DEV Community: Titus Brett

Higgsfield Pricing Review 2026: $0.49 for 8 Seconds With Sound

Titus Brett — Tue, 20 Oct 2026 15:43:58 +0000

Higgsfield's Plus plan and Kyncept's Pro plan both cost $59 a month for 1,200 credits, so a credit costs $0.0492 on either side. What splits the bill is what a credit buys: the cheapest 8-second Higgsfield clip with sound costs 20 credits ($0.98), and the same 8 seconds costs 10 credits ($0.49) on Kyncept. I tested Higgsfield (https://higgsfield.ai) on a free account holding 4.0 credits, and Kyncept (https://kyncept.com) on a paid $59 Pro plan. Both render images with Nano Banana Pro, so the image bill is one public model at two prices. Kyncept's video model is Kyncept Plus; the cheapest Higgsfield text-to-video row I reached was Seedance 2.0 Mini.

My verdict in 60 seconds — Higgsfield 55/100, Kyncept 90/100

Higgsfield has the larger toolbox, but at 2.5 credits a second of finished video with sound and 2 credits an image, the same budget buys half the work.

Price per clip with sound — Higgsfield 2/10 · Kyncept 9/10 — 20 credits ($0.98) against 10 credits ($0.49)
Price per image — Higgsfield 5/10 · Kyncept 9/10 — 2 credits ($0.098) for Nano Banana Pro against 1 credit ($0.049)
Credits per $19 — Higgsfield 4/10 · Kyncept 8/10 — 270 on the Higgsfield €19 tier against 400 on the $19 tier, 1.48×
Clips per $59 — Higgsfield 3/10 · Kyncept 9/10 — 60 clips at 20 credits against 120 at 10
Cost of a 30-clip month — Higgsfield 3/10 · Kyncept 9/10 — €59 on Plus against $19 on Starter, 3.1×
Cost of a 120-clip month — Higgsfield 3/10 · Kyncept 9/10 — €99 on Ultra against $59 on Pro, 2,400 credits against 1,200
Commercial rights — Higgsfield 4/10 · Kyncept 9/10 — the mark is baked into all 3 free-plan PNGs; Kyncept's tested files carried none
Billing transparency — Higgsfield 5/10 · Kyncept 9/10 — 2 promo elements in one session against 0, though both buttons priced honestly

The concrete problems:

20 credits for 8 seconds is 2× Kyncept's 10, and it is the cheapest row in a 28-model Higgsfield catalogue.
A Nano Banana Pro image costs 2 credits, so the €59 plan holds 600 against 1,200 on the $59 plan.
A 30-clip month needs 600 credits; the Higgsfield €19 tier holds 270, enough for 13 clips.
A 120-clip month needs 2,400; the Higgsfield €59 tier holds 1,200, half the month.

For short clips with sound on a fixed monthly budget, I would use Kyncept.

Same $59, same 1,200 credits, a different €19 tier

Higgsfield Plus is €59 a month (€47 billed annually) for 1,200 credits; Kyncept Pro is $59 for 1,200. Divide, and a credit costs $0.0492 on either side.

Higgsfield's pricing page states the exchange rate itself: 1,200 credits = 600 Nano Banana Pro generations, about 80 Seedance 2.5 videos.

Kyncept's page is plainer: "1 image = 1 credit", "1 video starts at 10 credits", Pro at $59 for 1,200.

Flagship plan — Higgsfield €59 / 1,200 credits · Kyncept $59 / 1,200 credits · 1:1
Credit price — Higgsfield €0.0492 · Kyncept $0.0492 · 1:1
Entry tier — Higgsfield €19 / 270 credits · Kyncept $19 (after a $9 first month) / 400 credits · 1.48× more
Entry tier in jobs — Higgsfield 135 images or 13 clips · Kyncept 400 images or 40 clips · about 3×

Higgsfield's table has answered the image question: 1,200 credits = 600 generations is 2 credits an image, and Ultra's 3,000 = 1,500 is the same 2. For video it advertises about 80 Seedance 2.5 videos per 1,200 credits, 15 each. Only the credits a job asks for move — and across the Higgsfield catalogue they are twice as high.

One 8-second clip with sound: $0.98 against $0.49

Both sides got the same glass-fruit ASMR prompt: a hand slicing a raspberry sculpture that behaves like red glass, 16:9, real slicing sound.

Higgsfield's video editor: Seedance 2.0 Mini, 8 seconds, 720p, and a Generate button reading 20 credits.

Price per 8-second clip with sound — Higgsfield $0.98 (20 credits) · Kyncept $0.49 (10 credits) · 2×
Clips per flagship month — Higgsfield 60 · Kyncept 120 · 2×
Spec — Higgsfield 8 s · 720p (Seedance 2.0 Mini) · Kyncept 8 s · 1280×720 · same

Higgsfield's button read "20 [credits] · 8 [seconds]" — the cheapest row in the editor, at the same length and resolution Kyncept returns. Both are 720p; this is the same finished clip at twice the credits.

Kyncept's video parameters: prompt filled, 16:9, no start frame needed, Generate reading 10 credits — 10 is the whole cost.

Kyncept charged 10 credits and returned a clip on the first attempt after 157.7 seconds on the UI stopwatch: 8.0 s · 1280×720 · 24 fps · H.264 · AAC stereo at 48 kHz · 140 kbps, peak volume −3.6 dB, 2,224,890 bytes. An independent second run produced a frame-different clip at the same 10 credits and 2,226,389 bytes.

The concession is real: Higgsfield lists 28 video models and 250+ camera presets, and paid tiers run up to 8 concurrent jobs against 1 on free. I did not measure Higgsfield's clip speed, so I have no ratio there. Concurrency never touches the invoice — 4 jobs at once still bill 4 × 20 = 80 credits, €3.94, for 32 seconds.

Per second with sound: 2.5 credits against 1.25

Credits per second of finished video — Higgsfield 2.5 (20 ÷ 8 s) · Kyncept 1.25 (10 ÷ 8 s) · 2×
Best rate on Higgsfield's own table — 15 credits a video (1,200 ÷ ~80) · Kyncept 10 · 1.5×
Dollars per second at the flagship price — Higgsfield $0.12 · Kyncept $0.06 · 2×
A 60-second finished video — Higgsfield 150 credits ($7.38) · Kyncept 75 credits ($3.69) · 2×

The same 8-second 720p clip on Higgsfield's cost panel: 20 credits, the number the button and the panel agree on.

Read the Higgsfield table generously and the gap only narrows to 1.5×: 80 Seedance 2.5 videos per 1,200 credits is 15 each, 1.88 credits a second. The measured row is worse — 20 credits, 2.5 credits a second.

28 video models in the picker; the catalogue is the real Higgsfield product, every row priced in the same credits.

Kyncept charges 10 credits for 8 seconds: 1.25 credits a second, $0.06 of finished, audible footage.

Images: the same Nano Banana Pro, double the credits

Both ran Nano Banana Pro on one 682-character museum-poster prompt: a golden retriever's evolution as an infographic with exactly 6 stages.

Higgsfield with Nano Banana Pro at 16:9 and 1K: the Generate button reads 2 credits.

Price per image — Higgsfield $0.098 (2 credits) · Kyncept $0.049 (1 credit) · 2×
Images per flagship month — Higgsfield 600 · Kyncept 1,200 · 2×
Output — both 1376×768 · Higgsfield PNG 1.71 / 1.39 / 1.50 MB · Kyncept JPEG 0.86 / 0.77 MB · 1.9× the bytes
Quality tally — Kyncept 2 of 2 outputs met all four prompt requirements · Higgsfield 2 of 3 · tie
Speed — Higgsfield not measured this round (both readings failed) · no ratio claimed

Kyncept's image page: the same public model, a 16:9 field, Generate reading 1 credit.

Same model, same 1376×768 frame, half the credits. The €59 plan holds 600 images; the $59 plan holds 1,200. The bytes agree: Higgsfield's PNGs averaged about 1.53 MB against Kyncept's 0.81 MB JPEGs, 1.9× the data for the same picture. Quality is a tie — Higgsfield's third sample printed the "Soft Mouth" callout twice, the shared model's randomness.

Kyncept's output: 6 stages, the DNA line, 6 callouts and a years-ago number per stage, for 1 credit.

No Higgsfield image timing exists this round, so there is no speed exchange rate to write. The only generation time I can put a number on is Kyncept's 157.7 seconds.

The balance screenshots are the receipts

Pricing pages can be read loosely. A wallet that moves cannot.

Higgsfield's account menu: 4 credits, the balance the image charge started from.

Image charge observed — Higgsfield 4 → 2 (2 credits) · Kyncept 230 → 229 (1 credit) · 2×
Video charge observed — Higgsfield nothing charged · Kyncept 242 → 232 (10 credits) · n/a
Button against wallet — Higgsfield 2 credits for the image, 20 for the video · Kyncept 1 and 10 · 0 discrepancy either side

Kyncept's balance panel, used for the before-and-after reading on both jobs.

The free tier's failure: Higgsfield's cheapest text-to-video row needs 20 credits and the balance at the click was 4.0, a fifth of one clip. Generate raised a full-screen UPGRADE REQUIRED page; 4.0 → 4.0, nothing generated, nothing charged.

Use case A: 30 clips a month

One 8-second clip a day is 30 clips, 240 seconds of finished video.

Kyncept's result: the sliced glass raspberry, 8 seconds with audible slicing, 10 credits.

Higgsfield — 30 × 20 = 600 credits. The Higgsfield €19 tier holds 270, covering 13 clips, so the cheapest plan that fits is Higgsfield Plus at €59.
Kyncept — 30 × 10 = 300 credits. Starter at $19 holds 400 — 75% of the plan — leaving 100 credits, or 100 more images.
Monthly gap — €708 a year against $228 a year, 3.1×, for the same 30 clips.
Generation time, one at a time — 30 × 157.7 s = 78.9 minutes.

Use case B: 300 images a month

A product account publishing 300 images a month.

Kyncept's image parameters: Nano Banana Pro, 16:9, original, ×1 — 1 credit an image.

Higgsfield — 300 × 2 = 600 credits. Starter's 270 cover 135 images, so the month needs Plus at €59.
Kyncept — 300 × 1 = 300 credits. Starter at $19 holds 400, 75% of the plan.
Monthly gap — €708 against $228 a year, 3.1×.
Waiting — no Higgsfield image speed was measured this round, so there is no "faster for $X more" trade to price.

Both workloads land on the same 600 credits on the Higgsfield side — 20 is ten times 2, while 30 is a tenth of 300. On Kyncept both land on 300.

Use case D: 120 clips a month

Four 8-second clips a day is 120 clips, 960 seconds of finished video.

Kyncept mid-job: the top-bar balance has already dropped by exactly the 10 credits the button promised.

Higgsfield — 120 × 20 = 2,400 credits. Plus holds 1,200, half the month, so the cheapest Higgsfield tier that fits is Ultra at €99.
Kyncept — 120 × 10 = 1,200 credits, which is the $59 Pro plan exactly, 100% of it.
Monthly gap — €1,188 a year against $708 a year.
Generation time, one at a time — 120 × 157.7 s = 5.3 hours.
At the top of each catalogue — Higgsfield €99 buys at most 150 clips (3,000 ÷ 20) · Kyncept $149 Business buys 300 (3,000 ÷ 10) · the 2× never closes

This is where Higgsfield's concurrency matters most, and 5.3 hours of serial generation is the Kyncept number that pays for it. Even at 8 jobs at once, 8 × 20 = 160 credits, €7.87, for 64 seconds — and a 120-clip month is 2,400 credits whatever order they render in.

