GPT-4.1 Nano vs Mistral Large
GPT-4.1 Nano leads Mistral Large on the Artificial Analysis Intelligence Index, 7.8 to 5.8, and GPT-4.1 Nano costs 15.0x less per output token ($0.40 vs $6.00 per million). Updated October 21, 2026; 39 benchmarks have a result for both.
GPT-4.1 Nano or Mistral Large: 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 128K tokens).
- Shared results
- 39
- GPT-4.1 Nano only
- 32
- Mistral Large only
- 5
- Like-for-like categories
- 6 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 5.8.
Same board, both measured
Reasoning
GPQA Diamond
GPT-4.1 Nano
GPT-4.1 Nano leads, 50.8% against 47.7%.
Same board, both measured
Math
AIME
GPT-4.1 Nano
GPT-4.1 Nano leads, 26.5% against 9.2%.
Same board, both measured
Knowledge
MMLU Pro
Mistral Large
Mistral Large leads, 69.7% 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 128K.
Listed by OpenRouter
Everyday cost
1K input + 500 output tokens
GPT-4.1 Nano
GPT-4.1 Nano $0.0003, Mistral Large $0.0050 per turn.
List prices
At a glance
- Coding index
- GPT-4.1 Nano not benchmarked, Mistral Large not benchmarked.
- Widest category
- Math, 9.2 points apart on average over 5 shared benchmarks.
- Widest single gap
- MGSM (Vals AI): GPT-4.1 Nano 69.3% against Mistral Large 87.2%, 18.0 points apart on a 0 to 100 scale.
- Output price
- GPT-4.1 Nano $0.40 against Mistral Large $6.00 per million output tokens, 15.0x apart.
- Context window
- GPT-4.1 Nano takes 1.05M tokens, Mistral Large 128K.
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 | Ties |
|---|---|---|---|---|
| Coding | 4 | 0 | ||
| Intelligence | 2 | 0 | ||
| Reasoning | 6 | 0 | ||
| Math | 4 | 1 | ||
| Knowledge | 5 | 3 | ||
| General | 13 | 1 | ||
| All shared boards | 34 | 5 |
CodingLike-for-likeSix of these boards feed the Vibe Coding Index40
| Benchmark | GPT-4.1 Nano | Mistral Large | 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% | 37.1% | GPT-4.1 Nano |
| LiveCodeBench (Artificial Analysis run) | 32.6% | 17.8% | 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 | 1275 | GPT-4.1 Nano |
| LMArena Text: software and IT | 1305 | 1274 | GPT-4.1 Nano |
| Neither model has a result on the other 31 boards here | |||
IntelligenceLike-for-likePublic boards, shown as published, kept out of the index20
| Benchmark | GPT-4.1 Nano | Mistral Large | Leader |
|---|---|---|---|
| AA Intelligence Index | 7.8 | 5.8 | GPT-4.1 Nano |
| Epoch Capabilities Index | 129.5 | 122.1 | GPT-4.1 Nano |
Agentic workDirectional onlyPublic boards, shown as published, kept out of the index2 boards, none shared
| Benchmark | GPT-4.1 Nano | Mistral Large | Leader |
|---|---|---|---|
| τ²-Bench Telecom | 17.3% | Not measured | Only GPT-4.1 Nano measured |
| 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 index60
| Benchmark | GPT-4.1 Nano | Mistral Large | Leader |
|---|---|---|---|
| GPQA Diamond | 50.8% | 47.7% | GPT-4.1 Nano |
| MMMU Pro (Vals AI) | 55.0% | Not measured | Only GPT-4.1 Nano measured |
| GPQA Diamond (Artificial Analysis run) | 51.2% | 35.1% | GPT-4.1 Nano |
| Humanity's Last Exam (Artificial Analysis run) | 3.8% | 3.5% | GPT-4.1 Nano |
| 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 | 1257 | GPT-4.1 Nano |
| LMArena Text: expert | 1271 | 1208 | GPT-4.1 Nano |
| LMArena Text: hard prompts in English | 1293 | 1268 | GPT-4.1 Nano |
| 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 index41
| Benchmark | GPT-4.1 Nano | Mistral Large | Leader |
|---|---|---|---|
| AIME | 26.5% | 9.2% | GPT-4.1 Nano |
| MATH 500 (Vals AI) | 80.2% | 74.4% | GPT-4.1 Nano |
| MGSM (Vals AI) | 69.3% | 87.2% | Mistral Large |
