GPT-4.1 Nano vs Mistral Medium 3.5
Mistral Medium 3.5 leads GPT-4.1 Nano on the Artificial Analysis Intelligence Index, 14.2 to 7.8, and GPT-4.1 Nano costs 18.8x less per output token ($0.40 vs $7.50 per million). Updated October 22, 2026; 48 benchmarks have a result for both.
GPT-4.1 Nano or Mistral Medium 3.5: which to pick
Only Mistral Medium 3.5 has a coding score, 25.5 on the Vibe Coding Index; GPT-4.1 Nano has none, so the two cannot be ranked on coding.
Pick GPT-4.1 Nano for output tokens 18.8x cheaper ($0.40 vs $7.50 per million) and a larger context window (1.05M against 262K tokens).
- Shared results
- 48
- GPT-4.1 Nano only
- 23
- Mistral Medium 3.5 only
- 33
- Like-for-like categories
- 8 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
Only Mistral Medium 3.5 has a coding score, so no call is made.
One side not measured
Intelligence
AA Intelligence Index
Mistral Medium 3.5
Mistral Medium 3.5 leads, 14.2 against 7.8.
Same board, both measured
Agentic work
AA Agentic Index
No call
Only Mistral Medium 3.5 has a AA Agentic Index result, 7.0.
Directional only
Reasoning
GPQA Diamond
GPT-4.1 Nano
GPT-4.1 Nano leads, 50.8% against 34.9%.
Same board, both measured
Math
AIME
No call
Only GPT-4.1 Nano has a AIME result, 26.5%.
Directional only
Knowledge
MMLU Pro
Mistral Medium 3.5
Mistral Medium 3.5 leads, 75.3% against 63.5%.
Same board, both measured
Web design
Design Arena: websites
Mistral Medium 3.5
Mistral Medium 3.5 leads, 1142 against 972.
Same board, both measured
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, Mistral Medium 3.5 $0.0053 per turn.
List prices
Cost per solved task
One SWE-bench Verified task, solved
No call
Only Mistral Medium 3.5 has the number, $6.02.
Directional only
At a glance
- Coding index
- GPT-4.1 Nano not benchmarked, Mistral Medium 3.5 25.5 from 3 benchmarks.
- Widest category
- Agentic work, 76.9 points apart on average over 1 shared benchmark.
- Widest single gap
- τ²-Bench Telecom: GPT-4.1 Nano 17.3% against Mistral Medium 3.5 94.2%, 76.9 points apart on a 0 to 100 scale.
- Output price
- GPT-4.1 Nano $0.40 against Mistral Medium 3.5 $7.50 per million output tokens, 18.8x apart.
- Context window
- GPT-4.1 Nano takes 1.05M tokens, Mistral Medium 3.5 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 | Mistral Medium 3.5 | Ties |
|---|---|---|---|---|
| Coding | 0 | 4 | ||
| Intelligence | 0 | 2 | ||
| Agentic work | 0 | 1 | ||
| Reasoning | 1 | 6 | ||
| Math | 0 | 2 | ||
| Knowledge | 1 | 7 | ||
| Web design | 0 | 6 | ||
| General | 0 | 18 | ||
| All shared boards | 2 | 46 |
CodingLike-for-likeSix of these boards feed the Vibe Coding Index04
| Benchmark | GPT-4.1 Nano | Mistral Medium 3.5 | Leader |
|---|---|---|---|
| Aider polyglot | 8.9% | Not measured | Only GPT-4.1 Nano measured |
| LMArena WebDev | Not measured | 1264 | Only Mistral Medium 3.5 measured |
| AA Coding Index | 11.1 | 46.9 | Mistral Medium 3.5 |
| LiveCodeBench (Vals AI) | 42.7% | Not measured | Only GPT-4.1 Nano measured |
| SWE-bench Verified (Vals AI) | Not measured | 66.4% | Only Mistral Medium 3.5 measured |
