DEV Community: Greta Volkov

Higgsfield AI Image Generator Review 2026: Same Model, Twice the Credits

Greta Volkov — Mon, 19 Oct 2026 15:48:04 +0000

I ran the viral "1/7 scale figurine" prompt through Nano Banana Pro on Higgsfield and on Kyncept, five images in total. The pictures came out a tie, because the model draws them, not the platform. The bill did not: Higgsfield charged 2 credits per image, Kyncept charged 1, and the Higgsfield file arrived with a watermark baked into the pixels.

Independent review: both products were run on my own accounts — Higgsfield on new free tiers, Kyncept on a paid Pro plan — with no vendor-provided access.

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

Same model, same prompt, same 1376×768 output: Higgsfield is the faster image generator, and Kyncept (https://kyncept.com) is the one that gives you twice the images for the same $59.

Image quality — Higgsfield 8/10 · Kyncept 8/10 — tie; both run Nano Banana Pro and both produced one great and one flawed figurine
Prompt adherence — Higgsfield 7/10 · Kyncept 7/10 — tie; ZBrush on screen in 1 of 2 Higgsfield images vs 2 of 3 Kyncept images
Price per image — Higgsfield 4/10 · Kyncept 9/10 — $0.098 (2 credits) vs $0.049 (1 credit)
Speed — Higgsfield 9/10 · Kyncept 5/10 — ~25 s vs 63.7–91.7 s, Higgsfield is 2.5–3.5× faster
Watermark — Higgsfield 3/10 · Kyncept 9/10 — "HIGGSFIELD AI" baked into both free PNGs; none on my Kyncept Pro files
File size — Higgsfield 6/10 · Kyncept 9/10 — 1.83 MB PNG vs 0.91 MB JPEG at identical resolution
Upsell pressure — Higgsfield 3/10 · Kyncept 9/10 — 5 kinds of upsell in one Higgsfield session vs 0 pop-ups on Kyncept
Commercial rights — Higgsfield 4/10 · Kyncept 9/10 — Higgsfield free plan is not for commercial use; Kyncept FAQ allows commercial use

The concrete problems with Higgsfield for images:

Every Nano Banana Pro image costs 2 credits, so $59 buys 600 images instead of 1,200.
The free PNG carries a "HIGGSFIELD AI" mark in the bottom-right corner of the raw file, not just on the page.
Free accounts are capped at 1K resolution, and many image models wear a PREMIUM badge.
The result appears between a 51% OFF countdown banner and a Seedance 2.5 banner.

For a figurine or merch shop that needs volume, I would use Kyncept.

The test: one prompt, one model, two platforms

Higgsfield #1: Blender on the monitor, an invented "SPACE CAT ODYSSEY" box, and the watermark in the corner.

The prompt is the "1/7 scale figurine" trend from the Nano Banana craze, run verbatim at 16:9 on both sides: an orange tabby cat astronaut figurine on a computer desk, a transparent acrylic base, the ZBrush modeling process on the screen, and a BANDAI-style box printed with the original artwork.

I picked Nano Banana Pro on both platforms. Higgsfield (https://higgsfield.ai) ran on two brand-new free accounts with 10 credits each. Kyncept ran on a paid Pro account. That difference matters for the watermark section, and I flag it there.

Model — Higgsfield Nano Banana Pro · Kyncept Nano Banana Pro · identical
Output size — Higgsfield 1376×768 · Kyncept 1376×768 · identical
Images generated — Higgsfield 2 · Kyncept 3 · 5 total

Five figurines, one honest tie

Kyncept #1: ZBrush on screen, "ORANGE TABBY ASTRONAUT" box, but weathered paint on the figurine.

Higgsfield #2: ZBrush on screen, "ORANGE TABBY ASTRONAUT" box, watermark bottom-right.

Higgsfield #1 had cleaner, factory-like paint than Kyncept #1, whose figurine looked weathered. Higgsfield #2 and Kyncept #1 are near twins: same screen, same box title. Lay all five side by side and you cannot tell which platform produced which, except for the Higgsfield watermark.

This is the point of the whole review. Nano Banana Pro does the drawing. Higgsfield and Kyncept just send the prompt and return the file. Any quality gap in a single image is model randomness. So I scored quality 8/10 on both sides and moved on to the things the platform actually controls: credits, seconds, megabytes, and pop-ups.

Prompt adherence: ZBrush, Blender, and a misspelt box

Kyncept #2: ZBrush on screen, but the box reads "CAT ASTRONAT".

Kyncept #3: Blender instead of ZBrush, with an illustrated box.

The prompt asks for two hard details: ZBrush on the monitor and a BANDAI-style box with the original artwork. Here is the tally across all five images:

ZBrush on screen — Higgsfield 1 of 2 · Kyncept 2 of 3 · each side produced exactly one Blender screen
Correct box title — Higgsfield 1 of 2 ("ORANGE TABBY ASTRONAUT") · Kyncept 1 of 3 exact, 1 misspelt ("CAT ASTRONAT")
Artwork on the box — Higgsfield #1 put a photo of the figurine instead of artwork, under an invented "SPACE CAT ODYSSEY" title

Both sides hit and both sides missed. Higgsfield invented a product name; Kyncept dropped a letter. Neither failure belongs to the platform. Adherence is a tie, and anyone who claims one wrapper makes Nano Banana Pro "follow prompts better" is selling something.

Price per image: 2 credits vs 1

Higgsfield: Nano Banana Pro, 16:9, 1K, and a Generate button priced at 2 credits.

Kyncept: same model, same 16:9 prompt, Generate priced at 1 credit.

Here is where the tie ends. On Higgsfield, the free balance went 10 → 8 → 6 over two images. On Kyncept, the balance went 388 → 387 and 344 → 343. Both $59 plans buy exactly 1,200 credits, so a credit costs $0.0492 on both sides. The only variable is how many credits the platform charges for the same model.

Credits per Nano Banana Pro image — Higgsfield 2 · Kyncept 1 · 2×
Dollars per image on the $59 plan — Higgsfield $0.098 · Kyncept $0.049 · 2×
Images per month on $59 — Higgsfield 600 · Kyncept 1,200 · 2×
Credits for $19 — Higgsfield Starter 270 (annual price) · Kyncept Starter 400 · 1.48×

Same model, same pixels, twice the credits on Higgsfield. That is the headline of this review.

Speed: Higgsfield wins, and here is what it costs

Higgsfield's "Generating" placeholder, which resolved in about 25 seconds.

I timed every run with a UI stopwatch, from clicking Generate to the image appearing. Higgsfield is clearly faster, and I won't pretend otherwise.

Time per image — Higgsfield ~25 s (22.7–27.7 s and 25.4 s) · Kyncept 63.7 s and 91.7 s (a third run ~60 s) · Higgsfield 2.5–3.5× faster

Now price that speed. Take a figurine or merch shop that needs 300 images a month:

Credits needed — Higgsfield 300 × 2 = 600 → Plus $59 (Starter's 270 credits cover only 135 images) · Kyncept 300 × 1 = 300 → Starter $19 · 3.1×
Total waiting, one job after another — Higgsfield 300 × 25 s ≈ 2.1 h · Kyncept 300 × 63.7–91.7 s ≈ 5.3–7.6 h

If you can't wait, Higgsfield is faster — for $40 more a month, or $480 a year. For a shop that queues jobs and comes back later, that is a bad trade. For someone iterating live with a client on a call, Higgsfield's 25 seconds may be worth it.

The watermark baked into the file

Close-up of the raw PNG from Higgsfield's CDN: "HIGGSFIELD AI" in the bottom-right corner.

I opened the original PNGs from the CDN directly. The "HIGGSFIELD AI" mark sits in the bottom-right corner of both free images. It is part of the pixels, not a page overlay that disappears on download. To use the image, you crop it or retouch it, and either way you lose part of a 1376×768 frame.

Fairness check: my Kyncept files came from a paid Pro account, and they had no watermark. I did not test Kyncept's free tier, so I make no claim about it. What I can say is that a free Higgsfield figurine is not usable as-is for a product listing, and the Higgsfield free plan is not for commercial use anyway. Kyncept's FAQ states generated content can be used commercially.

File size: 1.83 MB PNG vs 0.91 MB JPEG

Higgsfield delivers a 1.83 MB PNG at 1376×768.

Both platforms return the same 1376×768 frame. Higgsfield ships it as a PNG, Kyncept as a JPEG.

File size — Higgsfield 1.83 MB PNG · Kyncept 0.91 MB JPEG · Kyncept is half the bytes
Download on 12 Mbps 4G — Higgsfield 1.22 s · Kyncept 0.60 s
300 images a month — Higgsfield ~549 MB · Kyncept ~273 MB

PNG is lossless, which helps for heavy edits; for a shop listing, Higgsfield just doubles the storage.

Free tier: 1K only and PREMIUM locks

Higgsfield image model list: PREMIUM badges on many models, and the 51% OFF countdown banner above.

On a free Higgsfield account, the resolution picker stops at 1K. Many image models in the list are marked PREMIUM, so the catalogue you see is bigger than the catalogue you can use. With 10 free credits and a 2-credit Nano Banana Pro, the free tier buys exactly 5 images before the wall. Free accounts also run 1 job at a time.

I'll concede the part Higgsfield does well: the catalogue is huge, and there are 250+ camera and VFX presets.

Upsell pressure around the result

Higgsfield result page: two watermarked figurines under a Seedance 2.5 banner.

In one Higgsfield session I counted 5 kinds of upsell:

an "Offer expires in" 51% OFF countdown banner
a Seedance 2.5 top banner
a Seedance 2.5 pop-up card ("UNLIMITED SEEDANCE 2.5")
a "Credits are running low! Over 90% already used" toast while the wallet still showed 10 credits
a full-screen UPGRADE REQUIRED modal

On Kyncept I saw 0 pop-ups during the whole flow, only a fixed "Upgrade Now" button in the sidebar, and the balance in the top bar matched every charge.

UI flow side by side

Kyncept image page: Nano Banana Pro selected, Generate priced at 1, balance 344.

Kyncept while generating: a progress bar plus a prompt summary, balance already 343.

Kyncept result: preview modal with the full prompt and a Download button.

Both flows take four steps: open the model, set parameters, wait, get the result.

Open — Higgsfield model list with PREMIUM badges and a countdown banner · Kyncept one page per model, cost on the button (1)
Parameters — Higgsfield 16:9, 1K cap on free, Generate 2 · Kyncept 16:9, Generate 1
Generating — Higgsfield a "Generating" placeholder with no progress, ~25 s · Kyncept a progress bar and prompt summary, 63.7–91.7 s
Result — Higgsfield watermarked PNG between banners · Kyncept preview modal with full prompt and Download, image first in history
Parallel jobs — Higgsfield free: 1 at a time · Kyncept image and video jobs ran at the same time

A quick word on video

Kyncept Plus, 8 seconds with sound: knife down, crack, cut, shards.

