Models / Qwen/ Qwen3 VL 235B A22B Thinking

Qwen3 VL 235B A22B Thinking

Qwen · released Sep 22, 2025 · Qwen/Qwen3-VL-235B-A22B-Thinking

Input: text and images. Output: text.InputOutput
Type
Open weightsApache License 2.0
Params
236B
Context
131K

22B active per word · about 98K words of context

Our take

Written Aug 2, 2026

Qwen3 VL is a large multimodal model from Alibaba whose weights can be downloaded under a permissive Apache licence. It uses a mixture-of-experts design that keeps only 22 billion parameters active per token, and it scores highest on coding tasks while trailing on creative writing.

Who should pick it

Choose this for vision-language work that needs open weights and a permissive licence, or when coding performance is the priority. It suits cost-sensitive inference where the cheapest tracked input rate matters, or throughput-sensitive deployment where the fastest measured host is key. Skip it if creative writing quality is central, or if you need consistent output pricing across providers.

The case for it

  • Coding performance sits 59.2 points above its overall chat ranking on the Arena leaderboard.
  • Apache 2.0 licence allows commercial use, fine-tuning and redistribution.
  • 22 billion active parameters per token from 235.7 billion total — roughly tenfold parameter expansion.
  • Lowest input pricing among tracked offers, well under half the next-cheapest host.

The case against it

  • Creative writing is the weakest of its measured skills, 116.4 points below its own coding score and 57.2 points below its overall text ranking.
  • Output pricing varies sharply even for the same model from the same provider, with no disclosed reason.
  • The cheapest tracked offer lists no throughput figure; the measured speed comes from a pricier tier.
00

How good is it?

IntelligencePuzzles, maths, exam questions

2.5 of 5

Arena Text (overall)80th of 143 · 1395.3via Thinking

Arena Hard Prompts 75th of 143 via ThinkingArena Maths 73rd of 139 via Thinking

CodingWriting and fixing code on its own

2.5 of 5

Arena Coding71st of 143 · 1454.6via Thinking

Arena Coding is the only board that has scored it for this.

AgenticPlanning, calling tools, staying on task

not measured

Nobody we watch has scored Qwen3 VL 235B A22B Thinking for this. We would take the rating from Arena Agent (IPS).

WritingWe do not rate this

Scored, not ratedThe placings are on the right.

Two boards come close and neither tests writing: Arena Creative Writing asks people which of two replies they prefer, and LiveBench Language tests whether a model understood a passage. So we show where Qwen3 VL 235B A22B Thinking placed and give it no mark out of five.

Arena Creative Writing 87th of 143 · 1337.8 via Thinking
Also scored, on boards we give no mark for
Arena Instruction Following 79th of 143 via Thinking

These tests check whether a model follows instructions — a precondition for all the work above, but not a measure of how well that work is done, which is why they get no rating.

Every published score for this model6 scoresEvery figure we hold, from 6 boards, with who ran it and a link to the source — including the boards no rating above is built on.
1454.6via Thinkingindependentsource ↗
1337.8via Thinkingindependentsource ↗
1418.4via Thinkingindependentsource ↗
1383.1via Thinkingindependentsource ↗
1404.2via Thinkingindependentsource ↗
1395.3via Thinkingindependentsource ↗
01

Can you run it yourself?

Fits in memory
weights load entirely on the card
Spills to system RAM
some weights offload; much slower
Too large
will not load even with offload
est
size is calculated; the verdict could change by 10%
A card many people ownToo large

GeForce RTX 4090 · 24 GB

Weights at Q4_K_M148.6 / 24 GBest
Usable contextNot calculated for spilled setups
Decode speedNot estimated for spilled setups

Too large for this card. The weights do not fit even with part of them offloaded to system memory.

One step upToo large

Apple M1 Pro (16-core GPU) · 32 GB

Weights at Q4_K_M148.6 / 32 GBest
Usable contextNot calculated for spilled setups
Decode speedNot estimated for spilled setups

Too large for this card. The weights do not fit even with part of them offloaded to system memory.

Comfortable fit

On a MacFits in memory

Apple M3 Ultra (80-core GPU) · 512 GB

Weights at Q4_K_M148.6 / 512 GBest
Spare memory229.3 GB spare
Usable context131K of 131K
Decode speed37 tok/sest

Room to spare. 229.3 GB spare means a 10% error in the size would not change the answer.