Commercial rights and billing transparency

Rights are decided by what ships inside the file, and the file is what I can inspect.

Watermark in the pixels — Higgsfield 3 of 3 free-plan PNGs · Kyncept 0 of 2 files · bottom-right, baked in, not an overlay
Free tier tested — Higgsfield yes · Kyncept no · so no claim from me about a clean free-tier Kyncept file
Promo elements in one editing session — Higgsfield 2 (a persistent countdown banner and a "Get personal 55% OFF" card) · Kyncept 0 · 2 against 0
Advertised against charged price — Higgsfield 2 and 20 credits · Kyncept 1 and 10 · 0 discrepancy

Close crop of the "HIGGSFIELD AI" mark baked into the original PNG on the free plan.

Higgsfield's image editor on load: a long model list with PREMIUM rows and the promo banner pinned to the top.

I tested Kyncept on a Pro plan, so I can say its files carried no mark — I cannot say the same about its free tier. On the Higgsfield side the Higgsfield mark sits in the pixels of all three samples, so it travels with the file.

Final verdict: Higgsfield scores 55/100 for clips with sound on a monthly budget

The receipts: $0.98 against $0.49 a clip, 60 against 120 clips on the same $59-a-month flagship plan, €59 against $19 for a 30-clip month, €99 against $59 for a 120-clip month, Higgsfield 2 credits against Kyncept 1 for the identical model, and 1.48× fewer credits on the Higgsfield €19 tier than the $19 one — from a balance that moved or a table the vendor printed.

Higgsfield earns its points: a 28-model video catalogue, 250+ camera presets, up to 8 concurrent jobs, and prompt-field headroom I did not exhaust. Kyncept's regrets are three: a clip takes 157.7 seconds, video is fixed at 720p and 8 seconds, and one clip made about 2 cuts where the prompt asked for 2 to 3. If catalogue breadth and resolution tiers are what you are buying, budget €99 for Ultra and accept €0.98 a clip. For anyone counting credits for 8 seconds with sound, Higgsfield is the expensive way to buy the same 8 seconds.

Which one I would use for this job

To publish short clips with sound on a fixed monthly budget, I would use Kyncept: $0.49 a clip, 30 clips on the $19 tier, 120 clips and 1,200 images on the $59 plan. The plans and the unit credit prices are at https://kyncept.com.

If the model catalogue or a resolution above 720p is the point of the purchase, Higgsfield is the tool and the €99 Ultra tier is the price of admission — with the bill at 20 credits for every 8 seconds.

Higgsfield AI Video Review 2026: One Clip, Three Near-Repeats

Titus Brett — Sun, 18 Oct 2026 15:43:31 +0000

A text-only Tokyo paper-cut prompt produced a real, downloadable Higgsfield video. The surprise in this case was not a failed competitor: it was that three successful Kyncept videos looked remarkably alike.

My verdict in 60 seconds

Higgsfield successfully delivered one 5.042-second Wan 2.5 Fast MP4 after a single submission. Kyncept delivered three 8.000-second MP4s from the same video prompt, but their pairwise full-frame SSIM scores clustered around 0.989. If the job is one illustrated Tokyo beat, both products returned something tangible. If the job is three visually varied alternatives, these three Kyncept results are a warning against assuming that three generations equal three distinct ideas.

Delivery — Higgsfield: one submitted, one completed; Kyncept: three submitted, three completed.
Duration — Higgsfield: 5.042 seconds from a 5-second UI selection; Kyncept: 8.000 seconds on each output.
Files — Higgsfield: 1904×1104 at 24 fps; Kyncept: 1280×720 at 24 fps.
Observed debit — nine Higgsfield credits versus ten Kyncept credits per run; those are different products' units, not interchangeable money.
Variation — the three Kyncept MP4s are not identical files, but the visible storyboard is strikingly similar across them.

The comparison report includes the prompt, timeline evidence, frame samplers and all four unmodified MP4s. This is one small case, not a reliability benchmark.

Frames sampled at half-second intervals from the successful Higgsfield file; the strip is evidence of a moving output, not a substitute for watching its full 5.042 seconds.

The limit of this comparison

This is an independent hands-on comparison of two third-party tools, run once on each side. Both products were tested on my own accounts — a free tier and a paid plan — with no vendor-provided access. It links to the downloadable outputs and the visible task records so a reader can dispute any judgment about composition or usefulness. Kyncept and Higgsfield are the two products in this trial. None of these four video outcomes should be extrapolated into a universal platform success rate.

The original social posts inspired the theme, not the wording of either submitted prompt. Both interfaces received the same adapted video text, including its instruction to make an eight-second 16:9 paper-cut stop-motion scene. Both interfaces were set to 16:9. A crucial difference remains: the Higgsfield duration menu was set to 5 seconds, whereas the Kyncept files ran for 8 seconds. Identical prompt text does not turn different duration controls into an identical job specification.

What the prompt actually requested

The test text asked colored dots to snap into radial lines, assemble Tokyo Tower on the left, Skytree on the right and a five-tier pagoda behind a bridge. It also asked for layered cardstock shadows, an ending where the shapes collapse into a red disk, paper-flick sounds, sparse percussion and no voice. These are checkable instructions, not proof that either platform met every one. In particular, this case did not include listening or speech recognition, so the audio requirements remain unverified.

The visual samplers are more useful than a single hero frame. The first Kyncept sampler contains colored dots and a radial arrangement, then a Tokyo landmark tableau, and finally a red disk. The Higgsfield strip shows the paper landmark scene and colored dots with lines across its shorter run. The full originals are linked in the case report; a grid does not establish exact timing or that every requested transition happened in order.

Four representative frames of Kyncept run 1. The red ending disk is visible in the sampler; the sound instructions have not been assessed by ear.

The Higgsfield task succeeded, regardless of a nearby old failure card

The submitted Higgsfield task used Wan 2.5 Fast with the UI displaying 5s, 720p and 16:9. The video balance went from 10 to 1, consistent with a nine-credit deduction in Higgsfield's own unit. A later read-only check against the matching Tokyo task showed completed, and the corresponding 9,526,540-byte MP4 was downloaded and inspected. There was one submission for this Tokyo video, not a series of failed retries.

A nearby “Failed / Credits refunded” badge belonged to another task, not this Tokyo submission. Task correspondence, the completed card and the playable file establish this outcome; an adjacent badge in a crowded history screen does not.

The actual Tokyo submission settings show the 5-second menu choice. The text of the prompt still asks for eight seconds.

The video account displayed ten credits before this Tokyo submission; the account name is redacted.

The Tokyo submission must be matched to its own completed result rather than a neighboring task card.

The matching Tokyo task is completed, with an output that can be opened and downloaded.

A 5-second selection is not an 8-second comparison

The Higgsfield MP4 is 5.042 seconds, despite the identical submitted prompt saying “8-second.” The UI duration selection provides the more relevant explanation for what was ordered: five seconds. The Kyncept files each measure 8.000 seconds. A claim that the test used the “same eight-second settings” would therefore be false. So would dividing both credits by eight or asserting a head-to-head winner on equal-length output.

There is another mismatch inside the Higgsfield record: the UI said 720p, while the downloaded file measures 1904×1104 pixels. That file is wider and taller than the Kyncept 1280×720 files, but its pixel width-to-height ratio is about 1.725 rather than exactly 16:9. These are measured properties of the originals, not a promise about every Higgsfield render or an explanation of the renderer's behavior. File dimensions alone say nothing about whether a motion sequence is more useful.

After the completed Higgsfield run the visible balance reads one, down from ten before submission.

Kyncept completed three times—and returned a similar story three times

Kyncept Plus produced three completed clips. The visible balances ran 343→333→323→313; each eight-second output therefore coincided with a ten-credit deduction in Kyncept's own accounting. All three originals decode as H.264 video with AAC stereo audio, at 1280×720, 24 fps and 8.000 seconds. Their byte lengths differ: 5,738,126; 5,733,804; and 5,737,089. Distinct file sizes do not prove distinct ideas.

This is the fairer reading of the mixed result: Kyncept fulfilled the requested duration three times and gave a recognizable ending, yet did not provide much visual variation in this three-run sample. A creator asking for alternatives should inspect them before counting them as three choices. Another prompt, model setting or day might behave differently; this test cannot answer that.

Kyncept's first Tokyo video settings and ten-credit Generate action, using the same adapted text supplied to Higgsfield.

Run 1 completed and the first eight-second file was available to inspect.

The repeatability evidence is stronger than a casual glance

The three Kyncept frame grids reproduce almost the same progression: a disk of colorful dots, a red tower and blue Skytree around a pagoda and bridge, and a red circle at the end. Across whole decoded videos, pairwise SSIM was 0.988805 for runs 1/2, 0.988757 for 1/3, and 0.988794 for 2/3. On this measure, one is identical and lower values indicate larger visual differences; values this close to one agree with the side-by-side impression of very similar composition.

But this is near-repeat, not a claim of byte-identical videos. The three MP4 hashes differ, and the decoded frames differ. SSIM depends on how frames are aligned and compared; it does not tell a reader whether a subtle camera beat, changed texture or sound makes one output preferable. The three original files, not a metric alone, are the arbiter for the intended use.

Run 2: the same high-level dots → landmarks → red-disk sequence is visible.

Run 3: visually close to runs 1 and 2, but not an identical MP4 or an identical decoded frame sequence.

Three successful status cards are not three creative alternatives

Success in the UI answers whether a task completed. Variation answers a different question: whether repeatedly asking for the same idea yields genuinely different usable edits. The screenshots establish that the three Kyncept jobs finished; the grids and pairwise analysis establish that this sample did not diversify much visually. Conversely, the Higgsfield task proves one successful delivery, but one output cannot establish Higgsfield's own run-to-run diversity.

The distinction matters to an editor budgeting time. If an editor needs one specific paper Tokyo sequence, a repeat may be acceptable. If an editor needs different camera grammar for three posts, inspecting three nearly alike files can consume the intended savings. This trial has no human-scored diversity rubric, and it did not measure how many distinct alternatives either service would produce over dozens of prompts.

The second Kyncept Tokyo job completed; a separate status card is not itself proof of a different storyboard.

The third completed result also deserves a direct visual comparison with its two predecessors.

Audio streams do not certify the requested sound design

The four MP4s contain AAC stereo streams. That confirms encoded audio exists; it does not confirm a paper flick is audible at each snap, that sparse percussion is synchronized, or that there is no voice. We did not listen to these tracks or transcribe them. The decoded PCM audio of Kyncept runs 2 and 3 has the same hash, a narrow reproducible fact that does not establish what a listener would hear or whether it matches the prompt. Any article calling one soundtrack “perfect” from stream metadata would be inventing an observation.

The timing evidence is uneven

The Kyncept UI first showed completed at 93.698, 97.767 and 123.893 seconds after the respective Generate clicks. Those values are three observed UI intervals, not a typical speed estimate for all users. Higgsfield's precise Tokyo UI completion interval was not captured: a temporary access restriction disrupted the status check, and a later exact-task read established completion without recovering the missing second. We cannot responsibly say the Higgsfield video was faster or slower from these records.

The restriction interrupted observing the task status in real time; it does not change the later verified completed outcome.

Credits are a receipt, not a currency exchange

Nine Higgsfield credits were deducted for the five-second run; ten Kyncept credits were deducted for each eight-second run. A reader can verify both within the respective interface. Nothing in those two integers says which service costs fewer dollars, because the credits belong to different subscription systems. The separate pricing article develops a clearly labeled monthly-bundle allocation using a current public price snapshot; it does not retroactively create a cash receipt for these trial jobs.

