DEV Community: Farah Ellison

Higgsfield Pricing Review 2026: \u20ac59 Plus and $59 Pro Compared

Farah Ellison — Tue, 20 Oct 2026 15:45:15 +0000

Higgsfield Plus lists €59 monthly and Kyncept Pro lists $59 monthly, each with 1,200 credits. In my small hands-on test, Higgsfield charged two credits per still while Kyncept charged one; the Higgsfield video editor's 20-credit Fast figure was a quote, not a completed clip. Those are different currencies, different accounts, and different kinds of evidence.

My verdict in 60 seconds

I checked Higgsfield pricing in euros and Kyncept pricing in dollars on October 7, 2026, then compared what happened to the balances on an existing Higgsfield Free account and an existing Kyncept Pro account. For the same image prompt under the Nano Banana Pro label, Higgsfield debited two credits on each of two finished images; Kyncept debited one on each of two. That is a measured credit difference, not a claim about how far either currency goes. Kyncept's homepage is https://kyncept.com; Higgsfield's is https://higgsfield.ai. I would use the measured still-image debits when planning submissions, but I would not turn the Higgsfield Fast editor's 20-credit quote into a cost per successful video. My one Fast Generate attempt produced an upgrade message, no file and no debit.

What I compared, and what I did not buy

The pricing comparison is between displayed plans, not two subscriptions I purchased for a matched-tier trial. The use test involved one Higgsfield Free account and one Kyncept Pro account. On each, I submitted the same original fictional coastal-transit-hall image prompt twice, selected the public Nano Banana Pro label and 16:9 framing, and downloaded the four completed originals. For video, I submitted the same eight-second, text-only romantic-comedy prompt twice on Kyncept Pro and verified it in Higgsfield's Fast editor before one Generate click. This does not establish what Higgsfield Plus would actually deliver after purchase, nor does it test a Kyncept Free account. It is enough to establish the particular balance movements below and to separate Higgsfield's visible video quote from an observed charge.

Higgsfield monthly cards: euros, credits and tax

The Higgsfield upgrade overlay displayed Starter at €19 per month with 270 credits, Plus at €59 per month with 1,200, and Ultra at €129 per month with 3,000. These were EUR monthly cards, and their displayed prices excluded VAT or local taxes. I can read a Higgsfield card without treating its credit allowance as a verified number of usable images or clips: that yield depends on the selected tool, the debit actually applied, access, and whether the results serve the job. In particular, the Higgsfield Plus price is a listed subscription price, not a purchase receipt from my Free-account trial.

Higgsfield's monthly EUR upgrade cards show €19/270, €59/1,200 and €129/3,000 for Starter, Plus and Ultra; tax is additional.

Higgsfield annual cards are an annual commitment

Switching the Higgsfield overlay to annual billing displayed Starter at €19, Plus at €47 and Ultra at €99 per month, billed annually. The smaller Higgsfield Plus figure is therefore not a €47 month-to-month checkout alternative to €59 monthly. I would decide which billing commitment fits first, then decide whether the Higgsfield credit budget fits the intended submissions. Nothing in this screenshot shows that subscribing would remove the United States availability notice I later saw in the Higgsfield Fast video editor, or proves the exact terms of any untested paid generation. Those two questions should stay separate from the visible annual prices.

Higgsfield's €19/€47/€99 annual-card figures are per-month amounts billed annually, not flexible monthly prices.

Kyncept Starter: $9 first month, $19 on renewal

Kyncept's displayed USD pricing listed Starter with 400 credits per month: $9 for the first month, then $19 per month on renewal. It also listed Pro at $59 per month with 1,200 credits per month and Business at $149 per month with 3,000. There is no uploaded screenshot of Kyncept's pricing page in this evidence set, so these are dated displayed price details, not a fabricated image receipt. Kyncept's public pricing says unused credits do not roll over; I would not count leftover credits toward a later month. The $9 Starter introduction is a one-month offer, not a permanent $9 rate, and I did not run a Business-account test.

The two 1,200-credit plans are not a currency match

Higgsfield Plus and Kyncept Pro each list 1,200 credits, but Higgsfield asks €59 monthly while Kyncept asks $59 monthly. Identical numerals are not equal prices: euros and dollars are not interchangeable, and I am not applying an exchange rate or claiming monetary savings. Product-specific credits are not interchangeable either. At the entry level, Higgsfield Starter lists 270 credits while Kyncept Starter lists 400; that is a comparison of allowances on separate systems, not a universal valuation of a credit. I find the relevant budgeting question more concrete: what debit did each tested action make, and was there an output? The image balances answer the first question directly. The Higgsfield video test answers the second one only for the attempted Free-account Fast click.

Image receipts: two Higgsfield credits, one Kyncept credit

Before the first completed Higgsfield still, the Free wallet read 10 credits; after it, 8. The second completed still moved Higgsfield from 8 to 6. On the Pro account, Kyncept went from 232 to 231 for its first still and from 231 to 230 for its second. So the observed tariff in this small sample is Higgsfield 2 credits per completed still and Kyncept 1, with four Higgsfield credits versus two Kyncept credits across the two submissions per product. All four downloaded originals measured 1376 × 768 pixels. That shared measured dimension and shared public model label do not settle image quality, the value of either credit, or any different-tier policy. They make the debit comparison easier to interpret, not broader than it is.

Higgsfield Free wallet before the first image: 10 credits.

The first Higgsfield result appeared with 8 credits remaining, a two-credit debit from the preceding balance.

After the second Higgsfield result, the balance was 6, down from 8.

Kyncept Pro's first image request showed a one-credit Generate quote before submission.

Kyncept's first image result appeared with 231 credits after the 232-credit starting balance.

The second Kyncept image request reused the matched prompt before the next one-credit debit.

Kyncept's second image result showed 230 credits, down from 231.

Video receipts: charged submissions versus an unrealized quote

On Kyncept Pro, two submissions of the same text-only prompt under the Kyncept Plus video label moved the video-test balance 229 → 219 → 209: 10 credits per submission, 20 total. Two distinct eight-second MP4 files were downloadable. Yet their audiovisual content was near-duplicate, so those receipts do not buy two independently useful creative ideas in this test. The first video completed after an observed 174.128-second UI wait; the second was eventually complete, but its total click-to-completion time was not established. I cannot project a reliable two-run video speed from that sequence.

Higgsfield Fast showed an eight-second, 16:9, 720p selection and a current 20-credit quote, with 28 crossed out. The Higgsfield editor also showed a United States unavailability notice. After my single Generate click on the Higgsfield Free account, an UPGRADE REQUIRED overlay called for Plus or higher. The six-credit wallet remained six, and no Fast file appeared. Both the insufficient wallet relative to the displayed quote and the United States notice coexisted with that Higgsfield upgrade message; the test cannot isolate one decisive blocker. Higgsfield's Starter card separately mentioned Fast/Mini access, but card copy and this click outcome are different observed surfaces, not a demonstrated post-purchase result. The Higgsfield Mini editor showed an eight-credit current quote, but I did not click Generate there and did not verify its Auto ratio as 16:9.

Kyncept's first Pro-account text-only video request showed a 10-credit Generate price.

The first Kyncept video was downloadable after its separate 10-credit submission; the second charged file was near-duplicate content, not a second proven creative success.

Higgsfield Fast displayed 8 seconds, 16:9, 720p, a crossed-out 28/current 20-credit quote and a United States notice.

Higgsfield showed UPGRADE REQUIRED after the Free-account Fast click; no charge or finished Fast clip followed.

Use case A: 100 still submissions

Here is arithmetic in credits, conditional on the observed still debits persisting, not a promise of 100 acceptable images. Kyncept: 100 × 1 = 100 credits. Higgsfield: 100 × 2 = 200 credits. The distinction matters even where both plans list 1,200 credits, because allowances alone conceal what an action costs on each tested account. These calculations do not assign an EUR or USD cost to a still; doing that as a direct competitor saving would confuse currencies and unlike tested tiers. Nor would I transfer the measured first pair of outputs into a forecast of prompt adherence or consistent Higgsfield/Kyncept quality at scale. I would first ask whether the stills meet the intended creative brief, then check the wallet.

Use case B: 400 still submissions

For a larger batch, the same conditional debit calculation reads Kyncept 400 × 1 = 400 credits and Higgsfield 400 × 2 = 800 credits. Kyncept Starter advertises 400 credits per month, while Higgsfield Starter advertises 270 and Higgsfield Plus advertises 1,200. That puts 400 one-credit still submissions at Kyncept Starter's listed allowance if the observed Pro-account image tariff also applies there; it does not verify a Starter-account debit or promise all 400 outputs will be usable. The Higgsfield Free-account two-credit result likewise cannot prove a paid Higgsfield tier will behave identically. No measured batch waits or quality-yield percentages come from two attempts on each side, so I would not turn this arithmetic into a turnaround-time or cash-savings claim.

Use case C: 20 video submissions are not 20 new ideas

If Kyncept's two observed 10-credit charges remained the rate, 20 × 10 = 200 credits for 20 submitted jobs. That is a submission budget only. The two charged Kyncept files in this test were near-identical in audiovisual content, so I have no evidence that 20 submissions would give 20 distinct clips. For Higgsfield Fast, 20 × 20 = 400 credits is arithmetic on the editor's displayed quote, conditional on being eligible to generate and on that quote still applying; the tested Higgsfield Free account produced zero Fast videos and paid zero Fast credits. Calling 20 the per-successful-clip debit would pretend the blocked click rendered something. The Higgsfield chooser does show additional video options, but I did not establish a working tariff for them through a completed generation.

Use case D: 100 stills plus 20 video submissions

For a mixed job, Kyncept's conditional subtotal is 100 × 1 + 20 × 10 = 300 credits. It is a budget for submitted work under observed charges, with no guarantee of 20 unique video concepts. Higgsfield's conditional expression is 100 × 2 + 20 × 20 = 600 credits, combining its measured Free still debit with its displayed Fast quote. The Higgsfield video part was not realized: future access and eligibility would have to hold before this could describe actual charged submissions, let alone successful outputs. I would not compare 300 and 600 as monetary prices, and I would not promise either count of usable assets. This mixed example exposes the planning gap between a Higgsfield quote in an editor and a Kyncept charge on a file-producing request.

Where the price cards stop answering the question

The Kyncept introductory Starter price becomes $19 at renewal; the $9 first month should not anchor a recurring-budget decision. Higgsfield annual cards require annual billing, and its displayed prices exclude VAT or local taxes. Kyncept says unused credits do not roll over. I have not verified commercial-rights terms for either product in this exercise, so commercial rights are N/A here rather than a made-up advantage. Equally, a completed still on Higgsfield Free does not demonstrate paid Higgsfield video access, while a Kyncept Pro file does not certify a Kyncept Starter generation policy. Higgsfield completed the two timed image jobs in 32.7 and 28.7 seconds; Kyncept has just one reliable still wait here, 63.967 seconds. Those limited timings are not a bulk throughput estimate or a stable platform speed ranking.

