GPT-4.1 Nano vs Qwen2.5 7B Instruct
Qwen2.5 7B Instruct costs 2.0x less per output token than GPT-4.1 Nano ($0.20 vs $0.40 per million). Updated October 22, 2026; 5 benchmarks have a result for both.
GPT-4.1 Nano or Qwen2.5 7B Instruct: 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 33K tokens) and work measured by LMCA, where it leads.
- Shared results
- 5
- GPT-4.1 Nano only
- 66
- Qwen2.5 7B Instruct only
- 2
- Like-for-like categories
- 4 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
No call
Only GPT-4.1 Nano has a AA Intelligence Index result, 7.8.
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, 972.
Directional only
Long documents
Listed context window
GPT-4.1 Nano
GPT-4.1 Nano takes 1.05M tokens against 33K.
Listed by OpenRouter
Everyday cost
1K input + 500 output tokens
Qwen2.5 7B Instruct
GPT-4.1 Nano $0.0003, Qwen2.5 7B Instruct $0.0002 per turn.
List prices
At a glance
- Coding index
- GPT-4.1 Nano not benchmarked, Qwen2.5 7B Instruct not benchmarked.
- Widest category
- Math, 26.4 points apart on average over 1 shared benchmark.
- Widest single gap
- OTIS Mock AIME: GPT-4.1 Nano 28.9% against Qwen2.5 7B Instruct 2.5%, 26.4 points apart on a 0 to 100 scale.
- Output price
- GPT-4.1 Nano $0.40 against Qwen2.5 7B Instruct $0.20 per million output tokens, 2.0x apart.
- Context window
- GPT-4.1 Nano takes 1.05M tokens, Qwen2.5 7B Instruct 33K.
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 | Qwen2.5 7B Instruct | Ties |
|---|---|---|---|---|
| Intelligence | 1 | 0 | ||
| Reasoning | 1 | 0 | ||
| Math | 1 | 0 | ||
| General | 1 | 1 | ||
| All shared boards | 4 | 1 |
CodingDirectional onlySix of these boards feed the Vibe Coding Index8 boards, none shared
| Benchmark | GPT-4.1 Nano | Qwen2.5 7B Instruct | Leader |
|---|---|---|---|
| Aider polyglot | 8.9% | Not measured | Only GPT-4.1 Nano measured |
| AA Coding Index | 11.1 | Not measured | Only GPT-4.1 Nano measured |
| 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 |
| 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 | 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 31 boards here | |||
IntelligenceLike-for-likePublic boards, shown as published, kept out of the index10
| Benchmark | GPT-4.1 Nano | Qwen2.5 7B Instruct | Leader |
|---|---|---|---|
| AA Intelligence Index | 7.8 | Not measured | Only GPT-4.1 Nano measured |
| Epoch Capabilities Index | 129.5 | 118.5 | GPT-4.1 Nano |
Agentic workDirectional onlyPublic boards, shown as published, kept out of the index3 boards, none shared
| Benchmark | GPT-4.1 Nano | Qwen2.5 7B Instruct | Leader |
|---|---|---|---|
| τ²-Bench Telecom | 17.3% | Not measured | Only GPT-4.1 Nano measured |
| BALROG | Not measured | 7.8% | Only Qwen2.5 7B Instruct measured |
| 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 index10
| Benchmark | GPT-4.1 Nano | Qwen2.5 7B Instruct | 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% | Not measured | Only GPT-4.1 Nano measured |
| Humanity's Last Exam (Artificial Analysis run) | 3.8% | Not measured | Only GPT-4.1 Nano measured |
| GPQA Diamond (Epoch AI run) | 48.9% | 35.5% | GPT-4.1 Nano |
| 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 | |||
MathLike-for-likePublic boards, shown as published, kept out of the index10
| Benchmark | GPT-4.1 Nano | Qwen2.5 7B Instruct | 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% | 2.5% | GPT-4.1 Nano |
| 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 index13 boards, none shared
| Benchmark | GPT-4.1 Nano | Qwen2.5 7B Instruct | 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 |
| MMLU | Not measured | 72.9% | Only Qwen2.5 7B Instruct 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 5 boards here | |||
Web designDirectional onlyPublic boards, shown as published, kept out of the index6 boards, none shared
| Benchmark | GPT-4.1 Nano | Qwen2.5 7B Instruct | Leader |
|---|---|---|---|
| Design Arena: websites | 972 | Not measured | Only GPT-4.1 Nano measured |
| Design Arena: UI components | 913 | Not measured | Only GPT-4.1 Nano measured |
| Design Arena: game dev | 970 | Not measured | Only GPT-4.1 Nano measured |
| Design Arena: data viz | 891 | Not measured | Only GPT-4.1 Nano measured |
| Design Arena: 3D | 924 | Not measured | Only GPT-4.1 Nano measured |
| Design Arena: all code categories | 962 | 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 index11
| Benchmark | GPT-4.1 Nano | Qwen2.5 7B Instruct | Leader |
|---|---|---|---|
| IFBench | 32.0% | Not measured | Only GPT-4.1 Nano measured |
| Long context reasoning (AA-LCR) | 20.3% | Not measured | Only GPT-4.1 Nano measured |
| DTBench | 52.5% | 47.7% | GPT-4.1 Nano |
| LMCA | 5.5% | 6.4% | Qwen2.5 7B Instruct |
| 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 | Qwen2.5 7B Instruct | 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
Qwen2.5 7B Instruct costs less
Repository review
50K input + 3K output tokens
Qwen2.5 7B Instruct costs less
Agent loop
200K input + 10K output tokens
Qwen2.5 7B Instruct 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 | Qwen2.5 7B Instruct |
|---|---|---|
| Maker | OpenAI | Qwen |
| Weights | No public weights listed | Open weights |
| Released | April 14, 2025 | October 16, 2024 |
| Context window | 1.05M | 33K |
| Input $/Mtok | $0.10 | $0.10 |
| Output $/Mtok | $0.40 | $0.20 |
| 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 Qwen2.5 7B Instruct 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 33K tokens) and work measured by LMCA, where it leads.
Which is cheaper, GPT-4.1 Nano or Qwen2.5 7B Instruct?
GPT-4.1 Nano costs $0.10 per million input tokens and $0.40 per million output tokens; Qwen2.5 7B Instruct costs $0.10 and $0.20. Cost per solved task is known for neither of them.
Which has the bigger context window, GPT-4.1 Nano or Qwen2.5 7B Instruct?
GPT-4.1 Nano, with 1.05M tokens against 33K.
How many benchmarks is the GPT-4.1 Nano vs Qwen2.5 7B Instruct comparison based on?
5 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 Qwen2.5 7B Instruct's on 0. Where only one model has a result, the page says so instead of naming a winner.