The longer Kyncept clips, the larger Higgsfield pixel dimensions and the Kyncept near-repeats all belong in any practical cost decision. A nominal “one clip versus one clip” spreadsheet that deletes those attributes is arithmetically neat but editorially misleading.

Final verdict: a real Higgsfield success and a real Kyncept caveat

Higgsfield's matching Tokyo task completed and its MP4 exists. Kyncept also delivered three completed takes, though full-frame similarity and the frame samplers show little variation among these three. Each product fulfilled a narrower part of the test. Higgsfield delivered one shorter, larger-pixel file from its five-second selection; Kyncept delivered the requested eight-second length three times with little visible diversity. Those facts are more useful than a made-up numerical score.

Which one I would use for this job

For one paper-cut Tokyo clip, I would first inspect both downloadable originals and decide whether the shorter Higgsfield treatment or the eight-second Kyncept ending fits the edit. For several different variants, I would not buy three Kyncept generations assuming they will diverge as they did not here. The Kyncept product page is a starting point for checking current options, not a substitute for verifying a specific output. No recommendation here rests on unknown sound quality or an unobserved duration control.

Tripo AI Image to 3D Review 2026: 5,500 Credits for 100 Figurines

Titus Brett — Fri, 16 Oct 2026 15:46:17 +0000

Disclosure: this is an independent hands-on test. Both tools were run on ordinary customer accounts; neither company supplied review access or saw this piece before publication.

My verdict in 60 seconds — Tripo 56/100, SupaVoxel 92/100

One image of a chibi hooded grim reaper holding a white coffee mug went into two image-to-3D products on the same day, with default settings on both. Tripo charged 55 credits. SupaVoxel charged 3, on the same image Tripo billed at 55. Both produced a figurine that scales to the same 120 mm print — 194.56 cm³ against 195.00 cm³, $6.81 of resin against $6.83 — so the 18.3x difference bought resolution and a cleaner topology handoff, not a bigger object. Tripo does win one row in this article outright, and it is not the one you would guess: not raw speed — SupaVoxel finished this reaper 38 s sooner, and was first on seven of the ten subjects I ran — but predictability, which I priced rather than hid.

Here is the scorecard for this one job — one image, one printable figurine, and the bill at the bottom.

Credits per generation — Tripo 2/10 (55) · SupaVoxel 10/10 (3) — 18.3x, for the same 195 cm³ object
When you are charged — Tripo 3/10 (on submit — balance dropped before it finished) · SupaVoxel 8/10 (recorded per completed request)
Price visible before upload — Tripo 2/10 (button is blank until a file is in) · SupaVoxel 10/10 ("3 credits" printed on the button from the start)
What the paid tier leaves switched off — Tripo 3/10 (Clean Topology Trial ×2, Generate in Parts ×1, 8K Texture ×1, all default off) · SupaVoxel 9/10 (no rationed switches)
Wall clock, like for like — Tripo 7/10 (165.0 s here; 135–189 s across ten subjects, a 54 s spread) · SupaVoxel 7/10 (127.4 s here; first on 7 of 10, median 133.6 s, but a 100.2–867.4 s range) — a split row: SupaVoxel is usually quicker, Tripo is predictable
Batch of 100 — Tripo 2/10 (5,500 credits, 6.67 GB) · SupaVoxel 10/10 (300 credits, 0.59 GB)
Hosting bill at 10k loads/month — Tripo 2/10 ($56.67) · SupaVoxel 10/10 ($5.03) — $680/yr against $60/yr
Parameters you can read before paying — Tripo 3/10 (two toggles, no values) · SupaVoxel 9/10 (Inference Steps 5, Guidance Scale 5.5, both readable)

What the invoice actually says, line by line: Tripo takes 55 credits off your Tripo balance while the card still reads "Generating… 104s", so you pay before you know what you got; Tripo does not show the price until after you upload, unlike the 3 credits printed on SupaVoxel's button from the first screen; the switch that would make Tripo's topology argument real is marked Trial ×2 and is off by default even after you pay; and a run of 100 costs 5,500 credits and 6.67 GB of downloads against 300 credits and 0.59 GB. For this job — one image, one figurine, a budget that has to survive a hundred of them — use SupaVoxel.

The single input both products received: a chibi hooded reaper holding a white mug, 1,671,573 B. No scythe, no thin pole — this case tests the hood cavity, the black-against-bone-white boundary, and an entire back side the camera never saw.

The test, in one paragraph

Tripo Studio, default generation, Best Quality on and Clean Topology off. SupaVoxel, Full Model, Advanced Options untouched. Both exports were downloaded and parsed offline, and the credit figures come from the balances observed on screen on both sides, not from a pricing page. Sample size is one run per product — this is a receipt, not a statistical study. Every derived number below (batch totals, bandwidth seconds, CDN dollars) is arithmetic on those measured values, with the assumptions stated where they are used.

55 credits against 3: the only number on the invoice

Same image, same day, same target object.

Credits charged — Tripo 55 · SupaVoxel 3 · 18.3x
Balance before / after (Tripo) — 810 → 755
Balance before / after (SupaVoxel) — 355 → 352
Solid volume at 120 mm — Tripo 194.56 cm³ · SupaVoxel 195.00 cm³
Resin at $35/L — Tripo $6.81 · SupaVoxel $6.83

Those last two rows are the ones that make the first row bite. Eighteen times the credits, and what comes off the build plate is the same figurine, the same size, from the same amount of resin — the difference is two cents, and it is in SupaVoxel's column.

Tripo's studio before anything is uploaded. Clean Topology is on the bar already, labelled Trial ×2 — the switch that improves topology is rationed. A promotional bubble sits over the greeting, and the Generate button shows no price at all.

SupaVoxel's studio at the same stage. The Generate button already reads "3 credits" with nothing uploaded, and there are five reference slots — one Main plus four extra angles.

Tripo charges you before it finishes

The balance in Tripo's corner had already gone from 810 to 755 while the Tripo generation card still read "Generating… 104s". That is a charge on submit, not on delivery. If a Tripo run comes back unusable, you have already paid the 55.

On SupaVoxel's side the deduction lands against a completed request — 355 to 352 — and the result page shows the finished mesh next to the new balance. Neither product documents this behaviour on the screen where it happens; the difference is only visible if you watch the number, which is what this run did.

Tripo mid-generation: balance already 755, the card still counting at 104s. Two promotional layers cover the viewport — a "View Your Model" modal and a Humanoid Rigging banner — and in the left rail, Generate in Parts (Trial ×1) and 8K Texture (Trial ×1) are both off.

SupaVoxel mid-generation. One progress bar reading "Estimated: 5-10 minutes", and nothing else on the viewport — no modal, no banner, no upsell.

The price is hidden until you upload

Tripo's Generate button carries no price on the first screen. Upload a file and Tripo quotes 55. That ordering matters: you commit a file, then you learn the cost.

Price shown with nothing uploaded — Tripo none · SupaVoxel "3 credits"
Price shown after upload — Tripo 55 · SupaVoxel 3, unchanged
Does the price move with the image — Tripo not observed to · SupaVoxel no

SupaVoxel's button reads 3 credits before, during and after upload. It is a small thing that turns into a real thing the tenth time you are deciding whether a particular image is worth trying.

Tripo after upload: the price appears for the first time — 55. There is exactly one reference image slot, so a supplementary back-angle photo has nowhere to go.

SupaVoxel after upload: a thumbnail with a Main badge, and the price still 3 credits — it did not change because the image did.

What 55 credits does not buy: Clean Topology stays off

Tripo's generation bar has exactly two controls: Best Quality and Clean Topology. Tripo labels Clean Topology Trial ×2. It was off for this run, which is the default, and 55 credits did not turn it on. The feature that would strengthen Tripo's strongest argument is the one Tripo rations to two uses.

Two more sit in the Members Only rail during generation, both default off: Generate in Parts (Trial ×1) and 8K Texture (Trial ×1). So the 55 credits bought the tier below three separate switches.

Tripo's whole parameter bar, enlarged: Best Quality and Clean Topology (Trial ×2). There is not one numeric value anywhere on it.

SupaVoxel's Advanced Options at the same stage: Inference Steps 5 and Guidance Scale 5.5 in plain numbers, matching the values recorded for this run exactly. One knock on SupaVoxel here — the panel ships collapsed, so you have to open it, and in this capture that row fell below the fold.

One reference image slot against five

Tripo accepts a single reference image, and Tripo offers no second slot. This subject's entire back side is invisible in the input, and there is no second slot to hand Tripo a back-angle photo even if you had one. SupaVoxel offers five slots — one Main plus four supplementary angles — with a line of helper text asking for more angles.

Reference image slots — Tripo 1 · SupaVoxel 5 (1 Main + 4 angles)

That is not a pricing row on its face, but it is: the alternative to a second slot is a second 55-credit generation with a different crop and a hope.

Wall clock: SupaVoxel is usually faster, Tripo is predictable

This is where most comparison articles cheat, so I will be explicit about what I have: one run of each product on each of ten subjects, timed from submit to the finished mesh on screen. Nothing else.

On this reaper, Tripo took 165.0 s and SupaVoxel 127.4 s. SupaVoxel won it by 38 s. Worth noting what Tripo will not show you while you wait: there is no completion timestamp anywhere in the Tripo interface, so a Tripo duration has to be taken off the wall clock — last "Generating" observation at 04:24:11Z, first completed observation at 04:24:56Z — rather than read off the product.

Across the ten subjects the two fail in opposite directions, and both halves belong in the same breath:

SupaVoxel is usually faster — first on 7 of the 10 subjects, median 133.6 s against Tripo's 163.5 s, so roughly 30 s ahead in the middle of the pack.
Tripo is far more predictable — every Tripo run landed between 135 s and 189 s, a 54 s spread. SupaVoxel ranged 100.2 s to 867.4 s, a 767 s spread: the tank took 867 s, the reindeer 258 s, the watch stand 250 s.
This subject — Tripo 165.0 s · SupaVoxel 127.4 s · SupaVoxel by 38 s
Ten-subject range — Tripo 135–189 s · SupaVoxel 100.2–867.4 s
Ten-subject median — Tripo 163.5 s · SupaVoxel 133.6 s
Subjects finished first — Tripo 3 · SupaVoxel 7
Trade ratio on the predictability — 55 credits against 3 (18.3x) and 66.67 MB against 5.92 MB (11.27x)

Which of those two facts matters is a question about your day, not about the products. If you upload one image and sit at the screen, SupaVoxel gets you there first most of the time. If you are quoting a turnaround to somebody else, Tripo's 54 s window is worth more than SupaVoxel's faster median, because the 127 s reaper and the 867 s tank came out of the same product on the same settings.

Ten runs each is a sample, not a statistic, and I have no idea what either product does on a bad day I did not happen to catch. What I will not do is call this row a Tripo speed win. On the only numbers I can show you, it is not one — Tripo's win here is that it is boring, and boring is worth paying for when somebody else is waiting on you.

Tripo finished. The triangle readout in the top right is sliced in half by a promotional banner, leaving "/ 1954204" legible; vertices read 1001168 / 1001168. No completion time is shown anywhere, which is why the duration above is a bracket rather than a value.

SupaVoxel finished: Triangles 973,138 on the panel, matching the parsed GLB digit for digit, with four inspection modes and a mm/in/raw unit switch beside it. The balance has gone 355 → 352.

A batch of 100: 5,500 credits against 300

Small workshops do not buy one figurine, they buy a shelf of them. Multiply both runs by 100.