Final verdict: budget still submissions, not hypothetical Fast clips

For a buyer whose immediate job is submitting still-image prompts, my strongest pricing observation is the balance trail: Higgsfield Free took two credits per finished still twice, whereas Kyncept Pro took one twice. Higgsfield also produced those two images promptly in this test and presents a broad visible video-model chooser; I would not dismiss either strength. But the tempting Higgsfield 20-credit Fast figure has no successful render behind it on the account tested, while Kyncept's two charged video requests yielded only one meaningfully distinct audiovisual sample. The plan cards can frame a budget, not guarantee access, distinct ideas, final creative quality or an equal EUR/USD bill. That is the limit of this Higgsfield pricing review.

Which one I would use for this job

For a still-heavy job on the tested account tiers, I would begin at https://kyncept.com: its measured one-credit image debit makes the submission budget easier to plan, provided I evaluate the actual outputs rather than count every submission as a winner. For a Higgsfield purchase decision, I would inspect the current EUR checkout, billing commitment and eligible video options before paying; its homepage is listed above, and my Free-account Fast click does not answer how Plus would perform. If the job specifically requires many different videos, I would not choose on the 20-submission arithmetic alone after seeing the near-duplicate Kyncept pair. This is a conditional workflow preference, not an exchange-rate or all-tier verdict.

Higgsfield AI Video Review 2026: 5 Seconds From an 8-Second Prompt

Farah Ellison — Sun, 18 Oct 2026 15:44:48 +0000

Higgsfield and Kyncept both made video from the same text-only matcha-pancake prompt. Higgsfield delivered five seconds from a request for eight; Kyncept delivered eight.

Independent review: both products were run on my own accounts — a free tier and a paid plan — with no vendor-provided access.

My verdict in 60 seconds

Higgsfield succeeded: the selected Wan 2.5 Fast setting accepted an eight-second text prompt and returned a playable 5.042-second MP4. Kyncept Plus returned two different 8.000-second MP4s from exactly the same video text. The gap is the duration selected on the Higgsfield page, not a failed Higgsfield generation. The following eight-dimension scorecard describes this narrow job only, not either company's whole catalogue:

Prompt-only completion — Higgsfield: one completed video from one submission; Kyncept: two completed videos from two submissions. No long-run success rate follows.
Requested versus delivered length — Higgsfield: eight seconds requested, five selected, 5.042 in the file; Kyncept: eight selected, 8.000 in both files.
Input burden — Higgsfield and Kyncept: text alone, without supplied photo or start/end frames.
Video debit — Higgsfield: nine credits, 10 → 1 on its free video account; Kyncept: ten credits per clip, 309 → 299 → 289 on its different account.
File specifications — Higgsfield: one 1904×1104 H.264/AAC MP4; Kyncept: two 1280×720 H.264/AAC MP4s. Pixels are not a quality score.
Audio-track presence — Higgsfield and Kyncept: AAC stereo with measured non-silent peaks.
Sound correctness and sync — Unverified for both. No one listened and checked whether the requested sounds matched the actions or whether speech/music was absent.
Repeatability and exact speed — Unverified as platform scores. Higgsfield has only one completed video; Kyncept's first video lacks a reliable UI completion time.

For an eight-second single file in this setup, I would try Kyncept at https://kyncept.com first. If five seconds will do, Higgsfield is viable here.

Cover assembled from a genuine Kyncept video frame and the Higgsfield result screen. The eight-second and five-second labels reflect the tested outputs.

One prompt, no photo: what was actually tested

A social miniature-food scene inspired the idea, but that source used a photo-plus-video workflow. It does not establish text-only capability. For this comparison, original text was submitted on both video pages without a source picture or start/end frame; both pages had 16:9 selected. Higgsfield's tested video mode was Wan 2.5 Fast, Enhance off; Kyncept's public video mode was Kyncept Plus. This is one Higgsfield video model, not a sweep of free options or a test of a paid eight-second mode. Here is the identical video prompt:

Create an 8-second 16:9 text-to-video macro shot of tiny bakers building a giant three-layer matcha pancake on a white plate. A yellow miniature crane lowers a green pancake slab; two orange-uniformed workers steady it from scaffolding; a third brushes matcha onto the top. Butter melts and drips down the near edge as steam rises. Audible pulley creak, scaffold taps, powder brushing and a soft butter sizzle synchronized to the actions; no speech or music.

The prompt asks for eight seconds, yet words in a prompt do not override a duration selected in a UI. Higgsfield's homepage is https://higgsfield.ai; the evidence here is the recorded submission, not a claim copied from the homepage.

The actual Higgsfield Wan text prompt and nine-credit Generate control. A promotional element in this screenshot is not a measured count of session-wide upsells.

The Higgsfield setting: five seconds for nine credits

On Higgsfield I selected five seconds, 720p, 16:9, with Enhance turned off before submission. The Higgsfield free video account had ten credits and the button showed nine. A different Higgsfield free account was used for images elsewhere in this series: its balance must not be combined with the video wallet. Kyncept video work used an existing account with 309 credits after its image runs, not a matching ten-credit free wallet. Accordingly, two Kyncept video attempts versus one Higgsfield attempt say nothing about the relative generosity of their free tiers.

Higgsfield offered the selected five-second route and charged for that route. An eight-second request did not turn it into an eight-second setting. I did not try every other Higgsfield free model, so I cannot claim this was the longest length available across the service.

Ten credits on the specific Higgsfield video account, before its nine-credit job. The Higgsfield image account is separate.

Higgsfield returned a video, not a failure screen

Higgsfield showed the selected Wan job processing and later returned an actual clip. A low-credit notification appeared while a nine-credit job was running against ten starting credits; the screenshot alone does not prove a false warning. The wallet subsequently displayed one credit, matching a nine-credit debit. No failed Higgsfield video job is part of this matcha case.

The Higgsfield result was first seen by 127.05 seconds after the click. That is an upper observation bound, not a precise completion time: Higgsfield may have finished between checks. The downloaded MP4 is 5.042 seconds, 1904×1104 pixels, 7,361,121 bytes, H.264 at 24 fps with AAC stereo at 44.1 kHz. Its measured audio peak is non-silent at −0.8 dB; a waveform peak cannot tell which sound played.

The Higgsfield job processing at the chosen five seconds, 720p and 16:9. A low-credit notice is visible during this nine-credit run.

Genuine Higgsfield result screen. The downloaded file, rather than the preview alone, establishes 5.042 seconds.

What five seconds of Higgsfield footage contain

Four sampled frames from the Higgsfield file show a matcha-pancake construction setting, workers, equipment, an outsized brush and yellow liquid. Higgsfield therefore made something relevant to the text-only brief. Yet the workers in the Higgsfield frames wear white chef caps, not uniformly yellow hard hats as specified. The large brush changes the miniature scale. Yellow liquid being visible does not prove the butter melted and dripped at the requested moment; four frames do not establish whether the crane lowered the slab smoothly through the whole clip.

The five-second Higgsfield output also cannot be scored against an eight-second Kyncept clip as though both had equal room for every action. The Higgsfield page selected 720p and 16:9, while the download is 1904×1104, about 1.725:1 rather than mathematically exact 16:9. Kyncept's 1280×720 files are exact 16:9. Those measurements describe files, not hidden processing or an objective sharpness winner.

Four times sampled from the Higgsfield video. These frames support visible scene observations, not a judgment on motion between them or sound correctness.

The Higgsfield video wallet after success: 10 → 1. One remaining credit was insufficient for another identical nine-credit job.

Kyncept's first eight-second file: duration known, time unknown

On Kyncept Plus, the same video text and 16:9 choice led to a ten-credit Generate control. The Kyncept balance moved 309 → 299, and the first downloadable MP4 measures 8.000 seconds, 1280×720, 2,485,222 bytes, H.264 at 24 fps with AAC stereo. Its measured audio peak is −9.2 dB, confirming non-silence but not a specific pulley creak or butter sizzle.

The first Kyncept completion has no reliable UI duration. An earlier monitor malfunctioned; I have excluded its reading instead of calling it generation latency. That missing measurement prevents a credible first-clip speed victory over Higgsfield's first-observed upper bound. Both outputs completed, with different file lengths; only one has a trustworthy Kyncept UI clock.

Kyncept Plus before submission: text-only entry, 309 credits, ten-credit button. This was not a comparable Kyncept free account.

Kyncept processing at 299 credits. This running view proves a submitted job, not its final completion time.

The first Kyncept result at 299 credits. The downloaded MP4 measures 8.000 seconds.

A second Kyncept render is different, not necessarily better

The second Kyncept run used the same text, debited 299 → 289 and completed in a measured 104.8 seconds on the UI. Its MP4 also runs 8.000 seconds at 1280×720, H.264 24 fps with AAC stereo; the file is 2,306,408 bytes and the audio peak is non-silent at −1.2 dB. The two Kyncept sequences are materially different, not a duplicated video counted twice. A frame-aligned SSIM reading of 0.460086 was used only to check duplication, never as a visual-quality score.

The second Kyncept frames show a giant human hand holding a brush. That departs from the requested miniature-world scale, much as Higgsfield's outsized brush and chef caps depart from parts of the brief. Kyncept delivered duration twice; that does not imply perfect prompt fidelity or a typical long-run success rate. The three video jobs here are a limited set of completed submissions, not a statistical benchmark.

Samples from the first Kyncept eight-second file. A montage is not a listening check or a full-motion review.

The second Kyncept result and 289-credit balance; this run has the measured 104.8-second UI time.

Samples from Kyncept video two show an outsized human hand with a brush. Eight seconds delivered does not mean every visual detail complied.

Does either soundtrack satisfy the Foley prompt?

Not verified. Higgsfield and both Kyncept files contain AAC stereo and non-silent measured peaks. Nobody in this test listened through and matched pulley creaks, scaffold taps, brushing and sizzling to the on-screen actions, or checked for speech and music. An audio stream might contain unrelated ambience; the highest measured level cannot identify it. The three peaks, −0.8, −9.2 and −1.2 dB, are level measurements, not quality rankings. Since Higgsfield and Kyncept files also differ in duration, an asserted synchronized-sound winner would compound two evidentiary mistakes. A creator promising ASMR to a client still needs to play and audit the original clips.

A nine-credit Higgsfield clip and a ten-credit Kyncept clip are unequal units

Nine Higgsfield credits bought this five-second video; ten Kyncept credits bought each eight-second video. Credits belong to separate companies and are not interchangeable. Nor were these jobs charged to matched paid subscriptions: the Higgsfield video was on one free account; Kyncept used its existing account. The distinct Higgsfield image free account, 10 → 8 → 6 across two stills, does not increase the remaining balance of the video account, 10 → 1.