| Vibe Code Bench (Vals AI) | Not measured | 2.9% | Only Mistral Medium 3.5 measured |
| Code migration (Vals AI) | Not measured | 5.1% | Only Mistral Medium 3.5 measured |
| Terminal-Bench 2.1 (Vals AI) | Not measured | 39.0% | Only Mistral Medium 3.5 measured |
| LiveCodeBench (Artificial Analysis run) | 32.6% | Not measured | Only GPT-4.1 Nano measured |
| SciCode | Not measured | 40.2% | Only Mistral Medium 3.5 measured |
| Terminal-Bench Hard (Artificial Analysis run) | 3.8% | 33.3% | Mistral Medium 3.5 |
| WeirdML | 19.0% | Not measured | Only GPT-4.1 Nano measured |
| ALE-Bench | Not measured | 764.0 | Only Mistral Medium 3.5 measured |
| LMArena Text: coding | 1306 | 1460 | Mistral Medium 3.5 |
| LMArena Text: software and IT | 1305 | 1458 | Mistral Medium 3.5 |
| LMArena WebDev: React | Not measured | 1254 | Only Mistral Medium 3.5 measured |
| LMArena WebDev: HTML | Not measured | 1308 | Only Mistral Medium 3.5 measured |
| Neither model has a result on the other 22 boards here | |||
IntelligenceLike-for-likePublic boards, shown as published, kept out of the index02
| Benchmark | GPT-4.1 Nano | Mistral Medium 3.5 | Leader |
|---|---|---|---|
| AA Intelligence Index | 7.8 | 14.2 | Mistral Medium 3.5 |
| Epoch Capabilities Index | 129.5 | 141.4 | Mistral Medium 3.5 |
Agentic workLike-for-likePublic boards, shown as published, kept out of the index01
| Benchmark | GPT-4.1 Nano | Mistral Medium 3.5 | Leader |
|---|---|---|---|
| AA Agentic Index | Not measured | 7.0 | Only Mistral Medium 3.5 measured |
| Finance Agent (Vals AI) | Not measured | 32.1% | Only Mistral Medium 3.5 measured |
| Tax Agent Bench (Vals AI) | Not measured | 4.5% | Only Mistral Medium 3.5 measured |
| Legal Research Bench (Vals AI) | Not measured | 9.1% | Only Mistral Medium 3.5 measured |
| Excel Modeling Benchmark (Vals AI) | Not measured | 11.2% | Only Mistral Medium 3.5 measured |
| τ²-Bench Telecom | 17.3% | 94.2% | Mistral Medium 3.5 |
| Berkeley Function Calling Leaderboard (BFCL) | 33.1% | Not measured | Only GPT-4.1 Nano measured |
| Neither model has a result on the other 34 boards here | |||
ReasoningLike-for-likePublic boards, shown as published, kept out of the index16
| Benchmark | GPT-4.1 Nano | Mistral Medium 3.5 | Leader |
|---|---|---|---|
| GPQA Diamond | 50.8% | 34.9% | 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% | 74.8% | Mistral Medium 3.5 |
| Humanity's Last Exam (Artificial Analysis run) | 3.8% | 13.8% | Mistral Medium 3.5 |
| 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 | 1436 | Mistral Medium 3.5 |
| LMArena Text: expert | 1271 | 1432 | Mistral Medium 3.5 |
| LMArena Text: hard prompts in English | 1293 | 1445 | Mistral Medium 3.5 |
| LMArena Vision: diagrams | Not measured | 1254 | Only Mistral Medium 3.5 measured |
| LMArena Vision: homework | Not measured | 1230 | Only Mistral Medium 3.5 measured |
| Kagi LLM Benchmark | 33.3% | 41.4% | Mistral Medium 3.5 |
| Neither model has a result on the other 8 boards here | |||
MathLike-for-likePublic boards, shown as published, kept out of the index02