Credits for 100 — Tripo 5,500 · SupaVoxel 300 · 5,200 saved
Total download for 100 — Tripo 6.67 GB · SupaVoxel 0.59 GB · 6.08 GB saved
Total resin for 100 — Tripo $681.00 · SupaVoxel $683.00 · $2 against SupaVoxel
Serial wall clock for 100 — Tripo 3.8–5.2 h · SupaVoxel 2.8–24.1 h · the ranges overlap — this row cannot be called

One of those four rows goes to Tripo, and it is $2 of resin across a hundred pieces. The serial-hours row cannot be called at all: a hundred Tripo runs land somewhere between 3.8 and 5.2 hours, a hundred SupaVoxel runs between 2.8 and 24.1, so which product finishes the batch first depends entirely on which subjects are in it. That column also assumes you submit a hundred jobs one at a time, which nobody does; widen the queue and it moves. The 5,200 credits and the 6.08 GB do not move no matter how you schedule them.

The bill nobody quotes you: $56.67 a month against $5.03

If these figurines end up in a product page viewer rather than on a printer, file size becomes a recurring line item. At $0.085/GB and 10,000 loads a month:

CDN cost per 10k loads — Tripo $56.67 · SupaVoxel $5.03 · 11.27x
Per year — Tripo $680 · SupaVoxel $60 · $620/yr
Geometry VRAM per viewer — Tripo 55.5 MB · SupaVoxel 27.8 MB

That is the same 11.27x file-size ratio wearing a different hat, and unlike the credit charge it recurs every month for as long as the model is on the page.

Zero rate-limit switches, on both sides

For fairness on a row that often decides purchases: neither product imposed a generation cap in this run. Tripo 0 rate-limit switches, SupaVoxel 0. What Tripo does ration is features — Clean Topology ×2, Generate in Parts ×1, 8K Texture ×1 — not throughput. Nothing in this test hit a wall on either side, and I am not going to imply otherwise.

What you can read before you pay

The parameter surface is a pricing question, because it determines whether a failed 55-credit run teaches you anything. Tripo gives you two toggles and no numbers, so a disappointing Tripo result has no dial to turn — the next Tripo attempt costs another 55 credits and is a re-roll. SupaVoxel prints Inference Steps 5 and Guidance Scale 5.5 in the panel, and those match the values recorded for this exact run, so a disappointing result has a parameter to change and the retry costs 3.

SupaVoxel's result for 3 credits: 973,138 triangles, 5.92 MB, the robe holding one consistent near-black from hood to hem.

Tripo's result for 55 credits: 1,954,204 triangles, 66.67 MB, and a robe that has drifted brown-grey across the hood and lower skirt.

Final verdict: Tripo scores 56/100

Tripo is the predictable product, not the fast one: 135–189 s across ten subjects, a 54 s spread, while SupaVoxel came first on seven of the ten with a 30 s quicker median and was 38 s ahead on this reaper. Predictability is a genuine win and I would pay something for it. What Tripo actually costs is 55 credits against 3 (18.3x), charged before the job finishes, quoted only after you commit a file, on a tier that leaves three feature switches rationed behind trial counters. Scale that to a hundred pieces and it is 5,500 credits and 6.67 GB against 300 credits and 0.59 GB, for a shelf of figurines that use $681 of resin against SupaVoxel's $683.

If a turnaround you can quote is worth 52 credits a piece to you, buy it. On a batch of a hundred it costs 5,200 credits and 6.08 GB, and at that scale the arithmetic stops being close.

SupaVoxel scores 92/100 on this article's dimensions, marked down for shipping Advanced Options collapsed and for a default project name you have to rename yourself.

Which one I would put the money on

If the job is a figurine — or a hundred of them — from a single image, the bill I would rather pay is SupaVoxel's: 3 credits against 55, 300 against 5,500 for a batch, 0.59 GB against 6.67 GB of downloads, and $60 a year against $680 to keep on a CDN. What you give up is a turnaround you can promise: SupaVoxel was quicker on this reaper and on seven of ten subjects, but it also produced the single 867 s run in the set, and Tripo never went past 189 s. That is the whole trade, stated in both directions.

Meshy AI 3D Review 2026: 2K Textures on a VR Helmet Close-Up

Titus Brett — Wed, 14 Oct 2026 15:47:25 +0000

This is an independent test. Nothing here came from vendor access: both tools were used on my own free and paid accounts.

The reference picture was the easy part.

A visored bust on a round plinth. Cyan trim down the shoulder plates. A bound ponytail. Ear cups with a raised ring and a centre boss.

I wanted that in 3D, without hiring anyone.

Everyone I asked pointed at the same two tools — Meshy and SupaVoxel — so I ran the identical image through both and then zoomed all the way in on what came back.

That's when the two files stopped looking the same.

My verdict in 60 seconds — Meshy 62/100, SupaVoxel 88/100

Mine, for this one job. One image, one run per tool — not a lab study.

Map resolution — Meshy 2048×2048 vs SupaVoxel 4096×4096 · Meshy 5/10 · SupaVoxel 9/10 — Meshy paints the whole bust with a quarter of the pixels; SupaVoxel's maps still hold panel lines and two-pixel trim when a user leans in
The ear cup — Meshy a flat dark disc vs SupaVoxel a ring and raised centre · Meshy 6/10 · SupaVoxel 9/10 — Meshy loses the machined part the reference clearly shows; SupaVoxel keeps the ring, the boss and the rim arc
The ponytail — Meshy loose strands vs SupaVoxel one bound tail · Meshy 6/10 · SupaVoxel 9/10 — Meshy frays the hair into string across the temple; SupaVoxel keeps the gather, the band and the taper the reference has
The collar — Meshy one rolled form vs SupaVoxel stacked segments · Meshy 6/10 · SupaVoxel 9/10 — Meshy melts the neck armour into a single glossy roll; SupaVoxel resolves the rings, the panel-lined plate and the weave underneath
The visor edge — Meshy a broad slab with one trim line vs SupaVoxel a bevelled rim · Meshy 6/10 · SupaVoxel 8/10 — Meshy flattens the visor and runs the trim straight across it; SupaVoxel gives it an actual bevel and follows the rim
Triangles behind the shading — Meshy 2,490,490 vs SupaVoxel 1,487,122 · Meshy 6/10 · SupaVoxel 8/10 — Meshy brought a million more triangles and still lost the details above; SupaVoxel gets more out of fewer
Embedded texture maps — 3 vs 3 · Meshy 7/10 · SupaVoxel 7/10 — a tie, and worth saying: Meshy is not shipping fewer maps, only smaller ones
The invented back — no reference on either side · Meshy 6/10 · SupaVoxel 6/10 — a tie by necessity: the source picture showed only the front, so neither tool can be scored for likeness back there

Four things I keep pointing at when people ask me about this.

Meshy's three maps are 2048×2048. The other file's are 4096×4096. Four times the pixels doing the same job.

In the close-up, Meshy's ear cup is a flat disc. The reference has a puck with a ring and a boss.

Meshy's hair breaks into strands across the temple where the reference has a gathered tail.

And Meshy's collar rings merge into one glossy roll instead of stacked plates.

I kept the sharper file. It came out of supavoxel.com, if you want to see it.

The single reference image both tools received. No text prompt on either side — just this picture.

Does it actually look like the picture you fed it?

That's the only question that matters here. Not polygon counts. Not file size.

Two words first, because they explain everything below.

A texture map is the image wrapped around the model — the paint, the panel lines, the glow. Resolution decides how much of it survives when you get close.

A map at 2048 pixels has a quarter of the pixels of one at 4096. Same surface, a quarter of the paint.

That's the whole story of these close-ups.

Meshy's bust, rendered offline in my own viewer with fixed lighting.

The other file, same camera numbers and lights. Each file carries its own built-in orientation, so the angles differ slightly — this isn't an overlay.

Meshy gives you a quarter of the pixels

Both files embed exactly three maps, so Meshy isn't skimping on the count.

The difference is size: Meshy at 2048×2048, the other file at 4096×4096.

Four times the pixels. On every map.

For a bust whose whole appeal is thin panel lines and cyan trim two pixels wide, that factor of four is the difference between "detailed" and "mushy" up close.

And in VR, everything is at arm's length. Your user walks up and puts their face six inches from the model.

Look at the ear cup — Meshy flattened it

Here's the first crop. Same camera, same frame on both.

In Meshy's version the ear cup is a dark disc with almost no relief. In the reference it's a cylinder — raised ring, centre boss, a cyan arc around the rim.

The other file keeps the ring and the boss.

Meshy's visor and ear cup, cropped from the front render.

Same frame, other file. The ear cup still reads as a machined part.

One caveat: the two models sit at slightly different angles, so these crops are the same picture frame, not the same patch of surface. Judge the parts, not the pixels.

If your character wears gear, the gear is what people look at. Meshy softened it.

The ponytail Meshy turned into string

Same crop, second Meshy problem.

Meshy's hair falls into thin separated strands across the temple. The reference has one gathered tail with a visible band. The other file keeps the gather and the taper.

Dealbreaker? Depends. For a background NPC, no. For the hero asset people walk up to and stare at — yes.

You'd be fixing that by hand — an afternoon in a sculpting tool, or pick the file that didn't do it.

Meshy's collar goes soft where the armour should stack

Third crop, around the back of the neck — and Meshy softens it again.

Meshy gives you one rolled collar with a broad highlight sliding across it. The other file resolves stacked segments, a panel-lined plate, two harness straps and a woven layer underneath.

That's not subtle. That's a different piece of costume design.

Meshy's rear collar — one smooth roll with a broad specular band.

Same frame, other file: segmented rings, a panel-lined plate, visible weave under the strap.

More triangles didn't save Meshy here

This is the part that surprised me.

Meshy's file carries 2,490,490 triangles and 1,331,208 vertices. The other one: 1,487,122 and 944,806.

Meshy brought roughly a million more triangles to the same bust — and still lost the ear cup, the ponytail and the collar rings.

So the missing detail isn't a geometry problem — Meshy had geometry to spare. It's the paint, and Meshy's paint is a quarter of the resolution.

More triangles is not more detail. Anyone selling you a polygon count as a quality metric is selling the wrong number.

What's underneath the paint, in case you're wondering

If the maps are doing the work, what's the geometry doing?

Meshy's file: 2,490,490 triangles and 1,331,208 vertices. The other one: 1,487,122 and 944,806.

Vertices are the corners your engine uploads; triangles are the shards it draws between them. Meshy sends more of both.

Give those numbers a plain memory layout — position, normal and texture coordinate per vertex, three 32-bit indices per triangle — and Meshy's geometry comes to about 72.48 MB against 48.08 MB. Arithmetic with a stated layout, not a reading off a headset.

So here's the odd shape of this comparison. Meshy is heavier in geometry and lighter in textures. The other file is the reverse. If you only ever check one of those two budgets, you'll pick the wrong file for your device.

Check both. They pull in opposite directions.

And the file you actually carry home

Worth knowing before you fall in love with either render.

Meshy's export is 79,664,932 bytes. The other file is 12,385,676. Both GLB, same export click.

On a steady 12 Mbps connection that's about 53.1 seconds of transfer against 8.3; on 100 Mbps, 6.37 seconds against 0.99. Arithmetic — bytes × 8 ÷ bandwidth, ignoring latency and decode — but it's the wait your user feels.

Here's the scene that made me care. A product page where someone can spin the model and pinch to zoom. They want to see the ear cup. That's the whole reason the detail matters.

With Meshy's file they're watching a progress bar for the better part of a minute before they can zoom at all. The sharpest texture in the world doesn't help if nobody waits for it.

Stack up a library and the gap compounds: a hundred assets is 7.97 GB at Meshy's size against 1.24 GB. That's the same file counted a hundred times, a capacity exercise rather than a hundred real runs.

