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GPT-5 Nano vs Qwen3 235B A22B Thinking 2507

GPT-5 Nano leads Qwen3 235B A22B Thinking 2507 on the Artificial Analysis Intelligence Index, 13.0 to 12.7, and GPT-5 Nano costs 8.8x less per output token ($0.40 vs $3.50 per million). Updated October 22, 2026; 42 benchmarks have a result for both.

GPT-5 NanoOpenAI
Coding
No data
Overall
35.6
Output $/M
$0.40
6
Qwen3 235B A22B Thinking 2507Qwen
Coding
No data
Overall
52.9
Output $/M
$3.50
35
vs boards won of 42 shared, 1 tied

GPT-5 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-5 Nano for output tokens 8.8x cheaper ($0.40 vs $3.50 per million) and a larger context window (400K against 128K tokens).

Shared results
42
GPT-5 Nano only
46
Qwen3 235B A22B Thinking 2507 only
4
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-5 Nano not benchmarked, Qwen3 235B A22B Thinking 2507 not benchmarked.
Widest category
Knowledge, 20.9 points apart on average over 5 shared benchmarks.
Widest single gap
Fiction.LiveBench: GPT-5 Nano 44.4% against Qwen3 235B A22B Thinking 2507 75.0%, 30.6 points apart on a 0 to 100 scale.
Output price
GPT-5 Nano $0.40 against Qwen3 235B A22B Thinking 2507 $3.50 per million output tokens, 8.8x apart.
Context window
GPT-5 Nano takes 400K 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-5 NanoBarsQwen3 235B A22B Thinking 2507Ties
Coding03
Intelligence10
Reasoning041 tie
Math03
Knowledge05
Web design51
General019
All shared boards6351 tie
CodingLike-for-likeSix of these boards feed the Vibe Coding Index03
Coding benchmarks for GPT-5 Nano and Qwen3 235B A22B Thinking 2507
BenchmarkGPT-5 NanoQwen3 235B A22B Thinking 2507Leader
SWE-bench Verified34.8%Not measuredOnly GPT-5 Nano measured
AA Coding IndexNot measured22.1Only Qwen3 235B A22B Thinking 2507 measured
LiveCodeBench (Vals AI)70.2%Not measuredOnly GPT-5 Nano measured
LiveCodeBench (Artificial Analysis run)78.9%Not measuredOnly GPT-5 Nano measured
Terminal-Bench Hard (Artificial Analysis run)17.4%Not measuredOnly GPT-5 Nano measured
Terminal-Bench21.8%Not measuredOnly GPT-5 Nano measured
WeirdML38.1%41.0%Qwen3 235B A22B Thinking 2507
LMArena Text: coding13511424Qwen3 235B A22B Thinking 2507
LMArena Text: software and IT13451431Qwen3 235B A22B Thinking 2507
Neither model has a result on the other 34 boards here
IntelligenceLike-for-likePublic boards, shown as published, kept out of the index10
Intelligence benchmarks for GPT-5 Nano and Qwen3 235B A22B Thinking 2507
BenchmarkGPT-5 NanoQwen3 235B A22B Thinking 2507Leader
AA Intelligence Index13.012.7GPT-5 Nano
Epoch Capabilities Index139.2Not measuredOnly GPT-5 Nano measured
Agentic workDirectional onlyPublic boards, shown as published, kept out of the index4 boards, none shared
Agentic work benchmarks for GPT-5 Nano and Qwen3 235B A22B Thinking 2507
BenchmarkGPT-5 NanoQwen3 235B A22B Thinking 2507Leader
AA Agentic IndexNot measured1.3Only Qwen3 235B A22B Thinking 2507 measured
τ²-Bench Telecom36.5%Not measuredOnly GPT-5 Nano measured
Vending-Bench 2Not measured-$11Only Qwen3 235B A22B Thinking 2507 measured
Berkeley Function Calling Leaderboard (BFCL)51.4%Not measuredOnly GPT-5 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-5 Nano and Qwen3 235B A22B Thinking 2507
BenchmarkGPT-5 NanoQwen3 235B A22B Thinking 2507Leader
GPQA Diamond63.4%Not measuredOnly GPT-5 Nano measured
MMMU Pro (Vals AI)70.9%Not measuredOnly GPT-5 Nano measured
GPQA Diamond (Artificial Analysis run)67.6%Not measuredOnly GPT-5 Nano measured
Humanity's Last Exam (Artificial Analysis run)9.5%Not measuredOnly GPT-5 Nano measured
GPQA Diamond (Epoch AI run)69.4%80.1%Qwen3 235B A22B Thinking 2507
ARC-AGI20.7%Not measuredOnly GPT-5 Nano measured
ARC-AGI-22.6%Not measuredOnly GPT-5 Nano measured
Chess puzzles22.0%Not measuredOnly GPT-5 Nano measured
Mystery game puzzles9.0%9.0%Tie
VPCT37.2%Not measuredOnly GPT-5 Nano measured
LMArena Text: hard prompts13281416Qwen3 235B A22B Thinking 2507
LMArena Text: expert13211463Qwen3 235B A22B Thinking 2507
LMArena Text: hard prompts in English13381432Qwen3 235B A22B Thinking 2507
LMArena Vision: diagrams1141Not measuredOnly GPT-5 Nano measured
LMArena Vision: homework1157Not measuredOnly GPT-5 Nano measured
Kagi LLM Benchmark62.2%Not measuredOnly GPT-5 Nano measured
Neither model has a result on the other 7 boards here
MathLike-for-likePublic boards, shown as published, kept out of the index03
Math benchmarks for GPT-5 Nano and Qwen3 235B A22B Thinking 2507
BenchmarkGPT-5 NanoQwen3 235B A22B Thinking 2507Leader
AIME81.2%Not measuredOnly GPT-5 Nano measured