| 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 | 1261 | GPT-4.1 Nano |
| LMArena Text: mathematical work | 1274 | 1257 | GPT-4.1 Nano |
| Neither model has a result on the other 4 boards here | |||
KnowledgeLike-for-likePublic boards, shown as published, kept out of the index53
| Benchmark | GPT-4.1 Nano | Mistral Large | Leader |
|---|---|---|---|
| MMLU Pro | 63.5% | 69.7% | Mistral Large |
| 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% | 63.8% | Mistral Large |
| MortgageTax (Vals AI) | 52.8% | Not measured | Only GPT-4.1 Nano measured |
| MedQA (Vals AI) | 68.2% | 76.2% | Mistral Large |
| MMLU Pro (Artificial Analysis run) | 65.7% | 51.5% | GPT-4.1 Nano |
| SimpleQA Verified | 6.0% | Not measured | Only GPT-4.1 Nano measured |
| MMLU | Not measured | 68.8% | Only Mistral Large measured |
| LMArena Text: science | 1297 | 1260 | GPT-4.1 Nano |
| LMArena Text: medicine and healthcare | 1276 | 1255 | GPT-4.1 Nano |
| LMArena Text: legal and government | 1300 | 1275 | GPT-4.1 Nano |
| LMArena Text: business and finance | 1267 | 1238 | GPT-4.1 Nano |
| 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 | 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 index131
| Benchmark | GPT-4.1 Nano | Mistral Large | 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% | 60.8% | Mistral Large |
| 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 | 1265 | GPT-4.1 Nano |
| LMArena Text: instruction following | 1266 | 1249 | GPT-4.1 Nano |
| LMArena Text: creative writing | 1260 | 1242 | GPT-4.1 Nano |
| LMArena Vision | 1063 | Not measured | Only GPT-4.1 Nano measured |
| LMArena Text: English | 1303 | 1284 | GPT-4.1 Nano |
| LMArena Text: non-English | 1260 | 1235 | GPT-4.1 Nano |
| LMArena Text: Chinese | 1270 | 1239 | GPT-4.1 Nano |
| LMArena Text: German | 1288 | 1242 | GPT-4.1 Nano |
| LMArena Text: Japanese | 1198 | 1170 | GPT-4.1 Nano |
| LMArena Text: Korean | Not measured | 1202 | Only Mistral Large measured |
| LMArena Text: French | Not measured | 1325 | Only Mistral Large measured |
| LMArena Text: Spanish | Not measured | 1268 | Only Mistral Large measured |
| LMArena Text: Russian | 1261 | 1252 | GPT-4.1 Nano |
| LMArena Text: Polish | Not measured | 1157 | Only Mistral Large measured |
| LMArena Text: multi-turn | 1277 | 1260 | GPT-4.1 Nano |
| LMArena Text: longer queries | 1283 | 1261 | GPT-4.1 Nano |
| LMArena Text: writing and language | 1261 | 1261 | GPT-4.1 Nano |
| LMArena Text: entertainment and media | 1243 | 1230 | GPT-4.1 Nano |
| LMArena Vision: English | 1101 | Not measured | Only GPT-4.1 Nano measured |
| Neither model has a result on the other 22 boards here | |||
Image generationNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
| Benchmark | GPT-4.1 Nano | Mistral Large | 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 |
|---|---|---|
| Maker | OpenAI | Mistral AI |
| Weights | No public weights listed | No public weights listed |
| Released | April 14, 2025 | February 26, 2024 |
| Context window | 1.05M | 128K |
| 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 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 128K tokens).
Which is cheaper, GPT-4.1 Nano or Mistral Large?
GPT-4.1 Nano costs $0.10 per million input tokens and $0.40 per million output tokens; Mistral Large 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?
GPT-4.1 Nano, with 1.05M tokens against 128K.
How many benchmarks is the GPT-4.1 Nano vs Mistral Large comparison based on?
39 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 Mistral Large's on 0. Where only one model has a result, the page says so instead of naming a winner.