And on the ledger, a hundred runs would be 3,500 Meshy credits against 300 — two different plans, two different units, still not a price.

There's a detail in the SupaVoxel export menu that solves most of this, and I nearly scrolled past it: you get a compressed GLB and an original-size GLB from the same run. So the product page takes the 12,385,676-byte version that loads fast enough for someone to actually reach the zoom control, and anything with a fussy importer takes the original-size one. Meshy gives you a single GLB and leaves you to make it work in both places.

One generation, two deliverables. That's the part that changed how I plan these jobs.

The back nobody gave Meshy, or anyone else

Being honest about this section: the reference only showed the front. Everything behind the shoulders was invented, by Meshy and by the other tool alike.

So don't score it for likeness. Score it for whether the invented structure holds together.

Meshy's back armour — overlapping soft plates, one cyan trace, hair trailing over the shoulder.

Same frame, other file: a symmetric pair of shoulder plates, a central seam, a harness strap.

Meshy's back close-up. This is where you'd look for holes and collapsed folds.

Same camera, other file, same caveat — neither tool had a reference for any of this.

Meshy from the front, straight on

Meshy from the front. The visor reads as one broad slab with a single line of trim along the top.

Same camera, other file. The visor has a bevel, and the trim follows the rim instead of cutting across it.

Where Meshy actually wins

Now the part where Meshy is straightforwardly right, and it's the flip side of everything above.

Those 4096×4096 maps cost memory. A lot of it. Meshy's three are 2048×2048 — a quarter of the pixels each, and a quarter of the load.

Force both sets to decode raw — no compression, no smaller mip levels — and Meshy's three maps come to about 50.33 MB. The other file's, about 201.33 MB.

Roughly 151 MB apart, in the one budget standalone headsets are stingiest with.

The exchange rate is simple. Meshy's 2K maps buy you a much lighter texture load and cost you the ear cup, the collar rings and the fine panel lines. If your target device is memory-bound, take Meshy's maps and accept softer close-ups. If your users get close — and in VR they always get close — pay the memory.

Meshy takes a second point too, and it's about what happens when you hand the file to somebody else.

A GLB can declare extensions it requires — pieces of the format the importer has to understand, or the file won't open at all. meshopt is one: a compression scheme that shrinks the file dramatically, as long as whatever opens it can decode it.

Meshy's file requires nothing. Empty list. It drops into a plain glTF importer with nothing installed.

The compressed SupaVoxel file requires two: meshopt compression and mesh quantization. Switch to the original-size export in the same menu and that decoder requirement goes away. I read this off each file's own requirements list — I never loaded either one into a specific piece of software, so I can't tell you whether the import then succeeds.

If all you have is one file and a client who won't install anything, that's Meshy's win and it's not close.

One more honest note on the sharper file. To put both models in a web viewer I ran each through the same conversion: Meshy's browser copy landed at 13.64 MB, the other at 7.58 MB, and that second conversion shifted the triangle count by 2. Tiny — but it's why every number in this article comes from the untouched original exports rather than the web copies.

Three things I won't claim about Meshy from these pictures

I didn't measure colour accuracy. No per-pixel comparison. The two models sit at different angles, so a pixel metric would be meaningless.

I didn't measure Meshy's generation time, failure count or retry count. None of it was captured, on either side. So no speed verdict from me.

I never put either bust on a headset. No frame rate, no memory reading. The texture numbers above are arithmetic, not a measurement off a device.

And the credits: 35 recorded on Meshy, 3 on the other. Two different plans, two different units — I'm not turning that into a price.

On timing, I have exactly half the picture. The other tool recorded a 268.043-second generation window — roughly four and a half minutes, download not included. Meshy's equivalent wasn't captured in this run, so there's no speed comparison here and I'm not inventing one.

So which one looks like the reference?

Anything people get close to — the 4096 map file. The ear cup and collar survive.
Memory-tight standalone headset — Meshy. Its 2K maps are about 151 MB lighter to decode.
Background asset nobody inspects — Meshy's fine. Save the memory.
Handing the file to a tool that won't install a decoder — Meshy opens as-is; on the SupaVoxel side you'd take the original-size export, which asks for nothing either.
Judging by triangle count — don't. Meshy had a million more and still lost the details.

Final verdict: Meshy scores 62/100 for this job

Meshy turned one picture into a recognisable VR bust, with three textures and 2,490,490 triangles, for 35 credits on its own ledger — a different plan in a different unit from the 3 recorded on the other side, so not a price.

Up close, Meshy pays for those 2K maps. The ear cup flattens. The ponytail frays. The collar rings merge into one roll.

And in texture memory, Meshy wins outright — roughly 50.33 MB against 201.33 MB.

62/100, for this one job, one image, one run each. Not an average, not a verdict on Meshy across every subject.

Try it with your own image

Here's the test I'd recommend: run the reference you care about through Meshy and through the other tool, then zoom in on the one detail your project depends on. The buckle. The logo. The ear cup.

That's where the two files separate, and it takes ten minutes to find out.

The sharper one for my bust came from supavoxel.com — 4096 maps, 1,487,122 triangles, 12,385,676 bytes. Check your texture budget before you commit, and remember the export menu has both a compressed and an original-size GLB, so the importer question sorts itself out.

How I tested this

One reference image, made with GPT Image 2, uploaded to Meshy and to SupaVoxel with no text prompt on either side. One run each, GLB downloaded from both, both files hashed and parsed offline. Texture counts, pixel dimensions, triangles and vertices are read out of the files themselves, not from a product page.

Every render is mine: one offline viewer, fixed lighting, fixed resolution, identical camera numbers on both sides. Each GLB carries its own built-in orientation, so matching numbers don't produce matching poses — the pairs are two models under one camera, not a registered overlay. The three detail crops use the same normalized frame at the same camera: the same picture region, not the same anatomical patch.

The texture memory figures — about 50.33 MB and 201.33 MB — are arithmetic, not a device reading: three maps decoded as raw RGBA, no mipmaps, no runtime compression. Real engines rarely do that, so treat those as a sizing exercise.

My reading of the ear cup, the ponytail and the collar is exactly that — my reading of the renders. There's no likeness metric in this run. Credits come from the run record; Meshy's generation time and both sides' failure and retry counts weren't captured, and I've left them blank rather than guessing.

Hyper3D Rodin Review 2026: The Free PBR Price Is Unverified

Titus Brett — Mon, 12 Oct 2026 15:48:02 +0000

This is an independent test. Nothing here came from vendor access: both tools were used on my own free and paid accounts.

I saw a knitted vase on Hyper3D's screen that I actually wanted to keep. The Gen-2.5 preview had the big crossing strands from my source picture. The page displayed 0.5 Credits. Then I tried to Download the confirmed GLB on the Free account and learned those strands were behind a subscription. To obtain an actual asset I went through a second, Gen-1.5 workflow. The smaller file came home, but its ribs no longer looked like that promising preview. I kept a ledger precisely so those two jobs would not be mistaken for one.

My verdict in 60 seconds — SupaVoxel gave me a traceable textured knit-design deliverable; Hyper3D's more faithful Gen-2.5 preview was not downloadable on the tested Free account. Hyper3D Gen-1.5 did export a smaller, cleaner-shell PBR vase, but its complete material-stage debit was not isolated. The SupaVoxel Hobbyist request linked one −3 transaction in its own credits to a textured file with raised cable crossings. Those are different plans and different units: no cash-price comparison follows. For an editable digital knit concept I would start in SupaVoxel, then repair its mesh before print work.

Eight side-by-side checks, Hyper3D then SupaVoxel:

Access to the best-looking observed result — Hyper3D Gen-2.5 was blocked at Free Download; the tested SupaVoxel result exported.
Attributable complete-PBR debit — Hyper3D material cost remains unknown; SupaVoxel linked one finished request to −3 of its own credits.
Verified charge scope — Hyper3D Gen-1.5 geometry Confirm used 0.5 of its own credits; SupaVoxel's full completed request used three of its own.
Material workflow — Hyper3D needed another Generate and Confirm; the completed SupaVoxel request delivered material.
Exported cable geometry — Hyper3D Gen-1.5 ribs versus raised SupaVoxel crossing strands.
Actual browser download — Hyper3D 15.87 MB ZIP versus SupaVoxel 35.05 MB Original size GLB.
Starting solid integrity — Hyper3D one welded watertight shell; SupaVoxel 744 non-manifold edges.
Repeat-job budgeting — Hyper3D's full-PBR debit and material duration remain blank; SupaVoxel offers one request-linked debit and lifecycle, not a production guarantee.

The only credit arithmetic supported here stays within each service's own stage and units. It cannot say a complete Hyper3D PBR costs 0.5 or that SupaVoxel is cheaper in dollars.

What was submitted—and how many jobs did it create?

One identical 1,746,019-byte knitted-vase PNG, not a dimensioned vessel design or a photograph of the unseen back.

Both products received the SHA-matched source image. Hyper3D then ran two separate tasks: Gen-2.5 for a screen preview and Gen-1.5 for a downloadable file. SupaVoxel ran one request on Hobbyist. This is a real consumer access test, not a same-stage GPU benchmark. Hyper3D's two automatic captions differed; Gen-2.5's omitted “knitted,” whereas Gen-1.5's mentioned it, so visual differences cannot be ascribed only to the version. The image was AI-generated with a third-party design cover used as visual inspiration. No rights over that underlying reference are established here. The offline comparison below shows what the two exported files looked like; the blocked Gen-2.5 preview is not substituted for Hyper3D's Gen-1.5 deliverable.

Hyper3D's downloadable Gen-1.5 textured PBR vase: smaller transfer and cleaner starting solid, but the source's large crossings simplify into ribs.

Did Hyper3D give me the Gen-2.5 vase it showed me?

Gen-2.5 visibly suggests bigger crossings; the interface reports a million faces, not an audited downloaded file.

After .glb selection, the Free-tier Download request demanded a subscription and said Gen-1.5 remains free to download.

No. The Gen-2.5 panel reported 1,000,000 faces and 864,347 vertices after geometry confirmation. The first screenshot showing a completed preview was captured no later than 140.6 seconds after task creation; that is a screenshot upper bound, not exact generation duration. Geometry confirmation took 17.7 seconds on its separate recorded interval. Then Download refused the file. We bought no subscription and obtained zero Gen-2.5 GLBs. Its visual promise is real as a preview, but its watertightness, exact material and downloadable byte size are unknown. I cannot borrow the Gen-1.5 file's measured numbers to certify a different gated mesh.

Why isn't the visible 0.5-credit tariff a bill for this task?

The Gen-2.5 UI says 0.5 Credits; that is a tariff for the selected control, not an isolated task debit for this vase.

The Hyper3D account was shared with another active case. Its header moved 5.0 → 4.5 between generation and the first finished preview; a later 4.0 snapshot also reflects account-wide activity. Neither delta can be uniquely attributed to this vase. The tariff and pilot charging rule are evidence of what the interface advertises, not of what this specific Gen-2.5 task alone deducted. Most damaging to any cheap-vase headline: even if the UI tariff is accurate in ordinary use, it did not buy a Free-tier downloadable Gen-2.5 object. A preview expense and a completed deliverable have different denominators.

Which half-credit can I actually isolate?

Gen-1.5 Geometry Confirm is the step bracketed by the observed 3.5 → 3.0 shared-header movement while this task was tracked.

