GPT-4.1 Nano vs Qwen3.8 27B
Updated October 21, 2026. 43 benchmarks have a result for both. Coding is ranked on the Vibe Coding Index; every other board is shown as published.
Decision reading
Pick Qwen3.8 27B for coding: it scores 73.6 on the Vibe Coding Index against 8.9 for GPT-4.1 Nano.
Pick GPT-4.1 Nano when output tokens cost 3.4x less, or when the context window is larger, 1.05M against 1M tokens.
- Shared results
- 43
- GPT-4.1 Nano only
- 26
- Qwen3.8 27B only
- 42
- Like-for-like categories
- 6 of 9
Shared results can support a head-to-head reading. Results only one model has describe coverage, not superiority.
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, four public coding benchmarks
Qwen3.8 27B
Qwen3.8 27B scores 73.6 against 8.9.
1 and 2 benchmarks behind the scores
Intelligence
AA Intelligence Index
Qwen3.8 27B
Qwen3.8 27B leads, 33.7 against 7.8.
Same board, both measured
Agentic work
AA Agentic Index
No call
Only Qwen3.8 27B has a AA Agentic Index result, 45.8.
Directional only
Reasoning
GPQA Diamond
Qwen3.8 27B
Qwen3.8 27B leads, 88.9% against 50.8%.
Same board, both measured
Math
AIME
No call
Only GPT-4.1 Nano has a AIME result, 26.5%.
Directional only
Knowledge
MMLU Pro
Qwen3.8 27B
Qwen3.8 27B leads, 84.3% against 63.5%.
Same board, both measured
Web design
Design Arena: websites
No call
Only GPT-4.1 Nano has a Design Arena: websites result, 973.
Directional only
Long documents
Listed context window
GPT-4.1 Nano
GPT-4.1 Nano takes 1.05M tokens against 1M.
Listed by OpenRouter
Everyday cost
1K input + 500 output tokens
GPT-4.1 Nano
GPT-4.1 Nano $0.0003, Qwen3.8 27B $0.0007 per turn.
List prices
Cost per solved task
One SWE-bench Verified task, solved
No call
Only Qwen3.8 27B has the number, $1.26.
Directional only
At a glance
- Coding index
- GPT-4.1 Nano 8.9 from 1 benchmark, Qwen3.8 27B 73.6 from 2 benchmarks.
- Widest category
- Reasoning, 39.8 points apart on average over 9 shared benchmarks.
- Widest single gap
- ARC-AGI: GPT-4.1 Nano 0.0% against Qwen3.8 27B 87.5%, 87.5 points apart on a 0 to 100 scale.
- Output price
- GPT-4.1 Nano $0.40 against Qwen3.8 27B $1.35 per million output tokens, 3.4x apart.
- Context window
- GPT-4.1 Nano takes 1.05M tokens, Qwen3.8 27B 1M.
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 Nano | Bars | Qwen3.8 27B | Ties |
|---|---|---|---|---|
| Coding | 0 | 4 | ||
| Intelligence | 0 | 2 | ||
| Reasoning | 0 | 9 | ||
| Math | 0 | 2 | ||
| Knowledge | 0 | 8 | ||
| General | 0 | 18 | ||
| All shared boards | 0 | 43 |
CodingLike-for-likeFour of these boards feed the Vibe Coding Index04
| Benchmark | GPT-4.1 Nano | Qwen3.8 27B | Leader |
|---|---|---|---|
| Aider polyglot | 8.9% | Not measured | Only GPT-4.1 Nano measured |
| LiveBench coding | Not measured | 86.8% | Only Qwen3.8 27B measured |
| LMArena WebDev | Not measured | 1593 | Only Qwen3.8 27B measured |
| AA Coding Index | 11.1 | 68.1 | Qwen3.8 27B |
| LiveCodeBench (Vals AI) | 42.7% | 84.0% | Qwen3.8 27B |
| SWE-bench Verified (Vals AI) | Not measured | 86.0% | Only Qwen3.8 27B measured |
| Vibe Code Bench (Vals AI) | Not measured | 64.8% | Only Qwen3.8 27B measured |
| Code migration (Vals AI) | Not measured | 14.2% | Only Qwen3.8 27B measured |
| ProgramBench (Vals AI) | Not measured | 0.0% | Only Qwen3.8 27B measured |
| IOI (Vals AI) | Not measured | 39.1% | Only Qwen3.8 27B measured |
