MiniMax M2.1 vs Mistral Medium 3.5
MiniMax M2.1 leads Mistral Medium 3.5 on the Artificial Analysis Intelligence Index, 20.9 to 14.2, and MiniMax M2.1 costs 6.3x less per output token ($1.20 vs $7.50 per million). Updated October 22, 2026; 23 benchmarks have a result for both.
MiniMax M2.1 or Mistral Medium 3.5: which to pick
Only Mistral Medium 3.5 has a coding score, 25.5 on the Vibe Coding Index; MiniMax M2.1 has none, so the two cannot be ranked on coding.
Pick MiniMax M2.1 for output tokens 6.3x cheaper ($1.20 vs $7.50 per million) and a lower cost per solved task ($0.59 against $6.02).
- Shared results
- 23
- MiniMax M2.1 only
- 11
- Mistral Medium 3.5 only
- 58
- 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
Only Mistral Medium 3.5 has a coding score, so no call is made.
One side not measured
Intelligence
AA Intelligence Index
MiniMax M2.1
MiniMax M2.1 leads, 20.9 against 14.2.
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
MiniMax M2.1
MiniMax M2.1 leads, 78.5% against 34.9%.
Same board, both measured
Math
AIME
No call
Only MiniMax M2.1 has a AIME result, 77.9%.
Directional only
Knowledge
MMLU Pro
MiniMax M2.1
MiniMax M2.1 leads, 87.0% against 75.3%.
Same board, both measured
Web design
Design Arena: websites
MiniMax M2.1
MiniMax M2.1 leads, 1201 against 1142.
Same board, both measured
Long documents
Listed context window
Mistral Medium 3.5
Mistral Medium 3.5 takes 262K tokens against 205K.
Listed by OpenRouter
Everyday cost
1K input + 500 output tokens
MiniMax M2.1
MiniMax M2.1 $0.0009, Mistral Medium 3.5 $0.0053 per turn.
List prices
Cost per solved task
One SWE-bench Verified task, solved
MiniMax M2.1
MiniMax M2.1 $0.59, Mistral Medium 3.5 $6.02.
Published attempt cost and pass rate
At a glance
- Coding index
- MiniMax M2.1 not benchmarked, Mistral Medium 3.5 25.5 from 3 benchmarks.
- Widest category
- Reasoning, 20.4 points apart on average over 3 shared benchmarks.
- Widest single gap
- GPQA Diamond: MiniMax M2.1 78.5% against Mistral Medium 3.5 34.9%, 43.7 points apart on a 0 to 100 scale.
- Output price
- MiniMax M2.1 $1.20 against Mistral Medium 3.5 $7.50 per million output tokens, 6.3x apart.
- Context window
- MiniMax M2.1 takes 205K 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 | MiniMax M2.1 | Bars | Mistral Medium 3.5 | Ties |
|---|---|---|---|---|
| Coding | 1 | 2 | ||
| Intelligence | 1 | 0 | ||
| Agentic work | 0 | 1 | ||
| Reasoning | 3 | 0 | ||
| Knowledge | 4 | 1 | ||
| Web design | 7 | 0 | ||
| General | 2 | 1 | ||
| All shared boards | 18 | 5 |
CodingLike-for-likeSix of these boards feed the Vibe Coding Index12
| Benchmark | MiniMax M2.1 | Mistral Medium 3.5 | Leader |
|---|---|---|---|
| LMArena WebDev | Not measured | 1264 | Only Mistral Medium 3.5 measured |
| AA Coding Index | Not measured | 46.9 | Only Mistral Medium 3.5 measured |
| LiveCodeBench (Vals AI) | 81.8% | Not measured | Only MiniMax M2.1 measured |
| SWE-bench Verified (Vals AI) | 74.8% | 66.4% | MiniMax M2.1 |
| 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) | 81.0% | Not measured | Only MiniMax M2.1 measured |
| SciCode | Not measured | 40.2% | Only Mistral Medium 3.5 measured |
| Terminal-Bench Hard (Artificial Analysis run) | 28.8% | 33.3% | Mistral Medium 3.5 |
| Terminal-Bench | 36.6% | Not measured | Only MiniMax M2.1 measured |
| ALE-Bench | 623.8 | 764.0 | Mistral Medium 3.5 |
| LMArena Text: coding | Not measured | 1460 | Only Mistral Medium 3.5 measured |
| LMArena Text: software and IT | Not measured | 1458 | Only Mistral Medium 3.5 measured |
| 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 23 boards here | |||
IntelligenceLike-for-likePublic boards, shown as published, kept out of the index10
| Benchmark | MiniMax M2.1 | Mistral Medium 3.5 | Leader |
|---|---|---|---|
| AA Intelligence Index | 20.9 | 14.2 | MiniMax M2.1 |
