GPT-4.1 Nano vs Qwen3 Coder Next
Qwen3 Coder Next leads GPT-4.1 Nano on the Artificial Analysis Intelligence Index, 9.2 to 7.8, and GPT-4.1 Nano costs 2.0x less per output token ($0.40 vs $0.80 per million). Updated October 21, 2026; 9 benchmarks have a result for both.
GPT-4.1 Nano or Qwen3 Coder Next: 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 2.0x cheaper ($0.40 vs $0.80 per million) and a larger context window (1.05M against 262K tokens).
- Shared results
- 9
- GPT-4.1 Nano only
- 62
- Qwen3 Coder Next only
- 2
- Like-for-like categories
- 5 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
Qwen3 Coder Next
Qwen3 Coder Next leads, 9.2 against 7.8.
Same board, both measured
Agentic work
AA Agentic Index
No call
Only Qwen3 Coder Next has a AA Agentic Index result, 0.9.
Directional only
Reasoning
GPQA Diamond
No call
Only GPT-4.1 Nano has a GPQA Diamond result, 50.8%.
Directional only
Math
AIME
No call
Only GPT-4.1 Nano has a AIME result, 26.5%.
Directional only
Knowledge
MMLU Pro
No call
Only GPT-4.1 Nano has a MMLU Pro result, 63.5%.
Directional only
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 262K.
Listed by OpenRouter
Everyday cost
1K input + 500 output tokens
GPT-4.1 Nano
GPT-4.1 Nano $0.0003, Qwen3 Coder Next $0.0005 per turn.
List prices
At a glance
- Coding index
- GPT-4.1 Nano not benchmarked, Qwen3 Coder Next not benchmarked.
- Widest category
- Agentic work, 62.3 points apart on average over 1 shared benchmark.
- Widest single gap
- τ²-Bench Telecom: GPT-4.1 Nano 17.3% against Qwen3 Coder Next 79.5%, 62.3 points apart on a 0 to 100 scale.
- Output price
- GPT-4.1 Nano $0.40 against Qwen3 Coder Next $0.80 per million output tokens, 2.0x apart.
- Context window
- GPT-4.1 Nano takes 1.05M tokens, Qwen3 Coder Next 262K.
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 Coder Next | Ties |
|---|---|---|---|---|
| Coding | 0 | 3 | ||
| Intelligence | 0 | 1 | ||
| Agentic work | 0 | 1 | ||
| Reasoning | 0 | 2 | ||
| General | 0 | 2 | ||
| All shared boards | 0 | 9 |
CodingLike-for-likeSix of these boards feed the Vibe Coding Index03
| Benchmark | GPT-4.1 Nano | Qwen3 Coder Next | Leader |
|---|---|---|---|
| Aider polyglot | 8.9% | Not measured | Only GPT-4.1 Nano measured |
| AA Coding Index | 11.1 | 36.2 | Qwen3 Coder Next |
| LiveCodeBench (Vals AI) | 42.7% | Not measured | Only GPT-4.1 Nano measured |
| LiveCodeBench (Artificial Analysis run) | 32.6% | Not measured | Only GPT-4.1 Nano measured |
| SciCode | Not measured | 36.2% | Only Qwen3 Coder Next measured |
| Terminal-Bench Hard (Artificial Analysis run) | 3.8% | 18.2% | Qwen3 Coder Next |
| WeirdML | 19.0% | 34.4% | Qwen3 Coder Next |
| LMArena Text: coding | 1306 | Not measured | Only GPT-4.1 Nano measured |
| LMArena Text: software and IT | 1305 | Not measured | Only GPT-4.1 Nano measured |
| Neither model has a result on the other 30 boards here | |||
IntelligenceLike-for-likePublic boards, shown as published, kept out of the index01
| Benchmark | GPT-4.1 Nano | Qwen3 Coder Next | Leader |
|---|---|---|---|
| AA Intelligence Index | 7.8 | 9.2 | Qwen3 Coder Next |
| Epoch Capabilities Index | 129.5 | Not measured | Only GPT-4.1 Nano measured |
Agentic workLike-for-likePublic boards, shown as published, kept out of the index01
