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GPT-4.1 Nano vs Qwen3 235B A22B Thinking 2507

Qwen3 235B A22B Thinking 2507 leads GPT-4.1 Nano on the Artificial Analysis Intelligence Index, 12.7 to 7.8, and GPT-4.1 Nano costs 8.8x less per output token ($0.40 vs $3.50 per million). Updated October 22, 2026; 39 benchmarks have a result for both.

Replace GPT-4.1 Nano Replace Qwen3 235B A22B Thinking 2507
GPT-4.1 NanoOpenAI
Coding
No data
Overall
24.7
Output $/M
$0.40
1
Qwen3 235B A22B Thinking 2507Qwen
Coding
No data
Overall
52.9
Output $/M
$3.50
38
vs boards won of 39 shared

GPT-4.1 Nano or Qwen3 235B A22B Thinking 2507: which to pick

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

Pick GPT-4.1 Nano for output tokens 8.8x cheaper ($0.40 vs $3.50 per million) and a larger context window (1.05M against 128K tokens).

Model AGPT-4.1 NanoOpenAINot benchmarkedNo coding benchmarkNo cost per solved taskModel BQwen3 235B A22B Thinking 2507QwenNot benchmarkedNo coding benchmarkNo cost per solved task
Shared results
39
GPT-4.1 Nano only
32
Qwen3 235B A22B Thinking 2507 only
7
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-4.1 Nano not benchmarked, Qwen3 235B A22B Thinking 2507 not benchmarked.
Widest category
Math, 35.8 points apart on average over 3 shared benchmarks.
Widest single gap
OTIS Mock AIME: GPT-4.1 Nano 28.9% against Qwen3 235B A22B Thinking 2507 86.7%, 57.8 points apart on a 0 to 100 scale.
Output price
GPT-4.1 Nano $0.40 against Qwen3 235B A22B Thinking 2507 $3.50 per million output tokens, 8.8x apart.
Context window
GPT-4.1 Nano takes 1.05M tokens, Qwen3 235B A22B Thinking 2507 128K.