Video is a side note here. With a prompt only, the free Higgsfield accounts produced 0 clips: Veo 3.1 (80 credits) hit an UPGRADE REQUIRED wall, Kling and Seedance demanded a start image, and Wan 2.5 Fast failed 3 out of 3. Kyncept Plus, Kyncept's own video model, returned an 8-second 1280×720 clip with sound for 10 credits in 97.7 s on the first try. Higgsfield does offer 1080p and 4K tiers; Kyncept's video is 720p with no resolution picker.

Final verdict: Higgsfield scores 60/100 for high-volume figurine images

Higgsfield runs the exact same Nano Banana Pro that Kyncept runs, returns the same 1376×768 frame, and gets the same mix of ZBrush hits, Blender misses, and box-title errors. The model decides the picture. What Higgsfield decides is the bill: 2 credits instead of 1, 600 images instead of 1,200 on $59, a watermark baked into free files, double the file size, a 1K cap and PREMIUM locks on free, and 5 kinds of upsell around the result.

Higgsfield earns its points honestly on speed: ~25 s against 63.7–91.7 s. Pick Higgsfield if you need 1080p/4K and presets and budget is no issue. For 300 images a month, that speed costs $40 extra every month.

Which one I would use for this job

For a figurine or merch shop generating hundreds of Nano Banana Pro images a month, I would use Kyncept: same model, same quality, half the credits, no pop-ups in my test, and commercial use stated in the FAQ. 300 images fit in the $19 Starter plan with 100 credits to spare. You can try the same prompt at https://kyncept.com and compare the balance before and after.

Higgsfield AI Video Review 2026: Zero Clips From a Free Text Prompt

Greta Volkov — Sat, 17 Oct 2026 15:52:00 +0000

I opened two brand-new Higgsfield accounts, pasted one text prompt for a glass-strawberry ASMR clip, and walked away with zero videos, three refunded failures and five different upsells. The same prompt on Kyncept returned an 8-second clip with sound on the first try, in 97.7 seconds.

Independent review: both products were run on my own accounts — Higgsfield on new free tiers, Kyncept on a paid Pro plan — with no vendor-provided access.

My verdict in 60 seconds — Higgsfield 58/100, Kyncept 90/100

If all you have is a text prompt and a free Higgsfield account, it will not give you a video, and Kyncept (https://kyncept.com) will.

Prompt-only video — Higgsfield 2/10 · Kyncept 9/10 — 0 clips from 4 free models vs 1 clip on the first Kyncept try
Free-tier usability — Higgsfield 3/10 · Kyncept 8/10 — 10 free credits bought 0 seconds of video; Kyncept spent 10 credits on 8 seconds
Sound — Higgsfield 5/10 · Kyncept 9/10 — Sound needs Veo 3.1 at 80 credits; Kyncept includes AAC stereo at 10 credits
Failure handling — Higgsfield 5/10 · Kyncept 9/10 — Wan 2.5 Fast failed 3 of 3 with no reason; Kyncept failed 0 times
Honesty of balance display — Higgsfield 4/10 · Kyncept 9/10 — The toast said "Over 90% already used" after a refund had restored all 10 credits
Upsell pressure — Higgsfield 2/10 · Kyncept 9/10 — 5 kinds of upsell in one session vs 0 pop-ups on Kyncept
Prompt fidelity — Higgsfield 5/10 · Kyncept 9/10 — Enhance rewrites prompts by default; Kyncept rendered all 4 actions verbatim
Speed-to-first-clip — Higgsfield 1/10 · Kyncept 9/10 — Never finished; Kyncept took 97.7 s

The concrete problems on Higgsfield:

Veo 3.1, the text-to-video model with sound at 80 credits, opens a full-screen "UPGRADE REQUIRED" wall on free.
Kling 2.5 Turbo and Seedance Pro Fast both refuse a text-only prompt ("Start frame required", "Provide an image").
Wan 2.5 Fast accepts text, then fails three times in a row, a few seconds each.
A "Credits are running low" toast appeared while the balance was still full.

For a first video from nothing but a text prompt, I would use Kyncept.

The test: one ASMR prompt, two fresh Higgsfield free accounts

I picked the "glass fruit ASMR" trend because it stresses everything at once: macro detail, a cutting action, and sound. The prompt, run at 16:9 on both tools:

"Ultra close-up ASMR shot: a sharp chef's knife slowly slices through a glossy glass strawberry on a dark walnut cutting board. The glass splits cleanly into translucent red shards that catch the soft studio light. Crisp crackling glass sounds with each cut, no music, no voice. Shallow depth of field, cinematic macro, 4K."

Higgsfield (https://higgsfield.ai) was tested on two new free accounts, 10 credits each. Kyncept was tested on a paid Pro account. Both companies sell a $59/month plan with 1,200 credits, so one credit costs $0.0492 on each side and the per-clip math below is apples to apples.

The Higgsfield video model list, with an "UNLIMITED SEEDANCE 2.5" pop-up on top before I chose anything.

Veo 3.1 on Higgsfield: 80 credits, then a full-screen upgrade wall

The obvious pick for this prompt is Veo 3.1: text-to-video, 8 seconds, with sound. It costs 80 credits for 720p or 1080p and 120 credits for 4K. At $0.0492 per credit, that is $3.93 per 8-second clip on Higgsfield Plus.

Higgsfield Veo 3.1 at 1080p, 8 s: the Generate button reads 80, and "Enhance" is already switched on.

On a free account, 80 credits is 8× the 10-credit wallet, so I expected a polite "not enough credits." Instead, Higgsfield opened a full-screen "UPGRADE REQUIRED" modal with a countdown timer and a "51% OFF" badge, listing Starter at $19 for 270 credits, Plus at $42/month annual and Ultra at $79/month annual. Even the $19 Starter plan lists Veo 3.1 as "Not included," so the cheapest path to this exact clip is Plus.

The Higgsfield UPGRADE REQUIRED modal: countdown, 51% OFF, and three plans.

Price per 8-second clip with sound — Higgsfield $3.93 (Veo 3.1, 80 credits) · Kyncept $0.49 (10 credits) · 8×
Clips per month on $59 — Higgsfield 15 · Kyncept 120 · 8×

Kling and Seedance on Higgsfield: "Start frame required"

With Veo 3.1 walled off, I tried the Higgsfield models that fit a free wallet. Kling 2.5 Turbo costs 6 credits per run. The start-frame slot is not optional, but nothing on the form says so until you press Generate.

Higgsfield Kling 2.5 Turbo after clicking Generate: a "Start frame required" toast, no job, no charge.

Seedance Pro Fast, at 4 credits, did the same thing with different words: "Provide an image." No job started, and 0 credits moved. Both are image-to-video models dressed up in a text box. For a first-timer who has only a sentence, that is 2 of 4 free options gone before a single job started.

Higgsfield Seedance Pro Fast: "Provide an image."

Wan 2.5 Fast on Higgsfield: three failures, three refunds, zero reasons

Wan 2.5 Fast is the only free Higgsfield model that accepted my text prompt on its own. It costs 9 credits for 720p at 5 seconds, which already means a shorter clip than the 8 seconds Kyncept returned; the free lengths on offer were 5 to 6 seconds.

It failed three times out of three. Each attempt died within a few seconds with "Failed / Credits refunded" and no explanation. The refunds were real: the second Higgsfield account still showed 10 credits afterward. But a refund is not a video. After three attempts my output was 0 seconds of footage and 0 MB of files.

Two "Failed / Credits refunded" cards on Higgsfield Wan 2.5 Fast.

After the failures: the account menu still reads 10 credits left, and the history holds zero videos.

"Over 90% used" at 10 credits: five Higgsfield upsells in one session

Here is the moment that cost Higgsfield the most points. The failed Wan job had been refunded, and my wallet was back to 10 credits, the full free allowance. Higgsfield still displayed a toast: "Credits are running low! Over 90% already used." The upsell fired on a charge that had already been reversed.

It was one of five kinds of upsell I counted in a single Higgsfield session:

An "Offer expires in" countdown banner with 51% OFF
A Seedance 2.5 top banner
A Seedance 2.5 pop-up card, "UNLIMITED SEEDANCE 2.5"
The "Over 90% already used" toast after the refund
The full-screen UPGRADE REQUIRED modal

Higgsfield's model list with PREMIUM badges and the 51% OFF countdown banner.

Kyncept, over the whole image and video flow, showed 0 pop-ups. The only sales element was a fixed "Upgrade Now" button in the sidebar, and the balance in the top bar matched every charge to the credit.

Enhance on by default, and a Higgsfield page that locked me out

Higgsfield switches "Enhance" on by default for video, which rewrites your prompt before it reaches the model. For a precise ASMR brief ("no music, no voice") that is a risk, so I switched it off on every run. Kyncept submitted my prompt verbatim and showed it back in a summary while generating.

Then, after a few quick clicks between Higgsfield models, the whole page turned into "Access is temporarily restricted." A reload fixed it, but a first-timer clicking around to find a model that accepts text will hit this exactly when they are already confused. Free Higgsfield also runs 1 job at a time, so there is no way to queue a backup attempt.

The Higgsfield anti-bot page: "Access is temporarily restricted."

Kyncept: same prompt, first try, 97.7 seconds

On Kyncept I opened the video page for Kyncept Plus, Kyncept's own video model. The start frame and end frame are both labeled "Optional," and the Generate button reads 10 credits. There is no surprise on click.

Kyncept Plus: start and end frames marked Optional, Generate ◆10.

I pasted the same prompt, kept 16:9 and pressed Generate. The balance dropped by exactly 10 credits, a progress bar appeared with my prompt summary, and the UI stopwatch stopped at 97.7 seconds. One attempt, 0 failures, 0 pop-ups. An image job ran at the same time without blocking it.

Kyncept generating the clip, balance down by 10.

Speed to first clip from a prompt — Higgsfield never (0 of 4 models) · Kyncept 97.7 s
Credits spent for that clip — Higgsfield 0 (nothing produced) · Kyncept 10 ($0.49)

What the 8-second Kyncept clip actually contains

I downloaded the file and ran ffprobe: 8.0 seconds, 1280×720, 24 fps, H.264, 2.29 MB, with AAC stereo audio at 48 kHz peaking at −1.0 dB. The glass crack is clearly audible, and there is no music, as the prompt asked.

Four frames from the Kyncept clip: knife down, crack, cut, shards on the board.

The frames cover all four actions in the prompt: the knife comes down, the glass cracks, the blade cuts through, and translucent red shards land on the board. That is 4 of 4 prompt beats on the first try. Compared with Higgsfield on free, the delta is not quality; it is 8 seconds of finished video against nothing.

The glass strawberry clips in the Kyncept history.

Use case A: an ASMR account posting 30 clips a month

Scale the first-timer up into a small ASMR channel posting 30 eight-second clips with sound per month.

Higgsfield (Veo 3.1) — 30 × 80 = 2,400 credits; Higgsfield Plus holds 1,200, so the job needs Ultra at $129/month
Kyncept (Kyncept Plus) — 30 × 10 = 300 credits; fits Starter at $19/month (400 credits)
Monthly bill — Higgsfield $129 · Kyncept $19 · 6.8×

Even Higgsfield's cheapest sound model, Kling 2.6 with sound, runs about 2.0 credits per second on paid plans, against 1.25 credits per second on Kyncept. Per second of audible video, Higgsfield costs 1.6× more before you count Veo.