| Benchmark | GPT-4.1 Nano | Mistral Medium 3.5 | 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 |
| ProofBench | Not measured | 9.0% | Only Mistral Medium 3.5 measured |
| MATH level 5 | 70.0% | Not measured | Only GPT-4.1 Nano measured |
| LMArena Text: math | 1274 | 1431 | Mistral Medium 3.5 |
| LMArena Text: mathematical work | 1274 | 1440 | Mistral Medium 3.5 |
| Neither model has a result on the other 3 boards here | |||
KnowledgeLike-for-likePublic boards, shown as published, kept out of the index17
| Benchmark | GPT-4.1 Nano | Mistral Medium 3.5 | Leader |
|---|---|---|---|
| MMLU Pro | 63.5% | 75.3% | Mistral Medium 3.5 |
| LegalBench (Vals AI) | 61.1% | Not measured | Only GPT-4.1 Nano measured |
| CaseLaw (Vals AI) | Not measured | 44.2% | Only Mistral Medium 3.5 measured |
| CorpFin (Vals AI) | 42.1% | 58.8% | Mistral Medium 3.5 |
| TaxEval (Vals AI) | 60.8% | 68.0% | Mistral Medium 3.5 |
| MortgageTax (Vals AI) | 52.8% | 28.9% | GPT-4.1 Nano |
| MedQA (Vals AI) | 68.2% | Not measured | Only GPT-4.1 Nano measured |
| MedCode (Vals AI) | Not measured | 33.8% | Only Mistral Medium 3.5 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 | 1428 | Mistral Medium 3.5 |
| LMArena Text: medicine and healthcare | 1276 | 1409 | Mistral Medium 3.5 |
| LMArena Text: legal and government | 1300 | 1437 | Mistral Medium 3.5 |
| LMArena Text: business and finance | 1267 | 1426 | Mistral Medium 3.5 |
| 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-4.1 Nano | Mistral Medium 3.5 | Leader |
|---|---|---|---|
| Design Arena: websites | 972 | 1142 | Mistral Medium 3.5 |
| Design Arena: UI components | 913 | 1076 | Mistral Medium 3.5 |
| Design Arena: game dev | 970 | 1079 | Mistral Medium 3.5 |
| Design Arena: data viz | 891 | 1120 | Mistral Medium 3.5 |
| Design Arena: 3D | 924 | 1060 | Mistral Medium 3.5 |
| Design Arena: all code categories | 962 | 1121 | Mistral Medium 3.5 |
| Design Arena: SVG | Not measured | 949 | Only Mistral Medium 3.5 measured |
| Design Arena: ASCII art | Not measured | 1062 | Only Mistral Medium 3.5 measured |
| Neither model has a result on the other 1 board here | |||
GeneralLike-for-likePublic boards, shown as published, kept out of the index018
| Benchmark | GPT-4.1 Nano | Mistral Medium 3.5 | Leader |
|---|---|---|---|
| SAGE (Vals AI) | Not measured | 37.6% | Only Mistral Medium 3.5 measured |
| MedScribe (Vals AI) | Not measured | 67.7% | Only Mistral Medium 3.5 measured |
| Vals Multimodal Index | Not measured | 34.8% | Only Mistral Medium 3.5 measured |
| IFBench | 32.0% | 68.8% | Mistral Medium 3.5 |
| Long context reasoning (AA-LCR) | 20.3% | 69.3% | Mistral Medium 3.5 |
| DTBench | 52.5% | 75.5% | Mistral Medium 3.5 |
| LMCA | 5.5% | 26.1% | Mistral Medium 3.5 |
| Fiction.LiveBench | 25.0% | Not measured | Only GPT-4.1 Nano measured |
| Surface Evolver Bench | Not measured | 26.9% | Only Mistral Medium 3.5 measured |
| LMArena Text | 1285 | 1421 | Mistral Medium 3.5 |
| LMArena Text: instruction following | 1266 | 1415 | Mistral Medium 3.5 |
| LMArena Text: creative writing | 1260 | 1374 | Mistral Medium 3.5 |
