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GPT-4o (2024-08-06) vs Qwen3.6 35B A3B

Qwen3.6 35B A3B leads GPT-4o (2024-08-06) on the Artificial Analysis Intelligence Index, 18.2 to 7.7, and Qwen3.6 35B A3B costs 10.0x less per output token ($1.00 vs $10.00 per million). Updated October 22, 2026; 10 benchmarks have a result for both.

Replace GPT-4o (2024-08-06) Replace Qwen3.6 35B A3B
GPT-4o (2024-08-06)OpenAI
Coding
No data
Overall
26.3
Output $/M
$10.00
0
Qwen3.6 35B A3BQwen
Coding
No data
Overall
45.0
Output $/M
$1.00
10
vs boards won of 10 shared

GPT-4o (2024-08-06) or Qwen3.6 35B A3B: 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 AA Intelligence Index, where it leads.

Model AGPT-4o (2024-08-06)OpenAINot benchmarkedNo coding benchmarkNo cost per solved taskModel BQwen3.6 35B A3BQwenNot benchmarkedNo coding benchmarkNo cost per solved task
Shared results
10
GPT-4o (2024-08-06) only
48
Qwen3.6 35B A3B only
10
Like-for-like categories
6 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, Qwen3.6 35B A3B not benchmarked.
Widest category
Agentic work, 66.4 points apart on average over 1 shared benchmark.
Widest single gap
OTIS Mock AIME: GPT-4o (2024-08-06) 6.4% against Qwen3.6 35B A3B 86.7%, 80.3 points apart on a 0 to 100 scale.
Output price
GPT-4o (2024-08-06) $10.00 against Qwen3.6 35B A3B $1.00 per million output tokens, 10.0x apart.
Context window
GPT-4o (2024-08-06) takes 128K tokens, Qwen3.6 35B A3B 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)BarsQwen3.6 35B A3BTies
Coding01
Intelligence01
Agentic work01
Reasoning03
Math02
General02
All shared boards010
CodingLike-for-likeSix of these boards feed the Vibe Coding Index01
Coding benchmarks for GPT-4o (2024-08-06) and Qwen3.6 35B A3B
BenchmarkGPT-4o (2024-08-06)Qwen3.6 35B A3BLeader
Aider polyglot23.1%Not measuredOnly GPT-4o (2024-08-06) measured
SWE-bench Lite21.7%Not measuredOnly GPT-4o (2024-08-06) measured
AA Coding IndexNot measured41.9Only Qwen3.6 35B A3B measured
LiveCodeBench (Artificial Analysis run)31.7%Not measuredOnly GPT-4o (2024-08-06) measured
SciCodeNot measured36.6%Only Qwen3.6 35B A3B measured
Terminal-Bench Hard (Artificial Analysis run)8.3%34.8%Qwen3.6 35B A3B
Terminal-BenchNot measured24.6%Only Qwen3.6 35B A3B measured
WeirdMLNot measured34.5%Only Qwen3.6 35B A3B measured
LMArena Text: coding1283Not measuredOnly GPT-4o (2024-08-06) measured
LMArena Text: software and IT1278Not measuredOnly GPT-4o (2024-08-06) measured
Neither model has a result on the other 33 boards here
IntelligenceLike-for-likePublic boards, shown as published, kept out of the index01
Intelligence benchmarks for GPT-4o (2024-08-06) and Qwen3.6 35B A3B
BenchmarkGPT-4o (2024-08-06)Qwen3.6 35B A3BLeader
AA Intelligence Index7.718.2Qwen3.6 35B A3B
Epoch Capabilities IndexNot measured143.7Only Qwen3.6 35B A3B measured
Agentic workLike-for-likePublic boards, shown as published, kept out of the index01
Agentic work benchmarks for GPT-4o (2024-08-06) and Qwen3.6 35B A3B
BenchmarkGPT-4o (2024-08-06)Qwen3.6 35B A3BLeader
AA Agentic IndexNot measured13.1Only Qwen3.6 35B A3B measured
τ²-Bench Telecom28.9%95.3%Qwen3.6 35B A3B
Neither model has a result on the other 40 boards here
ReasoningLike-for-likePublic boards, shown as published, kept out of the index03
Reasoning benchmarks for GPT-4o (2024-08-06) and Qwen3.6 35B A3B
BenchmarkGPT-4o (2024-08-06)Qwen3.6 35B A3BLeader
MMMU Pro (Vals AI)64.0%Not measuredOnly GPT-4o (2024-08-06) measured
GPQA Diamond (Artificial Analysis run)52.1%84.1%Qwen3.6 35B A3B
Humanity's Last Exam (Artificial Analysis run)2.3%22.2%Qwen3.6 35B A3B
GPQA Diamond (Epoch AI run)49.2%84.8%Qwen3.6 35B A3B
SimpleBench17.8%Not measuredOnly GPT-4o (2024-08-06) measured
Chess puzzles6.0%Not measuredOnly GPT-4o (2024-08-06) measured
Mystery game puzzlesNot measured22.0%Only Qwen3.6 35B A3B measured
LMArena Text: hard prompts1264Not measuredOnly GPT-4o (2024-08-06) measured
LMArena Text: expert1241Not measuredOnly GPT-4o (2024-08-06) measured
LMArena Text: hard prompts in English1277Not measuredOnly GPT-4o (2024-08-06) measured
Neither model has a result on the other 13 boards here
MathLike-for-likePublic boards, shown as published, kept out of the index02
Math benchmarks for GPT-4o (2024-08-06) and Qwen3.6 35B A3B
BenchmarkGPT-4o (2024-08-06)Qwen3.6 35B A3BLeader
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%86.7%Qwen3.6 35B A3B
FrontierMath, tiers 1 to 30.4%20.4%Qwen3.6 35B A3B
MATH level 553.3%Not measuredOnly GPT-4o (2024-08-06) measured
LMArena Text: math1285Not measuredOnly GPT-4o (2024-08-06) measured
LMArena Text: mathematical work1283Not measuredOnly GPT-4o (2024-08-06) measured
Neither model has a result on the other 6 boards here
KnowledgeDirectional onlyPublic boards, shown as published, kept out of the index12 boards, none shared
Knowledge benchmarks for GPT-4o (2024-08-06) and Qwen3.6 35B A3B
BenchmarkGPT-4o (2024-08-06)Qwen3.6 35B A3BLeader
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: science1274Not measuredOnly GPT-4o (2024-08-06) measured
LMArena Text: medicine and healthcare1252Not measuredOnly GPT-4o (2024-08-06) measured
LMArena Text: legal and government1275Not measuredOnly GPT-4o (2024-08-06) measured
LMArena Text: business and finance1232Not measuredOnly GPT-4o (2024-08-06) measured
Neither model has a result on the other 8 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 Qwen3.6 35B A3B
BenchmarkGPT-4o (2024-08-06)Qwen3.6 35B A3BLeader
Neither model has a result on 9 boards here
GeneralLike-for-likePublic boards, shown as published, kept out of the index02
General benchmarks for GPT-4o (2024-08-06) and Qwen3.6 35B A3B
BenchmarkGPT-4o (2024-08-06)Qwen3.6 35B A3BLeader
IFBench36.0%64.4%Qwen3.6 35B A3B
Long context reasoning (AA-LCR)41.0%71.7%Qwen3.6 35B A3B
DTBenchNot measured73.9%Only Qwen3.6 35B A3B measured
LMCANot measured29.7%Only Qwen3.6 35B A3B measured
Surface Evolver BenchNot measured44.4%Only Qwen3.6 35B A3B measured
LMArena Text1283Not measuredOnly GPT-4o (2024-08-06) measured
LMArena Text: instruction following1267Not measuredOnly GPT-4o (2024-08-06) measured
LMArena Text: creative writing1274Not measuredOnly GPT-4o (2024-08-06) measured
LMArena Vision1065Not measuredOnly GPT-4o (2024-08-06) measured
LMArena Text: English1297Not measuredOnly GPT-4o (2024-08-06) measured
LMArena Text: non-English1262Not measuredOnly GPT-4o (2024-08-06) measured
LMArena Text: Chinese1254Not measuredOnly GPT-4o (2024-08-06) measured
LMArena Text: German1257Not measuredOnly GPT-4o (2024-08-06) measured
LMArena Text: Japanese1234Not measuredOnly GPT-4o (2024-08-06) measured
LMArena Text: Korean1218Not measuredOnly GPT-4o (2024-08-06) measured
LMArena Text: French1263Not measuredOnly GPT-4o (2024-08-06) measured
LMArena Text: Spanish1269Not measuredOnly GPT-4o (2024-08-06) measured
LMArena Text: Russian1272Not measuredOnly GPT-4o (2024-08-06) measured
LMArena Text: multi-turn1279Not measuredOnly GPT-4o (2024-08-06) measured
LMArena Text: longer queries1283Not measuredOnly GPT-4o (2024-08-06) measured
LMArena Text: writing and language1290Not measuredOnly GPT-4o (2024-08-06) measured
LMArena Text: entertainment and media1267Not measuredOnly GPT-4o (2024-08-06) measured
LMArena Vision: English1072Not measuredOnly GPT-4o (2024-08-06) measured
LMArena Vision: Chinese1009Not measuredOnly GPT-4o (2024-08-06) measured
Neither model has a result on the other 24 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 Qwen3.6 35B A3B
BenchmarkGPT-4o (2024-08-06)Qwen3.6 35B A3BLeader
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
Qwen3.6 35B A3B$0.0006Fits in one request

