GPT-4.1 vs Qwen3 32B
GPT-4.1 leads Qwen3 32B on the Artificial Analysis Intelligence Index, 12.7 to 7.3, and Qwen3 32B costs 20.0x less per output token ($0.40 vs $8.00 per million). Updated October 22, 2026; 44 benchmarks have a result for both.
GPT-4.1 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 for a larger context window (1.05M against 131K tokens) and work measured by LiveCodeBench (Artificial Analysis run) or Epoch Capabilities Index, where it leads.
- Shared results
- 44
- GPT-4.1 only
- 48
- Qwen3 32B only
- 1
- 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.
Coding
Vibe Coding Index, six public coding benchmarks
No call
Neither has a coding score yet.
One side not measured
Intelligence
AA Intelligence Index
GPT-4.1
GPT-4.1 leads, 12.7 against 7.3.
Same board, both measured
Reasoning
GPQA Diamond
No call
Only GPT-4.1 has a GPQA Diamond result, 65.4%.
Directional only
Math
AIME
No call
Only GPT-4.1 has a AIME result, 39.6%.
Directional only
Knowledge
MMLU Pro
No call
Only GPT-4.1 has a MMLU Pro result, 80.5%.
Directional only
Web design
Design Arena: websites
No call
Only GPT-4.1 has a Design Arena: websites result, 1038.
Directional only
Long documents
Listed context window
GPT-4.1
GPT-4.1 takes 1.05M tokens against 131K.
Listed by OpenRouter
Everyday cost
1K input + 500 output tokens
Qwen3 32B
GPT-4.1 $0.0060, Qwen3 32B $0.0003 per turn.
List prices
Cost per solved task
One SWE-bench Verified task, solved
No call
Only GPT-4.1 has the number, $0.37.
Directional only
At a glance
- Coding index
- GPT-4.1 not benchmarked, Qwen3 32B not benchmarked.
- Widest category
- Math, 24.0 points apart on average over 5 shared benchmarks.
- Widest single gap
- Long context reasoning (AA-LCR): GPT-4.1 68.3% against Qwen3 32B 0.0%, 68.3 points apart on a 0 to 100 scale.
- Output price
- GPT-4.1 $8.00 against Qwen3 32B $0.40 per million output tokens, 20.0x apart.
- Context window
- GPT-4.1 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.
| Category | GPT-4.1 | Bars | Qwen3 32B | Ties |
|---|---|---|---|---|
| Coding | 4 | 1 | ||
| Intelligence | 1 | 1 | ||
| Agentic work | 3 | 0 | ||
| Reasoning | 5 | 2 | ||
| Math | 0 | 5 | ||
| Knowledge | 5 | 0 | ||
| General | 16 | 1 | ||
| All shared boards | 34 | 10 |
CodingLike-for-likeSix of these boards feed the Vibe Coding Index41
| Benchmark | GPT-4.1 | Qwen3 32B | Leader |
|---|---|---|---|
| Aider polyglot | 52.4% | 40.0% | GPT-4.1 |
| SWE-bench Verified | 39.6% | Not measured | Only GPT-4.1 measured |
| SWE-bench Multimodal | 31.1% | Not measured | Only GPT-4.1 measured |
| LiveCodeBench (Vals AI) | 54.7% | Not measured | Only GPT-4.1 measured |
| LiveCodeBench (Artificial Analysis run) | 45.7% | 54.6% | Qwen3 32B |
| SciCode | Not measured | 36.0% | Only Qwen3 32B measured |
| Terminal-Bench Hard (Artificial Analysis run) | 13.6% | 3.0% | GPT-4.1 |
| WeirdML | 39.0% | Not measured | Only GPT-4.1 measured |
| ALE-Bench | 558.1 | Not measured | Only GPT-4.1 measured |
| LMArena Text: coding | 1390 | 1357 | GPT-4.1 |
| LMArena Text: software and IT | 1391 | 1366 | GPT-4.1 |
| SciCode | 4.6% | Not measured | Only GPT-4.1 measured |
| Neither model has a result on the other 27 boards here | |||
IntelligenceLike-for-likePublic boards, shown as published, kept out of the index11
| Benchmark | GPT-4.1 | Qwen3 32B | Leader |
|---|---|---|---|
| AA Intelligence Index | 12.7 | 7.3 | GPT-4.1 |
| Epoch Capabilities Index | 136.7 | 138.4 | Qwen3 32B |
Agentic workLike-for-likePublic boards, shown as published, kept out of the index30
| Benchmark | GPT-4.1 | Qwen3 32B | Leader |
|---|---|---|---|
| τ²-Bench Telecom | 47.1% | 29.8% | GPT-4.1 |
| Berkeley Function Calling Leaderboard (BFCL) | 54.0% | 48.7% | GPT-4.1 |
| BrowseComp-Plus | 36.4% | 10.7% | GPT-4.1 |
