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 Sep 30, 2026

Qwen3 VL 235B A22B Thinking is a downloadable model that takes text and images and returns text, with a licence that allows commercial use, changes and redistribution (Apache License 2.0). Its measured quality sits mid-field on the boards we hold, so it is a general-purpose pick rather than a leader.

Who should pick it

Use it for general chat, image questions and document work where you want a model you can download and run yourself under permissive terms. It accepts text and images in one request, and its active footprint per token is far below its total size. Skip it if you need a top-of-board result on coding, maths or creative writing, where it sits mid-field.

The case for it

  • You can download it and run it yourself, and the licence allows commercial use, changes and redistribution (Apache License 2.0).
  • Images go into the same request as the text, so a screenshot or diagram does not have to be described in words first.
  • 235.7 billion parameters in total, of which about 22 billion are used per token, so memory in use is far below its total size.

The case against it

  • Mid-field on the boards we hold: 100th of 168 on Arena Text (overall) via Thinking as of 25 Sep 2026, and 92nd of 168 on Arena Coding on the same date.
  • Those placings are human preference rankings, not correctness scores, so they say which answers people preferred rather than whether the work was right.
  • Released 2025-09-22, so it competes with newer releases; that is a date, not a measured quality gap.
00

How good is it?

EverydayGeneral questions and everyday reasoning

2.5 of 5

Arena Text (overall)100th of 168 · 1395

Arena Hard Prompts 100th of 168Arena Maths 98th of 163

CodingWriting and fixing code on its own

2.5 of 5

Arena Coding92nd of 168 · 1452

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

AgenticPlanning, calling tools, staying on task

not measured

Not yet scored on Arena Agent.

WritingDrafting and rewriting prose

2 of 5

Arena Creative Writing106th of 168 · 1339

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

Other boards it appears on
Arena Instruction Following 101st of 168

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.

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.
1452source ↗
1339source ↗
1417source ↗
1383source ↗
1399source ↗
1395source ↗
01

Can you run it yourself?

A card many people ownToo large

GeForce RTX 4090 · 24 GB

Weights at 148.6 / 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 148.6 / 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 148.6 / 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.