| Epoch Capabilities Index | Not measured | 141.4 | Only Mistral Medium 3.5 measured |
Agentic workLike-for-likePublic boards, shown as published, kept out of the index01
| Benchmark | MiniMax M2.1 | 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 | 85.4% | 94.2% | Mistral Medium 3.5 |
| Neither model has a result on the other 35 boards here | |||
ReasoningLike-for-likePublic boards, shown as published, kept out of the index30
| Benchmark | MiniMax M2.1 | Mistral Medium 3.5 | Leader |
|---|---|---|---|
| GPQA Diamond | 78.5% | 34.9% | MiniMax M2.1 |
| GPQA Diamond (Artificial Analysis run) | 83.0% | 74.8% | MiniMax M2.1 |
| Humanity's Last Exam (Artificial Analysis run) | 23.2% | 13.8% | MiniMax M2.1 |
| LMArena Text: hard prompts | Not measured | 1436 | Only Mistral Medium 3.5 measured |
| LMArena Text: expert | Not measured | 1432 | Only Mistral Medium 3.5 measured |
| LMArena Text: hard prompts in English | Not measured | 1445 | Only Mistral Medium 3.5 measured |
| 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 | Not measured | 41.4% | Only Mistral Medium 3.5 measured |
| Neither model has a result on the other 13 boards here | |||
MathDirectional onlyPublic boards, shown as published, kept out of the index8 boards, none shared
| Benchmark | MiniMax M2.1 | Mistral Medium 3.5 | Leader |
|---|---|---|---|
| AIME | 77.9% | Not measured | Only MiniMax M2.1 measured |
| MATH 500 (Vals AI) | 89.0% | Not measured | Only MiniMax M2.1 measured |
| MGSM (Vals AI) | 87.8% | Not measured | Only MiniMax M2.1 measured |
| AA Math Index | 82.7 | Not measured | Only MiniMax M2.1 measured |
| AIME 2025 (Artificial Analysis run) | 82.7% | Not measured | Only MiniMax M2.1 measured |
| ProofBench | Not measured | 9.0% | Only Mistral Medium 3.5 measured |
| LMArena Text: math | Not measured | 1431 | Only Mistral Medium 3.5 measured |
| LMArena Text: mathematical work | Not measured | 1440 | Only Mistral Medium 3.5 measured |
| Neither model has a result on the other 5 boards here | |||
KnowledgeLike-for-likePublic boards, shown as published, kept out of the index41
| Benchmark | MiniMax M2.1 | Mistral Medium 3.5 | Leader |
|---|---|---|---|
| MMLU Pro | 87.0% | 75.3% | MiniMax M2.1 |
| LegalBench (Vals AI) | 75.5% | Not measured | Only MiniMax M2.1 measured |
| CaseLaw (Vals AI) | 55.8% | 44.2% | MiniMax M2.1 |
| CorpFin (Vals AI) | 58.9% | 58.8% | MiniMax M2.1 |
| TaxEval (Vals AI) | 66.3% | 68.0% | Mistral Medium 3.5 |
| MortgageTax (Vals AI) | Not measured | 28.9% | Only Mistral Medium 3.5 measured |
| MedQA (Vals AI) | 91.2% | Not measured | Only MiniMax M2.1 measured |
| MedCode (Vals AI) | 34.1% | 33.8% | MiniMax M2.1 |
| MMLU Pro (Artificial Analysis run) | 87.5% | Not measured | Only MiniMax M2.1 measured |
| LMArena Text: science | Not measured | 1428 | Only Mistral Medium 3.5 measured |
| LMArena Text: medicine and healthcare | Not measured | 1409 | Only Mistral Medium 3.5 measured |
| LMArena Text: legal and government | Not measured | 1437 | Only Mistral Medium 3.5 measured |
| LMArena Text: business and finance | Not measured | 1426 | Only Mistral Medium 3.5 measured |
| Neither model has a result on the other 5 boards here | |||
Web designLike-for-likePublic boards, shown as published, kept out of the index70
| Benchmark | MiniMax M2.1 | Mistral Medium 3.5 | Leader |
|---|---|---|---|
| Design Arena: websites | 1201 | 1142 | MiniMax M2.1 |
| Design Arena: UI components | 1220 | 1076 | MiniMax M2.1 |
| Design Arena: game dev | 1134 | 1079 | MiniMax M2.1 |
| Design Arena: data viz | 1206 | 1120 | MiniMax M2.1 |
| Design Arena: 3D | 1164 | 1060 | MiniMax M2.1 |
| Design Arena: all code categories | 1190 | 1121 | MiniMax M2.1 |
| Design Arena: SVG | 1131 | 949 | MiniMax M2.1 |
| 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 index21
| Benchmark | MiniMax M2.1 | Mistral Medium 3.5 | Leader |