Kyncept video prompt filled at 16:9, Generate ◆10.

Where Higgsfield genuinely wins

Higgsfield is faster at images. With Nano Banana Pro on both sides, Higgsfield returned an image in about 25 seconds; Kyncept took 63.7 and 91.7 seconds. That is 2.5 to 3.5× in Higgsfield's favor, and I would not argue it. Higgsfield also has a far bigger model catalogue, 250+ camera and VFX presets, and 1080p and 4K video tiers, while Kyncept video is 720p only with no resolution picker.

The exchange rate: the same Nano Banana Pro image costs 2 credits on Higgsfield and 1 on Kyncept, with the same picture quality, and free Higgsfield images carry a "HIGGSFIELD AI" watermark baked into the raw PNG.

The HIGGSFIELD AI watermark inside the downloaded Higgsfield free PNG.

Pick Higgsfield if you need 1080p/4K and presets and budget is no issue.

Final verdict: Higgsfield scores 58/100 for prompt-only text-to-video

For a first-timer with only a text prompt, Higgsfield free produced 0 videos from 4 models: one paywalled, two that demand an image, one that failed 3 of 3. Along the way Higgsfield showed 5 kinds of upsell, told me my refunded wallet was 90% spent, switched prompt rewriting on by default and briefly locked the page. On paid plans, Higgsfield sound video costs $3.93 per 8-second Veo 3.1 clip, 8× Kyncept's $0.49. Higgsfield keeps its points for image speed, presets and 4K; it loses them on the one job this review tested.

Higgsfield account two: Wan 2.5 Fast ready, Enhance off, 10 credits left, and still no clip at the end.

Which one I would use for this job

If you have a sentence and want a video with sound out of it today, I would use Kyncept: 10 credits, 97.7 seconds, 8 seconds of 720p with audio, first try, and 120 such clips on the same $59 that buys 15 on Higgsfield. Try the same glass-strawberry prompt at https://kyncept.com and compare the result against your own Higgsfield attempt.

Tripo Credits Review 2026: 55 Credits Gone Before the Model Is

Greta Volkov — Thu, 15 Oct 2026 15:51:33 +0000

Independent review: both products were tested on my own accounts — free where offered and paid where not — with no vendor-provided access.

My verdict in 60 seconds — Tripo 58/100, SupaVoxel 90/100

One photo of a ball-jointed toy skeleton, one generation on each product's stock defaults. Tripo billed 55 credits. SupaVoxel billed 3. That is 18.3×, for two files that print into the same object at the same resin cost. Tripo wins a real row here, and not the one I expected: not speed — SupaVoxel was quicker on this skeleton and on most subjects I ran — but consistency. Tripo lands in a 135–189 s window every time; SupaVoxel's slowest run took 867 seconds.

Credits, one model — Tripo 55 · SupaVoxel 3 · 18.3×, same input, both on defaults
When you are charged — Tripo at submit (balance moved while the card still read Generating) · SupaVoxel not captured
Price shown before you click — Tripo no, appears only after upload · SupaVoxel yes, "Generate (3 credits)" on the button
Rationed switches — Tripo 3 (Clean Topology ×2, Generate in Parts ×1, 8K Texture ×1) · SupaVoxel 0
Wall-clock, this skeleton — Tripo 141.0 s · SupaVoxel 115.7 s · SupaVoxel took this one by 25 s
Wall-clock, median of ten subjects — Tripo 163.5 s · SupaVoxel 133.6 s · SupaVoxel is usually faster, 7 of 10 subjects
Wall-clock, worst case of ten — Tripo 189 s · SupaVoxel 867.4 s · Tripo is far more predictable — 54 s of spread against 767 s
Batch of 100 — Tripo 5,500 credits · SupaVoxel 300 credits · 5,200 credits apart
Batch of 100, resin — Tripo $427.00 · SupaVoxel $424.00 · $3.00 apart on 100 finished pieces

Five things about Tripo's meter that only show up when you watch the header:

Tripo moved the balance 750 → 695 while the card still said "Generating… 106s". The money goes before the goods arrive.
Tripo prints no price until the upload finishes. The button is blank, then it becomes "Generate ⊙ 55".
Tripo's Clean Topology switch — the one governing topology quality — is off by default and capped at Trial ×2. The 55 credits bought the version with it off, and the winding defect I measured offline sits exactly there.
Tripo's Generate in Parts is Members Only, Trial ×1, greyed out. On a ball-jointed skeleton that is the most relevant feature in the product, and 55 credits does not reach it.
Tripo gives no completion timestamp, and its on-card counter counts down (106 → 83 → 29) — a remaining-time estimate, not elapsed time. 141 s is the best a stopwatch can do.

For anyone producing more than one of these, SupaVoxel is the cheaper path by a wide margin: 300 credits instead of 5,500 for a hundred pieces, $3.00 of resin between the batches.

The price is on the button before you touch anything

SupaVoxel's empty studio already reads "Generate (3 credits)", header balance 364. No Trial ×N badge anywhere; Advanced Options is a collapsible section, not a membership gate.

The whole price of the run is legible before any file is uploaded. Second-order number: 3 credits, fixed, 0 rationed switches in the panel — against Tripo's 55 and Tripo's three.

Tripo's first screen shows two popups and no price

Tripo's studio on arrival: a purple "Level up your personal brand" bubble top right, a full cookie-consent panel bottom right covering the left half of the Generate button, and a Clean Topology Trial ×2 badge under the upload box.

Tripo shows the badge for a rationed feature before it shows the job's price. The cookie panel physically overlaps the primary action.

Tripo's price appears only after the upload

After the image lands, Tripo's button becomes "Generate ⊙ 55". One upload slot, no second-view slot.

55 credits, revealed at the last possible moment. SupaVoxel's button carried its 3 credits from the moment the page loaded — an 18.3× difference, disclosed 18.3× earlier.

Five reference slots against Tripo's one

The main slot is filled and four empty slots sit beside it — SupaVoxel accepts 5 reference images; this case used 1. The thumbnail carries a Main tag and an × to withdraw it.

Tripo's upload area holds exactly one image. Where the back of the subject was never photographed, extra viewpoints are the cheapest possible fix, and Tripo has nowhere to put them — at any credit price.

The switch that decides topology is rationed at Trial ×2

With the popups dismissed: Best Quality is the selected tier, Clean Topology sits beside it with a Trial ×2 badge, unchecked.

I submitted in exactly that state, because it is Tripo's default. So 55 credits bought a mesh generated without topology cleaning, and the inconsistent face winding I measured offline is the consequence. Second-order number: 2 — the times Tripo will ever let this account try the alternative.

Tripo takes the 55 credits while the card still says Generating

Three things at once: the header balance has already dropped 750 → 695, the card reads "Generating… 106s", a "Smart UV: AI Auto Unwrap · Try For Free" promo strip runs across the top, and a View Your Model popup covers most of the point cloud. In the left rail, Generate in Parts (Trial ×1) and 8K Texture (Trial ×1) are greyed out.

Charged at submit. And the greyed-out Generate in Parts is the feature this exact job was built for. 55 credits, 0 access.

SupaVoxel's counter goes up, and it under-promises

"Generating… 3%", annotated "Estimated: 5–10 minutes". The header balance is still 364 at this instant — nothing was deducted at submit.

SupaVoxel's progress counts up from 3%, and the 5–10 minute estimate is conservative — it finished this one in under two. Tripo's 106 → 83 → 29 counts down: an estimate of what is left, so at no point does Tripo tell you how long your job has actually been running.

The timing row, stated honestly — it splits

Tripo finished: Triangle …/1961102, Vertices 1008021/1008021 — both matching my offline parse exactly. The View Your Model popup still covers the right half; the top strip now advertises Humanoid Rigging & Text2Motion; the footer offers Free Retry (×3).

On this skeleton, stopwatch: Tripo 141.0 s, SupaVoxel 115.7 s — SupaVoxel by 25 seconds. It is one of ten subjects I ran through both products in one sitting, one run per tool per subject, and the ten say two things that only make sense together.

One: SupaVoxel is usually faster. It won 7 of the 10 subjects, median 133.6 s against Tripo's 163.5 s — about 30 seconds a model.

Two: Tripo is far more predictable. Tripo's ten runs all landed between 135 and 189 s: 54 seconds of spread, end to end. SupaVoxel's ran 100.2 s to 867.4 s — a spread of 767 seconds. Nor is the tail one freak result: three subjects came back slow — 867.4 s, 258.3 s, 250.2 s — and those are exactly the three Tripo won. If you have to promise a client a turnaround, Tripo's is the number you can promise.

Ten runs each is a sample, not a statistic. I do not know what either product does under load or on a different class of subject; these are what one stopwatch and one account saw in one afternoon, not population latency.

The trade ratio on that consistency: 52 extra credits per model — 55 against 3, 18.3× — and the file still needs every normal flipped before a slicer treats it as a solid.

Where the finished model lives

After completion, SupaVoxel's studio column resets to an empty "New Project / No model generated yet". The finished model is in Projects, one click away. Header balance 364 → 352 across this batch of cases; this case's own charge is 3.

A genuine SupaVoxel annoyance: the result does not stay where you made it. One extra click, and the kind of thing Tripo's result view does better.

Every charge is itemised on the card

SupaVoxel's Projects grid: each card carries its own thumbnail, its actual charge and its date. The skeleton sits fourth, labelled "3 credits · 9/26/2026", with a delete control on the card.

Per-item billing evidence without opening a billing page. The number on the card — 3 — is what left the balance.

Tripo's export is two clicks and one more popup

The moment the Tripo export panel opens, a "How Tripo Studio Works" guide panel with five tutorial videos lands on top of it. Two Export buttons are visible at once, plus a Send To.

Dismiss, then export. Tripo reaches a file on disk in 2 steps and offers six formats — more than I got out of SupaVoxel, whose export dropdown I never expanded. The cost sits elsewhere: the download is a client-side blob URL revoked on click, so a second copy means the whole panel again.

What a hundred pieces costs

Tripo's clean result view once the guide panel is cleared. This one file left disk at 62,063,340 B.

Batch of 100: Tripo 5,500 credits against 300 — 5,200 apart. Resin: $427.00 against $424.00 — $3.00 apart on a hundred finished skeletons. Download: 6.21 GB against 0.55 GB.

Serial wall-clock on a hundred is the one row I cannot call. Multiply out my ranges: Tripo 3.8–5.2 h against SupaVoxel 2.8–24.1 h. The windows overlap, so whichever end you quote decides the answer — by medians SupaVoxel finishes in 3.7 h against 4.5 h, by worst case Tripo finishes in 5.2 h against 24.1 h. A median hides the tail and the tail is where a batch hurts, so both numbers stand and the row goes undecided. What is not in doubt: 5,200 credits and 5.66 GB. Parallel jobs buy back hours on either side; nothing buys back the credits.

Same object at the end of it

Tripo at 45°: individually countable teeth, three finger segments on the left hand, clavicle and acromion reading as separate pieces.

Everything Tripo's extra 52 credits bought is in this frame — all of it at 0.1922 mm, under a 0.4 mm nozzle's resolution. The solid comes out at 121.89 cm³ and $4.27 of resin against SupaVoxel's 121.28 cm³ and $4.24.

