GPT-5 Nano vs Mistral Large 3 2512
GPT-5 Nano leads Mistral Large 3 2512 on the Artificial Analysis Intelligence Index, 13.0 to 9.3, and GPT-5 Nano costs 3.8x less per output token ($0.40 vs $1.50 per million). Updated October 22, 2026; 21 benchmarks have a result for both.
GPT-5 Nano or Mistral Large 3 2512: which to pick
Neither model has a coding score yet, so the two cannot be ranked on coding.
Pick GPT-5 Nano for output tokens 3.8x cheaper ($0.40 vs $1.50 per million) and a lower cost per solved task ($0.109 against $0.77).
- Shared results
- 21
- GPT-5 Nano only
- 66
- Mistral Large 3 2512 only
- 7
- 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-5 Nano
GPT-5 Nano leads, 13.0 against 9.3.
Same board, both measured
Agentic work
AA Agentic Index
No call
Only Mistral Large 3 2512 has a AA Agentic Index result, 1.0.
Directional only
Reasoning
GPQA Diamond
Mistral Large 3 2512
Mistral Large 3 2512 leads, 68.4% against 63.4%.
Same board, both measured
Math
AIME
GPT-5 Nano
GPT-5 Nano leads, 81.2% against 42.9%.
Same board, both measured
Knowledge
MMLU Pro
Mistral Large 3 2512
Mistral Large 3 2512 leads, 79.8% against 76.1%.
Same board, both measured
Web design
Design Arena: websites
Mistral Large 3 2512
Mistral Large 3 2512 leads, 1162 against 1101.
Same board, both measured
Long documents
Listed context window
GPT-5 Nano
GPT-5 Nano takes 400K tokens against 262K.
Listed by OpenRouter
Everyday cost
1K input + 500 output tokens
GPT-5 Nano
GPT-5 Nano $0.0003, Mistral Large 3 2512 $0.0013 per turn.
List prices
Cost per solved task
One SWE-bench Verified task, solved
GPT-5 Nano
GPT-5 Nano $0.109, Mistral Large 3 2512 $0.77.
Published attempt cost and pass rate
At a glance
- Coding index
- GPT-5 Nano not benchmarked, Mistral Large 3 2512 not benchmarked.
- Widest category
- Math, 21.1 points apart on average over 2 shared benchmarks.
- Widest single gap
- AIME: GPT-5 Nano 81.2% against Mistral Large 3 2512 42.9%, 38.3 points apart on a 0 to 100 scale.
- Output price
- GPT-5 Nano $0.40 against Mistral Large 3 2512 $1.50 per million output tokens, 3.8x apart.
- Context window
- GPT-5 Nano takes 400K tokens, Mistral Large 3 2512 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-5 Nano | Bars | Mistral Large 3 2512 | Ties |
|---|---|---|---|---|
| Coding | 1 | 0 | ||
| Intelligence | 1 | 0 | ||
| Reasoning | 1 | 1 | ||
| Math | 2 | 0 | ||
| Knowledge | 2 | 4 | ||
| Web design | 0 | 6 | ||
| General | 1 | 2 | ||
| All shared boards | 8 | 13 |
CodingLike-for-likeSix of these boards feed the Vibe Coding Index10
| Benchmark | GPT-5 Nano | Mistral Large 3 2512 | Leader |
|---|---|---|---|
| SWE-bench Verified | 34.8% | Not measured | Only GPT-5 Nano measured |
| AA Coding Index | Not measured | 20.1 | Only Mistral Large 3 2512 measured |
| LiveCodeBench (Vals AI) | 70.2% | 55.3% | GPT-5 Nano |
| SWE-bench Verified (Vals AI) | Not measured | 41.4% | Only Mistral Large 3 2512 measured |
| LiveCodeBench (Artificial Analysis run) | 78.9% | Not measured | Only GPT-5 Nano measured |
| Terminal-Bench Hard (Artificial Analysis run) | 17.4% | Not measured | Only GPT-5 Nano measured |
| Terminal-Bench | 21.8% | Not measured | Only GPT-5 Nano measured |
| WeirdML | 38.1% | Not measured | Only GPT-5 Nano measured |