MATH 500 (Vals AI)93.8%Not measuredOnly GPT-5 Nano measured
MGSM (Vals AI)89.3%Not measuredOnly GPT-5 Nano measured
AA Math Index83.7Not measuredOnly GPT-5 Nano measured
AIME 2025 (Artificial Analysis run)83.7%Not measuredOnly GPT-5 Nano measured
OTIS Mock AIME81.1%86.7%Qwen3 235B A22B Thinking 2507
FrontierMath, tiers 1 to 320.0%Not measuredOnly GPT-5 Nano measured
FrontierMath, tier 42.4%Not measuredOnly GPT-5 Nano measured
ProofBench12.0%Not measuredOnly GPT-5 Nano measured
MATH level 595.2%Not measuredOnly GPT-5 Nano measured
LMArena Text: math13171412Qwen3 235B A22B Thinking 2507
LMArena Text: mathematical work13261435Qwen3 235B A22B Thinking 2507
MathArena12.8%Not measuredOnly GPT-5 Nano measured
Neither model has a result on the other 1 board here
KnowledgeLike-for-likePublic boards, shown as published, kept out of the index05
Knowledge benchmarks for GPT-5 Nano and Qwen3 235B A22B Thinking 2507
BenchmarkGPT-5 NanoQwen3 235B A22B Thinking 2507Leader
MMLU Pro76.1%Not measuredOnly GPT-5 Nano measured
LegalBench (Vals AI)50.1%Not measuredOnly GPT-5 Nano measured
CaseLaw (Vals AI)52.6%Not measuredOnly GPT-5 Nano measured
TaxEval (Vals AI)67.4%Not measuredOnly GPT-5 Nano measured
MortgageTax (Vals AI)53.6%Not measuredOnly GPT-5 Nano measured
MedQA (Vals AI)93.3%Not measuredOnly GPT-5 Nano measured
MedCode (Vals AI)30.4%Not measuredOnly GPT-5 Nano measured
MMLU Pro (Artificial Analysis run)78.0%Not measuredOnly GPT-5 Nano measured
SimpleQA Verified11.7%40.4%Qwen3 235B A22B Thinking 2507
LMArena Text: science13181433Qwen3 235B A22B Thinking 2507
LMArena Text: medicine and healthcare13351456Qwen3 235B A22B Thinking 2507
LMArena Text: legal and government12981415Qwen3 235B A22B Thinking 2507
LMArena Text: business and finance13111413Qwen3 235B A22B Thinking 2507
Neither model has a result on the other 7 boards here
Web designLike-for-likePublic boards, shown as published, kept out of the index51
Web design benchmarks for GPT-5 Nano and Qwen3 235B A22B Thinking 2507
BenchmarkGPT-5 NanoQwen3 235B A22B Thinking 2507Leader
Design Arena: websites11011052GPT-5 Nano
Design Arena: UI components1064942GPT-5 Nano
Design Arena: game dev1044956GPT-5 Nano
Design Arena: data viz1056950GPT-5 Nano
Design Arena: 3D965997Qwen3 235B A22B Thinking 2507
Design Arena: all code categories10841032GPT-5 Nano
Neither model has a result on the other 3 boards here
GeneralLike-for-likePublic boards, shown as published, kept out of the index019
General benchmarks for GPT-5 Nano and Qwen3 235B A22B Thinking 2507
BenchmarkGPT-5 NanoQwen3 235B A22B Thinking 2507Leader
SAGE (Vals AI)30.4%Not measuredOnly GPT-5 Nano measured
MedScribe (Vals AI)72.9%Not measuredOnly GPT-5 Nano measured
IFBench67.6%Not measuredOnly GPT-5 Nano measured
Long context reasoning (AA-LCR)45.0%Not measuredOnly GPT-5 Nano measured
DTBench62.7%80.3%Qwen3 235B A22B Thinking 2507
LMCA7.9%29.3%Qwen3 235B A22B Thinking 2507
Fiction.LiveBench44.4%75.0%Qwen3 235B A22B Thinking 2507
Creative writing (Lech Mazur)Not measured82.4%Only Qwen3 235B A22B Thinking 2507 measured
LMArena Text13201415Qwen3 235B A22B Thinking 2507
LMArena Text: instruction following13061386Qwen3 235B A22B Thinking 2507
LMArena Text: creative writing12491384Qwen3 235B A22B Thinking 2507
LMArena Vision1159Not measuredOnly GPT-5 Nano measured
LMArena Text: English13211429Qwen3 235B A22B Thinking 2507
LMArena Text: non-English13131397Qwen3 235B A22B Thinking 2507
LMArena Text: Chinese13561481Qwen3 235B A22B Thinking 2507
LMArena Text: German13271408Qwen3 235B A22B Thinking 2507
LMArena Text: Japanese12271386Qwen3 235B A22B Thinking 2507
LMArena Text: Korean12691375Qwen3 235B A22B Thinking 2507
LMArena Text: Spanish13601389Qwen3 235B A22B Thinking 2507
LMArena Text: Russian12961401Qwen3 235B A22B Thinking 2507
LMArena Text: Polish13141390Qwen3 235B A22B Thinking 2507
LMArena Text: multi-turn13111406Qwen3 235B A22B Thinking 2507
LMArena Text: longer queries13121400Qwen3 235B A22B Thinking 2507
LMArena Text: writing and language12831388Qwen3 235B A22B Thinking 2507
LMArena Text: entertainment and media12681370Qwen3 235B A22B Thinking 2507
LMArena Vision: English1170Not measuredOnly GPT-5 Nano measured
LMArena Vision: Chinese1176Not measuredOnly GPT-5 Nano measured
LMArena Vision: reading text1152Not measuredOnly GPT-5 Nano measured
LMArena Vision: humor1110Not measuredOnly GPT-5 Nano 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-5 Nano and Qwen3 235B A22B Thinking 2507
BenchmarkGPT-5 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-5 Nano$0.0003Fits in one request
Qwen3 235B A22B Thinking 2507$0.0022Fits in one request

