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GPT-4o (2024-08-06) vs Qwen3.5 397B A17B

Qwen3.5 397B A17B leads GPT-4o (2024-08-06) on the Artificial Analysis Intelligence Index, 21.4 to 7.7, and Qwen3.5 397B A17B costs 4.3x less per output token ($2.34 vs $10.00 per million). Updated October 23, 2026; 40 benchmarks have a result for both.

Replace GPT-4o (2024-08-06) Replace Qwen3.5 397B A17B
GPT-4o (2024-08-06)OpenAI
Coding
No data
Overall
26.3
Output $/M
$10.00
0
Qwen3.5 397B A17BQwen
Coding
No data
Overall
57.2
Output $/M
$2.34
40
vs boards won of 40 shared

GPT-4o (2024-08-06) or Qwen3.5 397B A17B: 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 Terminal-Bench Hard (Artificial Analysis run) or LMArena Text: coding, where it leads.

Model AGPT-4o (2024-08-06)OpenAINot benchmarkedNo coding benchmarkNo cost per solved taskModel BQwen3.5 397B A17BQwenNot benchmarked1 independent benchmarkNo cost per solved task
Shared results
40
GPT-4o (2024-08-06) only
18
Qwen3.5 397B A17B only
26
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, Qwen3.5 397B A17B not benchmarked.
Widest category
Agentic work, 66.7 points apart on average over 1 shared benchmark.
Widest single gap
OTIS Mock AIME: GPT-4o (2024-08-06) 6.4% against Qwen3.5 397B A17B 88.9%, 82.5 points apart on a 0 to 100 scale.
Output price
GPT-4o (2024-08-06) $10.00 against Qwen3.5 397B A17B $2.34 per million output tokens, 4.3x apart.
Context window
GPT-4o (2024-08-06) takes 128K tokens, Qwen3.5 397B A17B 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)BarsQwen3.5 397B A17BTies
Coding03
Intelligence01
Agentic work01
Reasoning06
Math04
Knowledge04
General021
All shared boards040
CodingLike-for-likeSix of these boards feed the Vibe Coding Index03
Coding benchmarks for GPT-4o (2024-08-06) and Qwen3.5 397B A17B
BenchmarkGPT-4o (2024-08-06)Qwen3.5 397B A17BLeader
Aider polyglot23.1%Not measuredOnly GPT-4o (2024-08-06) measured
SWE-bench Lite21.7%Not measuredOnly GPT-4o (2024-08-06) measured
LMArena WebDevNot measured1400Only Qwen3.5 397B A17B measured
LiveCodeBench (Artificial Analysis run)31.7%Not measuredOnly GPT-4o (2024-08-06) measured
SciCodeNot measured44.8%Only Qwen3.5 397B A17B measured
Terminal-Bench Hard (Artificial Analysis run)8.3%40.9%Qwen3.5 397B A17B
ALE-BenchNot measured621.9Only Qwen3.5 397B A17B measured
LMArena Text: coding12831465Qwen3.5 397B A17B
LMArena Text: software and IT12781463Qwen3.5 397B A17B
LMArena WebDev: ReactNot measured1388Only Qwen3.5 397B A17B measured
LMArena WebDev: HTMLNot measured1409Only Qwen3.5 397B A17B measured
Neither model has a result on the other 32 boards here
IntelligenceLike-for-likePublic boards, shown as published, kept out of the index01
Intelligence benchmarks for GPT-4o (2024-08-06) and Qwen3.5 397B A17B
BenchmarkGPT-4o (2024-08-06)Qwen3.5 397B A17BLeader
AA Intelligence Index7.721.4Qwen3.5 397B A17B
Epoch Capabilities IndexNot measured146.8Only Qwen3.5 397B A17B measured
Agentic workLike-for-likePublic boards, shown as published, kept out of the index01
Agentic work benchmarks for GPT-4o (2024-08-06) and Qwen3.5 397B A17B
BenchmarkGPT-4o (2024-08-06)Qwen3.5 397B A17BLeader
τ²-Bench Telecom28.9%95.6%Qwen3.5 397B A17B
APEX-AgentsNot measured24.9%Only Qwen3.5 397B A17B measured
Neither model has a result on the other 40 boards here
ReasoningLike-for-likePublic boards, shown as published, kept out of the index06
Reasoning benchmarks for GPT-4o (2024-08-06) and Qwen3.5 397B A17B
BenchmarkGPT-4o (2024-08-06)Qwen3.5 397B A17BLeader
MMMU Pro (Vals AI)64.0%Not measuredOnly GPT-4o (2024-08-06) measured
GPQA Diamond (Artificial Analysis run)52.1%89.3%Qwen3.5 397B A17B
Humanity's Last Exam (Artificial Analysis run)2.3%29.0%Qwen3.5 397B A17B
GPQA Diamond (Epoch AI run)49.2%86.4%Qwen3.5 397B A17B
SimpleBench17.8%Not measuredOnly GPT-4o (2024-08-06) measured
Chess puzzles6.0%Not measuredOnly GPT-4o (2024-08-06) measured
Mystery game puzzlesNot measured18.0%Only Qwen3.5 397B A17B measured
LMArena Text: hard prompts12641448Qwen3.5 397B A17B
LMArena Text: expert12411462Qwen3.5 397B A17B
LMArena Text: hard prompts in English12771450Qwen3.5 397B A17B
LMArena Vision: diagramsNot measured1277Only Qwen3.5 397B A17B measured