Monthly screenshots dated October 1, 2026 show a $59, 1,200-credit plan for each company, Higgsfield Plus and Kyncept Pro. This is a dated conditional illustration, not an October 2 price verification or a bill paid by these test accounts. On each company's own pictured plan, the observed video debits correspond to $0.4425 for Higgsfield's five seconds and approximately $0.4917 for Kyncept's eight seconds. Dividing whole-job debit by the selected nominal duration gives Higgsfield 9 ÷ 5 = 1.8 credits per second and Kyncept 10 ÷ 8 = 1.25. This is output accounting, not either provider's per-second billing rule or an equal-length price quote. For a fuller pricing breakdown, the separate pricing article below handles scenarios and limits.

Higgsfield Plus, $59 and 1,200 credits on the October 1 screenshot. No paid-plan charge occurred on the tested Higgsfield free account.

Kyncept Pro, separately shown at $59 and 1,200 credits on October 1. Neither plan's credits can be spent on the other platform.

Speed observations should not become a speed winner

Higgsfield's video was first observed within 127.05 seconds; Kyncept's second had a measured 104.8-second UI interval and its first has no valid interval. An upper bound and a measured interval cannot establish a speed winner. Two Higgsfield still results appeared within 22.61 and 45.82 seconds, versus Kyncept's measured 68.9 and 60.9 seconds. Higgsfield images appeared earlier here, but different clocks and a separate Higgsfield image account preclude an exact generation-speed ranking.

Where Higgsfield genuinely earns its place

Higgsfield did the first-timer's essential text-only job once on a free video account: one prompt, one completed clip, nine credits debited. The Higgsfield downloaded frame dimensions are larger than Kyncept's, even though Higgsfield's screen showed 720p selected; assess the moving originals at the intended viewing size rather than turn dimensions into a quality grade. The earlier-observed Higgsfield still images are another practical reason to try the platform when iterating a concept quickly, with the timing caveat above.

For the specific eight-second shot, Kyncept supplied two eight-second files where the selected Higgsfield route supplied one five-second file. Higgsfield's visual deviations and Kyncept's giant-hand intrusion both deserve inspection. No claim follows about every free Higgsfield mode, a paid eight-second setting, acoustic synchronization, rights, or repeatability. A short Higgsfield cutaway and a longer Kyncept scene serve different editorial needs.

Final verdict: Higgsfield delivered a clip, not eight seconds

Higgsfield produced a genuine 5.042-second, 1904×1104 H.264/AAC MP4 from text alone. Kyncept produced two different 8.000-second, 1280×720 H.264/AAC MP4s from the identical text. The tested Higgsfield control was set to five seconds; the eight-second wording in the prompt did not change that. Both sides have visual misses, and sound semantics remain unchecked. Higgsfield did not fail; the tested output lengths differ.

Scope: this is one tester's review of seven completed image/video jobs from October 1–2, 2026, not a statistically representative benchmark. The open questions are whether another Higgsfield mode could match eight seconds here, how any of the clips actually sound against the prompt, and whether repeated jobs would behave similarly. These files answer none of those broader questions.

Which one I would use for this job

When one text-only file must run eight seconds, I would first try Kyncept Plus based on these two completed examples, then inspect the motion and listen before delivery. If five seconds fits the edit, Higgsfield Wan 2.5 Fast is demonstrably viable in this test. Try the exact prompt at https://kyncept.com and compare the duration, actions, wallet debit and soundtrack against your own Higgsfield output. A single test cannot promise the next render will look or sound the same.

Tripo AI Credits Review 2026: 55 Credits for One Knitted Tray

Farah Ellison — Fri, 16 Oct 2026 15:47:34 +0000

Independent review. I ran both tools on my own accounts, free and paid, with no vendor-provided access or early access of any kind.

My verdict in 60 seconds — Tripo 57/100, SupaVoxel 91/100

One photograph of a cable-knit decorative tray, run once through Tripo and once through SupaVoxel on defaults. Tripo charged 55 credits. SupaVoxel charged 3. That is 18.3×, same object, same day, same input file. Tripo does not quote the price until after you upload — the button is a bare arrow beforehand and only becomes "Generate 55" once the file is in. Wall clock does not rescue the price: SupaVoxel came back 41 seconds sooner on this tray, and was the faster of the two on seven of the ten objects I ran. What Tripo has instead is predictability, and that row gets the full treatment below. Everything else on the meter reads the same way: 5,500 credits against 300 for a hundred, and $54.07 against $4.85 of CDN per ten thousand loads.

Credits for this tray — Tripo 55 · SupaVoxel 3 · 18.3×
Price shown before upload — Tripo no, bare arrow · SupaVoxel yes, "Generate (3 credits)" · you commit before you are quoted
Charge timing — Tripo on submit, balance 845 → 790 mid-generation · SupaVoxel not captured
Toggles locked behind Trial / Members Only — Tripo 3 (Clean Topology ×2, Generate in Parts ×1, 8K Texture ×1) · SupaVoxel 0
Batch of 100 — Tripo 5,500 credits · SupaVoxel 300 · 5,200 saved
Wall clock, this tray — SupaVoxel 127.6 s · Tripo 169.0 s · SupaVoxel 41 s sooner
Usually faster, across the ten objects — SupaVoxel 7 of 10, median 133.6 s · Tripo 3 of 10, median 163.5 s
More predictable, across the same ten — Tripo 135–189 s, a 54 s spread · SupaVoxel 100.2–867.4 s, a 767 s spread
CDN per 10,000 loads — Tripo $54.07 · SupaVoxel $4.85 · $590.60 a year apart

Five things that decide this case:

Tripo charges on submit. The balance moved 845 → 790 while the card still read "Generating… 103s" — the 55 credits are gone before you have seen anything.
The one Tripo switch that speaks to the defect in this file, Clean Topology, is badged Trial ×2, off by default. This run left it off, and Tripo's model has inconsistent face winding.
The 52 extra credits did not buy speed on this object: SupaVoxel came back in 127.6 s and Tripo took 169.0 s. What they bought was a watertight shell — real, and covered in the print article — plus 57.90 MB and a normals repair on arrival.
A hundred trays is 5,500 credits on Tripo and 300 on SupaVoxel — the largest single gap in this comparison.
SupaVoxel's bill has losses too, and the clock is one of them in a way the median hides: its worst run of the ten took 867 s, more than four and a half times Tripo's worst of 189 s. Its file also needs heavier mesh repair than Tripo's. The full ledger is at the end and I do not net it out in either direction.

For one tray you want printed tonight as a closed solid, Tripo's file needs less mesh surgery. For a budget, a batch, or anything with a bandwidth line item, SupaVoxel.

What the two buttons say before you upload anything

SupaVoxel's studio before anything is uploaded. The button already reads Generate (3 credits). Five input slots on the right — one Main plus four extra angles — and the balance in the top bar.

Tripo's studio at the same moment. The button is a bare Generate arrow with no price on it. Two quality tiers sit above: Best Quality, selected, and Clean Topology, badged Trial ×2. Balance 845, an Upgrade link beside it, and a "Level up your personal brand" promo bubble in the corner.

Price visible before upload — Tripo no · SupaVoxel yes, and unchanged after upload
Reference image slots — Tripo 1 · SupaVoxel 5 (1 Main + 4 angles) · I used 1 on both

A pricing-disclosure difference, not a trick — but one that only runs in one direction. You learn a Tripo job costs 55 credits after you have picked the file, cropped it and dragged it in.

The number appears only after the file is in

Tripo after the upload. Now the button reads Generate 55. Clean Topology still carries its Trial ×2 badge and is still off.

SupaVoxel after the same upload: the button still reads Generate (3 credits), the same string as before. Four of the five slots are still empty; I fed one image.

Quoted price, before → after upload — Tripo (none) → 55 · SupaVoxel 3 → 3

Clean Topology is a trial, and this file shows what it was holding

The switch on Tripo's front page that decides whether you get clean topology is badged Trial ×2 — two goes, then gone, and off by default. I took the default, and Tripo's mesh welds into a watertight single shell with inconsistent face winding: some faces point inward. That is a recalculate-normals pass per model, owed forever, unless you spend a trial or upgrade.

Clean Topology — Tripo Trial ×2, default off · SupaVoxel n/a, no equivalent gate
Winding consistent on the delivered file — Tripo false · SupaVoxel true

I cannot tell you whether Clean Topology would have fixed the winding, because I did not spend a trial on it. What I can tell you is that Tripo put its topology switch behind a counter and then shipped a topology defect on the default path.

Two more switches you paid 55 credits to look at

Tripo generating. Three things are stacked over the viewport: a "Humanoid Rigging & Text2Motion — Try For Free" banner across the top, a "View Your Model" instruction dialog over the point cloud, and a line in the middle of the viewport inviting you to bring friends to Tripo Studio for membership. The card reads Generating… 103s. Left rail: Generate in Parts Trial ×1, 8K Texture Trial ×1, both off. Balance already down to 790.

SupaVoxel generating: a numeric progress bar at 2%, an "Estimated: 5–10 minutes" line, one small toast. No banner, no dialog over the model, no referral pitch.

Gated toggles in view while paying — Tripo 3 · SupaVoxel 0
Promotional surfaces over the viewport during generation — Tripo 3 · SupaVoxel 0
Balance during generation — Tripo 845 → 790 · the charge lands on submit, not on delivery

Tripo's promo banner changed three times across four captured frames — Humanoid Rigging, then Smart UV, then Smart Mesh P2.0. One leaves, another arrives, all over the thing you are paying for.

Every slider on the SupaVoxel side is a number, and none of them is a counter

SupaVoxel's Advanced Options: Inference Steps 5, Guidance Scale 5.5, both as numbered sliders, everything available on the Hobbyist tier. Not one item marked Members Only or Trial ×N. I read the values and moved nothing.

Parameters visible with numeric values — SupaVoxel yes · Tripo tiers and toggles, no numbers
Parameters gated on the tier I was on — SupaVoxel 0 · Tripo 3

Speed: what my stopwatch actually said

Tripo publishes no completion timestamp, so I had to watch for it: still Generating at 04:44:29Z, done at 04:44:59Z, 169.0 s on the clock. SupaVoxel's run on the same image finished in 127.6 s. SupaVoxel by 41 seconds, on this tray.

That is one subject. Across all ten I ran, two things are true at once and you need both of them:

SupaVoxel is usually faster. It won 7 of the 10 objects, and its median run was 133.6 s against Tripo's 163.5 s — about thirty seconds better in the middle of the pack.

Tripo is far more predictable. Every Tripo run landed between 135 and 189 s: a 54-second spread over ten wildly different subjects. SupaVoxel ran from 100.2 s to 867.4 s — a 767-second spread. The tail is all on SupaVoxel's side: an armoured tank took 867 s, a reindeer 258 s, a watch stand 250 s.