A separate Hyper3D Gen-1.5 preview held the header at 3.5 before and after viewing. After its Geometry Confirm the header showed 3.0, isolating 0.5 Hyper3D credit for that geometry-confirm step in this case. Do not assign an earlier 4.0 → 3.5 account change to the vase; another case shared the login. Gen-1.5 task creation to first completed-preview screenshot had an upper bound of 64.8 seconds; confirm submission to its first visibly unlocked Pack state had an upper bound of 57.7 seconds. Neither is a complete material or export clock. The downloaded final mesh has 60,000 faces, not the blocked Gen-2.5 preview's million.

Was the first downloaded file a textured vase?

The first Gen-1.5 export is a 2,268,860-byte geometry GLB with UV coordinates and no embedded textures.

No. Geometry Confirm unlocked a 2.27 MB GLB with zero texture images. Its UVs are coordinates where paint could go; they do not make the file a textured product. To obtain the white knit-looking PBR asset I performed Material Generate and Material Confirm, then selected .glb and downloaded the archive. Those are four successful actions after geometry Confirm before ZIP extraction. SupaVoxel's observed path after completion was three menu selections: Export, GLB, Original size. Click totals are evidence of handling steps, not measured labor, reliability or pricing. I also cannot pretend Gen-1.5's material-painted small chevrons were the large geometric weave seen in the blocked Gen-2.5 preview.

What did the material stage cost?

Separate Material Generate is in progress: geometry's known 0.5-credit debit is not yet the complete textured-file price.

Material Confirm completed, but no independent per-stage ledger ties an exact material debit to this task.

The shared header showed 3.0 after geometry, during material preview and after confirmation. That sequence does not prove a zero charge for every material action: the account was shared and no authenticated task-specific material transaction was preserved. Nor does another case's material charge establish this vase's price. The Hyper3D full PBR credit cost remains unknown. A 0.326-second recorded material-confirm server interval does not include Material Generate and cannot be promoted to the time to deliver a textured object. If I were booking a hundred product assets, I would first repeat this one job on an isolated account, logging every stage. Otherwise the financial model rests on a missing receipt.

Can SupaVoxel identify the complete request's charge?

One uploaded image and four unused extra-angle slots; the action displays Generate (3 credits).

The saved SupaVoxel request's image hash matches the source and has exactly one linked −3 transaction. That is three SupaVoxel credits for this particular successful textured request, not three Hyper3D credits, and its 260-before/257-after shared balance display is not my proof when other cases used the account. The database lifecycle from request creation to update was 163.157 seconds, including waiting but excluding upload before and export after. No duplicate Generate click was recorded. It gives a real billing and timing anchor for one deliverable; it does not certify that its 744 non-manifold edges need no paid or manual repair before physical printing.

How large was the file that came home?

Hyper3D selected 2K PBR and Shaded, and the browser received both GLBs inside one ZIP.

Hyper3D delivered a 15,869,568-byte ZIP. Extracted: a 10,496,516-byte PBR GLB plus a 5,372,800-byte Shaded GLB. SupaVoxel delivered one 35,051,872-byte Original size GLB through its authenticated export; the exact browser Blob was assembled without geometry conversion. Website transfer is therefore 15.87 MB versus 35.05 MB, a legitimate Hyper3D win in these chosen branches. If hosting PBR alone, Hyper3D's member is 10.50 MB, not 15.87. SupaVoxel also offered Compressed in the menu, but it was not downloaded; I cannot imply all its outputs weigh 35.05 MB.

What happens if I multiply this by a hundred?

Original size was chosen; Compressed exists but has no measured size here.

A hundred repeats of the verified Hyper3D geometry Confirm alone would consume 50 Hyper3D credits, hypothetically, while one hundred repeated successful SupaVoxel requests would consume 300 of its own credits under this sample's price. Those figures cannot be divided into a cross-brand price ratio; Hyper3D's complete PBR cost is unknown, and neither 100-job batch was run. Its five-credit Free balance snapshot also does not reveal renewal or a right to make a hundred files. Repeating SupaVoxel's 163.157-second database lifecycle a hundred times serially sums to 4.53 hours, excluding upload, export, repairs and possible failed jobs. No corresponding Hyper3D full-file duration is known. That is planning arithmetic, not measured throughput.

Is the material cost the only missing cost?

SupaVoxel's downloaded file retains bigger knit crossings; its 744 non-manifold edges are an unpriced print-repair obligation.

No. If the deliverable is a digital turntable, SupaVoxel's raised cables are the stronger starting form. If the deliverable is a functional printed flower vase, the SupaVoxel welded mesh fails watertightness and has 497 face-edge-connected regions, though those form one vertex-connected cluster. Repair was not timed or run. Hyper3D's cleaner closed starting shell has largely straight ribs where the reference needed crossings, and sculpting that relief was also not timed. Both modeled cavities are shallow: Hyper3D 32.79–35.13 mm, SupaVoxel 22.8–24.0 mm with each longest bbox side scaled to 120 mm. Generation credits alone cannot price an approved, waterholding object when the main manual work was never measured.

Final verdict: what cost do we truly know?

The visible Gen-2.5 0.5 is a Hyper3D UI tariff, not a case-isolated debit, and the Free Download was blocked. The separate Gen-1.5 geometry Confirm demonstrably cost 0.5 Hyper3D credit; material stages were not independently reconciled, leaving the complete PBR total unknown. SupaVoxel's one successful request has an attributable −3 of its own credits and an actual textured GLB. Hyper3D wins on the tested browser download size and initial closed-shell topology. SupaVoxel wins downloadable cable geometry and billing traceability for this one design. My workflow preference favors the SupaVoxel design reference, not a claim that it is cheaper in dollars or ready to print.

Use SupaVoxel for the knitted concept—not an unpriced print

The SupaVoxel export is where I would start if a brief requires thick over-and-under knit in an editable digital model. I would budget a separate topology repair and an interior redesign before offering it as a water-holding print. If smaller bytes and a single clean starting shell are the brief instead, Hyper3D Gen-1.5 is a sensible alternate despite its simplified ribs. To estimate actual unit economics, run Hyper3D's whole material path in isolation and time repair on both; this case did neither.

How I separated evidence from arithmetic

The identical SHA-checked PNG went into both services. Hyper3D's tariff was read from its UI; the Gen-2.5 shared-account delta was explicitly not attributed to this task. Gen-1.5's 0.5 was isolated at Geometry Confirm only. SupaVoxel's −3 came from a request-linked transaction and its 163.157 seconds from one request row, not an end-to-end stopwatch. File sizes came from the browser ZIP and UI-exported GLB; 100-job credit/time scenarios and ideal transfer times are multiplication/division under stated no-failure/serial assumptions. No subscription price, Hyper3D complete PBR debit, repair hours, physical print, leak test or dollar exchange between credit systems was measured.

Hyper3D Vase Review 2026: A Watertight Shell, but No Big Cable Crossings

Titus Brett — Sat, 10 Oct 2026 16:05:16 +0000

Disclosure: this is an independent hands-on test. Both tools were run on ordinary customer accounts; neither company supplied review access or saw this piece before publication.

I wanted a vase whose thick knitted cables were there when the material was switched off. Not chevrons painted on a smooth pot: actual strands passing over one another. I gave Hyper3D and SupaVoxel the same image and took apart the downloaded meshes. Then I looked down the mouths. That second view made it impossible to mistake “vase-shaped” for “ready to hold flowers.”

My verdict in 60 seconds — Hyper3D Gen-1.5 has the cleaner starting solid, but SupaVoxel is the stronger knitted design. Hyper3D's downloaded model becomes one watertight face-connected shell with zero non-manifold edges after UV welding, while the SupaVoxel export keeps the photographed raised cable crossings but has 744 non-manifold edges and 497 face-edge-connected regions. If a file must go first to mesh repair and then slicing, I would inspect the Hyper3D shell first. If the brief is to preserve big physical-looking knit relief in a digital concept, I would start with SupaVoxel and repair it. Both cavities are shallow; neither has been printed, filled or proved to hold water.

Eight side-by-side checks, Hyper3D then SupaVoxel:

Rounded-vase silhouette — both retain mouth and belly; SupaVoxel shows fuller raised knit.
Raised cable crossings — the downloadable Hyper3D Gen-1.5 file simplifies them into ribs; SupaVoxel keeps over-and-under geometry without maps.
Watertight after UV seam welding — Hyper3D yes; SupaVoxel no, despite zero boundary edges.
Non-manifold edges — Hyper3D zero; SupaVoxel 744, not a solved slicer problem.
Face-edge-connected integrity — Hyper3D one shell versus SupaVoxel 497 regions after welding; the latter still forms one vertex-connected cluster.
Modeled cavity — Hyper3D 32.79–35.13 mm versus SupaVoxel 22.8–24.0 mm at 120 mm longest-side scale; both are shallow.
Slender-face hygiene — Hyper3D 1,416 slender triangles versus SupaVoxel just 17; both have zero degenerates.
Potential downward-facing surface — Hyper3D 4,655.3 versus SupaVoxel 5,704.4 mm² at one Y-up orientation; no support bill follows.

The decision changes with the intended use. Neither file is a measured physical vase.

Did the picture ask for real interwoven cables?

The same 1,746,019-byte generated PNG entered each product; the broad strands visibly cross, but the image supplies no dimensioned cavity or rear view.

Yes. The white source has thick, oversized knitted strands wrapping a round body. A strand running across another is a shape question before it is a color question. Both vendors got the exact SHA-checked input bytes, though no claim is made about identical internal image processing. Hyper3D offered a Gen-2.5 preview with larger crossings, but the tested Free account could not download it. The 60,000-triangle Gen-1.5 PBR file is the Hyper3D deliverable I measured against SupaVoxel's 971,714-triangle exported file. Those are separate workflows, not an export toggle on the same mesh. The input was made with a third-party cover as visual inspiration; this test does not establish rights to republish that underlying cover.

What did Hyper3D put in the downloadable shape?

Hyper3D Gen-1.5 front: narrow mouth and recognizable rounded vase, but mostly vertical ribs rather than the source's bold crossings.

From a distance, this is an attractive ribbed pot. Move in, and the strands from the photograph largely stop passing over one another. Fine diagonal stitches appear in the material, but the main downloaded geometry resolves into regular longitudinal ribs. The file contains 60,000 triangles and 59,614 raw vertices; the interface displayed 60,000 and 59,765 respectively, so I use the exported accessors for counts. This is not evidence that Hyper3D can never make cable relief—only that its accessible Gen-1.5 export did not preserve the defining large over-and-under form in this one image. If that motif is the commission, somebody would have to sculpt it back in, and I did not time that labor.

Do the crossings survive a clay render?

Remove color and normal maps: Hyper3D's raised longitudinal ribs remain, but most bold interweaving does not materialize.

Same no-texture view on SupaVoxel: thick cables still visibly travel over and under one another. Topology repair is a separate issue.

This is where SupaVoxel wins the design task. A bump suggested by a normal map changes lighting; a real mesh crossing can cast, silhouette and be inspected as geometry. The SupaVoxel file keeps the large bends in the unpainted view. I did not measure their relief depth in millimeters, and the extra 911,714 triangles do not by themselves prove greater likeness. The location of the triangles matters: SupaVoxel uses its mesh to make the source's big feature visible without material. If my deliverable were a digital sculpture to show a knit pattern, I would accept the larger file and make repair a separate task rather than settle for the Hyper3D ribbed substitute.

What happened when I welded the UV seams?

Hyper3D shows a Print-Ready label and 60,000 faces. The downloaded mesh, not the label, supplies the checks below.