| ALE-Bench | Not measured | 264.7 | Only Mistral Large 3 2512 measured |
| LMArena Text: coding | 1351 | Not measured | Only GPT-5 Nano measured |
| LMArena Text: software and IT | 1345 | Not measured | Only GPT-5 Nano measured |
| Neither model has a result on the other 28 boards here | |||
IntelligenceLike-for-likePublic boards, shown as published, kept out of the index10
| Benchmark | GPT-5 Nano | Mistral Large 3 2512 | Leader |
|---|---|---|---|
| AA Intelligence Index | 13.0 | 9.3 | GPT-5 Nano |
| Epoch Capabilities Index | 139.2 | Not measured | Only GPT-5 Nano measured |
Agentic workDirectional onlyPublic boards, shown as published, kept out of the index3 boards, none shared
| Benchmark | GPT-5 Nano | Mistral Large 3 2512 | Leader |
|---|---|---|---|
| AA Agentic Index | Not measured | 1.0 | Only Mistral Large 3 2512 measured |
| τ²-Bench Telecom | 36.5% | Not measured | Only GPT-5 Nano measured |
| Berkeley Function Calling Leaderboard (BFCL) | 51.4% | Not measured | Only GPT-5 Nano measured |
| Neither model has a result on the other 38 boards here | |||
ReasoningLike-for-likePublic boards, shown as published, kept out of the index11
| Benchmark | GPT-5 Nano | Mistral Large 3 2512 | Leader |
|---|---|---|---|
| GPQA Diamond | 63.4% | 68.4% | Mistral Large 3 2512 |
| MMMU Pro (Vals AI) | 70.9% | 66.2% | GPT-5 Nano |
| GPQA Diamond (Artificial Analysis run) | 67.6% | Not measured | Only GPT-5 Nano measured |
| Humanity's Last Exam (Artificial Analysis run) | 9.5% | Not measured | Only GPT-5 Nano measured |
| GPQA Diamond (Epoch AI run) | 69.4% | Not measured | Only GPT-5 Nano measured |
| ARC-AGI | 20.7% | Not measured | Only GPT-5 Nano measured |
| ARC-AGI-2 | 2.6% | Not measured | Only GPT-5 Nano measured |
| Chess puzzles | 22.0% | Not measured | Only GPT-5 Nano measured |
| Mystery game puzzles | 9.0% | Not measured | Only GPT-5 Nano measured |
| VPCT | 37.2% | Not measured | Only GPT-5 Nano measured |
| LMArena Text: hard prompts | 1328 | Not measured | Only GPT-5 Nano measured |
| LMArena Text: expert | 1321 | Not measured | Only GPT-5 Nano measured |
| LMArena Text: hard prompts in English | 1338 | Not measured | Only GPT-5 Nano measured |
| LMArena Vision: diagrams | 1141 | Not measured | Only GPT-5 Nano measured |
| LMArena Vision: homework | 1157 | Not measured | Only GPT-5 Nano measured |
| Kagi LLM Benchmark | 62.2% | Not measured | Only GPT-5 Nano measured |
| Neither model has a result on the other 6 boards here | |||
MathLike-for-likePublic boards, shown as published, kept out of the index20
| Benchmark | GPT-5 Nano | Mistral Large 3 2512 | Leader |
|---|---|---|---|
| AIME | 81.2% | 42.9% | GPT-5 Nano |
| MATH 500 (Vals AI) | 93.8% | Not measured | Only GPT-5 Nano measured |
| MGSM (Vals AI) | 89.3% | 85.4% | GPT-5 Nano |
| AA Math Index | 83.7 | Not measured | Only GPT-5 Nano measured |
| AIME 2025 (Artificial Analysis run) | 83.7% | Not measured | Only GPT-5 Nano measured |
| OTIS Mock AIME | 81.1% | Not measured | Only GPT-5 Nano measured |
| FrontierMath, tiers 1 to 3 | 20.0% | Not measured | Only GPT-5 Nano measured |
| FrontierMath, tier 4 | 2.4% | Not measured | Only GPT-5 Nano measured |