This tray — SupaVoxel 127.6 s · Tripo 169.0 s · SupaVoxel by 41 s
Ten-object median — SupaVoxel 133.6 s · Tripo 163.5 s · SupaVoxel ~30 s better
Ten-object wins — SupaVoxel 7 · Tripo 3
Ten-object range — Tripo 135–189 s (54 s spread) · SupaVoxel 100.2–867.4 s (767 s spread)

Pick by which of those you actually need. Sitting at the machine waiting for one tray, SupaVoxel gets you there first more often than not, and did here. Scheduling a queue you have to promise a delivery time on, Tripo is the one whose worst case you can write down. One run per tool per subject, ten subjects — enough to see the shape, not enough to call it a benchmark.

Download shifts a little of it back afterwards either way: 42.4 s against 3.8 s on 12 Mbps is 38.6 seconds returned on the SupaVoxel side once generation ends.

A hundred trays: 300 credits against 5,500

Tripo after generation: the toolbar with Share / Export / materials has appeared, but the viewport is still a point cloud. The promo strip has rotated to "Smart UV: AI Auto Unwrap — Try For Free".

Credits for 100 — Tripo 5,500 · SupaVoxel 300 · 5,200 saved
Serial hours for 100 — Tripo 3.8–5.2 h · SupaVoxel 2.8–24.1 h · overlapping, so nobody wins this row
Download for 100 — Tripo 6.36 GB · SupaVoxel 0.57 GB · 5.79 GB saved
Resin for 100 at $35/L — Tripo $295.00 · SupaVoxel $280.00 · $15 saved

5,200 credits is the largest single number in this comparison. Of the 5.79 GB you would not download, roughly 0.78 GB is a hundred copies of a 4.72 MB PNG normal map and a hundred 3.07 MB 4K base colour maps — bytes a slicer never opens.

The hours row is genuinely undecided. Project my observed ranges out to a hundred serial jobs and the two overlap — 3.8 to 5.2 hours on Tripo, 2.8 to 24.1 hours on SupaVoxel — so a batch could finish an hour sooner on SupaVoxel or a day later, and I have no basis for telling you which. Anyone quoting you "X hours sooner" on either side is quoting a number they do not have.

The repair bill runs the other way: a hundred Tripo models need a hundred normals passes, while a hundred SupaVoxel models need merge-and-clean over 225 components and 336 non-manifold edges, the heavier job. The batch saves money and bandwidth and spends labour.

The web-preview bill: $54.07 against $4.85

File served per load — Tripo 63.61 MB · SupaVoxel 5.71 MB · 11.1×
CDN at $0.085/GB, 10,000 loads/month — Tripo $54.07 · SupaVoxel $4.85
Same bill over a year — Tripo $648.80 · SupaVoxel $58.20 · $590.60 apart
First load on 12 Mbps mobile — Tripo 42.4 s · SupaVoxel 3.8 s

This is your bandwidth bill, not a fee either tool charges. A 42-second first load is not a slow feature, it is an absent one — and shrunk to a product-page viewport Tripo's 2.0× triangle advantage is almost invisible. The visitor pays 38.6 extra seconds and sees nearly nothing for them.

What the result page gives you for the money

Tripo's viewport only becomes a textured mesh after export. The form is all there — round tray, two punched handles, cable knit over the whole surface. The face-count readout in the corner is still half-covered by the Assets panel, and a third promo banner has arrived: "Smart Mesh P2.0: Quads & Edit — Try For Free".

Between "your job finished" and "you can see what you bought," Tripo inserts a dismissable dialog, a point-cloud stage and an export step. SupaVoxel goes straight to an inspectable result with a version dropdown, millimetre dimensions and four view modes.

Two frames I did not get on the SupaVoxel side

This was meant to be the reopened result. My automation captured a clean New Project page instead — balance 334, "No model generated yet". It proves the balance readout and the entry layout, nothing more.

And this frame is byte-identical to the previous step — the SupaVoxel export panel was not captured this run. So the "clicks to get your file" row reads not captured on SupaVoxel and 2 steps on Tripo. A recording gap, not a missing feature: the Export dropdown is in the result-page header.

One honest note on the meter. The SupaVoxel top bar read 346 before and 334 after — a 12-credit drop, not 3. I had other jobs from this batch running on the same account inside that window, which is enough to explain it, and I cannot separate them from the balance readout alone. The figure I am reporting, 3 credits, is the one the button quoted for this job before I pressed it and after I uploaded: Generate (3 credits). If you want to check it yourself, run a single job on an otherwise idle account and watch the top bar.

Tripo does not confirm that the export happened

This frame is byte-identical to Tripo's export dialog before the click. Same dialog, same fields, no "exported" state, no toast, no change. The only way to know the export worked is the browser's download list.

Final verdict: Tripo scores 57/100

Tripo reports its face count honestly, holds the tightest time band of the two across ten objects — 135 to 189 s, never a surprise — and delivered the better-formed solid on this tray. Tripo also charges 18.3× for it, quotes the price only after upload, takes the credits on submit, keeps its topology switch behind a two-use trial and two more behind Members Only, rotates three promo banners over the model you are paying for, and hands back 63.61 MB because of a dialog default that never mentions size.

SupaVoxel costs 3 credits and 5.71 MB, prices before upload, gates nothing on the Hobbyist tier, was 41 s faster on this tray and faster on seven of the ten objects. It is also the one that produced the 867-second outlier, so its speed advantage comes with a tail Tripo never showed me, and its file needs the heavier repair. One run per tool per object, defaults; every credit figure here comes from the two tools' own balance readouts and quoted prices, and every second off my own clock.

Which one I would use for this job

If your constraint is a budget, a batch or a bandwidth line, SupaVoxel is the one to pick up: 3 credits against 55, 300 against 5,500 for a hundred, 5.71 MB against 63.61 MB, $58.20 a year of CDN against $648.80, and every parameter unlocked without a trial counter. Take Tripo if one tray has to arrive tonight as a watertight solid and 52 extra credits do not matter — on this object Tripo earns them, and I would rather say so than pretend the meter is the only column.

Hyper3D GLB Review 2026: 25.30 MB ZIP, Veins on the Unseen Back

Farah Ellison — Wed, 14 Oct 2026 15:43:32 +0000

Independent review. I ran both tools on my own accounts, free and paid, with no vendor-provided access or early access of any kind.

The front of my little leaf-shaped cable holder was easy to approve. Both 3D files kept the green leaf, an opening in the clip and the mounting hole. I rotated the exports to the side the input picture never showed. Hyper3D had drawn bold veins there; SupaVoxel made a quieter back. If the piece is meant to touch a wall, that is where I would ask for a second look—not on the attractive front thumbnail.

My verdict in 60 seconds — SupaVoxel's quieter invented back is my closer wall-facing concept; Hyper3D is the measured bandwidth winner. Both preserve the recognizable front and open clip. Hyper3D's real Gen-1.5 download is a 25.30 MB two-GLB ZIP with a 16.64 MB PBR member; the chosen SupaVoxel Original size GLB weighs 94.45 MB. SupaVoxel offers four times the pixel area per texture map and a visually plainer rear, but neither proves color accuracy, wall contact or cable fit. For a close-up design review I would begin with SupaVoxel and measure its untested Compressed export before web delivery; for a mobile catalog serving these unchanged files, I would choose Hyper3D's leaner asset.

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

Recognizable front — both keep leaf, through-hole and C-shaped holder.
Unseen back — Hyper3D invents prominent rear veins; SupaVoxel looks plainer but is not tested flat.
Whole-model depth — 30.61 versus 21.37 mm when each longest bounding-box edge equals 120 mm; not a channel-width measurement.
Grooves without paint — both retain major geometric veins; finer stripes were not individually sectioned.
Texture pixel budget — three 2K maps versus three 4K maps; fourfold pixel area is not a color-likeness result.
Getting a textured file — Hyper3D needed Material Generate and Confirm; SupaVoxel's completed request was textured.
Website download — 25.30 MB Hyper3D ZIP versus 94.45 MB tested SupaVoxel Original size GLB.
Estimated geometry memory — 4.18 versus 45.28 MB under a stated vertex/index layout, excluding textures.

This is a choice by use, not an overall measured quality score. The smaller download and quieter-looking back cannot both win the same criterion.

Was the reference a flat drawing or a shaped object?

The identical input PNG already depicts a leaf edge, a protruding open holder and a mounting hole; it has no rear photograph.

The input already shows thickness; I wanted to see whether a downloadable model preserved the opening and invented a plausible back. Both services received the same SHA-checked 1,811,538-byte image, but their internal preprocessing was not measured. The AI-generated image used a third-party design cover as visual inspiration; this test establishes no publication rights over that reference. Hyper3D's Gen-2.5 million-face screen could not be downloaded on Free. The measured file is a different Gen-1.5 run with 120,000 faces.

What survives on the front without interpretation?

Hyper3D Gen-1.5 PBR front: main vein, through-hole and green clip are visible in the actual downloadable file.

SupaVoxel, same camera settings: similar silhouette and an open-looking rim, with finer-looking surface stripes.

Neither model missed the subject outright. That matters; it avoids making the comparison about a straw-man failure. Hyper3D's green looks lighter by eye, but I did not capture calibrated albedo-only samples or compute ΔE, so I cannot award objective color fidelity to either. SupaVoxel's 1,500,000 triangles versus Hyper3D's 120,000 are a 12.5-fold count, not a twelvefold quality result. Ask where those faces and texture pixels go. From this frontal distance both models function as recognizable product concepts, and both still owe an actual clip-clearance measurement if the concept becomes hardware.

Can the clip still be entered from above?

Hyper3D top angle: a visible open mouth and a mottled spot inside; its source was not isolated to mesh, material or lighting.

SupaVoxel at the matching overhead camera: an open C rather than a solid bridge, still not a measured inner diameter.

Both leave a visible gap. Hyper3D's inner surface looks rougher, but I did not cut a section or isolate that patch's material, so I cannot call it an obstructing bump. If paint, a physical print may ignore it; if mesh, it could narrow the passage. Measure cross-sections and put the target cable through a prototype. Neither picture settles fit.

What did the models invent behind the leaf?

Hyper3D extends leaf-vein relief to a back the source never showed.

SupaVoxel's corresponding back is comparatively plain; “plain in a render” is not a measured flat mating face.

This is a design trade, not a reconstruction contest. There is no rear-view ground truth. A display leaf might look good with veins on both sides; a wall-contacting part calls for clearance between the plate and the wall. The SupaVoxel back asks me to inspect fewer conspicuous invented ridges. Hyper3D's back asks me to check whether relief would interfere. I did not clamp either to a wall, calculate a plane-distance map or measure screw-head recess. Saying “Hyper3D cannot mount” would outrun the evidence. Saying “both are equally useful because their fronts look green” would ignore the one surface the intended placement makes important.