| Benchmark | GPT-4.1 Nano | Qwen3 Coder Next | Leader |
|---|---|---|---|
| AA Agentic Index | Not measured | 0.9 | Only Qwen3 Coder Next measured |
| τ²-Bench Telecom | 17.3% | 79.5% | Qwen3 Coder Next |
| Berkeley Function Calling Leaderboard (BFCL) | 33.1% | Not measured | Only 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 index02
| Benchmark | GPT-4.1 Nano | Qwen3 Coder Next | Leader |
|---|---|---|---|
| GPQA Diamond | 50.8% | Not measured | Only GPT-4.1 Nano measured |
| MMMU Pro (Vals AI) | 55.0% | Not measured | Only GPT-4.1 Nano measured |
| GPQA Diamond (Artificial Analysis run) | 51.2% | 73.7% | Qwen3 Coder Next |
| Humanity's Last Exam (Artificial Analysis run) | 3.8% | 10.1% | Qwen3 Coder Next |
| GPQA Diamond (Epoch AI run) | 48.9% | Not measured | Only GPT-4.1 Nano measured |
| ARC-AGI | 0.0% | Not measured | Only GPT-4.1 Nano measured |
| ARC-AGI-2 | 0.0% | Not measured | Only GPT-4.1 Nano measured |
| Chess puzzles | 0.0% | Not measured | Only GPT-4.1 Nano measured |
| LMArena Text: hard prompts | 1286 | Not measured | Only GPT-4.1 Nano measured |
| LMArena Text: expert | 1271 | Not measured | Only GPT-4.1 Nano measured |
| LMArena Text: hard prompts in English | 1293 | Not measured | Only GPT-4.1 Nano measured |
| Kagi LLM Benchmark | 33.3% | Not measured | Only GPT-4.1 Nano measured |
| Neither model has a result on the other 10 boards here | |||
MathDirectional onlyPublic boards, shown as published, kept out of the index9 boards, none shared
| Benchmark | GPT-4.1 Nano | Qwen3 Coder Next | 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 |
| MATH level 5 | 70.0% | Not measured | Only GPT-4.1 Nano measured |
| LMArena Text: math | 1274 | Not measured | Only GPT-4.1 Nano measured |
| LMArena Text: mathematical work | 1274 | Not measured | Only GPT-4.1 Nano measured |
| Neither model has a result on the other 4 boards here | |||
KnowledgeDirectional onlyPublic boards, shown as published, kept out of the index12 boards, none shared
| Benchmark | GPT-4.1 Nano | Qwen3 Coder Next | Leader |
|---|---|---|---|
| MMLU Pro | 63.5% | Not measured | Only GPT-4.1 Nano measured |
| LegalBench (Vals AI) | 61.1% | Not measured | Only GPT-4.1 Nano measured |
| CorpFin (Vals AI) | 42.1% | Not measured | Only GPT-4.1 Nano measured |
| TaxEval (Vals AI) | 60.8% | Not measured | Only GPT-4.1 Nano measured |
| MortgageTax (Vals AI) | 52.8% | Not measured | Only GPT-4.1 Nano measured |
| MedQA (Vals AI) | 68.2% | Not measured | Only GPT-4.1 Nano 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 | Not measured | Only GPT-4.1 Nano measured |
| LMArena Text: medicine and healthcare | 1276 | Not measured | Only GPT-4.1 Nano measured |
| LMArena Text: legal and government | 1300 | Not measured | Only GPT-4.1 Nano measured |
| LMArena Text: business and finance | 1267 | Not measured | Only GPT-4.1 Nano measured |
| 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
| Benchmark | GPT-4.1 Nano | Qwen3 Coder Next | 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 | 890 | 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 |
| Neither model has a result on the other 3 boards here | |||
GeneralLike-for-likePublic boards, shown as published, kept out of the index02