Your hardware
Checking your profile…

Memory use by level

Against a 24 GB card.

Q4_K_M
recommended
148.6 GBest
Too large
Q5_K_M
174.4 GBest
Too large
Q8_0
260.5 GBest
Too large

All 3 sizes here are calculated, not measured. We hold no measured file for this model, so each size comes from the parameter count and every verdict above inherits that uncertainty.

Check against your own machine → · All 71 devices, with every size →

02

Or rent it from someone else

Cheapest published offer

Cheapest of 5 live listings. Picked at the widest standard context we hold, within one quantisation slice, so the numbers beside it are a price one host actually charges.

per 1M tokens
$0.26 in / $2.60 out
Context served
131K
Throughput
Not measured
Current provider offers with price, context and prompt-privacy answers
ProviderIn / out per 1M tokensContextThroughputTrains on promptsLogs promptsZero retention
Alibaba Cloud$0.26 / $2.60131Knot measuredNoYesunknown periodUnknown
Novita AI$0.98 / $3.95131Knot measuredUnknownUnknownUnknown
Novita AIbf16$0.98 / $3.95131K43 tok/sNoNoConfirmed
OpenRouter$0.40 / $4.00131Knot measuredUnknownUnknownUnknown
Alibaba Cloudfp8$0.40 / $4.00131K58 tok/sNoYesunknown periodUnknown

Across the 5 listings we hold: 3 say they do not train on prompts, 0 say they do and 2 do not say. 1 appear in the zero-retention registry we check; the rest are unknown to us rather than confirmed either way.

What each host's API supports

From the parameter list each endpoint publishes. Streaming is omitted: nothing we hold reports it, for any model.

Supported
Not supported
Not published
host gave no parameter list
API features per host
ProviderTool callingJSON outputStrict schema
Alibaba Cloud
Novita AI
Novita AIbf16
OpenRouter
Alibaba Cloudfp8

Tool calling: 3 of 5 listings say yes, 2 publish no parameter list. JSON output: 2 of 5 listings say yes, 1 says no, 2 publish no parameter list. Strict schema: 2 of 5 listings say yes, 1 says no, 2 publish no parameter list.

03

When we formed this view

Dates behind this page

Aug 2, 2026BenchmarkScored 1454.6 via Thinking on Arena Codingleaderboard
Aug 2, 2026BenchmarkScored 1337.8 via Thinking on Arena Creative Writingleaderboard
Aug 2, 2026BenchmarkScored 1418.4 via Thinking on Arena Hard Promptsleaderboard
Aug 2, 2026BenchmarkScored 1383.1 via Thinking on Arena Instruction Followingleaderboard
Aug 2, 2026BenchmarkScored 1404.2 via Thinking on Arena Mathsleaderboard
Aug 2, 2026BenchmarkScored 1395.3 via Thinking on Arena Text (overall)leaderboard
Jul 30, 2026Price changeOpenRouter raised Qwen3 VL 235B A22B Thinking pricing by 54%input +54% ($0.26 → $0.40 per 1M tokens); output +54% ($2.60 → $4.00 per 1M tokens)
Jul 26, 2026ListedListed on LLMapfirst indexed by our pipeline
Sep 22, 2025AnnouncedQwen3 VL 235B A22B Thinking announced by Qwen

Prices last checked 13h ago

What we do not know about this model yet

  • We hold no measured file for it, so all 3 sizes on this page are calculated from the parameter count.
  • 2 of 5 listings publish no parameter list, so what their API accepts is unknown to us.
  • Nothing we hold says whether an endpoint streams, so we do not show it either way.
  • 2 of 5 listings do not say whether they train on prompts.
  • We hold no cached-input rate for any of its listings.
04

Licence and identifiers

What the licence allowsApache License 2.0, what it allows commercially, and the identifiers you need to pull this model — its Hugging Face repo, our slug and a machine-readable card.

Licence

Apache License 2.0

permissiveCommercial use allowed

Fully permissive: commercial use, redistribution, and derivatives allowed. Requires attribution and a copy of the license. Includes an express patent grant.

Identifiers

Architecture
Mixture of experts
Modality record
text+image->text
Catalogue slug
qwen-qwen3-vl-235b-a22b-thinking

Machine-readable model card (omc.json) →

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