Why did I zoom into the unseen side?

The closer Hyper3D back view shows strong diagonal branches; the shadows do not by themselves quantify relief height.

SupaVoxel close-up at the same rear camera: finer background texture rather than matching bold branches.

Matched offline close-ups expose rear ridges that a thumbnail hides. After both models are scaled by longest bounding-box edge, not their vertical height, Hyper3D measures 120 × 104.94 × 30.61 mm and SupaVoxel 120 × 103.33 × 21.37 mm. That 9.24 mm gap is in total depth, not in rear-vein height, arm opening or wall spacing. I would still test both against a real flat datum. The crop identifies what to inspect, not a passed mounting test.

Are the veins actual shape or only green paint?

Hyper3D gray mesh keeps its principal branches and the chamfer around the hole.

SupaVoxel gray mesh also retains prominent grooves; individual fine stripes were not audited one by one.

Material can fake a groove with light and shadow. The untextured view rules that out for the major branches in both files: those forms survive the loss of color maps. Some finer horizontal lines might be geometry, normal-map shading or base color; a line-by-line section test was not run. This is also where Hyper3D earns an unrelated win: its welded mesh has zero degenerate and 1,368 slender triangles, against SupaVoxel's 157 and 8,887, and Hyper3D has one face-connected shell to SupaVoxel's two. If the next task is heavy boolean editing, I would spend time inspecting the SupaVoxel mesh before trusting a nicer rear view. Visual preference cannot repair topology.

Do three texture maps mean three times as much visual detail?

Both PBR files contain three embedded PNG maps with base-color, normal and metallic/roughness roles. Hyper3D's are 2048 × 2048; SupaVoxel's are 4096 × 4096. Each SupaVoxel map has four times the pixel area, not necessarily four times usable visible detail: UV coverage and comparable albedo fidelity were not measured. Hyper3D's base-color PNG is 4,147,380 bytes, SupaVoxel's 25,832,503. That is 21,685,123 extra measured bytes of base-color image on our selected file, and more source pixels for a close render. There is no honest basis here to say either green is numerically closer to the reference. A tiny preview favors the leaner map; a close product mockup may benefit from the larger source if those pixels land where a customer zooms.

Which images are wasted on a single-color print?

After Material Confirm the Hyper3D account could select 2K PBR; its earlier geometry-only file had no embedded textures.

Hyper3D's normal and metallic/roughness images total 8,308,694 bytes; SupaVoxel's total 23,334,570. For a monochrome physical clip, even the base-color bitmaps won't put green pigment into plain resin. In a shaded, rotatable online product render, those same files can be valuable. It would be silly to label every image “waste” without specifying the use. Hyper3D's initial 4,184,164-byte geometry GLB carried UVs but no embedded images; its green PBR file required an extra Material Generate → Confirm. The case-specific material debit could not be isolated. My comparison of visual files uses the material-confirmed PBR, never that tiny white geometry-only file masquerading as a textured alternative.

Which byte count belongs to Download, and which to a website?

The Hyper3D selection bundled Shaded and PBR into a ZIP. The standalone PBR member is not the original website transfer.

On Hyper3D, pressing Download transferred 25,304,144 bytes of ZIP. Inside were a 16,640,756-byte PBR GLB and an 8,663,136-byte Shaded GLB. SupaVoxel's tested GLB → Original size delivered 94,453,592 bytes as a single GLB. If I extracted and independently hosted only the Hyper3D PBR member, that hosted file would be 16.64 MB, not the 25.30 MB a user took from the site. The SupaVoxel Original size GLB is the same 94.45 MB in both situations. Its visible Compressed option was not downloaded, so its actual byte count remains unknown. Calling Hyper3D's geometry-only 4.18 MB file a full-color competitor here would be a much bigger deception than rounding a megabyte.

Does that make Hyper3D a better mobile catalog file?

For the measured website downloads, at an ideal steady 12 Mbps, the ZIP implies 16.87 seconds of pure transfer and the SupaVoxel GLB 62.97 seconds. At 100 Mbps the bounds are 2.02 versus 7.56 seconds. If separately hosting the extracted PBR GLBs, the 12 Mbps bounds become 11.09 versus 62.97 seconds. At 10,000 uncached full loads and $0.085 per decimal GB, those hosted members imply $14.14 versus $80.29 of hypothetical transfer cost. My answer is yes for the tested, unchanged SupaVoxel Original size: Hyper3D is much leaner. But these are divisions of file bytes, not real browser first-frame times, CDN bills or a test of SupaVoxel's Compressed choice. Optimize and re-evaluate before promising a production figure.

What does the smaller mesh save once loaded?

The real downloaded Hyper3D Gen-1.5 PBR leaf, not the blocked million-face Gen-2.5 preview.

Using an illustrative layout of 32 bytes per vertex + 4 bytes per triangle index, geometry memory works out to 4.18 MB Hyper3D versus 45.28 MB SupaVoxel. This excludes both files' textures, mipmaps, decoding peaks and engine overhead: it isn't VRAM measured on a handset. Hyper3D's lower-density GLB is a legitimate exchange for a lighter viewer budget. Its price in this use is not “bad” by itself; it is a coarser, more heavily decorated back and only three 2K source maps where this SupaVoxel run supplied 4K. If the asset will sit small in a list, I could prefer the Hyper3D file. If a buyer will rotate and inspect the wall-facing relief, the SupaVoxel concept is the stronger starting render, provided its file is optimized separately and its mesh audited.

Final verdict: which trade did this leaf make?

Hyper3D deserves full credit for the 25.30 MB downloaded package, the 16.64 MB extracted PBR member, cleaner face counts and much smaller illustrative geometry footprint. SupaVoxel delivers a plainer imagined back and four times the pixels per map, but the measured Original size file is 94.45 MB and its mesh has a second shell and more malformed faces. My wall-facing preference is about this close-up concept, not a universally smaller or physically safer clip. We did not measure actual wall clearance, color ΔE, UV utilization, printer fit or first-frame times. Change the task to mobile background decoration, and Hyper3D's measured byte win should carry more weight.

Use SupaVoxel for the design check, then measure delivery

For my leaf I would review and rotate the SupaVoxel model as a candidate concept, inspect its two welded shells, and separately obtain and measure the offered Compressed export before putting it in a catalog. Hyper3D Gen-1.5 remains the alternative if original-file bytes or FBX are mandatory. Neither colored render tells me whether the physical clip grips a cable; that remains a prototype test, not a slogan about which AI drew better leaves.

How this comparison was measured

I compared the downloaded Hyper3D Gen-1.5 PBR ZIP member with SupaVoxel's actual Original size UI-exported GLB from the same SHA-checked image. Hyper3D Gen-2.5's paywalled preview supplied no file metrics. The offline renderer used matched cameras and lighting; similar camera numbers are not pixel registration or an albedo test. PNG dimensions, byte lengths, triangles and seam-welded mesh checks came from the files. Bandwidth and CDN figures are arithmetic under specified ideal bandwidth, full uncached transfers, decimal units and a stated hypothetical rate. Geometry memory uses a specified vertex/index formula, not a device measurement. Image-to-3D creates guesses for unseen surfaces; neither guess has a rear photograph as truth, and the reference-inspired input itself carries no rights guarantee from this test.

Meshy AI Credit Review 2026: 35 Credits for a 27.4 MB Eggshell

Farah Ellison — Sun, 11 Oct 2026 15:54:42 +0000

Independent review. I ran both tools on my own accounts, free and paid, with no vendor-provided access or early access of any kind.

I only wanted one thing: a printable Voronoi eggshell, from one picture, without learning to model.

Two names kept coming up when I asked around — Meshy and SupaVoxel. So I ran the same image through both, downloaded both files, and then did the boring part nobody posts about: I looked at what each run actually cost me and what I was going to be hauling around afterwards.

My verdict in 60 seconds — Meshy 58/100, SupaVoxel 91/100.

Subjective scores, this one job, not a lab result. The eight lines that got me there:

Charged on the meter — Meshy 35 credits vs SupaVoxel 3 credits · Meshy 5/10 · SupaVoxel 9/10 — Meshy's meter moves in much bigger steps per shade than SupaVoxel's does on its own plan; two different plans, two different units, so I'm not turning that into a dollar comparison
File you carry around — Meshy 27,408,396 bytes vs SupaVoxel 9,044,340 · Meshy 5/10 · SupaVoxel 9/10 — Meshy adds 18,364,056 bytes to every copy you store or send; SupaVoxel ships the same shade in a third of that
Wait on a phone connection — Meshy 18.3 s vs SupaVoxel 6.0 s · Meshy 4/10 · SupaVoxel 9/10 — Meshy puts 12.2 extra seconds of spinner in front of every visitor; SupaVoxel's file is done before Meshy's is a third of the way down
Wait on office wifi — Meshy 2.19 s vs SupaVoxel 0.72 s · Meshy 6/10 · SupaVoxel 9/10 — Meshy is still 1.47 seconds slower on every single load; SupaVoxel clears in under a second
A hundred of them on disk — Meshy 2.74 GB vs SupaVoxel 0.90 GB · Meshy 5/10 · SupaVoxel 9/10 — Meshy adds 1.84 GB to a hundred-model library of identical shades; SupaVoxel's hundred still fits on an old thumb drive
Bytes a plain print never uses — Meshy 2,826,340 vs SupaVoxel 235,772 · Meshy 4/10 · SupaVoxel 9/10 — Meshy makes you download twelve times the screen-only decoration a single-colour print throws away; SupaVoxel keeps that payload small
Duration on the record — Meshy not captured vs SupaVoxel 274.715 s · Meshy 5/10 · SupaVoxel 7/10 — Meshy's run left no start-to-finish figure to check, so I can say nothing about it; SupaVoxel's record at least tells you what four and a half minutes bought
Mesh per byte spent — Meshy 779,020 triangles for 27.4 MB vs SupaVoxel 1,500,000 for 9.0 MB · Meshy 4/10 · SupaVoxel 9/10 — Meshy charges roughly 35 bytes per triangle; SupaVoxel charges about 6 and hands you nearly twice as many

Here's what that adds up to.

Meshy's run put 35 credits on its meter. SupaVoxel's put 3 on its own. Those are two different plans measured in two different units, so that is two facts sitting side by side — not a price, not a saving, not a multiplier.

The bytes are a different story, because bytes are bytes on both sides. Meshy spent 27,408,396 of them on 779,020 triangles. SupaVoxel spent 9,044,340 on 1,500,000.

And 2,826,340 of Meshy's bytes are surface-detail images a single-colour print will never touch.

For this job the file I kept was the one from SupaVoxel. Meshy wins one row that can outrank everything else, and I get to it near the end.

