GPT-4.1 Nano vs Mistral Large 2407
GPT-4.1 Nano leads Mistral Large 2407 on the Artificial Analysis Intelligence Index, 7.8 to 7.6, and GPT-4.1 Nano costs 15.0x less per output token ($0.40 vs $6.00 per million). Updated October 22, 2026; 14 benchmarks have a result for both.
GPT-4.1 Nano or Mistral Large 2407: 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 15.0x cheaper ($0.40 vs $6.00 per million) and a larger context window (1.05M against 131K tokens).
- Shared results
- 14
- GPT-4.1 Nano only
- 57
- Mistral Large 2407 only
- 3
- 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 Nano
GPT-4.1 Nano leads, 7.8 against 7.6.
Same board, both measured
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 131K.
Listed by OpenRouter
Everyday cost
1K input + 500 output tokens
GPT-4.1 Nano
GPT-4.1 Nano $0.0003, Mistral Large 2407 $0.0050 per turn.
List prices
At a glance
- Coding index
- GPT-4.1 Nano not benchmarked, Mistral Large 2407 not benchmarked.
- Widest category
- Math, 23.4 points apart on average over 4 shared benchmarks.
- Widest single gap
- MATH level 5: GPT-4.1 Nano 70.0% against Mistral Large 2407 44.8%, 25.2 points apart on a 0 to 100 scale.
- Output price
- GPT-4.1 Nano $0.40 against Mistral Large 2407 $6.00 per million output tokens, 15.0x apart.
- Context window
- GPT-4.1 Nano takes 1.05M tokens, Mistral Large 2407 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 Nano | Bars | Mistral Large 2407 | Ties |
|---|---|---|---|---|
| Coding | 1 | 0 | ||
| Intelligence | 1 | 0 | ||
| Agentic work | 0 | 1 | ||
| Reasoning | 2 | 1 | ||
| Math | 4 | 0 | ||
| Knowledge | 0 | 1 | ||
| General | 2 | 1 | ||
| All shared boards | 10 | 4 |
CodingLike-for-likeSix of these boards feed the Vibe Coding Index10
| Benchmark | GPT-4.1 Nano | Mistral Large 2407 | 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% | 26.7% | GPT-4.1 Nano |
| 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 | Mistral Large 2407 | Leader |
|---|---|---|---|
| AA Intelligence Index | 7.8 | 7.6 | GPT-4.1 Nano |
| 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 | Mistral Large 2407 | Leader |
|---|---|---|---|
| τ²-Bench Telecom | 17.3% | 33.0% | Mistral Large 2407 |
| Berkeley Function Calling Leaderboard (BFCL) | 33.1% | Not measured | Only GPT-4.1 Nano measured |
| Neither model has a result on the other 39 boards here | |||
ReasoningLike-for-likePublic boards, shown as published, kept out of the index21
| Benchmark | GPT-4.1 Nano | Mistral Large 2407 | 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% | 47.2% | GPT-4.1 Nano |
| Humanity's Last Exam (Artificial Analysis run) | 3.8% | 2.9% | GPT-4.1 Nano |
| GPQA Diamond (Epoch AI run) | 48.9% | 49.0% | Mistral Large 2407 |
| 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 |
| SimpleBench | Not measured | 22.5% | Only Mistral Large 2407 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 9 boards here | |||
MathLike-for-likePublic boards, shown as published, kept out of the index40
| Benchmark | GPT-4.1 Nano | Mistral Large 2407 | 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 | 0.0 | GPT-4.1 Nano |
| AIME 2025 (Artificial Analysis run) | 24.0% | 0.0% | GPT-4.1 Nano |
| OTIS Mock AIME | 28.9% | 8.5% | GPT-4.1 Nano |
| MATH level 5 | 70.0% | 44.8% | GPT-4.1 Nano |
| 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 | |||
KnowledgeLike-for-likePublic boards, shown as published, kept out of the index01
| Benchmark | GPT-4.1 Nano | Mistral Large 2407 | 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% | 68.3% | Mistral Large 2407 |
| SimpleQA Verified | 6.0% | Not measured | Only GPT-4.1 Nano measured |
| MMLU | Not measured | 80.0% | Only Mistral Large 2407 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 | Mistral Large 2407 | 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 index21
| Benchmark | GPT-4.1 Nano | Mistral Large 2407 | Leader |
|---|---|---|---|
| IFBench | 32.0% | 31.6% | GPT-4.1 Nano |
| Long context reasoning (AA-LCR) | 20.3% | 2.0% | GPT-4.1 Nano |
| DTBench | 52.5% | 61.2% | Mistral Large 2407 |
| LMCA | 5.5% | Not measured | Only GPT-4.1 Nano measured |
| Fiction.LiveBench | 25.0% | Not measured | Only GPT-4.1 Nano measured |
| Creative writing (Lech Mazur) | Not measured | 69.0% | Only Mistral Large 2407 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 25 boards here | |||
Image generationNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
| Benchmark | GPT-4.1 Nano | Mistral Large 2407 | 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 | Mistral Large 2407 |
|---|---|---|
| Maker | OpenAI | Mistral AI |
| Weights | No public weights listed | No public weights listed |
| Released | April 14, 2025 | November 19, 2024 |
| Context window | 1.05M | 131K |
| Input $/Mtok | $0.10 | $2.00 |
| Output $/Mtok | $0.40 | $6.00 |
| 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 Mistral Large 2407 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 15.0x cheaper ($0.40 vs $6.00 per million) and a larger context window (1.05M against 131K tokens).
Which is cheaper, GPT-4.1 Nano or Mistral Large 2407?
GPT-4.1 Nano costs $0.10 per million input tokens and $0.40 per million output tokens; Mistral Large 2407 costs $2.00 and $6.00. Cost per solved task is known for neither of them.
Which has the bigger context window, GPT-4.1 Nano or Mistral Large 2407?
GPT-4.1 Nano, with 1.05M tokens against 131K.
How many benchmarks is the GPT-4.1 Nano vs Mistral Large 2407 comparison based on?
14 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 Mistral Large 2407's on 0. Where only one model has a result, the page says so instead of naming a winner.