|---|---|---|---|
| SAGE (Vals AI) | Not measured | 37.6% | Only Mistral Medium 3.5 measured |
| MedScribe (Vals AI) | 80.8% | 67.7% | MiniMax M2.1 |
| Vals Multimodal Index | Not measured | 34.8% | Only Mistral Medium 3.5 measured |
| IFBench | 69.9% | 68.8% | MiniMax M2.1 |
| Long context reasoning (AA-LCR) | 67.7% | 69.3% | Mistral Medium 3.5 |
| DTBench | Not measured | 75.5% | Only Mistral Medium 3.5 measured |
| LMCA | Not measured | 26.1% | Only Mistral Medium 3.5 measured |
| Surface Evolver Bench | Not measured | 26.9% | Only Mistral Medium 3.5 measured |
| LMArena Text | Not measured | 1421 | Only Mistral Medium 3.5 measured |
| LMArena Text: instruction following | Not measured | 1415 | Only Mistral Medium 3.5 measured |
| LMArena Text: creative writing | Not measured | 1374 | Only Mistral Medium 3.5 measured |
| LMArena Vision | Not measured | 1222 | Only Mistral Medium 3.5 measured |
| LMArena Text: English | Not measured | 1433 | Only Mistral Medium 3.5 measured |
| LMArena Text: non-English | Not measured | 1404 | Only Mistral Medium 3.5 measured |
| LMArena Text: Chinese | Not measured | 1444 | Only Mistral Medium 3.5 measured |
| LMArena Text: German | Not measured | 1453 | Only Mistral Medium 3.5 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 | Not measured | 1395 | Only Mistral Medium 3.5 measured |
| LMArena Text: Polish | Not measured | 1430 | Only Mistral Medium 3.5 measured |
| LMArena Text: multi-turn | Not measured | 1423 | Only Mistral Medium 3.5 measured |
| LMArena Text: longer queries | Not measured | 1415 | Only Mistral Medium 3.5 measured |
| LMArena Text: writing and language | Not measured | 1384 | Only Mistral Medium 3.5 measured |
| LMArena Text: entertainment and media | Not measured | 1382 | Only Mistral Medium 3.5 measured |
| LMArena Vision: English | Not measured | 1240 | Only Mistral Medium 3.5 measured |
| 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 16 boards here | |||
Image generationNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
| Benchmark | MiniMax M2.1 | 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
MiniMax M2.1 costs less
Repository review
50K input + 3K output tokens
MiniMax M2.1 costs less
Agent loop
200K input + 10K output tokens
MiniMax M2.1 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 | MiniMax M2.1 | Mistral Medium 3.5 |
|---|---|---|
| Maker | MiniMax | Mistral AI |
| Weights | Open weights | No public weights listed |
| Released | December 23, 2025 | April 30, 2026 |
| Context window | 205K | 262K |
| Input $/Mtok | $0.30 | $1.50 |
| Output $/Mtok | $1.20 | $7.50 |
| Coding index | Not benchmarked | 25.5 |
| Independent measurements behind it | 1 | 3 |
| Cost per solved task | $0.59 | $6.02 |
| Source | OpenRouter listing | OpenRouter listing |
Questions
Is MiniMax M2.1 better than Mistral Medium 3.5 for coding?
Only Mistral Medium 3.5 has a coding score, 25.5 on the Vibe Coding Index; MiniMax M2.1 has none, so the two cannot be ranked on coding. Pick MiniMax M2.1 for output tokens 6.3x cheaper ($1.20 vs $7.50 per million) and a lower cost per solved task ($0.59 against $6.02).
Which is cheaper, MiniMax M2.1 or Mistral Medium 3.5?
MiniMax M2.1 costs $0.30 per million input tokens and $1.20 per million output tokens; Mistral Medium 3.5 costs $1.50 and $7.50. Per solved SWE-bench Verified task, MiniMax M2.1 costs $0.59 and Mistral Medium 3.5 $6.02.
Which has the bigger context window, MiniMax M2.1 or Mistral Medium 3.5?
Mistral Medium 3.5, with 262K tokens against 205K.
How many benchmarks is the MiniMax M2.1 vs Mistral Medium 3.5 comparison based on?
23 benchmarks have a result for both models as of October 22, 2026. MiniMax M2.1's coding index rests on 1 independent benchmark and Mistral Medium 3.5's on 3. Where only one model has a result, the page says so instead of naming a winner.