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-4.1 NanoBarsQwen3 235B A22B Thinking 2507Ties
Coding04
Intelligence01
Reasoning04
Math03
Knowledge05
Web design15
General016
All shared boards138
CodingLike-for-likeSix of these boards feed the Vibe Coding Index04
Coding benchmarks for GPT-4.1 Nano and Qwen3 235B A22B Thinking 2507
BenchmarkGPT-4.1 NanoQwen3 235B A22B Thinking 2507Leader
Aider polyglot8.9%Not measuredOnly GPT-4.1 Nano measured
AA Coding Index11.122.1Qwen3 235B A22B Thinking 2507
LiveCodeBench (Vals AI)42.7%Not measuredOnly GPT-4.1 Nano measured
LiveCodeBench (Artificial Analysis run)32.6%Not measuredOnly GPT-4.1 Nano measured
Terminal-Bench Hard (Artificial Analysis run)3.8%Not measuredOnly GPT-4.1 Nano measured
WeirdML19.0%41.0%Qwen3 235B A22B Thinking 2507
LMArena Text: coding13061424Qwen3 235B A22B Thinking 2507
LMArena Text: software and IT13051431Qwen3 235B A22B Thinking 2507
Neither model has a result on the other 35 boards here
IntelligenceLike-for-likePublic boards, shown as published, kept out of the index01
Intelligence benchmarks for GPT-4.1 Nano and Qwen3 235B A22B Thinking 2507
BenchmarkGPT-4.1 NanoQwen3 235B A22B Thinking 2507Leader
AA Intelligence Index7.812.7Qwen3 235B A22B Thinking 2507
Epoch Capabilities Index129.5Not measuredOnly GPT-4.1 Nano measured
Agentic workDirectional onlyPublic boards, shown as published, kept out of the index4 boards, none shared
Agentic work benchmarks for GPT-4.1 Nano and Qwen3 235B A22B Thinking 2507
BenchmarkGPT-4.1 NanoQwen3 235B A22B Thinking 2507Leader
AA Agentic IndexNot measured1.3Only Qwen3 235B A22B Thinking 2507 measured
τ²-Bench Telecom17.3%Not measuredOnly GPT-4.1 Nano measured
Vending-Bench 2Not measured-$11Only Qwen3 235B A22B Thinking 2507 measured
Berkeley Function Calling Leaderboard (BFCL)33.1%Not measuredOnly GPT-4.1 Nano measured
Neither model has a result on the other 38 boards here
ReasoningLike-for-likePublic boards, shown as published, kept out of the index04
Reasoning benchmarks for GPT-4.1 Nano and Qwen3 235B A22B Thinking 2507
BenchmarkGPT-4.1 NanoQwen3 235B A22B Thinking 2507Leader
GPQA Diamond50.8%Not measuredOnly GPT-4.1 Nano measured
MMMU Pro (Vals AI)55.0%Not measuredOnly GPT-4.1 Nano measured
GPQA Diamond (Artificial Analysis run)51.2%Not measuredOnly GPT-4.1 Nano measured
Humanity's Last Exam (Artificial Analysis run)3.8%Not measuredOnly GPT-4.1 Nano measured
GPQA Diamond (Epoch AI run)48.9%80.1%Qwen3 235B A22B Thinking 2507
ARC-AGI0.0%Not measuredOnly GPT-4.1 Nano measured
ARC-AGI-20.0%Not measuredOnly GPT-4.1 Nano measured
Chess puzzles0.0%Not measuredOnly GPT-4.1 Nano measured
Mystery game puzzlesNot measured9.0%Only Qwen3 235B A22B Thinking 2507 measured
LMArena Text: hard prompts12861416Qwen3 235B A22B Thinking 2507
LMArena Text: expert12711463Qwen3 235B A22B Thinking 2507
LMArena Text: hard prompts in English12931432Qwen3 235B A22B Thinking 2507
Kagi LLM Benchmark33.3%Not measuredOnly GPT-4.1 Nano measured
Neither model has a result on the other 10 boards here
MathLike-for-likePublic boards, shown as published, kept out of the index03
Math benchmarks for GPT-4.1 Nano and Qwen3 235B A22B Thinking 2507
BenchmarkGPT-4.1 NanoQwen3 235B A22B Thinking 2507Leader
AIME26.5%Not measuredOnly GPT-4.1 Nano measured
MATH 500 (Vals AI)80.2%Not measuredOnly GPT-4.1 Nano measured
MGSM (Vals AI)69.3%Not measuredOnly GPT-4.1 Nano measured
AA Math Index24.0Not measuredOnly GPT-4.1 Nano measured
AIME 2025 (Artificial Analysis run)24.0%Not measuredOnly GPT-4.1 Nano measured
OTIS Mock AIME28.9%86.7%Qwen3 235B A22B Thinking 2507
MATH level 570.0%Not measuredOnly GPT-4.1 Nano measured
LMArena Text: math12741412Qwen3 235B A22B Thinking 2507
LMArena Text: mathematical work12741435Qwen3 235B A22B Thinking 2507
Neither model has a result on the other 5 boards here
KnowledgeLike-for-likePublic boards, shown as published, kept out of the index05
Knowledge benchmarks for GPT-4.1 Nano and Qwen3 235B A22B Thinking 2507
BenchmarkGPT-4.1 NanoQwen3 235B A22B Thinking 2507Leader
MMLU Pro63.5%Not measuredOnly GPT-4.1 Nano measured
LegalBench (Vals AI)61.1%Not measuredOnly GPT-4.1 Nano measured
CorpFin (Vals AI)42.1%Not measuredOnly GPT-4.1 Nano measured
TaxEval (Vals AI)60.8%Not measuredOnly GPT-4.1 Nano measured
MortgageTax (Vals AI)52.8%Not measuredOnly GPT-4.1 Nano measured
MedQA (Vals AI)68.2%Not measuredOnly GPT-4.1 Nano measured
MMLU Pro (Artificial Analysis run)65.7%Not measuredOnly GPT-4.1 Nano measured
SimpleQA Verified6.0%40.4%Qwen3 235B A22B Thinking 2507
LMArena Text: science12971433Qwen3 235B A22B Thinking 2507
LMArena Text: medicine and healthcare12761456Qwen3 235B A22B Thinking 2507
LMArena Text: legal and government13001415Qwen3 235B A22B Thinking 2507
LMArena Text: business and finance12671413Qwen3 235B A22B Thinking 2507
Neither model has a result on the other 8 boards here
Web designLike-for-likePublic boards, shown as published, kept out of the index15
Web design benchmarks for GPT-4.1 Nano and Qwen3 235B A22B Thinking 2507
BenchmarkGPT-4.1 NanoQwen3 235B A22B Thinking 2507Leader
Design Arena: websites9731052Qwen3 235B A22B Thinking 2507
Design Arena: UI components913942Qwen3 235B A22B Thinking 2507
Design Arena: game dev970956GPT-4.1 Nano
Design Arena: data viz891950Qwen3 235B A22B Thinking 2507
Design Arena: 3D924997Qwen3 235B A22B Thinking 2507
Design Arena: all code categories9621032Qwen3 235B A22B Thinking 2507
Neither model has a result on the other 3 boards here
GeneralLike-for-likePublic boards, shown as published, kept out of the index016
General benchmarks for GPT-4.1 Nano and Qwen3 235B A22B Thinking 2507
BenchmarkGPT-4.1 NanoQwen3 235B A22B Thinking 2507Leader
IFBench32.0%Not measuredOnly GPT-4.1 Nano measured
Long context reasoning (AA-LCR)20.3%Not measuredOnly GPT-4.1 Nano measured
DTBench52.5%80.3%Qwen3 235B A22B Thinking 2507
LMCA5.5%29.3%Qwen3 235B A22B Thinking 2507
Fiction.LiveBench25.0%75.0%Qwen3 235B A22B Thinking 2507
Creative writing (Lech Mazur)Not measured82.4%Only Qwen3 235B A22B Thinking 2507 measured
LMArena Text12851415Qwen3 235B A22B Thinking 2507
LMArena Text: instruction following12661386Qwen3 235B A22B Thinking 2507
LMArena Text: creative writing12601384Qwen3 235B A22B Thinking 2507
LMArena Vision1063Not measuredOnly GPT-4.1 Nano measured
LMArena Text: English13031429Qwen3 235B A22B Thinking 2507
LMArena Text: non-English12601397Qwen3 235B A22B Thinking 2507
LMArena Text: Chinese12701481Qwen3 235B A22B Thinking 2507
LMArena Text: German12881408Qwen3 235B A22B Thinking 2507
LMArena Text: Japanese11981386Qwen3 235B A22B Thinking 2507
LMArena Text: KoreanNot measured1375Only Qwen3 235B A22B Thinking 2507 measured
LMArena Text: SpanishNot measured1389Only Qwen3 235B A22B Thinking 2507 measured
LMArena Text: Russian12611401Qwen3 235B A22B Thinking 2507
LMArena Text: PolishNot measured1390Only Qwen3 235B A22B Thinking 2507 measured
LMArena Text: multi-turn12771406Qwen3 235B A22B Thinking 2507
LMArena Text: longer queries12831400Qwen3 235B A22B Thinking 2507
LMArena Text: writing and language12611388Qwen3 235B A22B Thinking 2507
LMArena Text: entertainment and media12431370Qwen3 235B A22B Thinking 2507
LMArena Vision: English1101Not measuredOnly GPT-4.1 Nano 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-4.1 Nano and Qwen3 235B A22B Thinking 2507
BenchmarkGPT-4.1 NanoQwen3 235B A22B Thinking 2507Leader
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-4.1 Nano$0.0003Fits in one request
Qwen3 235B A22B Thinking 2507$0.0022Fits in one request

