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GPT-4.1 Nano vs Qwen3 32B

GPT-4.1 Nano leads Qwen3 32B on the Artificial Analysis Intelligence Index, 7.8 to 7.3, at about the same output price ($0.40 vs $0.40 per million). Updated October 22, 2026; 43 benchmarks have a result for both.

Replace GPT-4.1 Nano Replace Qwen3 32B
GPT-4.1 NanoOpenAI
Coding
No data
Overall
24.7
Output $/M
$0.40
3
Qwen3 32BQwen
Coding
No data
Overall
40.8
Output $/M
$0.40
40
vs boards won of 43 shared

GPT-4.1 Nano or Qwen3 32B: which to pick

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

Pick GPT-4.1 Nano for a larger context window (1.05M against 131K tokens) and work measured by Aider polyglot or LiveCodeBench (Artificial Analysis run), where it leads.

Model AGPT-4.1 NanoOpenAINot benchmarkedNo coding benchmarkNo cost per solved taskModel BQwen3 32BQwenNot benchmarkedNo coding benchmarkNo cost per solved task
Shared results
43
GPT-4.1 Nano only
28
Qwen3 32B only
2
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 32B not benchmarked.
Widest category
Math, 36.2 points apart on average over 5 shared benchmarks.
Widest single gap
Fiction.LiveBench: GPT-4.1 Nano 25.0% against Qwen3 32B 74.2%, 49.2 points apart on a 0 to 100 scale.
Output price
Both cost $0.40 per million output tokens.
Context window
GPT-4.1 Nano takes 1.05M tokens, Qwen3 32B 131K.