| ProofBench | 12.0% | Not measured | Only GPT-5 Nano measured |
| MATH level 5 | 95.2% | Not measured | Only GPT-5 Nano measured |
| LMArena Text: math | 1317 | Not measured | Only GPT-5 Nano measured |
| LMArena Text: mathematical work | 1326 | Not measured | Only GPT-5 Nano measured |
| Neither model has a result on the other 1 board here | |||
KnowledgeLike-for-likePublic boards, shown as published, kept out of the index24
| Benchmark | GPT-5 Nano | Mistral Large 3 2512 | Leader |
|---|---|---|---|
| MMLU Pro | 76.1% | 79.8% | Mistral Large 3 2512 |
| LegalBench (Vals AI) | 50.1% | 79.1% | Mistral Large 3 2512 |
| CaseLaw (Vals AI) | 52.6% | 61.4% | Mistral Large 3 2512 |
| CorpFin (Vals AI) | Not measured | 61.0% | Only Mistral Large 3 2512 measured |
| TaxEval (Vals AI) | 67.4% | 73.1% | Mistral Large 3 2512 |
| MortgageTax (Vals AI) | 53.6% | 52.1% | GPT-5 Nano |
| MedQA (Vals AI) | 93.3% | 82.2% | GPT-5 Nano |
| MedCode (Vals AI) | 30.4% | Not measured | Only GPT-5 Nano measured |
| MMLU Pro (Artificial Analysis run) | 78.0% | Not measured | Only GPT-5 Nano measured |
| SimpleQA Verified | 11.7% | Not measured | Only GPT-5 Nano measured |
| LMArena Text: science | 1318 | Not measured | Only GPT-5 Nano measured |
| LMArena Text: medicine and healthcare | 1335 | Not measured | Only GPT-5 Nano measured |
| LMArena Text: legal and government | 1298 | Not measured | Only GPT-5 Nano measured |
| LMArena Text: business and finance | 1311 | Not measured | Only GPT-5 Nano measured |
| Neither model has a result on the other 4 boards here | |||
Web designLike-for-likePublic boards, shown as published, kept out of the index06
| Benchmark | GPT-5 Nano | Mistral Large 3 2512 | Leader |
|---|---|---|---|
| Design Arena: websites | 1101 | 1162 | Mistral Large 3 2512 |
| Design Arena: UI components | 1064 | 1099 | Mistral Large 3 2512 |
| Design Arena: game dev | 1044 | 1080 | Mistral Large 3 2512 |
| Design Arena: data viz | 1056 | 1136 | Mistral Large 3 2512 |
| Design Arena: 3D | 965 | 1099 | Mistral Large 3 2512 |
| Design Arena: all code categories | 1084 | 1141 | Mistral Large 3 2512 |
| Design Arena: SVG | Not measured | 992 | Only Mistral Large 3 2512 measured |
| Design Arena: ASCII art | Not measured | 1069 | Only Mistral Large 3 2512 measured |
| Neither model has a result on the other 1 board here | |||
GeneralLike-for-likePublic boards, shown as published, kept out of the index12
| Benchmark | GPT-5 Nano | Mistral Large 3 2512 | Leader |
|---|---|---|---|
| SAGE (Vals AI) | 30.4% | 24.6% | GPT-5 Nano |
| MedScribe (Vals AI) | 72.9% | Not measured | Only GPT-5 Nano measured |
| IFBench | 67.6% | Not measured | Only GPT-5 Nano measured |
| Long context reasoning (AA-LCR) | 45.0% | Not measured | Only GPT-5 Nano measured |
| DTBench | 62.7% | 65.1% | Mistral Large 3 2512 |
| LMCA | 7.9% | 16.7% | Mistral Large 3 2512 |
| Fiction.LiveBench | 44.4% | Not measured | Only GPT-5 Nano measured |
| LMArena Text | 1320 | Not measured | Only GPT-5 Nano measured |
| LMArena Text: instruction following | 1306 | Not measured | Only GPT-5 Nano measured |
| LMArena Text: creative writing | 1249 | Not measured | Only GPT-5 Nano measured |
| LMArena Vision | 1159 | Not measured | Only GPT-5 Nano measured |