| Neither model has a result on the other 38 boards here | |||
ReasoningLike-for-likePublic boards, shown as published, kept out of the index52
| Benchmark | GPT-4.1 | Qwen3 32B | Leader |
|---|---|---|---|
| GPQA Diamond | 65.4% | Not measured | Only GPT-4.1 measured |
| MMMU Pro (Vals AI) | 72.4% | Not measured | Only GPT-4.1 measured |
| GPQA Diamond (Artificial Analysis run) | 66.6% | 66.8% | Qwen3 32B |
| Humanity's Last Exam (Artificial Analysis run) | 4.2% | 7.4% | Qwen3 32B |
| GPQA Diamond (Epoch AI run) | 66.9% | 65.7% | GPT-4.1 |
| ARC-AGI | 5.5% | Not measured | Only GPT-4.1 measured |
| ARC-AGI-2 | 0.4% | Not measured | Only GPT-4.1 measured |
| SimpleBench | 27.0% | Not measured | Only GPT-4.1 measured |
| Chess puzzles | 3.0% | Not measured | Only GPT-4.1 measured |
| LMArena Text: hard prompts | 1384 | 1334 | GPT-4.1 |
| LMArena Text: expert | 1364 | 1362 | GPT-4.1 |
| LMArena Text: hard prompts in English | 1393 | 1350 | GPT-4.1 |
| LMArena Vision: diagrams | 1215 | Not measured | Only GPT-4.1 measured |
| LMArena Vision: homework | 1248 | Not measured | Only GPT-4.1 measured |
| Kagi LLM Benchmark | 52.3% | 48.7% | GPT-4.1 |
| Neither model has a result on the other 7 boards here | |||
MathLike-for-likePublic boards, shown as published, kept out of the index05
| Benchmark | GPT-4.1 | Qwen3 32B | Leader |
|---|---|---|---|
| AIME | 39.6% | Not measured | Only GPT-4.1 measured |
| MATH 500 (Vals AI) | 87.2% | Not measured | Only GPT-4.1 measured |
| MGSM (Vals AI) | 87.7% | Not measured | Only GPT-4.1 measured |
| AA Math Index | 34.7 | 73.0 | Qwen3 32B |
| AIME 2025 (Artificial Analysis run) | 34.7% | 73.0% | Qwen3 32B |
| OTIS Mock AIME | 38.3% | 66.9% | Qwen3 32B |
| FrontierMath, tiers 1 to 3 | 6.0% | Not measured | Only GPT-4.1 measured |
| MATH level 5 | 83.0% | Not measured | Only GPT-4.1 measured |
| LMArena Text: math | 1369 | 1399 | Qwen3 32B |
| LMArena Text: mathematical work | 1360 | 1420 | Qwen3 32B |
| Neither model has a result on the other 3 boards here | |||
KnowledgeLike-for-likePublic boards, shown as published, kept out of the index50
| Benchmark | GPT-4.1 | Qwen3 32B | Leader |
|---|---|---|---|
| MMLU Pro | 80.5% | Not measured | Only GPT-4.1 measured |
| LegalBench (Vals AI) | 83.1% | Not measured | Only GPT-4.1 measured |
| CaseLaw (Vals AI) | 69.9% | Not measured | Only GPT-4.1 measured |
| CorpFin (Vals AI) | 63.0% | Not measured | Only GPT-4.1 measured |
| TaxEval (Vals AI) | 75.1% | Not measured | Only GPT-4.1 measured |
| MortgageTax (Vals AI) | 65.9% | Not measured | Only GPT-4.1 measured |
| MedQA (Vals AI) | 91.2% | Not measured | Only GPT-4.1 measured |
| MMLU Pro (Artificial Analysis run) | 80.6% | 79.8% | GPT-4.1 |
| SimpleQA Verified | 31.1% | Not measured | Only GPT-4.1 measured |
| GeoBench | 72.0% | Not measured | Only GPT-4.1 measured |
| LMArena Text: science | 1382 | 1372 | GPT-4.1 |
| LMArena Text: medicine and healthcare | 1379 | 1368 | GPT-4.1 |
| LMArena Text: legal and government | 1399 | 1324 | GPT-4.1 |
| LMArena Text: business and finance | 1369 | 1318 | GPT-4.1 |
| FACTS Benchmark Suite | 45.8% | Not measured | Only GPT-4.1 measured |
| Neither model has a result on the other 3 boards here | |||
Web designDirectional onlyPublic boards, shown as published, kept out of the index6 boards, none shared
| Benchmark | GPT-4.1 | Qwen3 32B | Leader |
|---|---|---|---|
| Design Arena: websites | 1038 | Not measured | Only GPT-4.1 measured |
| Design Arena: UI components | 997 | Not measured | Only GPT-4.1 measured |
| Design Arena: game dev | 1079 | Not measured | Only GPT-4.1 measured |
| Design Arena: data viz | 1104 | Not measured | Only GPT-4.1 measured |
| Design Arena: 3D | 849 | Not measured | Only GPT-4.1 measured |