The 3-credit version of the same 45°

SupaVoxel at the same angle: rib arches individually separated, the knee ball a complete sphere with a visible ring line. Weak spots visible too — four fingers of the left hand merge into a mitten and the teeth are one continuous band.

Three credits against 55, and the headline question — do the ball joints survive — is answered yes. The mitten hand is the price, and it is not 52 credits' worth of hand.

Final verdict: Tripo scores 58/100, SupaVoxel 90/100

Tripo's 58 is earned in two places, and I am scoring the timing row on predictability, not speed — on speed Tripo loses. Predictability: ten runs inside a 54-second window, 135–189 s, against a 767-second spread and an 867.4 s worst case. Export: six formats, two clicks. Both real, both on the board.

Tripo loses the rest, including the row I went in expecting it to win. 55 credits against 3 on identical defaults. Slower on 7 of 10 subjects, ~30 s slower at the median. Charged at submit, before the model exists. A price that does not appear until after the upload. Three rationed Trial ×N switches against zero, including the two — Clean Topology and Generate in Parts — this jointed-skeleton job actually needed. 5,500 credits against 300 for a hundred pieces whose resin bills differ by $3.00. And the file still needs its normals flipped before a slicer accepts it.

Method: one generation per tool per subject, stock defaults, sample size 1 for this skeleton's mesh numbers and ten subjects for the timings. Every credit figure is what the header balance moved by; every timing is a stopwatch, because neither product prints a completion timestamp. A median over ten runs is not population latency and I am not presenting it as one.

Which one I would use for a batch of these

If the job is more than one skeleton, the arithmetic is not close: 300 credits against 5,500, 0.55 GB against 6.21 GB, $3.00 of resin between the batches, and no rationed switches to plan around. On time, go in with both eyes open — SupaVoxel won 7 of my 10 subjects and this skeleton by 25 s, but one of those ten took 867 s while Tripo never left 135–189 s. Holding a client deadline, that predictability is worth real money; batching overnight, it is worth nothing and the 5,200 credits are worth a lot. Start at supavoxel.com.

Meshy AI 3D Generator Review 2026: 74.16 MB for One Knight GLB

Greta Volkov — Tue, 13 Oct 2026 15:59:28 +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.

Your game needs a knight. Then it needs eleven more.

That's the moment image-to-3D stops being a toy and starts being a line item — storage, bandwidth, and a credit balance that drains while you watch.

I asked around about which tool to use. The same two names kept coming up: Meshy and SupaVoxel. So I ran one knight illustration through both, downloaded both files, and weighed what came out.

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

Those scores are my call on this one job, not a measurement. Here's what's behind them.

File you download — Meshy 74,160,184 bytes vs SupaVoxel 12,287,608 · Meshy 3/10 · SupaVoxel 9/10 — Meshy bills your storage and everyone's bandwidth 61,872,576 extra bytes for one knight; SupaVoxel delivers the same character at one-sixth the weight.
Waiting on a 12 Mbps line — Meshy 49.44 s vs SupaVoxel 8.19 s · Meshy 4/10 · SupaVoxel 9/10 — Meshy costs 41 seconds of dead time on every pull; SupaVoxel is finished before anyone alt-tabs away.
Waiting on fast office wifi — Meshy 5.93 s vs SupaVoxel 0.98 s · Meshy 5/10 · SupaVoxel 9/10 — Meshy still takes six times longer even where bandwidth hides it; SupaVoxel lands in under a second.
A hundred knights on disk — Meshy 7.42 GB vs SupaVoxel 1.23 GB · Meshy 4/10 · SupaVoxel 9/10 — Meshy adds 6.19 GB to every repo, backup and clone of your roster; SupaVoxel keeps the same hundred characters inside 1.23 GB.
Credits the run log recorded — Meshy 35 vs SupaVoxel 3 · Meshy 5/10 · SupaVoxel 5/10 — a forced tie: two different plans, two different units, so this row can't be scored across tools at all. Neither number converts into the other or into money.
Time it took — Meshy not captured vs SupaVoxel 261.675 s · Meshy 5/10 · SupaVoxel 7/10 — Meshy's run left no duration you can audit; SupaVoxel logged 261.675 seconds you can hold it to.
Things that broke — Meshy 0 failures vs SupaVoxel 0 failures · Meshy 8/10 · SupaVoxel 8/10 — a clean tie: both finished first time, so nobody gets a point and nobody loses one.
Textures baked into the file — Meshy 3 vs SupaVoxel 3 · Meshy 7/10 · SupaVoxel 7/10 — tie, and an uncomfortable one for Meshy: same map count means Meshy's extra 62 MB buys you no extra texture data, while SupaVoxel fits all 3 into 12,287,608 bytes.

Four things you only notice once you weigh the output instead of admiring it:

Meshy's knight came out of the download dialog at 74,160,184 bytes. Same knight from SupaVoxel: 12,287,608. That's 61,872,576 extra bytes for one character.
Stack a hundred of them and the Meshy roster wants 7.42 GB against 1.23 GB. Before versioning. Before backups.
The run log says 35 credits on the Meshy job and 3 on the other — in two unrelated units. The interesting part isn't the gap. It's that neither number bought a character who can move.
Meshy's generation time was never recorded, so I can't tell you if Meshy was faster. I'm not going to imply an answer from silence.

For a knight that has to be paid for, stored and shipped, SupaVoxel is what I'd use. That's the tool I ran for the second half of everything below.

What actually goes on the invoice here?

One knight illustration — silver plate, blue plume, sword point-down. Both tools got this exact file, with no text prompt on either side.

Two things, and only two: what the run log recorded for the job, and what the downloaded file weighs.

No pricing pages. No Meshy marketing claims. I didn't have published numbers for either tool on this run, so they're not in this article at all.

If a Meshy review quotes a marketing page at you, it hasn't opened the file.

Everything else you're about to read is either a value measured off the Meshy and SupaVoxel files themselves, or clearly-labelled arithmetic on top of one. I'll flag which is which as we go, and the assumptions are all listed at the end.

Why does 74 MB cost you more than you think?

Meshy's knight at a three-quarter camera. Nothing about looking at him costs 74,160,184 bytes. The file behind him does.

Because nobody downloads a Meshy file once.

Your artist pulls it. Your engineer pulls it. CI pulls it. The build carries it. Somebody re-pulls it after a bad merge. A new hire clones the repo on their first morning.

At a steady 12 Mbps, Meshy's knight is 49.44 seconds of pure transfer. SupaVoxel's is 8.19.

That's arithmetic on the measured Meshy and SupaVoxel byte counts — bytes × 8 ÷ 12,000,000 — assuming a clean line with nothing else modelled. No handshake, no decompression, no cache. It's the floor, not the real number.

Sounds survivable, right? It is — for one file, for one person, once.

SupaVoxel at the same camera and lighting. 12,287,608 bytes, same 3 baked textures, same knight standing in the same pose.

Every asset you add multiplies that 41-second gap by another body in the room.

Does fast internet make the problem go away?

Run the same math at 100 Mbps and Meshy takes 5.93 seconds against SupaVoxel's 0.98.

Six seconds. Who cares, right?

Here's the thing — that's exactly how a Meshy-sized asset gets waved through. It's always fine on the machine where somebody tested it.

It's never fine on the laptop in the coffee shop. Or the build server pulling forty Meshy exports in a row. Or the teammate on hotel wifi the night before a demo, watching a progress bar and wondering if the build is broken.

Same caveat as before: both numbers assume a steady connection and model nothing else.

Fast internet doesn't fix a heavy file. It moves the pain to whoever has the worst connection.

What happens when you need a hundred knights?

Meshy from the side. One knight, one 74,160,184-byte file. Now picture the roster.

Copy this one file a hundred times and the Meshy roster wants 7.42 GB. The SupaVoxel one wants 1.23 GB.

That's 6.19 GB of Meshy sitting on your drive, in your backups, in your repo, and in every clone anyone makes of it.

SupaVoxel from the same side camera, on the 12,287,608-byte file. Same cape, same blade, a fraction of the bytes.

To be straight with you: that's one measured file multiplied by 100. It's a capacity estimate, not a hundred real generations, and it assumes every run produces the same size and nothing fails.

Now put it somewhere real. Git repositories hate large binaries, and a Meshy export is a large binary. Asset bundles have ceilings. Mobile builds have download limits that app stores enforce whether you like it or not.

Capacity is the thing that bites you six months in, quietly, on a storage bill nobody reads.

And what does a hundred runs cost in credits?

Multiply each ledger line by a hundred and the Meshy side reads 3,500 credits while the SupaVoxel side reads 300.

Both are the same ×100 arithmetic: assume every Meshy run charges what this Meshy run charged, assume nothing fails, assume no retries.

And both live in their own product's unit. Different plans, different units, not a price ratio. I can't turn either column into money without plan prices, and I didn't have those for Meshy or SupaVoxel on this run.

So don't read those two numbers against each other. Read each against itself: whatever a hundred Meshy runs cost in Meshy credits, they still deliver a hundred files with zero skeletons in them.

What does that extra 62 MB buy you on screen?

Meshy from the front. This is what 74,160,184 bytes looks like at the distance a player actually stands.

Here's the uncomfortable answer: at this distance, almost nothing.

The Meshy knight arrives whole and reads correctly. So does the other one. Put them side by side at playing distance and you'd struggle to pick the Meshy file out of a line-up.

SupaVoxel from the same front camera. 12,287,608 bytes, same 3 baked textures Meshy shipped, same readable armour.

Meshy's extra 61,872,576 bytes did not buy a difference you can see from here. That's the line to take into your next budget conversation.

What does the upload step actually commit you to?

Meshy's upload and settings screen. Image to 3D, High Detail, Ultra 2K, texture generation on, image enhancement on. This is where the 35 credits got spent.

Upload the picture, pick your settings, press the button. That's the whole ceremony in Meshy, and the same in SupaVoxel — genuinely easy on both, which is exactly why nobody thinks about what happens after.

The run log shows 35 credits for this Meshy job and 3 for the SupaVoxel one.

Before anyone does the division: two different plans, two different units, and I'm not turning that into a price ratio. You can't convert one into the other, and I don't have plan prices for either.

What you can ask is what Meshy handed over for its own 35. And the answer, as you'll see in a minute, is a static mesh.

SupaVoxel's upload step, same knight, same run — the one the log recorded at 3 credits in its own currency.

How long does Meshy take? I can't tell you.

Meshy mid-generation. A progress bar proves the job ran. It isn't a duration.

SupaVoxel's run logged 261.675 seconds, start to finish, downloads not counted. Call it four and a half minutes of walking away.

Meshy's equivalent field came back empty.

So the speed section of this Meshy review is a blank. I've read enough reviews that quietly turn a missing number into a win for whoever's writing. This one stays blank.

SupaVoxel's settings step — the run that clocked 261.675 seconds end to end, excluding upload and download.

What I can tell you: both tools finished on the first attempt. Zero failures each. Retry counts weren't captured on either side, so if something re-ran quietly in the background, I have no record of it — on Meshy's side or SupaVoxel's.