LMArena Vision: homeworkNot measured1292Only Qwen3.5 397B A17B measured
Neither model has a result on the other 11 boards here
MathLike-for-likePublic boards, shown as published, kept out of the index04
Math benchmarks for GPT-4o (2024-08-06) and Qwen3.5 397B A17B
BenchmarkGPT-4o (2024-08-06)Qwen3.5 397B A17BLeader
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
OTIS Mock AIME6.4%88.9%Qwen3.5 397B A17B
FrontierMath, tiers 1 to 30.4%31.2%Qwen3.5 397B A17B
MATH level 553.3%Not measuredOnly GPT-4o (2024-08-06) measured
LMArena Text: math12851454Qwen3.5 397B A17B
LMArena Text: mathematical work12831454Qwen3.5 397B A17B
MathArenaNot measured19.2%Only Qwen3.5 397B A17B measured
Neither model has a result on the other 5 boards here
KnowledgeLike-for-likePublic boards, shown as published, kept out of the index04
Knowledge benchmarks for GPT-4o (2024-08-06) and Qwen3.5 397B A17B
BenchmarkGPT-4o (2024-08-06)Qwen3.5 397B A17BLeader
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
SimpleQA Verified26.0%Not measuredOnly GPT-4o (2024-08-06) measured
MMLU84.3%Not measuredOnly GPT-4o (2024-08-06) measured
LMArena Text: science12741457Qwen3.5 397B A17B
LMArena Text: medicine and healthcare12521455Qwen3.5 397B A17B
LMArena Text: legal and government12751441Qwen3.5 397B A17B
LMArena Text: business and finance12321435Qwen3.5 397B A17B
Neither model has a result on the other 8 boards here
Web designDirectional onlyPublic boards, shown as published, kept out of the index7 boards, none shared
Web design benchmarks for GPT-4o (2024-08-06) and Qwen3.5 397B A17B
BenchmarkGPT-4o (2024-08-06)Qwen3.5 397B A17BLeader
Design Arena: websitesNot measured1190Only Qwen3.5 397B A17B measured
Design Arena: UI componentsNot measured1161Only Qwen3.5 397B A17B measured
Design Arena: game devNot measured1140Only Qwen3.5 397B A17B measured
Design Arena: data vizNot measured1175Only Qwen3.5 397B A17B measured
Design Arena: 3DNot measured1162Only Qwen3.5 397B A17B measured
Design Arena: all code categoriesNot measured1179Only Qwen3.5 397B A17B measured
Design Arena: SVGNot measured1139Only Qwen3.5 397B A17B measured
Neither model has a result on the other 2 boards here
GeneralLike-for-likePublic boards, shown as published, kept out of the index021
General benchmarks for GPT-4o (2024-08-06) and Qwen3.5 397B A17B
BenchmarkGPT-4o (2024-08-06)Qwen3.5 397B A17BLeader
IFBench36.0%78.8%Qwen3.5 397B A17B
Long context reasoning (AA-LCR)41.0%77.3%Qwen3.5 397B A17B
DTBenchNot measured87.5%Only Qwen3.5 397B A17B measured
LMCANot measured37.9%Only Qwen3.5 397B A17B measured
LMArena Text12831438Qwen3.5 397B A17B
LMArena Text: instruction following12671423Qwen3.5 397B A17B
LMArena Text: creative writing12741402Qwen3.5 397B A17B
LMArena Vision10651262Qwen3.5 397B A17B
LMArena Text: English12971443Qwen3.5 397B A17B
LMArena Text: non-English12621430Qwen3.5 397B A17B
LMArena Text: Chinese12541499Qwen3.5 397B A17B
LMArena Text: German12571444Qwen3.5 397B A17B
LMArena Text: Japanese12341430Qwen3.5 397B A17B
LMArena Text: Korean12181385Qwen3.5 397B A17B
LMArena Text: French12631462Qwen3.5 397B A17B
LMArena Text: Spanish12691441Qwen3.5 397B A17B
LMArena Text: Russian12721430Qwen3.5 397B A17B
LMArena Text: PolishNot measured1448Only Qwen3.5 397B A17B measured
LMArena Text: multi-turn12791447Qwen3.5 397B A17B
LMArena Text: longer queries12831441Qwen3.5 397B A17B
LMArena Text: writing and language12901416Qwen3.5 397B A17B
LMArena Text: entertainment and media12671399Qwen3.5 397B A17B
LMArena Vision: English10721268Qwen3.5 397B A17B
LMArena Vision: Chinese10091314Qwen3.5 397B A17B
LMArena Vision: reading textNot measured1271Only Qwen3.5 397B A17B measured
LMArena Vision: humorNot measured1262Only Qwen3.5 397B A17B measured
LMArena Vision: captioningNot measured1230Only Qwen3.5 397B A17B measured
LMArena Vision: recognizing thingsNot measured1265Only Qwen3.5 397B A17B measured
LMArena Vision: creative writingNot measured1260Only Qwen3.5 397B A17B measured
Neither model has a result on the other 19 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 Qwen3.5 397B A17B
BenchmarkGPT-4o (2024-08-06)Qwen3.5 397B A17BLeader
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
Qwen3.5 397B A17B$0.0016Fits in one request