GPT-4.1 Nano costs less

Repository review

50K input + 3K output tokens

GPT-4.1 Nano$0.0062Fits in one request
Qwen3 235B A22B Thinking 2507$0.033Fits in one request

GPT-4.1 Nano costs less

Agent loop

200K input + 10K output tokens

GPT-4.1 Nano$0.024Fits in one request
Qwen3 235B A22B Thinking 2507$0.125Exceeds the context window

GPT-4.1 Nano 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-4.1 Nano and Qwen3 235B A22B Thinking 2507
SpecificationOpenAI GPT-4.1 NanoQwen3 235B A22B Thinking 2507
MakerOpenAIQwen
WeightsNo public weights listedOpen weights
ReleasedApril 14, 2025July 25, 2025
Context window1.05M128K
Input $/Mtok$0.10$0.45
Output $/Mtok$0.40$3.50
Coding indexNot benchmarkedNot benchmarked
Independent measurements behind it00
Cost per solved taskNot computableNot computable
SourceOpenRouter listingOpenRouter listing

Questions

Is GPT-4.1 Nano better than Qwen3 235B A22B Thinking 2507 for coding?

Neither model has a coding score yet, so the two cannot be ranked on coding. Pick GPT-4.1 Nano for output tokens 8.8x cheaper ($0.40 vs $3.50 per million) and a larger context window (1.05M against 128K tokens).

Which is cheaper, GPT-4.1 Nano or Qwen3 235B A22B Thinking 2507?

GPT-4.1 Nano costs $0.10 per million input tokens and $0.40 per million output tokens; Qwen3 235B A22B Thinking 2507 costs $0.45 and $3.50. Cost per solved task is known for neither of them.

Which has the bigger context window, GPT-4.1 Nano or Qwen3 235B A22B Thinking 2507?

GPT-4.1 Nano, with 1.05M tokens against 128K.

How many benchmarks is the GPT-4.1 Nano vs Qwen3 235B A22B Thinking 2507 comparison based on?

39 benchmarks have a result for both models as of October 22, 2026. GPT-4.1 Nano's coding index rests on 0 independent benchmarks and Qwen3 235B A22B Thinking 2507's on 0. Where only one model has a result, the page says so instead of naming a winner.

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