| Terminal-Bench 2.1 (Vals AI) | Not measured | 58.4% | Only Qwen3.8 27B measured |
| SkillsBench (Vals AI) | Not measured | 38.1% | Only Qwen3.8 27B measured |
| CyberBench (Vals AI) | Not measured | 67.8% | Only Qwen3.8 27B measured |
| LiveCodeBench (Artificial Analysis run) | 32.6% | Not measured | Only GPT-4.1 Nano measured |
| SciCode | Not measured | 46.6% | Only Qwen3.8 27B measured |
| Terminal-Bench Hard (Artificial Analysis run) | 3.8% | Not measured | Only GPT-4.1 Nano measured |
| WeirdML | 19.0% | Not measured | Only GPT-4.1 Nano measured |
| LMArena Text: coding | 1306 | 1483 | Qwen3.8 27B |
| LMArena Text: software and IT | 1305 | 1478 | Qwen3.8 27B |
| LMArena WebDev: React | Not measured | 1602 | Only Qwen3.8 27B measured |
| LMArena WebDev: HTML | Not measured | 1570 | Only Qwen3.8 27B measured |
| LMArena WebDev: from an image | Not measured | 1577 | Only Qwen3.8 27B measured |
| Neither model has a result on the other 16 boards here | |||
IntelligenceLike-for-likePublic boards, shown as published, kept out of the index02
| Benchmark | GPT-4.1 Nano | Qwen3.8 27B | Leader |
|---|---|---|---|
| AA Intelligence Index | 7.8 | 33.7 | Qwen3.8 27B |
| Epoch Capabilities Index | 129.5 | 151.7 | Qwen3.8 27B |
Agentic workDirectional onlyPublic boards, shown as published, kept out of the index11 boards, none shared
| Benchmark | GPT-4.1 Nano | Qwen3.8 27B | Leader |
|---|---|---|---|
| AA Agentic Index | Not measured | 45.8 | Only Qwen3.8 27B measured |
| LiveBench research | Not measured | 59.4% | Only Qwen3.8 27B measured |
| LiveBench browser | Not measured | 32.0% | Only Qwen3.8 27B measured |
| LiveBench long agentic | Not measured | 27.9% | Only Qwen3.8 27B measured |
| Finance Agent (Vals AI) | Not measured | 48.5% | Only Qwen3.8 27B measured |
| Tax Agent Bench (Vals AI) | Not measured | 30.9% | Only Qwen3.8 27B measured |
| Legal Research Bench (Vals AI) | Not measured | 36.1% | Only Qwen3.8 27B measured |
| Excel Modeling Benchmark (Vals AI) | Not measured | 59.7% | Only Qwen3.8 27B measured |
| Terminal-Bench-Science (Vals AI) | Not measured | 0.0% | Only Qwen3.8 27B measured |
| τ²-Bench Telecom | 17.3% | Not measured | Only GPT-4.1 Nano measured |
| APEX-Agents | Not measured | 47.5% | Only Qwen3.8 27B measured |
| Neither model has a result on the other 28 boards here | |||
ReasoningLike-for-likePublic boards, shown as published, kept out of the index09
| Benchmark | GPT-4.1 Nano | Qwen3.8 27B | Leader |
|---|---|---|---|
| GPQA Diamond | 50.8% | 88.9% | Qwen3.8 27B |
| LiveBench reasoning | Not measured | 80.0% | Only Qwen3.8 27B measured |
| MMMU Pro (Vals AI) | 55.0% | 83.9% | Qwen3.8 27B |
| GPQA Diamond (Artificial Analysis run) | 51.2% | 90.5% | Qwen3.8 27B |
| Humanity's Last Exam (Artificial Analysis run) | 3.8% | 33.9% | Qwen3.8 27B |
| GPQA Diamond (Epoch AI run) | 48.9% | Not measured | Only GPT-4.1 Nano measured |
| ARC-AGI | 0.0% | 87.5% | Qwen3.8 27B |
| ARC-AGI-2 | 0.0% | 42.4% | Qwen3.8 27B |
| Chess puzzles | 0.0% | Not measured | Only GPT-4.1 Nano measured |
| LMArena Text: hard prompts | 1286 | 1459 | Qwen3.8 27B |
| LMArena Text: expert | 1271 | 1479 | Qwen3.8 27B |
| LMArena Text: hard prompts in English | 1293 | 1461 | Qwen3.8 27B |
| LMArena Vision: diagrams | Not measured | 1285 | Only Qwen3.8 27B measured |
| LMArena Vision: homework | Not measured | 1295 | Only Qwen3.8 27B measured |