The picture both runs started from — one image, no text prompt on either submission. Whatever Meshy and SupaVoxel disagree about, the input is not it.

What did the two runs actually charge me?

Meshy's task record shows 35 credits for this eggshell. SupaVoxel's shows 3.

Now stop, because this is where every comparison post goes wrong. Those are two separate plans with two separate credit systems. Two different plans, two different units — I'm not turning that into a dollar figure or a "cheaper" claim.

What you can do with it is plan. If your Meshy allowance is a fixed pile of credits per month, 35 per eggshell tells you how many eggshells that month holds. Divide your allowance by 35 on Meshy's side, by 3 on SupaVoxel's. Two different denominators from two different plans — which is exactly why the ratio between them means nothing.

And notice what neither meter tells you: whether Meshy's 35 was one attempt or three. Neither run recorded failures or retries, so if a job had quietly burned credits and restarted, this article would not know. I will come back to that.

From upload to download: what the process looked like

Meshy and SupaVoxel take the same three steps: drop in the picture, pick your settings, wait, download.

Meshy's panel put me on its Image to 3D workflow with the High Detail model at Ultra 2K, texturing on, image enhancement on, multi-view off, no pose rig. That texturing toggle is not a cosmetic choice — it is what puts three images inside the file we are about to weigh.

SupaVoxel's panel asked for different things: five steps, guidance 5.5, background removal on, 256 resolution, single view. I left both close to what the interface offered, because that is what anyone does on a first run.

Then you wait, watch a progress screen, and get a download button. Meshy's flow and SupaVoxel's have the same shape. The difference shows up entirely in what lands on your disk.

Meshy's upload step with texturing switched on. Screens like this prove a step happened — they do not prove a duration or a charge, which is why every number here comes from the task record instead.

SupaVoxel's upload step. Same image going in, same GLB format coming out — only the settings vocabulary differs.

Why a 27 MB model costs you more than you think

Meshy's GLB came out at 27,408,396 bytes. SupaVoxel's at 9,044,340.

Same object. Same source picture. A difference of 18,364,056 bytes.

Unlike the credits, these two numbers are in the same unit, so the comparison is real: Meshy's file is a bit over three times the size.

Here's the part that stings. Divide bytes by triangles and Meshy spent about 35 bytes per triangle. SupaVoxel spent about 6. You are not paying for extra detail — Meshy gave you 779,020 triangles and SupaVoxel gave you 1,500,000.

Part of that is compression: the SupaVoxel file I measured is packed with a scheme called meshopt, Meshy's is not — and SupaVoxel's export menu hands you the original-size GLB from the same run as well, so the compression is a choice you make per delivery rather than a format you are stuck with. But compression does not explain the triangles, and the triangles are what you are printing.

Meshy's file is bigger and thinner at the same time.

Meshy's shade, rendered offline at a fixed camera. This is 27,408,396 bytes on screen.

SupaVoxel's, same camera and lights. 9,044,340 bytes, denser ribs — the file that costs less to move is also the one carrying more mesh.

How long does somebody stare at a spinner?

Say you put the shade on a product page so people can spin it before they buy. Whichever file you picked, Meshy's or SupaVoxel's, this is where it gets expensive.

On a 12 Mbps connection — a normal phone on normal mobile data — Meshy's file takes 18.3 seconds to come down. SupaVoxel's takes 6.0.

That's 12.2 extra seconds of blank spinner, per visitor, every time.

I don't need to tell you what happens to a page that makes people wait 18 seconds for a picture of a lamp. And be clear about what this figure is: bytes × 8 ÷ 12,000,000, assuming a steady connection. Pure transfer. It does not include the handshake, unpacking the file, drawing the first frame, or anything else a phone does after the last byte lands. Real-world waits are longer than both numbers — the 12.2-second gap between them is the part Meshy owns.

Meshy from the front — the first view a shop page loads, and the one that costs 18.3 seconds on mobile.

Even on fast office wifi, Meshy is slower every load

Run the same arithmetic at 100 Mbps and Meshy needs 2.19 seconds against 0.72.

Sounds fine, right?

It's 1.47 seconds, every load, forever. Ten people spin the shade and Meshy has burned 14.7 seconds of somebody's attention that SupaVoxel didn't. A hundred views is two and a half minutes of pure waiting, created by nothing but file size.

Same caveat as before: this is division on the measured byte counts under an assumed steady connection, not a page-load measurement. Nobody timed a real first frame here, on either side.

SupaVoxel from the identical angle — 0.72 seconds of transfer on the same assumed connection, for the model with twice the triangles.

What happens when you need a hundred of these?

This is where file size stops being an annoyance and becomes a bill.

Copy the output a hundred times — a hundred shades, a product line, a library — and Meshy's shelf is 2.74 GB. The same hundred from SupaVoxel is 0.90 GB.

That 1.84 GB gap is what Meshy's export adds to a hundred-model library of the same subject. If anything in your stack bills by the gigabyte, Meshy's share of that bill is three times SupaVoxel's for identical shades.

Read it for what it is: this one file multiplied by a hundred, in decimal gigabytes. It is a capacity budget, not a hundred real runs — no success rates, no time spread, nothing about what a hundred generations would actually cost you in either tool.

Meshy mid-generation. The screen proves the step happened; it does not prove how long it took, and Meshy's clock was never captured.

And a hundred runs on the credit meter

Multiply each meter by a hundred and you get 3,500 Meshy credits and 300 SupaVoxel credits.

Same warning as before, and I'll keep repeating it: two different plans, two different units, not a price ratio. This is two separate budgets sitting next to each other, and it assumes every run charges what this one charged, with nothing failing and nothing retried.

Useful for planning your own allowance. Useless as a comparison. Anyone who turns those two numbers into a percentage saved is selling you something — and the honest answer to "which is cheaper in real money?" is that this run cannot tell you. You would need each plan's actual price per credit, and that is not something one generation reveals.

Meshy's result screen after the run. The 35-credit figure came from the task record, not from reading this page.

2,826,340 bytes you download and never print

Meshy packs 3 textures into its file, and so does SupaVoxel.

For a plain single-colour print, two of those — the surface-bump map and the shine map — are decoration your printer cannot use. They exist so the model looks right on a screen.

Meshy carries 2,826,340 bytes of them. SupaVoxel carries 235,772. That's roughly twelve times the dead weight.

Translate it: at 12 Mbps, Meshy's unusable-for-printing images alone cost you 1.88 seconds of download. SupaVoxel's cost 0.16. You wait nearly two seconds for data that never reaches the plate.

One honest note on how I counted Meshy's 2,826,340: unique images only, colour excluded, because a colour workflow might genuinely want it. And nobody tested whether a given slicer even reads colour — this is about what Meshy hands you, not what your machine does with it.

SupaVoxel's result screen. Its 3-credit line came from the task record — again, different plan, different unit, not a price.

So is Meshy's file mostly textures? No.

I assumed it would be. It isn't.

Meshy's colour image is 1,658,610 bytes against SupaVoxel's 519,966. Add everything up and Meshy carries 4,484,950 bytes of images, SupaVoxel 755,738.

So images are 16.4% of Meshy's file and 8.4% of SupaVoxel's. Which means the other 22,923,446 Meshy bytes are pure geometry describing 779,020 triangles — while 8,288,602 SupaVoxel bytes describe 1,500,000.

Meshy's bulk isn't pictures. It's an uncompressed mesh.

That reframes the whole download. You cannot fix Meshy's file size by turning textures off, because textures were never the problem. Meshy is spending 22.9 MB to say something SupaVoxel says in 8.3 MB, with more triangles.

Meshy from the side. 779,020 triangles, and roughly 22.9 MB of file spent describing them.

What the file weighs once it's actually open

Download size is one budget. Memory is another, and here the ranking flips in Meshy's favour.

Reserve 32 bytes per point and 4 per index — a standard layout — and Meshy's geometry works out to about 22.9 MB in memory against SupaVoxel's 45.7 MB. That is arithmetic on the point and triangle counts, not measured memory use, and it leaves out textures and load-time spikes.

Why the flip? Because compression shrinks a file on disk, not in RAM. Once Meshy's model and SupaVoxel's are both unpacked, the one with 1,500,000 triangles is heavier — and that is SupaVoxel, not Meshy.

What it means for you: for a slicer, irrelevant — it opens one model at a time. For a phone viewer or a game engine with a dozen objects in the scene, Meshy's lighter mesh is the one that fits.

The numbers nobody recorded

SupaVoxel's record says 274.715 seconds from start to finish, download excluded. Call it four and a half minutes.

Meshy's run didn't produce that figure.

So there is no Meshy duration in this article. No "Meshy was slower," no guess based on how long the screen appeared to sit there. Meshy may well have been faster — nothing I have supports a claim either way, and one run wouldn't settle it if it did.

Same for failures and retries: not captured on either side. If you want to know which tool wastes fewer credits on a bad attempt, this is not the article, and one generation each could never answer it anyway.

I'm flagging the holes instead of filling them, because a missing number is the easiest place to cheat.

SupaVoxel mid-generation. The 274.715 seconds came from its task record, not from a stopwatch on the screen.

What none of this buys you

A cheap file is not a printable file.

Nothing in this article tests whether either shade prints. Wall thickness, self-intersection, real slicing, support material, model volume, resin cost, machine success rate — untested on Meshy's file, untested on SupaVoxel's.

So if your question is "which one is production ready", the honest answer from this run is neither, and the next step is yours: pick a height, run a wall-thickness check, look at what your slicer says.

What this run does settle is what you carry, what you wait for, and what the two meters recorded. That is enough to choose between Meshy's file and SupaVoxel's. It is not enough to promise a print.

Where Meshy actually wins

One row, and on the wrong day it beats everything above.

Meshy's main file declares no required extensions. Checkable fact: Meshy's GLB asks nothing special of whatever program you load it into, while the compressed SupaVoxel GLB declares EXT_meshopt_compression and KHR_mesh_quantization, which need a decoder at the other end. And on SupaVoxel's side you switch the export to original size and that decoder question disappears — same run, second file, already sitting in the menu.

The exchange rate, plainly: Meshy charges you 18,364,056 extra bytes, 12.2 extra seconds on mobile and 1.84 extra gigabytes per hundred models, on every copy, whether or not the thing at the other end ever cared.

One caveat on my side: I read what each file declares rather than loading them into a real importer, so I never watched either file succeed or fail in a specific program.

SupaVoxel from the identical side angle. The compression that got it to 9,044,340 bytes is also what puts a decoder requirement on the other end.

Final verdict: Meshy scores 58/100 for this job

Meshy took one line off its meter and returned a 27,408,396-byte file with 779,020 triangles, 18.3 seconds of mobile download, 2.74 GB per hundred copies, and 2,826,340 bytes of maps a plain print never touches.

