Enter opens the highlighted result. Esc closes.

GPT-4o (2024-08-06) vs Qwen3 VL 235B A22B Instruct

Qwen3 VL 235B A22B Instruct leads GPT-4o (2024-08-06) on the Artificial Analysis Intelligence Index, 9.9 to 7.7, and Qwen3 VL 235B A22B Instruct costs 5.3x less per output token ($1.90 vs $10.00 per million). Updated October 22, 2026; 38 benchmarks have a result for both.

Replace GPT-4o (2024-08-06) Replace Qwen3 VL 235B A22B Instruct
GPT-4o (2024-08-06)OpenAI
Coding
No data
Overall
26.3
Output $/M
$10.00
0
Qwen3 VL 235B A22B InstructQwen
Coding
No data
Overall
No data
Output $/M
$1.90
38
vs boards won of 38 shared

GPT-4o (2024-08-06) or Qwen3 VL 235B A22B Instruct: which to pick

Neither model has a coding score yet, so the two cannot be ranked on coding.

Pick GPT-4o (2024-08-06) for work measured by LiveCodeBench (Artificial Analysis run) or Terminal-Bench Hard (Artificial Analysis run), where it leads.

Model AGPT-4o (2024-08-06)OpenAINot benchmarkedNo coding benchmarkNo cost per solved taskModel BQwen3 VL 235B A22B InstructQwenNot benchmarkedNo coding benchmarkNo cost per solved task
Shared results
38
GPT-4o (2024-08-06) only
20
Qwen3 VL 235B A22B Instruct only
11
Like-for-like categories
7 of 9

Only benchmarks that tested both models count toward a pick; a result only one model has is shown but not compared.

Which one for your work

Each card is decided by one number both models carry. Where only one has it, the card says so and makes no call.

At a glance

Coding index
GPT-4o (2024-08-06) not benchmarked, Qwen3 VL 235B A22B Instruct not benchmarked.
Widest category
Knowledge, 29.7 points apart on average over 4 shared benchmarks.
Widest single gap
LMArena Vision: Chinese: GPT-4o (2024-08-06) 1009 against Qwen3 VL 235B A22B Instruct 1283, 45.6 points apart on a 0 to 100 scale.
Output price
GPT-4o (2024-08-06) $10.00 against Qwen3 VL 235B A22B Instruct $1.90 per million output tokens, 5.3x apart.
Context window
GPT-4o (2024-08-06) takes 128K tokens, Qwen3 VL 235B A22B Instruct 262K.

Benchmark by benchmark

First the score: boards each model leads, per category, where both have a result. Then every board, a category to a fold; bars are each result on a 0 to 100 scale, so compare the two in a row, not one row with another.