| Neither model has a result on the other 7 boards here | |||
MathLike-for-likePublic boards, shown as published, kept out of the index02
| Benchmark | GPT-4.1 Nano | Qwen3.8 27B | Leader |
|---|---|---|---|
| AIME | 26.5% | Not measured | Only GPT-4.1 Nano measured |
| MATH 500 (Vals AI) | 80.2% | Not measured | Only GPT-4.1 Nano measured |
| MGSM (Vals AI) | 69.3% | Not measured | Only GPT-4.1 Nano measured |
| AA Math Index | 24.0 | Not measured | Only GPT-4.1 Nano measured |
| AIME 2025 (Artificial Analysis run) | 24.0% | Not measured | Only GPT-4.1 Nano measured |
| OTIS Mock AIME | 28.9% | Not measured | Only GPT-4.1 Nano measured |
| ProofBench | Not measured | 16.0% | Only Qwen3.8 27B measured |
| MATH level 5 | 70.0% | Not measured | Only GPT-4.1 Nano measured |
| LMArena Text: math | 1274 | 1455 | Qwen3.8 27B |
| LMArena Text: mathematical work | 1274 | 1458 | Qwen3.8 27B |
| Neither model has a result on the other 3 boards here | |||
KnowledgeLike-for-likePublic boards, shown as published, kept out of the index08
| Benchmark | GPT-4.1 Nano | Qwen3.8 27B | Leader |
|---|---|---|---|
| MMLU Pro | 63.5% | 84.3% | Qwen3.8 27B |
| LegalBench (Vals AI) | 61.1% | 82.4% | Qwen3.8 27B |
| CorpFin (Vals AI) | 42.1% | Not measured | Only GPT-4.1 Nano measured |
| TaxEval (Vals AI) | 60.8% | 70.8% | Qwen3.8 27B |
| MortgageTax (Vals AI) | 52.8% | 64.9% | Qwen3.8 27B |
| MedQA (Vals AI) | 68.2% | Not measured | Only GPT-4.1 Nano measured |
| MedCode (Vals AI) | Not measured | 28.7% | Only Qwen3.8 27B measured |
| MMLU Pro (Artificial Analysis run) | 65.7% | Not measured | Only GPT-4.1 Nano measured |
| SimpleQA Verified | 6.0% | Not measured | Only GPT-4.1 Nano measured |
| LMArena Text: science | 1297 | 1467 | Qwen3.8 27B |
| LMArena Text: medicine and healthcare | 1276 | 1437 | Qwen3.8 27B |
| LMArena Text: legal and government | 1300 | 1447 | Qwen3.8 27B |
| LMArena Text: business and finance | 1267 | 1438 | Qwen3.8 27B |
| Neither model has a result on the other 4 boards here | |||
Web designDirectional onlyPublic boards, shown as published, kept out of the index7 boards, none shared
| Benchmark | GPT-4.1 Nano | Qwen3.8 27B | Leader |
|---|---|---|---|
| Design Arena: websites | 973 | Not measured | Only GPT-4.1 Nano measured |
| Design Arena: UI components | 916 | Not measured | Only GPT-4.1 Nano measured |
| Design Arena: game dev | 971 | Not measured | Only GPT-4.1 Nano measured |
| Design Arena: data viz | 891 | Not measured | Only GPT-4.1 Nano measured |
| Design Arena: 3D | 925 | Not measured | Only GPT-4.1 Nano measured |
| Design Arena: all code categories | 963 | Not measured | Only GPT-4.1 Nano measured |
| LiveBench design | Not measured | 75.9% | Only Qwen3.8 27B measured |
| Neither model has a result on the other 2 boards here | |||
GeneralLike-for-likePublic boards, shown as published, kept out of the index018
| Benchmark | GPT-4.1 Nano | Qwen3.8 27B | Leader |
|---|---|---|---|
| LiveBench data analysis | Not measured | 76.6% | Only Qwen3.8 27B measured |
| SAGE (Vals AI) | Not measured | 52.4% | Only Qwen3.8 27B measured |
| MedScribe (Vals AI) | Not measured | 83.8% | Only Qwen3.8 27B measured |
| IFBench | 32.0% | Not measured | Only GPT-4.1 Nano measured |
| Long context reasoning (AA-LCR) | 20.3% | 82.0% | Qwen3.8 27B |
| DTBench | 52.5% | 88.0% | Qwen3.8 27B |
| LMCA | 5.5% | 41.4% | Qwen3.8 27B |