| Design Arena: all code categories | 1025 | Not measured | Only GPT-4.1 measured |
| Neither model has a result on the other 3 boards here | |||
GeneralLike-for-likePublic boards, shown as published, kept out of the index161
| Benchmark | GPT-4.1 | Qwen3 32B | Leader |
|---|---|---|---|
| IFBench | 43.0% | 36.3% | GPT-4.1 |
| Long context reasoning (AA-LCR) | 68.3% | 0.0% | GPT-4.1 |
| DTBench | 68.3% | 67.5% | GPT-4.1 |
| LMCA | 25.6% | 17.3% | GPT-4.1 |
| Fiction.LiveBench | 63.9% | 74.2% | Qwen3 32B |
| LMArena Text | 1383 | 1340 | GPT-4.1 |
| LMArena Text: instruction following | 1367 | 1305 | GPT-4.1 |
| LMArena Text: creative writing | 1363 | 1297 | GPT-4.1 |
| LMArena Vision | 1210 | Not measured | Only GPT-4.1 measured |
| LMArena Text: English | 1392 | 1359 | GPT-4.1 |
| LMArena Text: non-English | 1370 | 1317 | GPT-4.1 |
| LMArena Text: Chinese | 1382 | 1357 | GPT-4.1 |
| LMArena Text: German | 1381 | 1341 | GPT-4.1 |
| LMArena Text: Japanese | 1319 | Not measured | Only GPT-4.1 measured |
| LMArena Text: Korean | 1338 | Not measured | Only GPT-4.1 measured |
| LMArena Text: French | 1382 | Not measured | Only GPT-4.1 measured |
| LMArena Text: Spanish | 1376 | Not measured | Only GPT-4.1 measured |
| LMArena Text: Russian | 1377 | 1311 | GPT-4.1 |
| LMArena Text: Polish | 1389 | Not measured | Only GPT-4.1 measured |
| LMArena Text: multi-turn | 1397 | 1331 | GPT-4.1 |
| LMArena Text: longer queries | 1385 | 1327 | GPT-4.1 |
| LMArena Text: writing and language | 1371 | 1298 | GPT-4.1 |
| LMArena Text: entertainment and media | 1365 | 1279 | GPT-4.1 |
| LMArena Vision: English | 1209 | Not measured | Only GPT-4.1 measured |
| LMArena Vision: Chinese | 1226 | Not measured | Only GPT-4.1 measured |
| LMArena Vision: reading text | 1218 | Not measured | Only GPT-4.1 measured |
| LMArena Vision: humor | 1196 | Not measured | Only GPT-4.1 measured |
| LMArena Vision: captioning | 1207 | Not measured | Only GPT-4.1 measured |
| LMArena Vision: recognizing things | 1208 | Not measured | Only GPT-4.1 measured |
| LMArena Vision: creative writing | 1210 | Not measured | Only GPT-4.1 measured |
| Neither model has a result on the other 16 boards here | |||
Image generationNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
| Benchmark | GPT-4.1 | Qwen3 32B | Leader |
|---|---|---|---|
| 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
Qwen3 32B costs less
Repository review
50K input + 3K output tokens
Qwen3 32B costs less
Agent loop
200K input + 10K output tokens
Qwen3 32B 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.
| Specification | OpenAI GPT-4.1 | Qwen3 32B |
|---|---|---|
| Maker | OpenAI | Qwen |
| Weights | No public weights listed | Open weights |
| Released | April 14, 2025 | April 28, 2025 |
| Context window | 1.05M | 131K |
| Input $/Mtok | $2.00 | $0.14 |
| Output $/Mtok | $8.00 | $0.40 |
| Coding index | Not benchmarked | Not benchmarked |
| Independent measurements behind it | 0 | 0 |
| Cost per solved task | $0.37 | Not computable |
| Source | OpenRouter listing | OpenRouter listing |
Questions
Is GPT-4.1 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 for a larger context window (1.05M against 131K tokens) and work measured by LiveCodeBench (Artificial Analysis run) or Epoch Capabilities Index, where it leads.
Which is cheaper, GPT-4.1 or Qwen3 32B?
GPT-4.1 costs $2.00 per million input tokens and $8.00 per million output tokens; Qwen3 32B costs $0.14 and $0.40. Cost per solved task is known for GPT-4.1 only.
Which has the bigger context window, GPT-4.1 or Qwen3 32B?
GPT-4.1, with 1.05M tokens against 131K.
How many benchmarks is the GPT-4.1 vs Qwen3 32B comparison based on?
44 benchmarks have a result for both models as of October 22, 2026. GPT-4.1'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.