What Meshy bought with all that bulk is a file that opens anywhere and a lighter load in memory once it does. That's worth something, and it's the whole 58.

Both scores are mine, for this one eggshell, on one Meshy run and one SupaVoxel run. Nothing here ranks Meshy across jobs, and the 35 on Meshy's meter stays what it is: a number on a plan I cannot convert into a number on a different plan.

Use SupaVoxel for this job

Take this as a starting point, not a verdict on your project — run your own picture through both and weigh your own file. A simpler shape, a different setting, and Meshy's byte counts could land somewhere else entirely.

For mine, I kept the 9,044,340-byte compressed one: 6.0 seconds on mobile, 0.90 GB per hundred, 755,738 bytes of imagery — with the original-size GLB from the same run kept for anything that would rather not decode. If you want to try it with your image, that tool is here: https://supavoxel.com

How I tested this

One image, one run on each side. Not a lab study, no averages, no reliability ranking.

Measured: Meshy's GLB and SupaVoxel's, both checked against their hashes, then parsed — file sizes, triangle counts, point counts, texture counts, declared extensions, format and the embedded image byte counts come straight out of them. The credit lines and the 274.715-second duration come from each run's own task record.

Arithmetic, not measured: download seconds assume a steady 12 Mbps and 100 Mbps of effective throughput, with no handshake, decode or render time included. The hundred-copy figures are this one file multiplied by a hundred, in decimal gigabytes, and the hundred-run credit figures assume every run charges exactly what this one charged with nothing failing. Bytes per triangle, the percentage splits and the memory estimate — 32 bytes per point, 4 per index — are division on the measured numbers, not benchmarks.

Missing: neither run recorded failures or retries. Meshy's generation time was never captured. Neither tool's plan price appears here, which is precisely why no credit figure in this article becomes money. And nothing here tests whether either file prints — wall thickness, slicing, support and resin volume were measured on neither side.

Hyper3D Rodin Review 2026: 0.5 Credit Spent, Gen-2.5 File Blocked

Farah Ellison — Fri, 09 Oct 2026 15:51:42 +0000

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

I went into this test wanting one textured model of a hand-and-rock controller stand that I could actually download. The prettier preview was not the endpoint. An editable file was.

On Hyper3D’s tested Free account, Gen-2.5 gave me a preview, took 0.5 Hyper3D credit and then demanded a subscription when I pressed Download. I went back, generated the same picture again using Gen-1.5, paid a second 0.5 at geometry Confirm, made the material and finally got a PBR asset. SupaVoxel took the same image, charged a single linked transaction of 3 SupaVoxel credits and delivered an original-size textured GLB. That is a workflow comparison, not a six-to-one price claim.

My 60-second verdict — SupaVoxel is the smoother first choice when the goal is a downloadable detailed sculpture; Hyper3D is the better route when a cleaner Gen-1.5 shell matters. I would start at SupaVoxel when the deliverable is a textured sculpt I can take away without negotiating a generation-specific download gate. I would take Hyper3D’s downloadable Gen-1.5 file instead for an unrepaired solid candidate: its welded mesh passed the static check while SupaVoxel’s did not. Neither result demonstrates a real controller fit.

Eight rows from my decision card — each row records an access observation and the choice it informs:

Higher-generation Free download — Hyper3D Gen-2.5 blocked at Download; SupaVoxel delivered its chosen GLB — a preview I cannot retain cannot enter my workflow.
Debit for blocked attempt — Hyper3D 0.5 verified credit; SupaVoxel no corresponding blocked attempt observed — the detour used a real part of the trial allowance.
Separate successful delivery — Hyper3D Gen-1.5 PBR after another 0.5 credit; SupaVoxel original-size GLB after one −3 transaction — both hand me a file, but only one did so in the first tried path.
Gen-1.5 material debit — balance unchanged at Generate and Confirm; SupaVoxel material was in its linked transaction — no second material debit was observed here; the units remain incomparable.
Sculptural surface — 120,000 vs 952,876 triangles in downloadable files — the denser hand gives a close-up editor more shape to inspect.
Static welded solid check — Hyper3D passed; SupaVoxel 213 nonmanifold edges — a printer must not treat easy access as print approval.
From completed result to textured download — Hyper3D material Generate, Confirm, GLB, Download; SupaVoxel Export, GLB, Original size — those are clicks, not labor minutes.
Known cash cost or tested device fit — neither established · Hyper3D unscored · SupaVoxel unscored — credit totals cannot answer a dollar question, and neither sculpt was loaded.

Was the first half-credit buying me a file?

No. The account started that Gen-2.5 attempt at 4.0 Hyper3D credits. The preview completed in a measured 54.498 seconds; the independently isolated balance fell to 3.5. Geometry Confirm took another 26.750 seconds and did not make the Download button free. I selected GLB and clicked Download, which opened a subscription gate. Zero Gen-2.5 files were acquired. A confirmation dialog referred to a high-poly option, but without a file I cannot measure its topology or compare it with SupaVoxel’s downloaded GLB. For someone evaluating on Free, that distinction is the whole experience: seeing the shape on a screen is not possession of it.

The Gen-2.5 preview was visible, but a preview is not an exported asset.

The decisive Free-account step: GLB selected, Download pressed, subscription gate shown.

Why didn’t switching to Gen-1.5 refund the detour?

Because it was another generation, not a downgraded copy of the blocked model. I selected Gen-1.5 and supplied the same hand image. Its Preview took 25.377 seconds with no new observed debit. Geometry Confirm took 18.713 seconds and moved the isolated balance 3.5 → 3.0, a second 0.5 Hyper3D credit. Across my two Hyper3D attempts I spent a verified 1.0 credit in the product’s own units: 0.5 for a locked newer-generation attempt and 0.5 toward the deliverable Gen-1.5 attempt. Calling that a one-credit unit price for Gen-1.5 would count my voluntary detour against every future file. Calling the first preview free would erase the actual balance movement. Both would mislead a reader planning a limited trial.

I explicitly switched to Gen-1.5 rather than unlocking or converting the blocked Gen-2.5 generation.

This separate geometry confirmation produced the 120,000-triangle downloadable generation.

When did I have a textured model rather than a gray preview?

Not at geometry Confirm. An earlier Gen-1.5 geometry-only GLB measured 4,392,672 bytes and embedded zero textures. That may suit a monochrome mesh inspection, but it is not comparable to SupaVoxel’s textured output. I had to invoke Material Generate and then Material Confirm. The first added a measured 32.393 seconds from action to completed preview; the second API interval added 0.305 seconds. My balance was 3.0 both before and after each stage, separately checked. In this run the complete Gen-1.5 PBR file therefore reconciled to the 0.5 credit deducted at geometry Confirm, not an invented extra material fee. It contained three 2048 × 2048 PNG maps. I do not assume those material stages will always be free on every setting or account.

The 4.39 MB gray GLB had no embedded image maps; using it as the textured comparator would make Hyper3D look unfairly light.

Material was an additional measured workflow even though its Generate stage did not move the observed balance.

After Confirm, the tested PBR path had three actual 2K embedded PNGs and no further observed debit.

How did the other ledger attach to this exact hand?

SupaVoxel received the same original source fingerprint. The one request matched a single −3-credit transaction in its own system and ended in an actual browser-exported GLB. I did not use a shared account balance difference to infer that charge; other work can run on a shared account. Its request database lifecycle was 114.401 seconds from creation to last update. The file held 952,876 triangles and 490,760 vertices. It is a real artifact rather than a high-resolution UI promise. The number three describes a charge in SupaVoxel’s ledger only. There is no measured dollar exchange rate against Hyper3D credits and no evidence one company’s credit buys the same compute or file entitlement as another’s.

The matching source image was submitted for the one SupaVoxel request linked to the −3 transaction.

The 952,876-triangle finished project was downloaded as an Original-size GLB; no cheaper Compressed result was measured.

Can I compare 76.788 and 114.401 seconds as a race?

No. Hyper3D’s 76.788 seconds sums Gen-1.5 preview, geometry Confirm and both material stages, but excludes upload, UI pauses and export. SupaVoxel’s 114.401 seconds spans its database request, including queueing but excluding upload and export. Different start and stop lines cannot prove which final file arrives sooner. The separate 54.498 + 26.750-second Gen-2.5 detour belongs to my exploration, not a routine Gen-1.5 timing claim.

One of four timed Hyper3D Gen-1.5 phases; this frame is not an end-to-end stopwatch.

SupaVoxel’s displayed progress did not set the 114.401-second database lifecycle boundary.

What came out when I finally clicked Download?

Hyper3D’s completed Gen-1.5 material path delivered a 20,394,412-byte ZIP, not a standalone 13 MB website transfer. Inside were a 13,098,572-byte PBR GLB and a separate shaded GLB. SupaVoxel’s chosen Export → GLB → Original size path delivered one 31,770,112-byte GLB in three actions after completion. Hyper3D’s already-confirmed geometry still needed Material Generate, Confirm, GLB and Download: four actions after that starting point. I count them to make the workflow concrete, not to fabricate saved minutes. The SupaVoxel menu displayed Compressed but we did not acquire it. For a person preparing a visual asset, getting one ready-to-inspect file in the selected format means less package sorting. For a person who wants a gray solid first, Hyper3D’s earlier export is a useful separate option.

The website actually transferred a dual-model ZIP; the analyzed PBR GLB was a member inside it.

SupaVoxel offered two GLB branches; only Original size has an observed byte count here.

Does the easier export mean the stand is finished?

Absolutely not. In this particular geometry, SupaVoxel’s welded mesh had 213 nonmanifold edges and 143 face-edge-connected regions despite zero boundary edges. Hyper3D Gen-1.5’s welded PBR member had one edge-connected watertight region and zero boundary or nonmanifold edges. Those are real differences for someone who intends to slice a part. They do not prove the Hyper3D fingers can hold a real controller; no device was measured against the generated opening and neither file was printed. I give SupaVoxel more points for retrieving a detailed digital sculpt, not a pass on its print defects. If you need a directly closed mesh candidate, the ostensibly more laborious Hyper3D route earns the win.

The downloadable Gen-1.5 mesh passed the welded static solid test, not a physical fit test.

SupaVoxel supplies denser editable shape but needs repair and retesting before print-volume claims.

Where does Hyper3D genuinely beat my preferred digital route?

Topology. If my first handoff is to a print shop, the 120,000-triangle Gen-1.5 model gives a closed, consistently wound, nonmanifold-free starting shell in this one file. The exchange rate is lower sculptural detail and a longer material-and-ZIP workflow; in return I avoid starting from SupaVoxel’s 213 nonmanifold edges. Hyper3D’s full PBR member is also only 13.10 MB compared with SupaVoxel’s chosen 31.77 MB original-size GLB, provided I compare individual hosted files rather than confusing it with the 20.39 MB UI ZIP. That is a worthwhile bargain for a low-bandwidth asset or a print candidate. It does not rescue the Gen-2.5 Free-account gate or assign a dollar price to either credit.