| Fiction.LiveBench | 25.0% | Not measured | Only GPT-4.1 Nano measured |
| Surface Evolver Bench | Not measured | 45.0% | Only Qwen3.8 27B measured |
| LMArena Text | 1285 | 1440 | Qwen3.8 27B |
| LMArena Text: instruction following | 1266 | 1436 | Qwen3.8 27B |
| LMArena Text: creative writing | 1260 | 1383 | Qwen3.8 27B |
| LMArena Vision | 1063 | 1269 | Qwen3.8 27B |
| LMArena Text: English | 1303 | 1448 | Qwen3.8 27B |
| LMArena Text: non-English | 1260 | 1428 | Qwen3.8 27B |
| LMArena Text: Chinese | 1270 | 1501 | Qwen3.8 27B |
| LMArena Text: German | 1288 | 1443 | Qwen3.8 27B |
| LMArena Text: Japanese | 1198 | 1387 | Qwen3.8 27B |
| LMArena Text: Korean | Not measured | 1384 | Only Qwen3.8 27B measured |
| LMArena Text: French | Not measured | 1465 | Only Qwen3.8 27B measured |
| LMArena Text: Spanish | Not measured | 1449 | Only Qwen3.8 27B measured |
| LMArena Text: Russian | 1261 | 1415 | Qwen3.8 27B |
| LMArena Text: Polish | Not measured | 1439 | Only Qwen3.8 27B measured |
| LMArena Text: multi-turn | 1277 | 1440 | Qwen3.8 27B |
| LMArena Text: longer queries | 1283 | 1448 | Qwen3.8 27B |
| LMArena Text: writing and language | 1261 | 1405 | Qwen3.8 27B |
| LMArena Text: entertainment and media | 1243 | 1393 | Qwen3.8 27B |
| LMArena Vision: English | 1101 | 1267 | Qwen3.8 27B |
| LMArena Vision: Chinese | Not measured | 1326 | Only Qwen3.8 27B measured |
| LMArena Vision: reading text | Not measured | 1281 | Only Qwen3.8 27B measured |
| LMArena Vision: humor | Not measured | 1235 | Only Qwen3.8 27B measured |
| LMArena Vision: creative writing | Not measured | 1237 | Only Qwen3.8 27B measured |
| Neither model has a result on the other 14 boards here | |||
Image generationNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
| Benchmark | GPT-4.1 Nano | Qwen3.8 27B | 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
GPT-4.1 Nano costs less
Repository review
50K input + 3K output tokens
Qwen3.8 27B costs less
Agent loop
200K input + 10K output tokens
Qwen3.8 27B 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 Nano | Qwen3.8 27B |
|---|---|---|
| Maker | OpenAI | Qwen |
| Weights | No public weights listed | Open weights |
| Released | April 14, 2025 | August 14, 2026 |
| Context window | 1.05M | 1M |
| Input $/Mtok | $0.10 | $0.04 |
| Output $/Mtok | $0.40 | $1.35 |
| Coding index | 8.9 | 73.6 |
| Independent measurements behind it | 1 | 2 |
| Cost per solved task | Not computable | $1.26 |
| Source | OpenRouter listing | OpenRouter listing |
Questions
Is GPT-4.1 Nano better than Qwen3.8 27B for coding?
Pick Qwen3.8 27B for coding: it scores 73.6 on the Vibe Coding Index against 8.9 for GPT-4.1 Nano. Pick GPT-4.1 Nano when output tokens cost 3.4x less, or when the context window is larger, 1.05M against 1M tokens.
Which is cheaper, GPT-4.1 Nano or Qwen3.8 27B?
GPT-4.1 Nano costs $0.10 per million input tokens and $0.40 per million output tokens; Qwen3.8 27B costs $0.04 and $1.35. Cost per solved task is known for Qwen3.8 27B only.
Which has the bigger context window, GPT-4.1 Nano or Qwen3.8 27B?
GPT-4.1 Nano, with 1.05M tokens against 1M.
How many benchmarks is the GPT-4.1 Nano vs Qwen3.8 27B comparison based on?
43 benchmarks have a result for both models as of October 21, 2026. GPT-4.1 Nano's coding index rests on 1 independent benchmark and Qwen3.8 27B's on 2. Where only one model has a result, the page says so instead of naming a winner.