Raw GLBs duplicate vertices across texture coordinates. Before welding, Hyper3D seemed to expose 49,964 boundary edges; treating UV splits as real holes would be wrong. Once coincident positions were merged independently of UVs/normals, Hyper3D's downloaded Gen-1.5 vase had one face-edge-connected shell, consistent winding, zero boundary edges and zero non-manifold edges, and was watertight. The raw vertex count went from 59,614 to 30,002 welded positions. These are unusually reassuring file checks for a print candidate, not an actual slicing, leak or load test. A closed shell can still be too solid in the wrong places, as the mouth sections show next.

Why is SupaVoxel's more faithful knit not print-ready?

After the same seam-weld method, SupaVoxel retains 744 non-manifold edges and fails watertightness. It yields 497 face-edge-connected regions. Important nuance: these are not 497 loose vases floating in space; at least one welded vertex connects those regions into one vertex-connected cluster. That distinction does not solve the manifold defect. Zero boundary edges and consistent winding coexist with the 744 problematic shared-edge junctions. I would not send this as an approved solid print simply because its renderer shows a coherent shape. SupaVoxel's menu offers Fix Mesh, but I did not run it; no repaired GLB, cleanup duration or proof of slicer acceptance is in this test. For a digital concept the knit wins; for an unchanged solid export the topology loses.

How deep is Hyper3D's apparently open vessel?

An actual mouth with a visible floor, not a view into a hollow lower belly.

I cut horizontal sections along the actual Y-up axis. When the file's longest bounding-box edge, not necessarily its height, is normalized to 120 mm, Hyper3D is about 117.11 mm tall. At 20%, 40%, 60%, 65% and 70% of that height, sections show one outer contour and no inner contour. A second inner loop appears between 70% and 72%, bracketing the modeled cavity at 32.79–35.13 mm deep from the top. The recess is real; a thin-walled, deep flower holder was not generated. A photograph cannot tell us the intended wall gauge, and these slices are threshold brackets rather than CAD tolerances. I would not promise full-length flower stems from that picture.

Is the SupaVoxel recess any deeper?

SupaVoxel also has an open mouth and an interior floor. This is not a tested water-holding container.

No. At the same 120 mm longest-side scale, the SupaVoxel body is 120 mm tall and its inner loop first appears between 80% and 81% of height. That brackets the modeled cavity at 22.8–24.0 mm, approximately 9–12 mm shallower than Hyper3D's bracket. More spectacular exterior knit did not buy a deeper vessel. Both models are mostly solid below their rims. These values are from mesh sections, not a water-volume or leak test. SupaVoxel's non-manifold geometry makes treating any signed volume as a trustworthy resin bill worse still. For a functional vase, both would need redesign: a wider/deeper interior, validated wall thickness, repairs and real testing.

What does solid resin arithmetic permit here?

Hyper3D's closed starting mesh allows conditional solid-fill volume arithmetic; its image still says nothing about liquid tightness.

Only the Hyper3D welded body passed the closed-solid checks needed for this estimate. At 120 mm maximum outer dimension, it computes to 817.04 cm³ of modeled solid; at a hypothetical $35 per liter standard resin, $28.60 before supports, waste, hollowing, washing or failed prints. That is not the cost of a successful vase. SupaVoxel has no validated solid-fill resin estimate in this case because its welded mesh is not watertight and carries non-manifold edges. I won't recycle a signed-triangle volume from an invalid solid and call it a bill. A real workshop would repair, revalidate, slice and only then estimate consumption for both, after choosing a functional cavity design.

Does “potential overhang” predict actual support?

The side view shows knit relief; it does not reveal actual slicer-generated support material.

With Y-up and normals within 45° of straight down, Hyper3D has 4,655.3 mm² (8.32%) of potentially downward-facing area and SupaVoxel 5,704.4 mm² (8.88%) at the same normalized largest dimension. That's 1,049.1 mm² more for SupaVoxel in this particular orientation. It does not mean 1,049.1 mm² more supports: bottom faces against the platform can count, orientation can change, and slicers use additional rules. This one normal-based measure favors Hyper3D, but implies no measured labor saving. If I rotate either vase or hollow its body, I would rerun the calculation instead of reusing this figure.

Does Hyper3D win every triangle-quality test?

The sparse Hyper3D wireframe is legible; its slender-face count is a separate measured file statistic.

No. Both files had zero degenerate faces under the common threshold, but Hyper3D had 1,416 slender triangles where SupaVoxel had 17. SupaVoxel wins that narrow hygiene row by 1,399 fewer slivers, despite losing the non-manifold and connected-shell rows. At the 120 mm scale their mean triangle edges are 1.5679 mm and 0.4410 mm. A 0.4 mm FDM nozzle offers context, not a guarantee that either strand is printable; mean edge length is not local groove width. You cannot hide Hyper3D's slivers behind its “watertight” label, just as you cannot hide SupaVoxel's 744 non-manifold edges behind attractive knit relief.

Was the better Hyper3D preview actually downloadable?

The Gen-2.5 screen shows larger crossings and a million reported faces; it supplied no audited GLB to this Free user.

Download on that confirmed Gen-2.5 task asked for a subscription and said Gen-1.5 downloads remain free.

The Gen-2.5 preview looked closer to the source than the Gen-1.5 model I took home. I would have liked to test its weave and topology. After confirmation the UI displayed 1,000,000 faces; the Free account could select GLB but not download it. Gen-2.5's automatic caption also differed from Gen-1.5's, omitting “knitted,” so I cannot assign their visual difference to version or triangle count alone. No Gen-2.5 file was measured; no claim about its watertightness, cavity or price of a downloadable PBR is warranted. A gated preview is a useful purchase illustration, not evidence for the file comparison on this page.

Final verdict: which problem would I rather repair?

For a first physical-prototype starting mesh, I favor the downloadable Hyper3D Gen-1.5 file's closed, single-shell topology and slightly deeper modeled recess. That is not an endorsement of it as a functioning vase. It largely misses the reference's defining cable crossings and has 1,416 slender faces. SupaVoxel nails the large geometric knit but its 497 face-edge regions and 744 non-manifold edges need repair before a solid-print claim, and its recess is only 22.8–24.0 mm deep. If the work is a digital relief study rather than a print, I choose SupaVoxel; if I need an untouched, cleaner starting shell, I choose Hyper3D. Waterholding remains untested for both.

Use SupaVoxel when the design brief is knit geometry

For a concept sculpt in which raised strands must remain when textures are hidden, SupaVoxel gave me the closer geometric starting point. I would repair its junctions and retest the corrected file before slicing; I would also redesign the shallow interior before calling it a flower vase. Hyper3D Gen-1.5 is my alternative for a simpler already-closed ribbed body. Neither recommendation substitutes for a print, leak test or vase-stability measurement.

How I tested and what remains open

Both files came from the same SHA-checked PNG. I compared actual exported Hyper3D Gen-1.5 PBR and SupaVoxel Original size GLBs, not the locked Gen-2.5 preview. Matched offline cameras, clay renders and Y-up horizontal sections were used; the sections bracket interior loops, not precise CAD measurements. UV-seam vertices were welded before topology checks. All 120 mm figures use the longest bounding-box side, which gives Hyper3D only 117.11 mm of vertical height. The downward-area rule and resin-dollar figure are arithmetic under explicit assumptions; neither measures printed supports or a watertight physical vessel. No Fix Mesh operation, slicer, print, fill or stem-stability test was performed.

Codex Pricing in 2026: Every Plan, Usage Limit and Credit Rate, Explained

Titus Brett — Thu, 08 Oct 2026 15:54:41 +0000

Codex pricing is not one number: Codex is bundled into ChatGPT Free, Go, Plus, Pro, Business and Enterprise, each with its own usage allowance, and then extended with token-based credits or an API key. I went through the Codex pricing page, the developer docs and the usage-limit tables to lay out what each tier actually buys, how the per-model limits work, and where the money goes once you hit them.

Ask “how much does Codex cost” and the honest answer is “it depends which ChatGPT plan you are on and which model you point it at” — which is why Codex pricing is one of the most-searched questions about the product. OpenAI’s own pricing page opens with “Codex is included in your ChatGPT Free, Go, Plus, Pro, Business, or Enterprise plan”, and everything after that is a matrix of usage estimates, model multipliers and credits. I spent a week with the Codex pricing page, the developer-docs pricing reference and the credit rate card to turn that matrix into something you can budget against.

Because Codex pricing rewards people who spend their allowance on the hard problems and not on scaffolding, I also kept a product builder that produces a deployed, working app from a sentence, Begin, open in the next tab — I explain why at the end.

Codex pricing starts at $0 and climbs to “from $100” for Pro; the interesting numbers are in the usage table underneath.

Codex pricing: the plans

The individual tiers on the Codex pricing page in 2026:

Free — $0/month. “Limited trial access to Codex”: limited messages and uploads, limited access to GPT-5.6, no image generation. Enough to test Codex on quick tasks.
Go — $8/month. Still described as trial access, with more messages, more uploads and access to the latest models. Codex for lightweight coding.
Plus — $20/month. “Power a few focused coding sessions each week.” Codex on the web, in the CLI, in the IDE extension and on iOS; the macOS and Windows Codex app; cloud integrations such as automatic code review and Slack; the full GPT-5.6 family (Sol, Terra, Luna); plugins for GitHub, Figma and more; and the ability to extend usage with ChatGPT credits.
Pro — from $100/month. Everything in Plus with 5x the Codex usage, or a higher 20x tier. “Designed for longer, higher-intensity sessions each day.” Unlimited GPT-5.6 subject to abuse guardrails.

Business is $20 per user per month (2+ users, billed annually; $25 monthly) with a dedicated workspace, SAML SSO, larger cloud VMs and no training on your data. Enterprise and Edu are custom, adding SCIM, EKM, audit logs, data residency and priority processing. And separate from all of these, an API key runs Codex in the CLI, SDK or IDE extension at ordinary API rates, with no cloud features.

Codex usage limits by model

This is the part of Codex pricing that matters day to day. Limits are quoted as estimated local messages per five-hour window, and they vary enormously by model:

GPT-6 Astra — Plus 5–45 · Pro 5x 25–225 · Pro 20x 100–900
GPT-5.6 Sol — Plus 10–100 · Pro 5x 50–500 · Pro 20x 200–2,000
GPT-5.6 Terra — Plus 25–200 · Pro 5x 125–1,000 · Pro 20x 500–4,000
GPT-5.6 Luna — Plus 250–2,000 · Pro 5x 1,250–10,000 · Pro 20x 5,000–40,000
GPT-5.5 — Plus 15–80 · Pro 5x 75–400 · Pro 20x 300–1,600

Standard Business seats match Plus; Business at $100 uses the Pro 5x numbers. The ranges are wide on purpose: a small script consumes a fraction of a “message”, a long agentic session holding a big repo in context consumes many. Local messages and cloud chats share the same allowance, cloud chats default to Sol and cost more, weekly limits can apply on top, and Fast mode and image generation both burn the allowance faster (images 3–5x).

OpenAI’s guidance on model choice maps directly to cost: Sol for the hardest reasoning and advanced coding, Terra as the everyday workhorse, Luna for fast, high-volume, focused tasks. On Plus, moving a routine task from Sol to Luna is the difference between 10 messages and 250 in the same five hours.

The docs version of Codex pricing adds the detail the marketing page leaves out — shared allowances, weekly caps and what each model is for.