| LMArena Text: English | 1321 | Not measured | Only GPT-5 Nano measured |
| LMArena Text: non-English | 1313 | Not measured | Only GPT-5 Nano measured |
| LMArena Text: Chinese | 1356 | Not measured | Only GPT-5 Nano measured |
| LMArena Text: German | 1327 | Not measured | Only GPT-5 Nano measured |
| LMArena Text: Japanese | 1227 | Not measured | Only GPT-5 Nano measured |
| LMArena Text: Korean | 1269 | Not measured | Only GPT-5 Nano measured |
| LMArena Text: Spanish | 1360 | Not measured | Only GPT-5 Nano measured |
| LMArena Text: Russian | 1296 | Not measured | Only GPT-5 Nano measured |
| LMArena Text: Polish | 1314 | Not measured | Only GPT-5 Nano measured |
| LMArena Text: multi-turn | 1311 | Not measured | Only GPT-5 Nano measured |
| LMArena Text: longer queries | 1312 | Not measured | Only GPT-5 Nano measured |
| LMArena Text: writing and language | 1283 | Not measured | Only GPT-5 Nano measured |
| LMArena Text: entertainment and media | 1268 | Not measured | Only GPT-5 Nano measured |
| LMArena Vision: English | 1170 | Not measured | Only GPT-5 Nano measured |
| LMArena Vision: Chinese | 1176 | Not measured | Only GPT-5 Nano measured |
| LMArena Vision: reading text | 1152 | Not measured | Only GPT-5 Nano measured |
| LMArena Vision: humor | 1110 | Not measured | Only GPT-5 Nano measured |
| Neither model has a result on the other 18 boards here | |||
Image generationNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
| Benchmark | GPT-5 Nano | Mistral Large 3 2512 | 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-5 Nano costs less
Repository review
50K input + 3K output tokens
GPT-5 Nano costs less
Agent loop
200K input + 10K output tokens
GPT-5 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-5 Nano | Mistral Large 3 2512 |
|---|---|---|
| Maker | OpenAI | Mistral AI |
| Weights | No public weights listed | No public weights listed |
| Released | August 7, 2025 | December 1, 2025 |
| Context window | 400K | 262K |
| Input $/Mtok | $0.05 | $0.50 |
| Output $/Mtok | $0.40 | $1.50 |
| Coding index | Not benchmarked | Not benchmarked |
| Independent measurements behind it | 0 | 1 |
| Cost per solved task | $0.109 | $0.77 |
| Source | OpenRouter listing | OpenRouter listing |
Questions
Is GPT-5 Nano better than Mistral Large 3 2512 for coding?
Neither model has a coding score yet, so the two cannot be ranked on coding. Pick GPT-5 Nano for output tokens 3.8x cheaper ($0.40 vs $1.50 per million) and a lower cost per solved task ($0.109 against $0.77).
Which is cheaper, GPT-5 Nano or Mistral Large 3 2512?
GPT-5 Nano costs $0.05 per million input tokens and $0.40 per million output tokens; Mistral Large 3 2512 costs $0.50 and $1.50. Per solved SWE-bench Verified task, GPT-5 Nano costs $0.109 and Mistral Large 3 2512 $0.77.
Which has the bigger context window, GPT-5 Nano or Mistral Large 3 2512?
GPT-5 Nano, with 400K tokens against 262K.
How many benchmarks is the GPT-5 Nano vs Mistral Large 3 2512 comparison based on?
21 benchmarks have a result for both models as of October 22, 2026. GPT-5 Nano's coding index rests on 0 independent benchmarks and Mistral Large 3 2512's on 1. Where only one model has a result, the page says so instead of naming a winner.