| Benchmark | GPT-4.1 Nano | Qwen3 Coder Next | Leader |
|---|---|---|---|
| IFBench | 32.0% | 35.2% | Qwen3 Coder Next |
| Long context reasoning (AA-LCR) | 20.3% | 47.0% | Qwen3 Coder Next |
| DTBench | 52.5% | Not measured | Only GPT-4.1 Nano measured |
| LMCA | 5.5% | Not measured | Only GPT-4.1 Nano measured |
| Fiction.LiveBench | 25.0% | Not measured | Only GPT-4.1 Nano measured |
| LMArena Text | 1285 | Not measured | Only GPT-4.1 Nano measured |
| LMArena Text: instruction following | 1266 | Not measured | Only GPT-4.1 Nano measured |
| LMArena Text: creative writing | 1260 | Not measured | Only GPT-4.1 Nano measured |
| LMArena Vision | 1063 | Not measured | Only GPT-4.1 Nano measured |
| LMArena Text: English | 1303 | Not measured | Only GPT-4.1 Nano measured |
| LMArena Text: non-English | 1260 | Not measured | Only GPT-4.1 Nano measured |
| LMArena Text: Chinese | 1270 | Not measured | Only GPT-4.1 Nano measured |
| LMArena Text: German | 1288 | Not measured | Only GPT-4.1 Nano measured |
| LMArena Text: Japanese | 1198 | Not measured | Only GPT-4.1 Nano measured |
| LMArena Text: Russian | 1261 | Not measured | Only GPT-4.1 Nano measured |
| LMArena Text: multi-turn | 1277 | Not measured | Only GPT-4.1 Nano measured |
| LMArena Text: longer queries | 1283 | Not measured | Only GPT-4.1 Nano measured |
| LMArena Text: writing and language | 1261 | Not measured | Only GPT-4.1 Nano measured |
| LMArena Text: entertainment and media | 1243 | Not measured | Only GPT-4.1 Nano measured |
| LMArena Vision: English | 1101 | Not measured | Only 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
| Benchmark | GPT-4.1 Nano | Qwen3 Coder Next | 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
GPT-4.1 Nano costs less
Agent loop
200K input + 10K output tokens
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.
| Specification | OpenAI GPT-4.1 Nano | Qwen3 Coder Next |
|---|---|---|
| Maker | OpenAI | Qwen |
| Weights | No public weights listed | Open weights |
| Released | April 14, 2025 | February 4, 2026 |
| Context window | 1.05M | 262K |
| Input $/Mtok | $0.10 | $0.12 |
| Output $/Mtok | $0.40 | $0.80 |
| Coding index | Not benchmarked | Not benchmarked |
| Independent measurements behind it | 0 | 0 |
| Cost per solved task | Not computable | Not computable |
| Source | OpenRouter listing | OpenRouter listing |
Questions
Is GPT-4.1 Nano better than Qwen3 Coder Next 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 2.0x cheaper ($0.40 vs $0.80 per million) and a larger context window (1.05M against 262K tokens).
Which is cheaper, GPT-4.1 Nano or Qwen3 Coder Next?
GPT-4.1 Nano costs $0.10 per million input tokens and $0.40 per million output tokens; Qwen3 Coder Next costs $0.12 and $0.80. Cost per solved task is known for neither of them.
Which has the bigger context window, GPT-4.1 Nano or Qwen3 Coder Next?
GPT-4.1 Nano, with 1.05M tokens against 262K.
How many benchmarks is the GPT-4.1 Nano vs Qwen3 Coder Next comparison based on?
9 benchmarks have a result for both models as of October 21, 2026. GPT-4.1 Nano's coding index rests on 0 independent benchmarks and Qwen3 Coder Next's on 0. Where only one model has a result, the page says so instead of naming a winner.