Qwen3.5 397B A17B costs less

Repository review

50K input + 3K output tokens

GPT-4o (2024-08-06)$0.155Fits in one request
Qwen3.5 397B A17B$0.027Fits in one request

Qwen3.5 397B A17B costs less

Agent loop

200K input + 10K output tokens

GPT-4o (2024-08-06)$0.600Exceeds the context window
Qwen3.5 397B A17B$0.101Fits in one request

Qwen3.5 397B A17B 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 Qwen3.5 397B A17B
SpecificationOpenAI GPT-4o (2024-08-06)Qwen3.5 397B A17B
MakerOpenAIQwen
WeightsNo public weights listedOpen weights
ReleasedAugust 6, 2024February 16, 2026
Context window128K262K
Input $/Mtok$2.50$0.39
Output $/Mtok$10.00$2.34
Coding indexNot benchmarkedNot benchmarked
Independent measurements behind it01
Cost per solved taskNot computableNot computable
SourceOpenRouter listingOpenRouter listing

Questions

Is GPT-4o (2024-08-06) better than Qwen3.5 397B A17B 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 Terminal-Bench Hard (Artificial Analysis run) or LMArena Text: coding, where it leads.

Which is cheaper, GPT-4o (2024-08-06) or Qwen3.5 397B A17B?

GPT-4o (2024-08-06) costs $2.50 per million input tokens and $10.00 per million output tokens; Qwen3.5 397B A17B costs $0.39 and $2.34. Cost per solved task is known for neither of them.

Which has the bigger context window, GPT-4o (2024-08-06) or Qwen3.5 397B A17B?

Qwen3.5 397B A17B, with 262K tokens against 128K.

How many benchmarks is the GPT-4o (2024-08-06) vs Qwen3.5 397B A17B comparison based on?

40 benchmarks have a result for both models as of October 23, 2026. GPT-4o (2024-08-06)'s coding index rests on 0 independent benchmarks and Qwen3.5 397B A17B's on 1. Where only one model has a result, the page says so instead of naming a winner.

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