What is quantisation? →
148.6 GBest
Too large
174.4 GBest
Too large
260.5 GBest
Too large
This model on every device we track71 devicesThe Q4 build most people download, on each device: what the weights come to, how much context the memory leaves, and whether it runs. Smallest device that runs it first.
B200 (SXM 192GB)192 GB148.6 GBest131KFits in memory
Instinct MI300X192 GB148.6 GBest131KFits in memory
Apple M3 Ultra (80-core GPU)512 GB148.6 GBest131KFits in memory
NVIDIA DGX Spark (GB10)128 GB148.6 GBestnot calculatedSpills to system RAM
Ryzen AI Max+ 395 (Radeon 8060S)128 GB148.6 GBestnot calculatedSpills to system RAM
H200 141GB SXM141 GB148.6 GBestnot calculatedSpills to system RAMest
Apple M2 Ultra (76-core GPU)192 GB148.6 GBestnot calculatedSpills to system RAMest
Apple M1 Ultra (64-core GPU)128 GB148.6 GBestnot calculatedToo largeest
Apple M3 Max (40-core GPU)128 GB148.6 GBestnot calculatedToo largeest
Apple M4 Max (40-core GPU)128 GB148.6 GBestnot calculatedToo largeest
Apple M5 Max (40-core GPU)128 GB148.6 GBestnot calculatedToo largeest
Apple M2 Max (38-core GPU)96 GB148.6 GBestnot calculatedToo large
RTX PRO 6000 Blackwell96 GB148.6 GBestnot calculatedToo largeest
A100 80GB SXM80 GB148.6 GBestnot calculatedToo large
H100 80GB SXM80 GB148.6 GBestnot calculatedToo large
Apple M1 Max (32-core GPU)64 GB148.6 GBestnot calculatedToo large
Apple M4 Max (32-core GPU)64 GB148.6 GBestnot calculatedToo large
Apple M4 Pro (20-core GPU)64 GB148.6 GBestnot calculatedToo large
Apple M5 Max (32-core GPU)64 GB148.6 GBestnot calculatedToo large
Apple M5 Pro (20-core GPU)64 GB148.6 GBestnot calculatedToo large
L40S48 GB148.6 GBestnot calculatedToo large
RTX 6000 Ada48 GB148.6 GBestnot calculatedToo large
Apple M3 Pro (18-core GPU)36 GB148.6 GBestnot calculatedToo large
Apple M1 Pro (16-core GPU)32 GB148.6 GBestnot calculatedToo large
Apple M2 Pro (19-core GPU)32 GB148.6 GBestnot calculatedToo large
Apple M4 (10-core GPU)32 GB148.6 GBestnot calculatedToo large
Apple M5 (10-core GPU)32 GB148.6 GBestnot calculatedToo large
GeForce RTX 509032 GB148.6 GBestnot calculatedToo large
Apple M2 (10-core GPU)24 GB148.6 GBestnot calculatedToo large
Apple M3 (10-core GPU)24 GB148.6 GBestnot calculatedToo large
GeForce RTX 309024 GB148.6 GBestnot calculatedToo large
GeForce RTX 3090 Ti24 GB148.6 GBestnot calculatedToo large
GeForce RTX 409024 GB148.6 GBestnot calculatedToo large
Radeon RX 7900 XTX24 GB148.6 GBestnot calculatedToo large
Radeon RX 7900 XT20 GB148.6 GBestnot calculatedToo large
Apple M1 (8-core GPU)16 GB148.6 GBestnot calculatedToo large
GeForce RTX 4060 Ti 16GB16 GB148.6 GBestnot calculatedToo large
GeForce RTX 4070 Ti SUPER16 GB148.6 GBestnot calculatedToo large
GeForce RTX 4080 SUPER16 GB148.6 GBestnot calculatedToo large
GeForce RTX 5060 Ti 16GB16 GB148.6 GBestnot calculatedToo large
GeForce RTX 5070 Ti16 GB148.6 GBestnot calculatedToo large
GeForce RTX 508016 GB148.6 GBestnot calculatedToo large
Radeon RX 907016 GB148.6 GBestnot calculatedToo large
Radeon RX 9070 XT16 GB148.6 GBestnot calculatedToo large
Arc B58012 GB148.6 GBestnot calculatedToo large
GeForce RTX 3060 12GB12 GB148.6 GBestnot calculatedToo large
GeForce RTX 4070 SUPER12 GB148.6 GBestnot calculatedToo large
GeForce RTX 507012 GB148.6 GBestnot calculatedToo large
Arc B57010 GB148.6 GBestnot calculatedToo large
GeForce RTX 3080 10GB10 GB148.6 GBestnot calculatedToo large
Android phone · 16 GB · 2024 or newer8 GB148.6 GBestnot calculatedToo large
Apple M1 (8-core GPU, 8GB unified)8 GB148.6 GBestnot calculatedToo large
Apple M2 (8-core GPU, 8GB unified)8 GB148.6 GBestnot calculatedToo large
GeForce RTX 3060 8GB8 GB148.6 GBestnot calculatedToo large
GeForce RTX 4060 8GB8 GB148.6 GBestnot calculatedToo large
Radeon RX 66008 GB148.6 GBestnot calculatedToo large
iPhone 17 Pro6.6 GB148.6 GBestnot calculatedToo large
Android phone · 12 GB · 2023 or newer6 GB148.6 GBestnot calculatedToo large
GeForce GTX 1660 SUPER6 GB148.6 GBestnot calculatedToo large
iPhone 15 Pro4.4 GB148.6 GBestnot calculatedToo large
iPhone 164.4 GB148.6 GBestnot calculatedToo large
iPhone 16 Pro4.4 GB148.6 GBestnot calculatedToo large
iPhone 174.4 GB148.6 GBestnot calculatedToo large
Android phone · 8 GB · 2020–20224 GB148.6 GBestnot calculatedToo large
Android phone · 8 GB · 2023 or newer4 GB148.6 GBestnot calculatedToo large
iPhone 143.3 GB148.6 GBestnot calculatedToo large
iPhone 153.3 GB148.6 GBestnot calculatedToo large
Android phone · 6 GB3 GB148.6 GBestnot calculatedToo large
iPhone 132.2 GB148.6 GBestnot calculatedToo large
iPhone SE (3rd gen)2.2 GB148.6 GBestnot calculatedToo large
Android phone · 4 GB2 GB148.6 GBestnot calculatedToo large