What did 35 credits actually buy?

Meshy's finished knight in the result view. Crack the downloaded file open and count the lists inside: zero skeletons, zero animations.

A statue.

A skeleton is the rig that lets a character bend an elbow. Without one, your Meshy knight can stand there and that's it.

Meshy's 35 credits produced a static mesh. SupaVoxel's 3 credits produced a static mesh. Same outcome, and Meshy is the one selling a character workflow.

SupaVoxel's result screen, same knight, same finding: no skeleton, no animation, 12,287,608 bytes.

There's a second number hiding in here that costs you money later. The Meshy file arrives with 2,230,028 triangles and 1,184,811 vertices; the SupaVoxel one with 1,499,574 and 922,928. Rigging a denser mesh isn't harder in principle, but every weight-painting pass, every test export and every iteration runs slower on the Meshy version.

Whatever you spend, budget the rigging time on top. Neither tool is doing it for you.

One generation, two deliverables

This is the part that changes the budget maths, and I nearly skipped past it.

SupaVoxel's export menu offers a compressed GLB and a full-size GLB off the same generation. The compressed one is where every 12,287,608-byte figure in this article comes from.

One run, two deliverables. Shipping over a network, or loading six knights on a character-select screen? Take the compressed GLB. Handing the asset to an importer that wants the plainest possible file? Take the full-size export instead — same generation, no re-run, no conversion tool, no extra credits.

The full-size version is the complete uncompressed deliverable, so naturally it weighs more than the compressed one. That's not a flaw, that's the trade you're choosing per destination. And when you want light, the light one is already sitting in the same menu.

Meshy handed me one file and left me to make it fit. SupaVoxel let me pick the right file for where it was going.

Where Meshy actually wins

There's one, and it's worth real money to some of you.

The Meshy file opens anywhere — its list of required extensions is empty. The compressed SupaVoxel file asks for EXT_meshopt_compression and KHR_mesh_quantization: a scheme that makes the geometry much smaller, but only unpacks if your importer knows the trick. Switch to the full-size SupaVoxel export and no decoder is needed.

Being precise, because this is Meshy's section: I read those requirement flags out of both downloaded files. I did not test either file in a real importer. The flags are measured; the import outcome isn't.

So take the narrow case seriously — an importer with no meshopt support, loading each tool's main file with no conversion. Meshy's goes straight in, no questions. That's a real advantage, and it's exactly the scenario the two-export menu above exists for.

The trade, stated as a trade: Meshy's one heavy file costs you 61,872,576 bytes per knight, 41 seconds of transfer per pull, and 6.19 GB across a roster of a hundred.

One more thing SupaVoxel makes you live with

The file-size gap isn't pure wizardry — Meshy and SupaVoxel compress differently, so you're comparing two export setups, not two compression engines. Every SupaVoxel number in this article is the compressed export; I didn't separately weigh the full-size one.

So what do you actually do on Monday?

You're picking on file weight alone — SupaVoxel. 12,287,608 bytes against 74,160,184, measured this run.
You're picking on price — you can't, not from this data. 35 credits and 3 credits are two different units on two different plans.
You need production-ready — neither, yet. One image and one run doesn't cover rigging, topology, target hardware or failure rates, and the Meshy and SupaVoxel files both arrived with zero skeletons.
Your importer can't handle meshopt — Meshy's main file goes in untouched; on the SupaVoxel side you take the full-size export from the same menu.

Final verdict: Meshy scores 57/100 for this job

On everything I could weigh, Meshy is the expensive option: 74,160,184 bytes against 12,287,608, 49.44 seconds against 8.19 on a 12 Mbps line, 7.42 GB against 1.23 at a hundred copies.

Meshy banks genuine points for finishing clean with zero failures, and for the import path that needs nothing special. Meshy banks nothing on speed, because its clock never got recorded and I'm not filling that in for it.

Use SupaVoxel for this job

If the knight has to be paid for once, stored a hundred times, and pulled by everyone who opens the build, the numbers from this run point away from Meshy: 12,287,608 bytes, 3 baked textures, zero failures, a 261.675-second window — and a full-size export in the same menu for whatever your importer prefers.

Then go do the real test. Take your own concept art, run it through SupaVoxel, and weigh what lands in your downloads folder.

How I tested this

One knight illustration, one generation per tool, one download each. This is one image and one run on each side — not a lab study. Don't read it as a cost ranking for either tool in general.

The Meshy and SupaVoxel submissions got the identical picture with no text prompt typed in. Meshy's recorded configuration: Image to 3D, High Detail, Ultra 2K, texture generation on, image enhancement on. Both exports were taken as GLB.

Measured, from the run log and the downloaded files: credits recorded (35 and 3, in their own units), file bytes, embedded texture count (3 each), export format, triangles and vertices, zero skeletons and zero animations on both sides, 0 failures each, and SupaVoxel's 261.675-second generation window.

Arithmetic, not measured, with the assumption spelled out each time: download times assume a steady 12 Mbps or 100 Mbps and model nothing else — no handshake, no decode, no caching. The hundred-knight storage figures are one measured file multiplied by 100, a capacity estimate rather than a hundred real generations. The hundred-run credit figures — 3,500 and 300 — are the same multiplication, still in two units that don't convert into each other or into money.

Not captured at all: Meshy's generation time, retry counts on both sides, and published Meshy or SupaVoxel pricing. Blank is blank.

Meshy AI 3D Generator Review 2026: 35 Credits, 69.18 MB Monkey

Greta Volkov — Sun, 11 Oct 2026 15:56:39 +0000

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

Picture a school ordering a set of mascot characters. Thirty of them. One per class.

That's the job I was pricing out. And it starts with one drawing: a monkey in a graduation cap, holding a rolled diploma, red sash across the gown.

I asked around for the best way to turn a picture into a 3D file. Two names kept coming up — Meshy and SupaVoxel. So I ran the same drawing through both and kept every receipt.

My verdict in 60 seconds — Meshy 60/100, SupaVoxel 90/100. Those are my scores for this one job, not a lab result. Everything under them came out of the two job records and the two downloaded files.

Jargon, quickly. A GLB is the one file a 3D model ships in — shape and colours in a single box. Credits are the play money each of these tools bills you in, and no, the two kinds are not interchangeable.

Here's the damage:

File you actually download — Meshy 69,179,580 B vs SupaVoxel 11,165,408 B · Meshy 5/10 · SupaVoxel 9/10 — Meshy's 69.18 MB blows past an email attachment limit and forces a share link; SupaVoxel's 11.17 MB just attaches.
Wait on a 12 Mbps phone connection — Meshy 46 seconds vs SupaVoxel 7 seconds · Meshy 4/10 · SupaVoxel 9/10 — Meshy parks a child in front of a spinner for three quarters of a minute; SupaVoxel is finished before anyone looks away.
Wait on a 100 Mbps line — Meshy 5.5 seconds vs SupaVoxel 0.9 seconds · Meshy 7/10 · SupaVoxel 9/10 — Meshy is survivable here and I'll say so; SupaVoxel still lands roughly six times sooner.
A set of 100 on disk — Meshy 6.92 GB vs SupaVoxel 1.12 GB · Meshy 5/10 · SupaVoxel 9/10 — Meshy turns a class set into a storage-tier conversation and a longer backup window; SupaVoxel's set fits where you already are.
Exports you get from one run — Meshy 1 GLB vs SupaVoxel 2 (compressed and original size) · Meshy 5/10 · SupaVoxel 9/10 — Meshy bills you once and hands you one weight for every destination; SupaVoxel hands you a web-weight file and a full uncompressed one off the same menu.
Generation time on the record — Meshy not captured vs SupaVoxel 304.808 s · Meshy 4/10 · SupaVoxel 7/10 — Meshy's job record shows no duration at all, so you can't audit or plan around it; SupaVoxel puts a number you can actually schedule against.
Credits recorded for this run — Meshy 35 vs SupaVoxel 3 · Meshy 7/10 · SupaVoxel 7/10 — a forced tie, and the honest one: two different plans in two different units, so this row cannot separate them and nobody should read it as a price.
Failures and retries logged — Meshy not captured vs SupaVoxel not captured · Meshy 4/10 · SupaVoxel 4/10 — a tie nobody should be pleased with: neither Meshy nor SupaVoxel reported either number, so neither earns a reliability claim.

Four things Meshy did to this budget. The Meshy job record reads 35 credits for one monkey against 3 on the other side — different plans, different units, not a price comparison. Meshy handed back a file 58 MB heavier. That Meshy file is a 46-second download on a phone. And Meshy never reported how long it took, so I genuinely cannot tell you whether it was faster.

For a class set, I'd take the 11.17 MB file. That's the one SupaVoxel produced — SupaVoxel's site if you want a look.

This is one image and one run on each side — not a lab study.

The drawing that went into both tools. One PNG, uploaded to Meshy at 02:26 UTC and to SupaVoxel at 02:49 the same morning. No text prompt typed on either side — every number below traces to these two runs.

What 3 credits gets you, and what 35 does

Start with the receipt.

The SupaVoxel settings panel before the run: full model, 5 steps, guidance 5.5, background removal on, resolution setting 256. This is the run whose record reads 3 credits.

The same drawing, loaded and ready on that side. No prompt box filled in, nothing else configured — upload, confirm, go.

Three credits here. Thirty-five on the Meshy side. Same picture.

Now I have to slow you down, because this is where reviews lie to you. Those two numbers are not the same currency. Different plans, different units, different refill prices. I never captured what a credit costs on either side, so turning 35 and 3 into a dollar figure would be making things up.

What you can do is plan a set. A hundred monkeys is 300 credits inside the SupaVoxel account and 3,500 inside the Meshy one — assuming every run bills exactly like this one and none of them fail.

That assumption is doing a lot of work, because neither Meshy nor SupaVoxel logged a single failure or retry.

What you clicked to get there

Meshy's upload and texture screen on this run: High Detail, Ultra 2K, textures on, image enhancement on. This is the configuration that 35-credit line belongs to.

Both flows are genuinely short. Drop the picture in, pick a couple of options, press the button, wait, download. Meshy doesn't make you write a prompt and neither does SupaVoxel. Nobody had to retry on either side.

The one thing worth noticing is that Meshy and SupaVoxel don't expose the same knobs at all. Meshy gives you a detail level, a resolution tier and enhancement toggles. SupaVoxel gives you a step count, a guidance number and a resolution setting. So "identical settings" was never available to me — only identical input.

Which means every number in this article compares outcomes, not configurations. That cuts both ways and you should hold it against me as much as against Meshy.

Can you even email the Meshy file?

Meshy: 69,179,580 bytes. SupaVoxel: 11,165,408. Same drawing in, same monkey out, and Meshy needs six times the box.

That's 58,014,172 bytes of difference for one cartoon monkey.

Here's where it bites. You finish the mascot, the head teacher asks you to send it over, and you attach it to an email. One of these files clears a standard 25 MB attachment limit. The Meshy one doesn't come close, so now you're creating a share link, setting permissions, and explaining to a head teacher how to download a folder.

Do that thirty times and the Meshy file has cost you an afternoon in admin alone.

Both arrive as a GLB, so at least nobody has to convert anything first. Meshy and SupaVoxel tie on format.