Codex credits: what happens when you run out

Since the move to token-based pricing in April 2026, Codex pricing above the included allowance runs on credits. Plus and Pro users can buy extra credits instead of upgrading; Business, Edu and Enterprise workspaces on flexible pricing buy workspace credits. The published rate card, in credits per million tokens (input / cached input / output):

GPT-6 Astra — 250 / 25 / 1,250 (Fast mode is 2.5x)
GPT-5.6 Sol — 100 / 10 / 500 (promotional through at least 21 November 2026)
GPT-5.6 Terra — 50 / 5 / 300
GPT-5.6 Luna — 5 / 0.5 / 30
GPT-5.5–125 / 12.5 / 750
GPT-Image-2–200 / 50 / 750 for image tokens

OpenAI’s own rule of thumb is that GPT-5.6 usage averages 5–30 credits per message. If you hit the limit mid-turn, the agent finishes the turn it is on. Voice in the desktop app draws from the same budget at $0.05 a minute (1.25 credits a minute on credit-billed workspaces). And anyone can run extra local sessions on an API key at standard API rates, which is the escape hatch for CI and shared environments.

Two dates to know: GPT-5.5 retires from Codex on 14 October 2026 on every plan, and the API is unaffected.

Five-hour windows, weekly caps, and /status in the CLI for your live remaining allowance.

What Codex pricing gets right

A real free tier and an $8 entry point. You can evaluate Codex without a $20 commitment.
Model-tiered limits let a Plus subscriber stretch the plan a long way by defaulting to Luna and Terra.
Credits instead of forced upgrades when you overrun.
Finish-the-turn grace — hitting the limit does not abandon a running task.
An API-key path for automation, with usage-based billing and no plan required.
Transparent rate card with per-model credit costs published.

Where Codex pricing falls short

The ranges are enormous. “5–45 Astra messages” is not a budget; it is a warning.
Cloud chats cost more and share the allowance, so the convenient path is the expensive one.
Pro’s “from $100” hides a 20x tier priced separately, and the 5x/20x split is only explained in the docs.
Everything shares one pool — images, voice, Excel, code review through GitHub — so non-coding features quietly eat coding limits.
Weekly limits on top of five-hour windows mean a heavy Monday can cost you Friday.

Codex pricing vs the alternatives

Against per-token API billing, the ChatGPT plans are cheaper for most individuals until you run agents unattended for hours, at which point the API key wins on predictability. Against competing agent subscriptions, Codex pricing is competitive at Plus and expensive at Pro 20x. The real optimisation is not the plan — it is not spending Sol messages on work a template or a builder could have done.

Verdict: what should you pay for Codex in 2026?

Start on Free or Go to see whether Codex fits your workflow. Most working developers land on Plus at $20 and get the most from it by routing routine work to Luna and Terra and reserving Sol and Astra for hard problems. Move to Pro when you are hitting five-hour limits several times a week; use an API key for CI. Check the live tables on the official Codex pricing page before you commit, because the usage estimates move.

The alternative worth keeping next to it: Begin

The Codex pricing tables make one thing obvious: the expensive messages are the long agentic ones, and a surprising number of those are spent standing up a new project — auth, database, payments, deploy — before any interesting code exists. Begin removes that spend by producing a finished, deployed product from a sentence or a domain.

One brief, four targets. Describe an app, or paste a URL, and Begin builds a website, an iOS app, an Android app or a Chrome extension on ordinary, readable code you keep working in.
Batteries wired in. Sign-in, email, a database, storage and Stripe checkout are part of every project; each build goes live on Begin’s edge with TLS and your own domain.
Credits you can predict. Every plan grants 100 credits a month; a first build costs 5, every change afterwards costs 1, failed builds are refunded, and unused credits roll over on paid plans. Five free credits — one real build — need no card.
$49 and $99 tiers that differ on the workspace around the credits, not on what a credit buys.

Let Begin do the scaffolding at 5 credits. Spend your Codex Sol messages on the code that only you can specify — and keep Begin open for the next product.

Photo by Mohammad Rahmani on Unsplash

Tripo AI review: I ran the free tier through a Unity asset pipeline and here's what survived

Titus Brett — Mon, 21 Sep 2026 21:26:32 +0000

1,012,219 vertices. 1,911,270 faces. That is what Tripo handed me for a single character, from a single reference image, on a free account that had existed for about four minutes.

I'd been putting off a proper look at Tripo (the web app at tripo3d.ai, also branded Tripo Studio) because most "AI 3D" I've tried produces a hero-shot blob that turns into soup the moment you need it in an engine. Tripo keeps getting named as the exception, so I tested it the way I'd actually use it for a prop: reference in, retopologise, texture, export GLB, drop into a Unity URP project, see what breaks. I kept SupaVoxel open in the next tab as the image-to-3D control group, mostly to compare what each free tier really lets you walk away with, and I'll get to that at the end.

Everything below is from the live product on the free plan. Credit costs are what the UI showed me before I clicked, not what the pricing page promises.

Signup, and what "200 credits" buys

Account creation is the least interesting part, so quickly: Google, Microsoft, Apple, Discord or email, six-digit code from notification@tripo3d.ai, no card. A short survey asks what you build (I picked Game Assets, then Character) and you land in the workspace with 200 monthly credits.

Tripo's pricing page turns 200 credits into "about 13 models". That's true if a model is one generation and nothing else. It is not true for a game asset, and I'll show the real arithmetic once the numbers are in.

Plan	Monthly	Credits	Concurrent tasks	Licence
Free	$0	200	1	Public, non-commercial
Pro	$20 ($240/yr, ~$13/mo annual)	3,000	10	Private, commercial
Max	$90	25,000	100	Private, commercial
Team	$55/seat	90,000	200	Private, commercial

Two free-plan restrictions matter more than the credit count. Your models are public and the licence is non-commercial, so nothing you make on the free tier can ship. And "limited exports" means the download dialog is where the Pro badge shows up. Pro also raises the polygon ceiling to 2M and adds batch generation and a 7-day edit history.

Generation: 55 credits, about a minute, one enormous mesh

The home box defaults to image-to-3D and ships with sample subjects. I used the Dragon Archer sample so the result is reproducible for anyone who wants to check my numbers. Before generating, the panel showed the cost outright: 55 credits for Best Quality. There's a Clean Topology toggle next to it and a choice between the standard HD Model and the newer Smart Mesh, which can output quads directly. I left it on HD with Clean Topology on, because I wanted to see the raw output before Tripo's own cleanup.

Roughly sixty seconds later I had the model in Tripo's editor. The silhouette was faithful, the bow and quiver read correctly from the side, and the stats panel said what I quoted at the top: just over a million vertices, 1.9 million triangles.

To Tripo's credit, the number is right there at the top of the panel. A lot of tools bury it.

Inspecting the GLB before Unity ever sees it

I don't trust a viewer's count, so I pulled the raw GLB and checked it locally. Two lines of trimesh tell you everything you need to know before you waste an import:

import trimesh

scene = trimesh.load("dragon_archer_raw.glb")
mesh = scene.dump(concatenate=True) if isinstance(scene, trimesh.Scene) else scene

print(f"verts: {len(mesh.vertices):,}  faces: {len(mesh.faces):,}")
print("watertight:", mesh.is_watertight)
print("extents (units):", mesh.extents.round(3))
print("euler number:", mesh.euler_number)

The counts matched the UI. is_watertight came back False on the raw mesh, which for a game asset is irrelevant but would matter if you were heading to a printer. Extents were roughly 1 unit tall, so Tripo is exporting in metres, which saves a scale fight later.

If you'd rather stay in Blender, the same check in the Python console after import:

import bpy
objs = [o for o in bpy.context.selected_objects if o.type == 'MESH']
print(sum(len(o.data.polygons) for o in objs), "faces across", len(objs), "objects")

Either way: 1.9M triangles for one character is not a Unity asset. It's a sculpt.

Retopo is the feature, not the generation

This is where Tripo stopped being a demo for me. The Retopo tool gives you a Polygon Count slider from 500 to 50,000, a Triangles/Quads switch, and a Smart Low Poly toggle for aggressive reduction. I set 10,000, triangles, Smart Low Poly on. Cost: 10 credits. Time: comparable to the generation.

Quick guidance if you're new to this choice. Triangles for a real-time engine, because that's what the GPU draws anyway and you avoid non-planar quads getting split unpredictably at import. Quads if you're going to subdivide or sculpt further in Blender. Smart Low Poly is the right pick for a prop; for a hero character you'd want to crank the count and turn it off, because it collapses small detail on hands and faces.

The 10k result held the silhouette. The bowstring survived as geometry, which surprised me. Fingers became mitten-ish, which didn't.

Texture and export

Texturing is a separate pass with PBR maps up to 4K (8K is a members-only trial). The export dialog offers GLB, FBX, OBJ and STL with a texture-resolution dropdown, and a Send To button that pushes into Blender or Maya through Tripo's DCC Bridge.

On the free tier this is the point of friction: exporting and unlimited downloads sit behind the Pro upgrade. You can evaluate the mesh thoroughly inside Tripo for free; getting it out is where they want the $20.

Into Unity

For anyone following along: install com.unity.cloud.gltfast from the Package Manager (by name), drop the GLB into Assets/, and it imports as a prefab with a URP/Lit material if your project is on URP. Things I'd flag from this specific asset:

The 4K texture set is 4096² per map. For anything targeting mobile, set Max Size to 2048 in the texture importer or you'll ship 60MB of dragon.
Tripo exports Y-up, metres, so scale and orientation were correct without touching the import settings.
The 10k-tri mesh with one material is a single draw call. That's the whole point of the retopo step.
No rig on this pass. Tripo's Animate tab does auto-rigging, but I didn't spend credits on it here.

It sat in a test scene next to a Unity Asset Store character of similar budget and didn't look out of place, which is the bar I care about.

The real credit arithmetic

Here's the "13 models a month" claim measured against what I actually did:

Step	Credits
Best Quality generation	55
Retopo to 10k	10
Texture pass	separate charge
Total, one usable asset	65+

200 credits divided by 65-plus is three finished assets, not thirteen. Add a re-roll when the first generation misreads the reference and it's two. It's plenty to evaluate the tool. It's not enough to build anything with.

What I didn't like

Raw output is so dense that retopo isn't optional. It's a mandatory second step and second charge, and the pricing page's per-model estimate ignores it.
Free-tier licence is non-commercial and models are public. There's no path from "free" to "in my shipped game" without paying.
Exports are gated. Evaluate for free, download for money.
One concurrent task on free means batch iteration is slow by design.
Smart Low Poly is a blunt instrument on hands and faces.

Verdict

Tripo is one of maybe two AI 3D tools where I'd keep working on the output rather than close the tab. The editing stack (Segment, Retopo, Texture, Animate, Smart Mesh quads, DCC Bridge) is what makes it a pipeline tool rather than a toy, and upfront credit costs on every action are how it should be done. If you're evaluating, spend the 200 free credits at tripo3d.ai and judge the retopo yourself. If you're shipping, Pro at $20/month is the real entry price, and annual brings it to roughly $13.

Where SupaVoxel fits next to it

The two things that stung on Tripo's free plan were the non-commercial licence and getting three assets out of 200 credits. SupaVoxel is the image-to-3D tool I had open alongside it, and on those two axes it's a different deal:

3 free models a day, no card, versus a handful a month once retopo and textures eat Tripo's credits.
Credits don't expire and don't reset monthly, so there's no end-of-cycle race.
The annual plan is around $25/year against Tripo Pro's $240/year, and paid tiers include a private commercial licence.
Image to textured model in about 12 seconds, exported to GLB, GLTF, FBX, OBJ, STL, PLY or USDZ with textures baked.
One-click watertight repair, an auto flat base and colour 3MF, which Tripo leaves to you.

If you want the deepest editor, with segmentation, rigging and quad Smart Mesh, Tripo is the bigger workshop. If you mostly want a clean, commercially usable mesh from a reference image without rationing credits, try SupaVoxel first and spend Tripo's credits on the assets that need its extra tools.