Boards won, where both have a result
CategoryGPT-4o (2024-08-06)BarsQwen3 VL 235B A22B InstructTies
Coding04
Intelligence01
Agentic work01
Reasoning05
Math02
Knowledge04
General021
All shared boards038
CodingLike-for-likeSix of these boards feed the Vibe Coding Index04
Coding benchmarks for GPT-4o (2024-08-06) and Qwen3 VL 235B A22B Instruct
BenchmarkGPT-4o (2024-08-06)Qwen3 VL 235B A22B InstructLeader
Aider polyglot23.1%Not measuredOnly GPT-4o (2024-08-06) measured
SWE-bench Lite21.7%Not measuredOnly GPT-4o (2024-08-06) measured
LiveCodeBench (Artificial Analysis run)31.7%64.6%Qwen3 VL 235B A22B Instruct
Terminal-Bench Hard (Artificial Analysis run)8.3%11.4%Qwen3 VL 235B A22B Instruct
LMArena Text: coding12831438Qwen3 VL 235B A22B Instruct
LMArena Text: software and IT12781442Qwen3 VL 235B A22B Instruct
Neither model has a result on the other 37 boards here
IntelligenceLike-for-likePublic boards, shown as published, kept out of the index01
Intelligence benchmarks for GPT-4o (2024-08-06) and Qwen3 VL 235B A22B Instruct
BenchmarkGPT-4o (2024-08-06)Qwen3 VL 235B A22B InstructLeader
AA Intelligence Index7.79.9Qwen3 VL 235B A22B Instruct
Neither model has a result on the other 1 board here
Agentic workLike-for-likePublic boards, shown as published, kept out of the index01
Agentic work benchmarks for GPT-4o (2024-08-06) and Qwen3 VL 235B A22B Instruct
BenchmarkGPT-4o (2024-08-06)Qwen3 VL 235B A22B InstructLeader
τ²-Bench Telecom28.9%54.1%Qwen3 VL 235B A22B Instruct
Neither model has a result on the other 41 boards here
ReasoningLike-for-likePublic boards, shown as published, kept out of the index05
Reasoning benchmarks for GPT-4o (2024-08-06) and Qwen3 VL 235B A22B Instruct
BenchmarkGPT-4o (2024-08-06)Qwen3 VL 235B A22B InstructLeader
MMMU Pro (Vals AI)64.0%Not measuredOnly GPT-4o (2024-08-06) measured
GPQA Diamond (Artificial Analysis run)52.1%77.2%Qwen3 VL 235B A22B Instruct
Humanity's Last Exam (Artificial Analysis run)2.3%11.9%Qwen3 VL 235B A22B Instruct
GPQA Diamond (Epoch AI run)49.2%Not measuredOnly GPT-4o (2024-08-06) measured
SimpleBench17.8%Not measuredOnly GPT-4o (2024-08-06) measured
Chess puzzles6.0%Not measuredOnly GPT-4o (2024-08-06) measured
LMArena Text: hard prompts12641428Qwen3 VL 235B A22B Instruct
LMArena Text: expert12411442Qwen3 VL 235B A22B Instruct
LMArena Text: hard prompts in English12771439Qwen3 VL 235B A22B Instruct
LMArena Vision: diagramsNot measured1255Only Qwen3 VL 235B A22B Instruct measured
LMArena Vision: homeworkNot measured1277Only Qwen3 VL 235B A22B Instruct measured
Neither model has a result on the other 12 boards here
MathLike-for-likePublic boards, shown as published, kept out of the index02
Math benchmarks for GPT-4o (2024-08-06) and Qwen3 VL 235B A22B Instruct
BenchmarkGPT-4o (2024-08-06)Qwen3 VL 235B A22B InstructLeader
AIME14.0%Not measuredOnly GPT-4o (2024-08-06) measured
MATH 500 (Vals AI)75.2%Not measuredOnly GPT-4o (2024-08-06) measured
MGSM (Vals AI)90.7%Not measuredOnly GPT-4o (2024-08-06) measured
AA Math IndexNot measured88.3Only Qwen3 VL 235B A22B Instruct measured
AIME 2025 (Artificial Analysis run)Not measured88.3%Only Qwen3 VL 235B A22B Instruct measured
OTIS Mock AIME6.4%Not measuredOnly GPT-4o (2024-08-06) measured
FrontierMath, tiers 1 to 30.4%Not measuredOnly GPT-4o (2024-08-06) measured
MATH level 553.3%Not measuredOnly GPT-4o (2024-08-06) measured
LMArena Text: math12851425Qwen3 VL 235B A22B Instruct
LMArena Text: mathematical work12831419Qwen3 VL 235B A22B Instruct
Neither model has a result on the other 4 boards here
KnowledgeLike-for-likePublic boards, shown as published, kept out of the index04
Knowledge benchmarks for GPT-4o (2024-08-06) and Qwen3 VL 235B A22B Instruct
BenchmarkGPT-4o (2024-08-06)Qwen3 VL 235B A22B InstructLeader
MMLU Pro74.1%Not measuredOnly GPT-4o (2024-08-06) measured
LegalBench (Vals AI)80.1%Not measuredOnly GPT-4o (2024-08-06) measured
CorpFin (Vals AI)39.4%Not measuredOnly GPT-4o (2024-08-06) measured
TaxEval (Vals AI)71.1%Not measuredOnly GPT-4o (2024-08-06) measured
MortgageTax (Vals AI)61.0%Not measuredOnly GPT-4o (2024-08-06) measured
MedQA (Vals AI)88.2%Not measuredOnly GPT-4o (2024-08-06) measured
MMLU Pro (Artificial Analysis run)Not measured83.6%Only Qwen3 VL 235B A22B Instruct measured
SimpleQA Verified26.0%Not measuredOnly GPT-4o (2024-08-06) measured
MMLU84.3%Not measuredOnly GPT-4o (2024-08-06) measured
LMArena Text: science12741444Qwen3 VL 235B A22B Instruct
LMArena Text: medicine and healthcare12521431Qwen3 VL 235B A22B Instruct
LMArena Text: legal and government12751438Qwen3 VL 235B A22B Instruct
LMArena Text: business and finance12321433Qwen3 VL 235B A22B Instruct
Neither model has a result on the other 7 boards here
Web designNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
Web design benchmarks for GPT-4o (2024-08-06) and Qwen3 VL 235B A22B Instruct
BenchmarkGPT-4o (2024-08-06)Qwen3 VL 235B A22B InstructLeader
Neither model has a result on 9 boards here
GeneralLike-for-likePublic boards, shown as published, kept out of the index021
General benchmarks for GPT-4o (2024-08-06) and Qwen3 VL 235B A22B Instruct
BenchmarkGPT-4o (2024-08-06)Qwen3 VL 235B A22B InstructLeader
IFBench36.0%56.5%Qwen3 VL 235B A22B Instruct
Long context reasoning (AA-LCR)41.0%63.7%Qwen3 VL 235B A22B Instruct
LMArena Text12831420Qwen3 VL 235B A22B Instruct
LMArena Text: instruction following12671406Qwen3 VL 235B A22B Instruct
LMArena Text: creative writing12741366Qwen3 VL 235B A22B Instruct
LMArena Vision10651247Qwen3 VL 235B A22B Instruct
LMArena Text: English12971429Qwen3 VL 235B A22B Instruct
LMArena Text: non-English12621405Qwen3 VL 235B A22B Instruct
LMArena Text: Chinese12541463Qwen3 VL 235B A22B Instruct
LMArena Text: German12571424Qwen3 VL 235B A22B Instruct
LMArena Text: Japanese12341385Qwen3 VL 235B A22B Instruct
LMArena Text: Korean12181394Qwen3 VL 235B A22B Instruct
LMArena Text: French12631452Qwen3 VL 235B A22B Instruct
LMArena Text: Spanish12691428Qwen3 VL 235B A22B Instruct
LMArena Text: Russian12721408Qwen3 VL 235B A22B Instruct
LMArena Text: PolishNot measured1419Only Qwen3 VL 235B A22B Instruct measured
LMArena Text: multi-turn12791428Qwen3 VL 235B A22B Instruct
LMArena Text: longer queries12831419Qwen3 VL 235B A22B Instruct
LMArena Text: writing and language12901392Qwen3 VL 235B A22B Instruct
LMArena Text: entertainment and media12671368Qwen3 VL 235B A22B Instruct
LMArena Vision: English10721256Qwen3 VL 235B A22B Instruct
LMArena Vision: Chinese10091283Qwen3 VL 235B A22B Instruct
LMArena Vision: reading textNot measured1253Only Qwen3 VL 235B A22B Instruct measured
LMArena Vision: humorNot measured1209Only Qwen3 VL 235B A22B Instruct measured
LMArena Vision: captioningNot measured1281Only Qwen3 VL 235B A22B Instruct measured
LMArena Vision: recognizing thingsNot measured1249Only Qwen3 VL 235B A22B Instruct measured
LMArena Vision: creative writingNot measured1220Only Qwen3 VL 235B A22B Instruct measured
Neither model has a result on the other 21 boards here
Image generationNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
Image generation benchmarks for GPT-4o (2024-08-06) and Qwen3 VL 235B A22B Instruct
BenchmarkGPT-4o (2024-08-06)Qwen3 VL 235B A22B InstructLeader
Neither model has a result on 5 boards here