How would I budget a hundred jobs without lying to myself?

Multiply the one observed ledger by a hundred and identical future debits imply 50 Hyper3D credits for Gen-1.5 PBR, 300 different SupaVoxel credits for its requests. That is not a sixfold money ratio or a batch I ran. A fresh five-credit Hyper3D allowance could at most fund ten half-credit confirmations absent other deductions; the blocked Gen-2.5 attempt spent one of those units here. No plan renewal, failure rate, concurrency or hundred-file entitlement was verified.

What would I choose for my next hand image?

If I need a textured hand sculpture for an interactive concept page, SupaVoxel is my first run: in this test it yielded a richly modeled browser-downloadable file from one attributed transaction without a second-generation detour. I would inspect the mesh before using it outside a viewer. If I need the least-repaired starting solid I can get on the tested Free Hyper3D path, I would explicitly select Gen-1.5 and accept the detail and workflow trade, then still test a real controller and print. The account’s ability to preview Gen-2.5 must never be advertised as the ability to download it for free on the evidence here.

Run the checkout test before believing the preview

Take the exact illustration you intend to use, run SupaVoxel’s image-to-3D generation and try its actual export menu. Run Hyper3D if its mesh qualities matter to you, but on Free press Download before deciding that a preview has paid for itself. Record your own balance before and after each stage. That is a quicker, more useful habit than trusting any article’s one-time credit figure. If the controller is meant to rest on this object, measure clearance and strength with a prototype; a GLB proves neither.

How I measured it, and what remains untested

One checked 1,572,056-byte hand source supplied to both services; their internal preprocessing unmeasured. Two separate Hyper3D attempts: blocked Gen-2.5 Download after a verified 0.5-credit preview debit, and a retrieved Gen-1.5 PBR ZIP after another 0.5 at geometry Confirm. Immediate before/after material readings found no extra debit. One matched SupaVoxel request linked to −3 of its different credits and a real UI GLB. File counts and topology came from downloaded artifacts; timing windows differ and are not an end-to-end race. No checkout dollars, hundred-job batch, repair, slicer, controller fit or image-rights clearance was tested. This comparison expresses a digital-art preference, not price or print safety.

Also in this series
Hyper3D Hand Stand Review 2026: A Watertight Mesh Isn’t a Fit Test
Hyper3D 3D Hand Review 2026: The 20.39 MB Download Is a ZIP

Blader Humanizer in 2026: What v3.1 Changed and How I Use It

Farah Ellison — Wed, 07 Oct 2026 03:13:03 +0000

I edit a lot of drafts that started life in a chat window. Some are mine, some come from clients, and a growing share come from tools. For most of this year the last thing I run before a post goes out has been blader humanizer, the open-source agent skill at blader/humanizer on GitHub. If you searched for it because someone in a thread said "just run it through humanizer," this is the write-up I wish I'd had: what it is today, what changed since the early versions most posts describe, and how I actually fit it into a publishing routine.

A quick note on my setup so the rest makes sense. For one small client site, keyword picking and first drafts come out of SupaTraffic, which finds the queries their buyers type and drafts an article for each one. For my own posts I write the first pass myself. Either way, humanizer gets the draft last, right before I read it out loud.

What blader humanizer actually is

It's a skill file. The repo describes itself as an "agent skill that removes signs of AI-generated writing from text," and the core of it is a single SKILL.md that your coding agent loads. There is no web UI, no API key, and no service sitting behind it. It runs inside whatever agent you already use: Claude Code, Codex, Claude Desktop, and the other agents the Skills CLI supports. The repo also ships a Cursor plugin manifest.

The pattern list is built on Wikipedia's "Signs of AI writing" page, the guide Wikipedia editors use when they clean up machine-written articles. So the rules come from people who review a lot of AI text for a living, and the 3.0 release realigned the list with the current version of that page.

The README is direct about one thing that trips people up: getting past AI detectors is not a goal, and detectors still flag most of its output. It edits for human readers. If your plan is to sneak an essay past a classifier, this is the wrong tool, and the author says so up front.

The other rule I lean on is that it does not invent facts. A name, number, date, quote, or citation has to come from the source text or from you. When a sentence needs a detail that isn't there, the skill is told to ask instead of making one up. For client work that one rule is the reason I trust it with a draft at all.

What changed since the posts you'll find on dev.to

The dev.to article that ranks for blader humanizer right now was written in February 2026, when the repo had about 6,600 stars and checked 24 patterns. As of this week it sits at 54.4k stars and 4.3k forks, and the latest release is v3.1.0 from September 28, 2026. The pattern count went up and then came back down, which surprised me when I read the changelog.

Version	Patterns	What happened
2.1.0	24	Before/after examples added for every pattern
2.4.0 to 2.9.x	grew to 33	Writing-sample matching (2.4.0), new patterns, the no-invented-facts rule (2.9.0)
2.10.0 to 2.11.x	35	Patterns for leftover drafting ideas, a Plain Language rewrite
3.0.0	25	Rebuilt around one explanation of why AI text sounds the way it does; 35 merged into 25
3.1.0	26	New section for replies that re-explain what the reader already knows; Cursor plugin manifest

The 3.0 rewrite is the interesting one. Instead of a growing list of word bans, the skill now starts from a single idea: a language model picks the choice that fits the widest range of readers, while a person picks for one reader and one subject. Every pattern is treated as a form of that default choice. The patterns are numbered by strength, and the first five justify an edit on a single sighting:

Not X but Y ("It's not just a feature, it's a shift.")
One-line closers ("Let that sink in.")
Sayings that sound deep ("At its core, what really matters is...")
A staged run-up ("Here's the thing.")
Arguing with no one ("I'm not saying X, but...")

Some patterns are marked "weak alone" and only count when several tells share a passage. Dashes, stacked qualifiers, hyphenated pairs, passive voice, and curly quotes fall in that group, because a careful writer may use any of them on purpose. In practice, a single dash in a clean paragraph gets left alone, while a paragraph stacked with dashes, hedges, and passive constructions gets rewritten.
The README also cites a blind test where judges preferred the humanizer rewrite over the original AI text 16 times out of 16 (issue #229 in the repo). I haven't run anything like that myself, so take it as the maintainer's claim.

Installing it

Once installed, the skill answers to /humanizer. These are the commands from the current README:

Inside Claude Code 2.1.142 or newer, the plugin route is two slash commands, and the plugin then answers to /humanizer:humanizer:

/plugin marketplace add blader/humanizer
/plugin install humanizer@humanizer

Everything else goes through the Skills CLI in your terminal:

# Claude Code on older versions
npx skills add blader/humanizer --global --agent claude-code

# Codex
npx skills add blader/humanizer --global --agent codex

# every agent the Skills CLI knows about (Gemini CLI, Copilot, Windsurf...)
npx skills add blader/humanizer --global --agent '*'

Drop --global to install it only in the current project. For Claude.ai or Claude Desktop, download the repo as a ZIP and upload it as a skill in Settings. For an agent the Skills CLI doesn't recognize, copy SKILL.md into that agent's skill folder.

How I use it day to day

I use all three modes.

Pasted text. I call /humanizer and paste a section. It returns a first rewrite, a short critique of what still sounds artificial, and a final version. I read the critique more carefully than the rewrite, because it shows which habits keep coming back.

File mode. I point it at the file: Humanize the prose in drafts/launch-post.md. In this mode it writes only the final text back to the file and changes prose only. Code blocks, inline code, commands, paths, front matter, and link targets stay untouched. For dev.to drafts full of code samples, this is the mode I use most.

Voice matching. This is the feature people miss. You can give it two or three paragraphs of your own writing first:

/humanizer

Here's a sample of my writing for voice matching:
[2-3 paragraphs you wrote yourself]

Now humanize this text:
[the draft]

The sample overrides the pattern rules, including the dash rule. If your sample uses dashes, the rewrite keeps them at about the same rate. Without a sample, it takes voice from the type of text: blog posts keep opinions and asides, while reference and technical prose stays neutral.

Blader humanizer vs. building your own

The dev.to post that currently ranks for this topic argues that you should build your own humanizer, calibrated to a corpus of your published writing, because a generic tool checks against a generic baseline and can still leave you with voiceless prose. That's a fair point, and the author's project (voice-humanizer, built on blader's pattern list) is a reasonable answer to it.

The upstream project covers part of that gap. Voice matching from a sample has been there since 2.4.0, and in the current SKILL.md the sample explicitly wins over the pattern list. You don't get a persistent corpus file, so you paste the sample each time or keep it in a snippet. For me that's enough. If you write in several registers (technical posts vs. newsletters, say), keeping two short samples is simpler than maintaining a corpus.

Need	Upstream blader humanizer	A corpus-based fork
Strip common AI tells from any draft	Yes, 26 patterns	Yes, inherits the list
Match your voice	Paste a sample per run	Reads your corpus file automatically
Leave code and front matter alone	Yes, in file mode	Depends on the fork
Get updates when Wikipedia's guide changes	Yes, tracked in the changelog	Only if the fork merges upstream
Setup effort	One install command	Install plus building a corpus

My suggestion: start with upstream. Fork it if you find yourself pasting the same sample fifty times a week.

Where it falls short

It won't make a thin draft good. If the source has no specifics, the no-invention rule means the rewrite has none either, and you get a shorter, plainer version of the same empty post. When it asks for a missing detail, answer it; that's where the improvement comes from.

Pattern 26, re-explaining what the reader already knows, only applies to replies, so don't expect it to restructure a long article around a decision.

The full pattern list is in the repo README, grouped into six sections with a before and after for each. It's worth reading once even if you never install anything.

FAQ

Is blader humanizer free?
Yes. It's MIT licensed and runs inside the agent you already pay for or use. There's no separate subscription.

Does blader humanizer bypass AI detectors like GPTZero or Turnitin?
The README says getting past detectors is not a goal and that detectors still flag most of its output. Use it to make text better for readers.

How do I update blader humanizer to v3.1.0?
Reinstall with the same command you used before, for example npx skills add blader/humanizer --global --agent codex. The version is listed in the metadata.version field of SKILL.md.

Does it work outside Claude Code?
Yes. The README covers Codex, Claude.ai and Claude Desktop (ZIP upload), and any agent the Skills CLI supports, including Gemini CLI, GitHub Copilot, and Windsurf. There's a Cursor plugin manifest in the repo too.

Closing thoughts

Humanizer earned its spot as the final pass in my routine because it is conservative: it keeps facts, leaves code alone, and tells me when it needs something it doesn't have. The research and drafting happen earlier, and for the client site that part runs through SupaTraffic, which gives me a calendar of drafts tied to real search queries that I can review and edit before anything goes live. Then the draft goes through humanizer, I read it aloud, and I publish.