Qwen3.6 35B A3B costs less

Repository review

50K input + 3K output tokens

GPT-4o (2024-08-06)$0.155Fits in one request
Qwen3.6 35B A3B$0.0080Fits in one request

Qwen3.6 35B A3B costs less

Agent loop

200K input + 10K output tokens

GPT-4o (2024-08-06)$0.600Exceeds the context window
Qwen3.6 35B A3B$0.030Fits in one request

Qwen3.6 35B A3B 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 Qwen3.6 35B A3B
SpecificationOpenAI GPT-4o (2024-08-06)Qwen3.6 35B A3B
MakerOpenAIQwen
WeightsNo public weights listedOpen weights
ReleasedAugust 6, 2024April 27, 2026
Context window128K262K
Input $/Mtok$2.50$0.10
Output $/Mtok$10.00$1.00
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 Qwen3.6 35B A3B 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 AA Intelligence Index, where it leads.

Which is cheaper, GPT-4o (2024-08-06) or Qwen3.6 35B A3B?

GPT-4o (2024-08-06) costs $2.50 per million input tokens and $10.00 per million output tokens; Qwen3.6 35B A3B costs $0.10 and $1.00. Cost per solved task is known for neither of them.

Which has the bigger context window, GPT-4o (2024-08-06) or Qwen3.6 35B A3B?

Qwen3.6 35B A3B, with 262K tokens against 128K.

How many benchmarks is the GPT-4o (2024-08-06) vs Qwen3.6 35B A3B comparison based on?

10 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 Qwen3.6 35B A3B's on 0. Where only one model has a result, the page says so instead of naming a winner.

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