Check against your own machine → · Where to rent it hosted →

02

Or rent it from someone else

Prices checked between 1 hour and 7 hours ago — each listing carries its own date.

Cheapest published offer

Alibaba Cloud, through OpenRouter

Cheapest of the 2 listings we can compare like for like — at 131K of context, out of 3 in the table below. One cheaper row there is outside that comparison: a different quantisation.

per 1M tokens
$0.40 in / $4.00 out
Context served
131K
Throughput
~57 tok/s
Current provider offers with price, context and prompt-privacy answers
ProviderIn / out per 1M tokensContextThroughputTrains on promptsLogs promptsZero retention
Novita AIbf16Direct and through OpenRouter$0.98 / $3.95checked 1 hour ago131K33K max reply through OpenRouter43 tok/sthrough OpenRouterDirectUnknownThrough OpenRouterNoDirectUnknownThrough OpenRouterNoDirectUnknownThrough OpenRouterConfirmed
OpenRouterOpenRouter's own listing$0.40 / $4.00checked 1 hour ago131Knot measuredUnknownUnknownUnknown
Alibaba CloudThrough OpenRouter$0.40 / $4.00checked 7 hours ago131K33K max reply57 tok/sNoYesunknown periodUnknown

Across the 3 listings we hold: 2 say they do not train on prompts (1 of them only through OpenRouter), 0 say they do and 1 does not say. 1 appears in the zero-retention registry we check (only through OpenRouter); the rest are unknown to us.

What each host's API supports

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

API features per host
ProviderTool callingJSON outputStrict schema
Novita AIbf16Direct and through OpenRouter✓✗✗
OpenRouterOpenRouter's own listing✓✓✓
Alibaba CloudThrough OpenRouter✓✓✓

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

03

When we formed this view

Recent changes

Sep 25, 2026BenchmarkScored 1452 via Thinking on Arena Coding
What movedleaderboard
Sep 25, 2026BenchmarkScored 1339 via Thinking on Arena Creative Writing
What movedleaderboard
Sep 25, 2026BenchmarkScored 1417 via Thinking on Arena Hard Prompts
What movedleaderboard
Sep 25, 2026BenchmarkScored 1383 via Thinking on Arena Instruction Following
What movedleaderboard
Sep 25, 2026BenchmarkScored 1399 via Thinking on Arena Maths
What movedleaderboard
Sep 25, 2026BenchmarkScored 1395 via Thinking on Arena Text (overall)
What movedleaderboard
Jul 26, 2026ListedListed on LLMap
What movedfirst indexed by our pipeline
Sep 22, 2025AnnouncedQwen3 VL 235B A22B Thinking announced by Qwen

Each date is the day we first saw the change, or the day the maker announced it.

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.
  • Nothing we hold says whether an endpoint streams, so we do not show it either way.
  • 1 of 3 listings does not say whether it trains on prompts, and 1 answers only through OpenRouter, not for its own listing.
  • We hold no cached-input rate for any of its listings.
  • We hold no batch or off-peak rate for any of its listings.
  • We hold a decode speed for it, but no prompt-processing (prefill) figure, so how long the input side of a job takes is unknown to us.
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

Open, few conditionsCommercial 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
Takes in, gives back
Text and images in, text out
Catalogue slug
qwen-qwen3-vl-235b-a22b-thinking

Machine-readable model card (omc.json) →

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