GPT-5 Nano costs less

Repository review

50K input + 3K output tokens

GPT-5 Nano$0.0037Fits in one request
Qwen3 235B A22B Thinking 2507$0.033Fits in one request

GPT-5 Nano costs less

Agent loop

200K input + 10K output tokens

GPT-5 Nano$0.014Fits in one request
Qwen3 235B A22B Thinking 2507$0.125Exceeds the context window

GPT-5 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-5 Nano and Qwen3 235B A22B Thinking 2507
SpecificationOpenAI GPT-5 NanoQwen3 235B A22B Thinking 2507
MakerOpenAIQwen
WeightsNo public weights listedOpen weights
ReleasedAugust 7, 2025July 25, 2025
Context window400K128K
Input $/Mtok$0.05$0.45
Output $/Mtok$0.40$3.50
Coding indexNot benchmarkedNot benchmarked
Independent measurements behind it00
Cost per solved task$0.109Not computable
SourceOpenRouter listingOpenRouter listing

Questions

Is GPT-5 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-5 Nano for output tokens 8.8x cheaper ($0.40 vs $3.50 per million) and a larger context window (400K against 128K tokens).

Which is cheaper, GPT-5 Nano or Qwen3 235B A22B Thinking 2507?

GPT-5 Nano costs $0.05 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 GPT-5 Nano only.

Which has the bigger context window, GPT-5 Nano or Qwen3 235B A22B Thinking 2507?

GPT-5 Nano, with 400K tokens against 128K.

How many benchmarks is the GPT-5 Nano vs Qwen3 235B A22B Thinking 2507 comparison based on?

42 benchmarks have a result for both models as of October 22, 2026. GPT-5 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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