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 32BTies
Coding14
Intelligence11
Agentic work02
Reasoning07
Math05
Knowledge05
General116
All shared boards340
CodingLike-for-likeSix of these boards feed the Vibe Coding Index14
Coding benchmarks for GPT-4.1 Nano and Qwen3 32B
BenchmarkGPT-4.1 NanoQwen3 32BLeader
Aider polyglot8.9%40.0%Qwen3 32B
AA Coding Index11.1Not measuredOnly GPT-4.1 Nano measured
LiveCodeBench (Vals AI)42.7%Not measuredOnly GPT-4.1 Nano measured
LiveCodeBench (Artificial Analysis run)32.6%54.6%Qwen3 32B
SciCodeNot measured36.0%Only Qwen3 32B measured
Terminal-Bench Hard (Artificial Analysis run)3.8%3.0%GPT-4.1 Nano
WeirdML19.0%Not measuredOnly GPT-4.1 Nano measured
LMArena Text: coding13061357Qwen3 32B
LMArena Text: software and IT13051366Qwen3 32B
Neither model has a result on the other 30 boards here
IntelligenceLike-for-likePublic boards, shown as published, kept out of the index11
Intelligence benchmarks for GPT-4.1 Nano and Qwen3 32B
BenchmarkGPT-4.1 NanoQwen3 32BLeader
AA Intelligence Index7.87.3GPT-4.1 Nano
Epoch Capabilities Index129.5138.4Qwen3 32B
Agentic workLike-for-likePublic boards, shown as published, kept out of the index02
Agentic work benchmarks for GPT-4.1 Nano and Qwen3 32B
BenchmarkGPT-4.1 NanoQwen3 32BLeader
τ²-Bench Telecom17.3%29.8%Qwen3 32B
Berkeley Function Calling Leaderboard (BFCL)33.1%48.7%Qwen3 32B
BrowseComp-PlusNot measured10.7%Only Qwen3 32B measured
Neither model has a result on the other 38 boards here
ReasoningLike-for-likePublic boards, shown as published, kept out of the index07
Reasoning benchmarks for GPT-4.1 Nano and Qwen3 32B
BenchmarkGPT-4.1 NanoQwen3 32BLeader
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%66.8%Qwen3 32B
Humanity's Last Exam (Artificial Analysis run)3.8%7.4%Qwen3 32B
GPQA Diamond (Epoch AI run)48.9%65.7%Qwen3 32B
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
LMArena Text: hard prompts12861334Qwen3 32B
LMArena Text: expert12711362Qwen3 32B
LMArena Text: hard prompts in English12931350Qwen3 32B
Kagi LLM Benchmark33.3%48.7%Qwen3 32B
Neither model has a result on the other 10 boards here
MathLike-for-likePublic boards, shown as published, kept out of the index05
Math benchmarks for GPT-4.1 Nano and Qwen3 32B
BenchmarkGPT-4.1 NanoQwen3 32BLeader
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 Index24.073.0Qwen3 32B
AIME 2025 (Artificial Analysis run)24.0%73.0%Qwen3 32B
OTIS Mock AIME28.9%66.9%Qwen3 32B
MATH level 570.0%Not measuredOnly GPT-4.1 Nano measured
LMArena Text: math12741399Qwen3 32B
LMArena Text: mathematical work12741420Qwen3 32B
Neither model has a result on the other 4 boards here
KnowledgeLike-for-likePublic boards, shown as published, kept out of the index05
Knowledge benchmarks for GPT-4.1 Nano and Qwen3 32B
BenchmarkGPT-4.1 NanoQwen3 32BLeader
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%79.8%Qwen3 32B
SimpleQA Verified6.0%Not measuredOnly GPT-4.1 Nano measured
LMArena Text: science12971372Qwen3 32B
LMArena Text: medicine and healthcare12761368Qwen3 32B
LMArena Text: legal and government13001324Qwen3 32B
LMArena Text: business and finance12671318Qwen3 32B
Neither model has a result on the other 6 boards here
Web designDirectional onlyPublic boards, shown as published, kept out of the index6 boards, none shared
Web design benchmarks for GPT-4.1 Nano and Qwen3 32B
BenchmarkGPT-4.1 NanoQwen3 32BLeader
Design Arena: websites972Not measuredOnly GPT-4.1 Nano measured
Design Arena: UI components913Not measuredOnly GPT-4.1 Nano measured
Design Arena: game dev970Not measuredOnly GPT-4.1 Nano measured
Design Arena: data viz891Not measuredOnly GPT-4.1 Nano measured
Design Arena: 3D924Not measuredOnly GPT-4.1 Nano measured
Design Arena: all code categories962Not measuredOnly GPT-4.1 Nano measured
Neither model has a result on the other 3 boards here
GeneralLike-for-likePublic boards, shown as published, kept out of the index116
General benchmarks for GPT-4.1 Nano and Qwen3 32B
BenchmarkGPT-4.1 NanoQwen3 32BLeader
IFBench32.0%36.3%Qwen3 32B
Long context reasoning (AA-LCR)20.3%0.0%GPT-4.1 Nano
DTBench52.5%67.5%Qwen3 32B
LMCA5.5%17.3%Qwen3 32B
Fiction.LiveBench25.0%74.2%Qwen3 32B
LMArena Text12851340Qwen3 32B
LMArena Text: instruction following12661305Qwen3 32B
LMArena Text: creative writing12601297Qwen3 32B
LMArena Vision1063Not measuredOnly GPT-4.1 Nano measured
LMArena Text: English13031359Qwen3 32B
LMArena Text: non-English12601317Qwen3 32B
LMArena Text: Chinese12701357Qwen3 32B
LMArena Text: German12881341Qwen3 32B
LMArena Text: Japanese1198Not measuredOnly GPT-4.1 Nano measured
LMArena Text: Russian12611311Qwen3 32B
LMArena Text: multi-turn12771331Qwen3 32B
LMArena Text: longer queries12831327Qwen3 32B
LMArena Text: writing and language12611298Qwen3 32B
LMArena Text: entertainment and media12431279Qwen3 32B
LMArena Vision: English1101Not measuredOnly GPT-4.1 Nano measured
Neither model has a result on the other 26 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 32B
BenchmarkGPT-4.1 NanoQwen3 32BLeader
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 32B$0.0003Fits 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 32B$0.0082Fits 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 32B$0.032Exceeds 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 32B
SpecificationOpenAI GPT-4.1 NanoQwen3 32B
MakerOpenAIQwen
WeightsNo public weights listedOpen weights
ReleasedApril 14, 2025April 28, 2025
Context window1.05M131K
Input $/Mtok$0.10$0.14
Output $/Mtok$0.40$0.40
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 32B for coding?

Neither model has a coding score yet, so the two cannot be ranked on coding. Pick GPT-4.1 Nano for a larger context window (1.05M against 131K tokens) and work measured by Aider polyglot or LiveCodeBench (Artificial Analysis run), where it leads.

Which is cheaper, GPT-4.1 Nano or Qwen3 32B?

GPT-4.1 Nano costs $0.10 per million input tokens and $0.40 per million output tokens; Qwen3 32B costs $0.14 and $0.40. Cost per solved task is known for neither of them.

Which has the bigger context window, GPT-4.1 Nano or Qwen3 32B?

GPT-4.1 Nano, with 1.05M tokens against 131K.

How many benchmarks is the GPT-4.1 Nano vs Qwen3 32B comparison based on?

43 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 32B's on 0. Where only one model has a result, the page says so instead of naming a winner.

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