What each workload costs

Three fixed token mixes at each model's listed price, so per-token rates become a bill.

Chat turn

1K input + 500 output tokens

GPT-4o (2024-08-06)$0.0075Fits in one request
Qwen3 VL 235B A22B Instruct$0.0012Fits in one request

Qwen3 VL 235B A22B Instruct costs less

Repository review

50K input + 3K output tokens

GPT-4o (2024-08-06)$0.155Fits in one request
Qwen3 VL 235B A22B Instruct$0.016Fits in one request

Qwen3 VL 235B A22B Instruct costs less

Agent loop

200K input + 10K output tokens

GPT-4o (2024-08-06)$0.600Exceeds the context window
Qwen3 VL 235B A22B Instruct$0.061Fits in one request

Qwen3 VL 235B A22B Instruct costs less

Listed OpenRouter rates, without cache or batch discounts. A workload that does not fit a model's context window would need splitting.

Specification differences

The leading value in a row is marked; a row with a missing value names no leader. Our two rows are marked in red.

Specifications of GPT-4o (2024-08-06) and Qwen3 VL 235B A22B Instruct
SpecificationOpenAI GPT-4o (2024-08-06)Qwen3 VL 235B A22B Instruct
MakerOpenAIQwen
WeightsNo public weights listedOpen weights
ReleasedAugust 6, 2024September 23, 2025
Context window128K262K
Input $/Mtok$2.50$0.21
Output $/Mtok$10.00$1.90
Coding indexNot benchmarkedNot benchmarked
Independent measurements behind it00
Cost per solved taskNot computableNot computable
SourceOpenRouter listingOpenRouter listing

Questions

Is GPT-4o (2024-08-06) better than Qwen3 VL 235B A22B Instruct for coding?

Neither model has a coding score yet, so the two cannot be ranked on coding. Pick GPT-4o (2024-08-06) for work measured by LiveCodeBench (Artificial Analysis run) or Terminal-Bench Hard (Artificial Analysis run), where it leads.

Which is cheaper, GPT-4o (2024-08-06) or Qwen3 VL 235B A22B Instruct?

GPT-4o (2024-08-06) costs $2.50 per million input tokens and $10.00 per million output tokens; Qwen3 VL 235B A22B Instruct costs $0.21 and $1.90. Cost per solved task is known for neither of them.

Which has the bigger context window, GPT-4o (2024-08-06) or Qwen3 VL 235B A22B Instruct?

Qwen3 VL 235B A22B Instruct, with 262K tokens against 128K.

How many benchmarks is the GPT-4o (2024-08-06) vs Qwen3 VL 235B A22B Instruct comparison based on?

38 benchmarks have a result for both models as of October 22, 2026. GPT-4o (2024-08-06)'s coding index rests on 0 independent benchmarks and Qwen3 VL 235B A22B Instruct's on 0. Where only one model has a result, the page says so instead of naming a winner.

Compare next