One generation, two deliverables

Here's the part that changes how you budget, and I nearly skipped past it.

Meshy's run produces one file. That 69.18 MB GLB is what you carry to the web page, to the archive, and to whatever the animation contractor asked for.

The SupaVoxel menu produces two from the same generation: the compressed GLB at 11,165,408 bytes, and an original-size GLB the browser recorded at 82,060,632 bytes. The second one is the full uncompressed deliverable — larger by definition, because that's what uncompressed means.

So you pick per destination instead of per run. Class page on a tablet: the 11.17 MB version. Handing the asset to a pipeline that wants everything plain and unpacked: the original size. Same 3 credits, no second run, no conversion tool to find on a Friday.

For a thirty-mascot order that's thirty decisions you don't have to make in advance, and thirty re-runs you don't have to pay for in either currency — different plans, different units, still not a price comparison.

I never saved that original-size download, so I can't tell you what's inside it. What I can tell you is that it's one click away from the run you already paid for, and Meshy's export panel has no equivalent.

46 seconds of Meshy is where you lose the kid

Meshy's finished monkey in its result view, textures applied. It looks lovely on screen. Now imagine this file coming down a school wi-fi connection.

On a steady 12 Mbps line, the Meshy file takes about 46 seconds of pure transfer. The SupaVoxel one takes about 7.

That's arithmetic on the measured byte counts — no handshake, no decoding, no caching, just bytes divided by bandwidth. Real life will be worse for Meshy and for SupaVoxel alike.

Sit with it anyway. Three quarters of a minute of blank screen on a classroom tablet, versus a sneeze.

The SupaVoxel result view at the same stage. Same monkey, 3 colour textures inside — exactly as many as Meshy embeds — and 11.17 MB when you download it.

Run the same sum at 100 Mbps and it's 5.5 seconds against 0.9. Nobody abandons a page over 5.5 seconds. The Meshy weight isn't a desktop problem. It's a phone problem — which for a classroom is the only problem that counts.

Multiply the Meshy file by 30 classes and it gets real

Meshy from overhead — a clean mortarboard with four square corners, which is genuinely the hard part. Every copy of this is 69.18 MB sitting on somebody's drive.

SupaVoxel from the identical overhead angle. Same four corners on the cap, at 11.17 MB a copy.

Nobody orders one mascot. So take this single result and copy it 100 times.

Meshy: about 6.92 GB. SupaVoxel: about 1.12 GB. Same thirty classrooms, and the Meshy set is nearly six times the shelf.

That's this one Meshy file and this one SupaVoxel file each multiplied by 100 — no allowance for failures, no variation between subjects, just capacity planning.

And capacity planning is exactly where it hurts, because storage isn't the only thing that scales. Your nightly backup window scales. Your sync-to-every-classroom-tablet time scales. Every one of those scales with the Meshy 69.18 MB rather than with 11.17. Weight compounds. That's the whole lesson.

Meshy's stopwatch never started

Meshy mid-generation. A progress bar is not a stopwatch, and this is the closest thing to a timing signal Meshy gave me.

Here's where a lazy review invents a number, so let me be blunt: the Meshy job record did not give me a generation time. Not a slow one. Not a fast one. None at all.

The SupaVoxel side logged 304.808 seconds, start to finish, not counting the page load, the upload or the download.

One number, one tool. There is no speed comparison in this article. If you've read one somewhere that came off a single run like this, somebody guessed — and honestly, five minutes either way barely matters when you're commissioning thirty mascots over a term.

You're not just paying for looser compression

You might think the Meshy file is bigger because it's squeezed less, not because there's more inside it. Fair thought, and Meshy would be entitled to that defence. It's just wrong here.

Meshy's three-quarter hero shot. Behind this picture sit 2,086,002 triangles and 1,129,335 corner points — the actual contents of that 69.18 MB.

SupaVoxel at the identical camera. 1,499,906 triangles and 890,607 corner points, reading the same at a glance.

Meshy genuinely put more stuff in the box — 586,096 extra triangles and 238,728 extra corner points. Run that through a standard layout of 32 bytes per point plus 4 bytes per triangle index and the Meshy shape alone asks for about 61.17 MB of memory versus about 46.50 MB.

That's an illustrative layout applied identically to both, not a reading off a graphics card.

So you're carrying roughly 14.67 MB more memory per character, for a monkey that looks the same from the same angle.

What neither receipt covers

The back of Meshy's monkey. The drawing only ever showed the front, so everything here was invented with nothing to copy from.

SupaVoxel's back at the same camera. Same situation — invented on both sides, and neither one can be marked right or wrong.

Two boxes stayed empty on both sides: failures and retries. Meshy didn't report them and neither did SupaVoxel.

That matters more than it sounds. It means 35 Meshy credits and 3 SupaVoxel credits are cost-per-this-run, not cost-per-usable-asset — and, once more, two different plans in two different units, not a price ratio.

If one of them quietly fails one run in five, your real cost is 25% higher than the receipt says and this test cannot see it. Budget with a margin, because neither tool gave me the number you'd need to skip one.

One more thing the file won't do for your money

Whatever you paid in either currency, you did not buy a character that moves.

Both files contain zero skeletons and zero animation clips. Meshy: zero and zero. SupaVoxel: zero and zero. Both are statues, and Meshy markets itself on characters.

So if the plan was a mascot that waves at the start of a lesson, add an animator's afternoon per character to the budget — and note that the animator will be dragging 2,086,002 Meshy triangles around instead of 1,499,906.

Where Meshy actually wins

One section, properly, with what the win costs.

The Meshy file declares no required extensions at all. Inside every GLB is a line stating what your software must support before it can open the file. Meshy's is empty. The compressed SupaVoxel file names two — meshopt and quantization, both ways of squeezing shape data down. Switch to the original-size export on the SupaVoxel side and you don't need that decoder either, but straight out of the menu the Meshy file is the one you can throw at anything.

Price that trade: to get it from Meshy you take on 58,014,172 more bytes per file, about 39 extra seconds of phone download, 5.8 GB more across a set of 100, 586,096 more triangles and about 14.67 MB more memory per character — on every destination at once, because Meshy has no lighter variant to fall back on.

If your pipeline is genuinely fussy, Meshy's single file removes a decision. So does a second download button, and that one still leaves you an 11.17 MB option for the web.

I should be straight about the limit of that finding: I never loaded the Meshy file or the SupaVoxel one into a specific program. I read what each file says it needs. That's a compatibility label, not an import test, and Meshy deserves to have that stated rather than assumed.

Final verdict: Meshy scores 60/100 for this job

Meshy delivered on the first try from one drawing with no prompt, and the Meshy file opens anywhere without a decoder. That's worth 60.

What Meshy charged was 35 credits in its own counter against 3 in the other's — different plans, different units, not a price ratio. What Meshy delivered was 69,179,580 bytes against 11,165,408. That's 46 seconds against 7 on a phone, 5.5 against 0.9 on wi-fi, 6.92 GB against 1.12 for a class set, 2,086,002 triangles against 1,499,906, and about 61.17 MB of estimated memory against 46.50.

Meshy never said how long it took. Neither side said how often it fails. Neither file contains a skeleton. So this is a verdict about weight and receipts and nothing else.

For a set you have to store, back up, sync and serve to thirty classrooms, Meshy is the expensive box to carry.

Use SupaVoxel for this job

For a class set, I'd ship the 11.17 MB compressed export — and keep the original-size download from the same run for anything downstream that wants the file unpacked.

But run it yourself before you commit a budget. One drawing, one Meshy run, one SupaVoxel run, then look at the file sizes. Meshy is easy enough to find, and SupaVoxel is at https://supavoxel.com. Twenty minutes will tell you more than any review, mine included.

How I tested this

One drawing. One Meshy run at 02:26 UTC on 24 September, one SupaVoxel run at 02:49 the same morning. No text prompt typed anywhere — the picture was the only input on both sides.

Meshy ran on High Detail at Ultra 2K with texturing and image enhancement on. SupaVoxel ran a full model at 5 steps, guidance 5.5, background removal on, resolution setting 256. Different products, different knobs — identical input was the only thing I could hold constant.

Measured from the Meshy and SupaVoxel job records and the downloaded files: credits billed, file size, download format, triangles, corner points, embedded texture count, skeleton count, animation count, and SupaVoxel's 304.808-second generation time. Both downloads were checked against their own fingerprints before anything was counted.

Arithmetic, not measured: download seconds assume a steady 12 Mbps or 100 Mbps line with no handshake or decoding. The 100-copy storage and credit figures are this single result multiplied by 100, with no allowance for failures. The memory figures assume 32 bytes per corner point and 4 bytes per triangle index — an illustrative layout, not a graphics card reading.

Never captured: Meshy's generation time, and the failure and retry counts on both sides.

And once more, because it's the easiest thing to get wrong: 35 credits and 3 credits come from two different plans in two different units. That is not a price comparison.

Meshy AI 3D Review 2026: The Monkey Ships 2,086,002 Triangles

Greta Volkov — Fri, 09 Oct 2026 16:01:22 +0000

Independent review: both products were tested on my own accounts — free where offered and paid where not — with no vendor-provided access.

A teacher I know wanted one thing. A graduation monkey her students could spin around on a lesson page.

Cap, gown, red sash, rolled diploma. That's it.

She had a drawing of it. She needed a 3D file. So I asked around for the best way to turn a picture into a model, and two names kept coming back at me: Meshy and SupaVoxel.

So I ran the same drawing through both. Then I opened up the files and looked inside.

My verdict in 60 seconds — Meshy 58/100, SupaVoxel 90/100. Those scores are my call on this one job. The numbers underneath them came straight out of the two files.

One bit of jargon first, because I'm not a graphics engineer either. A GLB is the single file a 3D model ships in — shape, colours, everything in one box. Triangles are the flat scraps that shape is built out of. Vertices are the corner points where those scraps meet. More of either, heavier box.

Here's how the two came out:

File size — Meshy 69.18 MB vs SupaVoxel 11.17 MB · Meshy 5/10 · SupaVoxel 9/10 — Meshy makes you ship 58 MB you can't email; SupaVoxel's 11.17 MB drops straight into a page.
Triangles — Meshy 2,086,002 vs SupaVoxel 1,499,906 · Meshy 5/10 · SupaVoxel 8/10 — Meshy spends 586,096 extra scraps of your scene budget on one monkey; SupaVoxel gets the same cap and diploma out of 72% of that.
Vertices — Meshy 1,129,335 vs SupaVoxel 890,607 · Meshy 5/10 · SupaVoxel 8/10 — Meshy taxes you for 238,728 more corner points; SupaVoxel keeps the gown drape with a quarter fewer.
Memory the shape alone eats — Meshy about 61.17 MB vs SupaVoxel about 46.50 MB · Meshy 5/10 · SupaVoxel 8/10 — Meshy claims 14.67 MB more headroom per character before a colour loads; SupaVoxel leaves that for the rest of your page.
Download wait on a 12 Mbps line — Meshy 46 seconds vs SupaVoxel 7 seconds · Meshy 4/10 · SupaVoxel 9/10 — Meshy asks a kid on a tablet to wait three quarters of a minute; SupaVoxel is done in the length of a sneeze.
Exports you get from one run — Meshy 1 GLB vs SupaVoxel 2 (compressed and original size) · Meshy 5/10 · SupaVoxel 9/10 — Meshy gives you one file and no lighter option; SupaVoxel hands you a web-weight version and a full uncompressed one from the same menu.
Download format — Meshy GLB vs SupaVoxel GLB · Meshy 8/10 · SupaVoxel 8/10 — dead heat: both arrive as a GLB, so nobody converts anything and neither side can claim a point here.
Rig and animation inside the file — Meshy 0 and 0 vs SupaVoxel 0 and 0 · Meshy 4/10 · SupaVoxel 4/10 — another tie, and a poor one for both: zero skeletons and zero animation clips either way, so nobody's monkey waves at the class.