| LMArena Vision | 1063 | 1222 | Mistral Medium 3.5 |
| LMArena Text: English | 1303 | 1433 | Mistral Medium 3.5 |
| LMArena Text: non-English | 1260 | 1404 | Mistral Medium 3.5 |
| LMArena Text: Chinese | 1270 | 1444 | Mistral Medium 3.5 |
| LMArena Text: German | 1288 | 1453 | Mistral Medium 3.5 |
| LMArena Text: Japanese | 1198 | Not measured | Only GPT-4.1 Nano measured |
| LMArena Text: Korean | Not measured | 1384 | Only Mistral Medium 3.5 measured |
| LMArena Text: French | Not measured | 1448 | Only Mistral Medium 3.5 measured |
| LMArena Text: Spanish | Not measured | 1411 | Only Mistral Medium 3.5 measured |
| LMArena Text: Russian | 1261 | 1395 | Mistral Medium 3.5 |
| LMArena Text: Polish | Not measured | 1430 | Only Mistral Medium 3.5 measured |
| LMArena Text: multi-turn | 1277 | 1423 | Mistral Medium 3.5 |
| LMArena Text: longer queries | 1283 | 1415 | Mistral Medium 3.5 |
| LMArena Text: writing and language | 1261 | 1384 | Mistral Medium 3.5 |
| LMArena Text: entertainment and media | 1243 | 1382 | Mistral Medium 3.5 |
| LMArena Vision: English | 1101 | 1240 | Mistral Medium 3.5 |
| LMArena Vision: Chinese | Not measured | 1244 | Only Mistral Medium 3.5 measured |
| LMArena Vision: reading text | Not measured | 1233 | Only Mistral Medium 3.5 measured |
| LMArena Vision: humor | Not measured | 1216 | Only Mistral Medium 3.5 measured |
| LMArena Vision: creative writing | Not measured | 1198 | Only Mistral Medium 3.5 measured |
| Neither model has a result on the other 14 boards here | |||
Image generationNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
| Benchmark | GPT-4.1 Nano | Mistral Medium 3.5 | 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 Medium 3.5 |
|---|---|---|
| Maker | OpenAI | Mistral AI |
| Weights | No public weights listed | No public weights listed |
| Released | April 14, 2025 | April 30, 2026 |
| Context window | 1.05M | 262K |
| Input $/Mtok | $0.10 | $1.50 |
| Output $/Mtok | $0.40 | $7.50 |
| Coding index | Not benchmarked | 25.5 |
| Independent measurements behind it | 0 | 3 |
| Cost per solved task | Not computable | $6.02 |
| Source | OpenRouter listing | OpenRouter listing |
Questions
Is GPT-4.1 Nano better than Mistral Medium 3.5 for coding?
Only Mistral Medium 3.5 has a coding score, 25.5 on the Vibe Coding Index; GPT-4.1 Nano has none, so the two cannot be ranked on coding. Pick GPT-4.1 Nano for output tokens 18.8x cheaper ($0.40 vs $7.50 per million) and a larger context window (1.05M against 262K tokens).
Which is cheaper, GPT-4.1 Nano or Mistral Medium 3.5?
GPT-4.1 Nano costs $0.10 per million input tokens and $0.40 per million output tokens; Mistral Medium 3.5 costs $1.50 and $7.50. Cost per solved task is known for Mistral Medium 3.5 only.
Which has the bigger context window, GPT-4.1 Nano or Mistral Medium 3.5?
GPT-4.1 Nano, with 1.05M tokens against 262K.
How many benchmarks is the GPT-4.1 Nano vs Mistral Medium 3.5 comparison based on?
48 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 Medium 3.5's on 3. Where only one model has a result, the page says so instead of naming a winner.