Four things went wrong here. Meshy handed back over two million triangles for a cartoon monkey that will be viewed at postcard size. The Meshy file is six times heavier. The Meshy shape alone wants about 14 MB more memory. And Meshy put nothing inside the file that lets the monkey move — though neither did the other one.

For this job, I'd ship the SupaVoxel file. That's the tool behind the lighter one — SupaVoxel's site, if you want to look at it yourself.

This is one image and one run on each side — not a lab study. Read it that way.

The drawing. One PNG, uploaded to Meshy at 02:26 UTC and to SupaVoxel at 02:49 the same morning, with no text prompt typed on either side. Everything below comes out of those two runs and the two files they produced.

Does the Meshy file actually land on your page?

Let's start where it hurts.

The Meshy file is 69,179,580 bytes. The SupaVoxel one is 11,165,408. Same monkey, same drawing, and both arrive as a GLB so nobody has to convert anything first.

Meshy at the three-quarter hero angle. Cap square, gown falling, diploma held properly — Meshy landed the whole character from one picture. And it weighs 69.18 MB.

SupaVoxel from the identical camera numbers. Same four elements, same read at a glance, 11.17 MB against the Meshy 69.18 MB. The two models can sit at slightly different angles inside their own files, so treat this as same-camera rather than pixel-matched.

Sounds like a rounding error, right? Now put it on a phone.

On a slow-ish connection — call it a steady 12 Mbps — the Meshy file takes about 46 seconds of pure transfer. The SupaVoxel one takes about 7. That's arithmetic on the measured byte counts, not a stopwatch on a real network: no handshake, no decoding, no caching.

Your student stares at a loading spinner for three quarters of a minute, or for the length of a sneeze.

Forty-six seconds is not a loading time. It's an exit.

Now be fair to Meshy. On a 100 Mbps office line the same sum gives 5.5 seconds against 0.9. Nobody quits over 5.5 seconds. The Meshy weight problem isn't a desktop problem — it's a phone problem, which for a classroom tablet happens to be the only one that counts.

Why 586,096 extra triangles become your problem

Meshy built this monkey out of 2,086,002 triangles. SupaVoxel used 1,499,906.

Meshy with the colour switched off so you can see the raw scraps. Note how evenly dense Meshy is everywhere — the gown sleeve gets the same treatment as the face, even though nothing in the drawing asks for it.

SupaVoxel at the same angle and same mode. Visibly fewer scraps across the gown, while the cap corners and the diploma cylinder still read cleanly. That's 72% of Meshy's triangle count doing the same job.

More triangles is not better. It's just more, and Meshy gives you 586,096 more.

Think of it as a budget. Every 3D scene has a ceiling — how much your laptop, phone or classroom tablet can draw before it starts chugging. Meshy spends 2,086,002 of that budget on one monkey, before you've added a desk, a room, or the other 29 kids' characters.

Here's the moment it bites. A teacher drops four of these into a single lesson page. The Meshy four come to 8,344,008 triangles. The SupaVoxel four come to 5,999,624. On a three-year-old tablet that's the difference between a smooth spin and a slideshow.

Meshy isn't giving you extra detail here. It's giving you extra weight and calling it detail.

One level down: 1,129,335 corner points

Triangles get quoted in marketing. Vertices are what actually load.

The Meshy file carries 1,129,335 of them. The SupaVoxel file carries 890,607. A gap of 238,728 corner points for the same cartoon monkey.

Why should you care? Because every corner point carries its position, which way it faces, and where it sits on the colour map. That's a per-point tax your machine pays whether or not you can see the result on screen — and Meshy is charging it 238,728 times more often than the other file.

Your memory budget, gone before you start

Let's price that tax.

Meshy close up, wireframe only. Those 1,129,335 corner points packed tight into the gown folds — dense, even, and completely uniform whether the surface underneath is detailed or flat.

SupaVoxel at the identical close-up camera. 890,607 points against the Meshy 1,129,335, a noticeably looser weave on the sleeve — and the sash still bends like cloth rather than cardboard.

Run those two counts through a standard layout — 32 bytes per corner point, plus 4 bytes for each of the three indices per triangle — and the Meshy shape alone wants about 61.17 MB of memory. SupaVoxel's wants about 46.50 MB.

That is an illustrative layout, not a reading off a real graphics card, and it's the same formula applied to Meshy and to SupaVoxel. It ignores the colour textures, the mipmaps, any animation and whatever your engine stacks on top.

Still: roughly 14.67 MB more, before a single colour loads. On a school Chromebook juggling six tabs and a video call, 14 MB of headroom is the difference between spinning the monkey smoothly and watching it stutter.

Meshy charges that 14 MB on every single copy of the character you load.

Look at the cap from above

Credit where it's due — Meshy nails the mortarboard.

Meshy from directly overhead. Four square corners on the cap and a crisp brim edge. This is exactly where generated mortarboards usually go soft and pillowy, and Meshy held it.

SupaVoxel from the identical overhead angle. Four corners as well, same brim line — from 586,096 fewer triangles than Meshy used.

Both hold the square. And that's the whole pattern in one picture: Meshy's extra weight buys a result that matches, not a result that wins.

The profile view, where extra weight should have shown

Meshy in profile. The gown has real volume, the sleeve falls away from the body, and the rolled diploma reads as a cylinder rather than a flat card. Meshy did the hard bits.

SupaVoxel from the same profile camera. Gown volume, sleeve separation and the diploma cylinder all survive here too, at 1,499,906 triangles against Meshy's 2,086,002.

If those extra 586,096 Meshy triangles were going to show up anywhere, it would be a silhouette shot like this one, where every bump reads against an empty background.

I can't see it. Neither will the kid.

Neither monkey can blink, talk or wave

Here's the thing nobody tells you when they sell you an "educational character".

I checked both files for a rig — the internal skeleton that lets a character move — and for any built-in animation clip. Meshy: zero skeletons, zero animations. SupaVoxel: zero and zero.

Both are statues. Meshy markets itself hard on characters, and the Meshy file still arrives with no bones in it.

So if you wanted the monkey to nod when a student gets an answer right, that's your afternoon, not the tool's. And when you're weight-painting a skeleton by hand, you'd much rather be pushing 1,499,906 triangles around than 2,086,002. That's a real difference in how fast the software answers every brush stroke.

Same 3 textures, so why is the Meshy box six times bigger?

This one surprised me enough to check twice.

Both files embed exactly 3 colour textures. That's a count, not a resolution — I measured how many there are, not how big each one is.

Meshy needed 69.18 MB to deliver its three. SupaVoxel needed 11.17 MB to deliver its three.

Now scale it the way a school would. A class set of 100 characters comes to about 6.92 GB from Meshy against about 1.12 GB. That is this single result copied 100 times and nothing more — no allowance for failed runs, no variation between subjects.

That's a storage tier, a backup window and an IT ticket, purely because of file size. Same contents. Six times the shipping cost.

Both files are GLB — but only one run gives you two of them

Format-wise this is a clean tie. Meshy downloads as a GLB. SupaVoxel downloads as a GLB. No conversion step on either side, and nothing new to learn before you start.

The difference is how many files you walk away with.

Meshy gives you one. The SupaVoxel menu gives you two from the same generation: a compressed GLB at 11,165,408 bytes, and an original-size GLB that the browser recorded at 82,060,632 bytes. That second one is the full uncompressed deliverable, so of course it's the larger file — that's what uncompressed means.

One run, two deliverables, and you pick per destination. Web page, lesson deck, anything a kid loads on a phone: take the 11.17 MB version. Handing the asset to a picky pipeline that wants everything plain and unpacked: take the original size. No re-running, no hunting for a conversion tool, no second charge against your credits.

Meshy has no equivalent button. Whatever Meshy's flow produces is what you carry, at 69.18 MB, to every destination you have.

I never saved that original-size download to disk, so I can't tell you what's inside it or how many triangles it carries. What I can tell you is that it exists, one click away, from the run you already paid for.

What the export screens actually let you pick

Both flows are short. Upload the drawing, set a couple of options, wait, download. Meshy doesn't ask you to write a prompt and neither does SupaVoxel, and neither run needed a retry.

Meshy's export panel at the end of the run. This is where the 69.18 MB GLB comes from — and there's no lighter variant sitting next to it.

SupaVoxel's export menu, the same step in the flow as the Meshy panel above. The 11.17 MB figure comes from the compressed option, and the original-size button sits right beside it.

That difference in the two menus is the quiet story of this article: one run, two deliverables, versus one run, one.

Where Meshy actually wins

One section, everything Meshy takes, with the price of each win.

Meshy's file declares no required extensions at all. Inside every GLB is a line saying what your software must support before it can open the file. Meshy's list is empty; the compressed SupaVoxel file names two — meshopt and quantization, both ways of squeezing shape data down. Switch to the original-size export on the SupaVoxel side and you don't need that decoder either, but out of the box the Meshy file is the one you can throw at anything.

Now price it. To get that from Meshy you take on 586,096 more triangles, 238,728 more corner points, about 14.67 MB more memory, 58,014,172 more bytes on disk, roughly 39 extra seconds of phone download, and 5.8 GB more across a set of 100 — on every destination, because Meshy has no lighter variant to fall back to.

If your pipeline is genuinely fussy, Meshy's single file removes one decision. The other side removes it too, with a second download button, and still leaves you an 11.17 MB option for the web.

I have to be straight about the limit here: I never loaded either file into a specific program. I read what each file declares it needs. That's a compatibility label, not an import test, and anyone claiming otherwise off a single run is guessing.

What neither tool would tell me

Three blanks, and I'm not filling any of them.

How long Meshy took. Its job record didn't expose a start-to-finish time. SupaVoxel's logged 304.808 seconds. One number from one tool is not a speed comparison, and if you've read one somewhere off a single run like this, somebody guessed.

How often either one fails. Neither Meshy nor SupaVoxel reported a failure count or a retry count. So nobody here, me included, has a reliability figure for either.

What this really cost. The job record shows 35 credits on the Meshy side and 3 on the SupaVoxel side. Two different plans, two different units — I'm not turning that into a dollar comparison. Multiplied across a class set it's 3,500 against 300 in each tool's own counter, which is a planning number and still not a price.

Final verdict: Meshy scores 58/100 for this job

Meshy did the job on the first go. One drawing in, one complete monkey out, cap corners intact, gown volume right, diploma reading as a cylinder, and a file that opens anywhere without a decoder. That's real work and it's why Meshy clears 58.

But everything after "it opened" goes against Meshy. 2,086,002 triangles against 1,499,906. 1,129,335 corner points against 890,607. About 61.17 MB of estimated memory against 46.50. 69,179,580 bytes against 11,165,408. Three colour textures — the same three the small file carries. Zero skeletons and zero animations, from a tool sold on characters.

Meshy built a beautiful, extremely heavy statue of a cartoon monkey. For a lesson page a kid opens on a phone, heavy is the whole problem.

Use SupaVoxel for this job

If your target is a web page on a school device, the 11.17 MB compressed export is the one I'd hand over rather than the 69.18 MB Meshy file. If something downstream wants everything plain and unpacked instead, the original-size download is on the same menu — pick per destination, from the one run.

But don't take my word for it. Take your picture, run it through Meshy, run it through SupaVoxel, and open both files. Meshy is easy enough to find, and SupaVoxel is at https://supavoxel.com. Two runs and twenty minutes will tell you more than any review, including this one.

How I tested this

One drawing. One run on Meshy at 02:26 UTC on 24 September, one on SupaVoxel at 02:49 the same morning. No text prompt typed on either side — the picture was the only input.

Meshy ran through its image-to-3D flow on the High Detail setting at Ultra 2K, texturing on, image enhancement on. SupaVoxel ran a full model at 5 steps, guidance 5.5, background removal on, resolution setting 256. Meshy and SupaVoxel don't expose the same knobs, so "identical settings" was never on the table. Identical input was, and that limit cuts against my conclusions as much as it cuts against Meshy.

Measured straight from the Meshy and SupaVoxel files: triangles, vertices, file size, embedded texture count, skeleton count, animation count, download format, and the list of things each file requires of an importer. Both downloads were checked against their own fingerprints before anything got counted.

Worked out with arithmetic, not measured: download seconds assume a steady 12 Mbps or 100 Mbps line with no handshake or decoding. Memory figures assume 32 bytes per corner point and 4 bytes per triangle index — an illustrative layout, not a graphics card reading. The 100-copy storage and credit figures are this one result multiplied by 100.

Not captured at all: Meshy's generation time, and the failure and retry counts on both sides.

All renders came from one offline viewer with one light rig and identical camera numbers on both models, so what you compared above is the model rather than a screenshot. The interface shots are the redacted versions.

SiliconFlow API Review 2026: Setup, Models and Real Pricing

Greta Volkov — Wed, 07 Oct 2026 03:15:56 +0000

Verdict up front: SiliconFlow is a solid, OpenAI-compatible way to call open-weight LLMs (DeepSeek, Qwen, GLM, Kimi, MiniMax) from one key, with an Anthropic-compatible endpoint as a bonus. It is not automatically the cheapest host for every model. Check the specific model you need before you commit. Prices below were read off each provider's public pricing page on October 6, 2026.

I went looking for a current, practical write-up on SiliconFlow and mostly found launch posts. SiliconFlow's own dev.to account announces models as they arrive, which is useful, but its two newest posts there cover GLM-4.6V and GLM-4.7, and both have since been deprecated on the platform. So this is the guide I wanted: how to connect, how to find the model IDs that actually exist today, what it costs compared with the alternatives, and where it falls short.

One limit to know early: SiliconFlow is mostly a text and LLM platform. Its pricing page lists a handful of FLUX image models, two Wan 2.2 video models and two TTS models. For image, video and speech work I use synexa instead, which runs 100+ media models behind a single predictions endpoint. The rest of this post is about the LLM side, where SiliconFlow is strongest.

1. Connecting to the SiliconFlow API

Setup is the standard OpenAI-compatible routine. Create an account, open API Keys in the console, generate a key, and point any OpenAI SDK at https://api.siliconflow.com/v1. New accounts get $1 in free credits, and billing after that is pay-as-you-go with no minimum commitment.

The first thing I do with any new provider is list the models through the API instead of trusting the marketing page:

export SILICONFLOW_API_KEY="sk-..."

# Chat models only; other sub_type values: embedding, reranker,
# text-to-image, image-to-image, speech-to-text, text-to-video
curl -s "https://api.siliconflow.com/v1/models?sub_type=chat" \
  -H "Authorization: Bearer $SILICONFLOW_API_KEY" | jq -r '.data[].id'

Then a minimal Python call with the official openai package:

import os
from openai import OpenAI

client = OpenAI(
    api_key=os.environ["SILICONFLOW_API_KEY"],
    base_url="https://api.siliconflow.com/v1",
)

resp = client.chat.completions.create(
    model="deepseek-ai/DeepSeek-V4.1-Flash",
    messages=[{"role": "user", "content": "Summarise this changelog in 3 bullets: ..."}],
    max_tokens=512,
)
print(resp.choices[0].message.content)
print(resp.usage)  # log this; it's what you're billed on

Model IDs use the org/Model form (deepseek-ai/..., zai-org/..., moonshotai/..., Qwen/...), so copy them exactly from the /models output.

2. Why `/models` beats the API reference

This is the most useful thing I found. The chat completions reference documents the model parameter with an enum of allowed values, and that list is out of date. It still includes zai-org/GLM-4.7, zai-org/GLM-4.6 and nex-agi/DeepSeek-V3.1-Nex-N1, which the release notes say were deprecated in May and June 2026. It doesn't include deepseek-ai/DeepSeek-V4.1-Flash, which has a live model page and price.

So treat the reference as a guide to parameters, not to models. Two parameters worth knowing about:

enable_thinking switches thinking mode on or off, but only for the models listed in the reference. Since it isn't a standard OpenAI field, pass it via extra_body in the Python SDK.
thinking_budget caps chain-of-thought tokens for reasoning models (minimum 128, maximum 32,768, default 4,096).

3. Deprecations come with traffic migration

SiliconFlow retires models on a regular schedule and posts notices in its release notes. Sometimes it also reroutes traffic. On June 11, 2026, calls to zai-org/GLM-5 were automatically routed to zai-org/GLM-5.1, and moonshotai/Kimi-K2.5 to Kimi-K2.6, at the same price.

That's convenient, but it means the model answering your request can change while your code stays the same. If you run evals or care about reproducible output, pin the successor name yourself and watch the release notes.

4. SiliconFlow pricing compared with alternatives

Prices are per 1M tokens, shown as input / cached input / output. A dash means I couldn't find the model on that provider's pricing page.

Model	SiliconFlow	DeepSeek direct	Together AI	DeepInfra
DeepSeek V4.1 Flash	$0.15 / $0.003 / $0.60	$0.15 / $0.003 / $0.60 off-peak; $0.30 / $0.006 / $1.20 peak	$0.30 / $0.006 / $1.20	—
DeepSeek V4 Pro 0813	$1.32 / $0.044 / $3.96	$0.66 / $0.022 / $1.98 off-peak; $1.32 / $0.044 / $3.96 peak	$1.32 / $0.13 / $3.96	—
DeepSeek V4 Flash 0731	$0.22 / $0.014 / $0.66	retired, served by V4.1 Flash	$0.14 / $0.03 / $0.28	$0.06 / $0.015 / $0.18
GLM-5.3	$1.40 / $0.26 / $4.40	—	$1.40 / $0.26 / $4.40	—
Kimi K3	$2.70 / $0.27 / $13.50	—	$2.70 / $0.27 / $13.50	$2.85 / $0.285 / $14.25
Kimi K2.6	$0.77 / $0.14 / $3.40	—	—	$0.75 / $0.15 / $3.50
MiniMax M3	$0.30 / $0.06 / $1.20	—	$0.30 / $0.06 / $1.20	—

What the table actually says:

DeepSeek V4.1 Flash is SiliconFlow's best deal. You pay DeepSeek's off-peak rate at all hours. DeepSeek's own peak windows (01:00–04:00 and 06:00–10:00 UTC, weekdays) cost double, and Together charges the peak rate all the time.
DeepSeek V4 Pro 0813 is the opposite. SiliconFlow charges DeepSeek's peak rate at all hours. If your jobs can run off-peak, going direct costs half.
Older revisions can cost much more. For V4 Flash 0731, SiliconFlow's input and output prices are about 3.7x DeepInfra's.
Mainstream models are at parity. GLM-5.3, Kimi K3 and MiniMax M3 match Together's prices to the cent, so for those the decision comes down to rate limits and reliability, not price.

If you'd rather keep several providers behind one key, OpenRouter says it passes provider prices through without markup and charges a 5.5% fee ($0.80 minimum) when you buy credits.

5. Rate limits start low for long-context work

Limits are per account, not per key, and they apply per model. Hitting the limit on one model doesn't throttle the others. Your tier depends on monthly spend (the higher of last month and month-to-date) and upgrades automatically:

Tier	RPM	TPM
L0 (new accounts)	1,000	40,000
L2	2,000	80,000
L4	8,000	500,000
L5	10,000	2,000,000

The catch: many current models advertise context windows of around 1M tokens, but an L0 account gets 40,000 tokens per minute. If you plan to send whole repositories or long documents, budget for spend that moves you up a tier, or test early whether you'll hit 429s.

6. Bonus: using SiliconFlow with Claude Code

SiliconFlow also exposes an Anthropic-style Messages endpoint, and its docs include a Claude Code integration. The manual version is three environment variables:

export ANTHROPIC_BASE_URL="https://api.siliconflow.com/"
export ANTHROPIC_MODEL="your-preferred-model"   # a model ID from /models
export ANTHROPIC_API_KEY="sk-your-siliconflow-key"

The docs also offer a one-line setup script piped from a URL. Read it before you run it; the docs say so too.

Who should use SiliconFlow

Good fit: you want many open-weight LLMs behind one OpenAI-compatible key, your traffic peaks during DeepSeek's peak hours, or you want Claude Code-style tooling on top of open models.

Look elsewhere (or compare per model): you're cost-sensitive on older model revisions, you can batch DeepSeek Pro jobs off-peak, or your workload is mainly image and video generation.

FAQ

Is SiliconFlow OpenAI-compatible?
Yes. Use base URL https://api.siliconflow.com/v1 with the OpenAI SDK. It supports most OpenAI parameters, plus its own fields like enable_thinking and thinking_budget.

Does SiliconFlow have a free tier?
New accounts get $1 in free credits. After that it's pay-as-you-go, with no minimum commitment.

What are SiliconFlow's rate limits?
They're tiered by monthly spend, starting at 1,000 RPM and 40,000 TPM for new accounts and going up to 10,000 RPM and 2,000,000 TPM at L5. Limits apply per account and per model.

Is SiliconFlow cheaper than calling DeepSeek directly?
For DeepSeek V4.1 Flash during DeepSeek's peak hours, yes. Off-peak it costs the same. For V4 Pro 0813, going direct off-peak costs half as much.

Wrapping up

SiliconFlow earns its place as a single endpoint for current open-weight LLMs, and the official quickstart gets you to a first call in minutes. Just don't assume it's cheapest on every model: list models through the API, pin model names, and compare each model's price before you commit. For the media side of my projects (images, video, speech) I keep using synexa, where the same /v1/predictions request shape works across its model gallery.