MiniMax M2.1 vs Mistral Large 2407
MiniMax M2.1 leads Mistral Large 2407 on the Artificial Analysis Intelligence Index, 20.9 to 7.6, and MiniMax M2.1 costs 5.0x less per output token ($1.20 vs $6.00 per million). Updated October 22, 2026; 10 benchmarks have a result for both.
MiniMax M2.1 or Mistral Large 2407: which to pick
Neither model has a coding score yet, so the two cannot be ranked on coding.
Pick MiniMax M2.1 for output tokens 5.0x cheaper ($1.20 vs $6.00 per million) and a larger context window (205K against 131K tokens).
- Shared results
- 10
- MiniMax M2.1 only
- 24
- Mistral Large 2407 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
MiniMax M2.1
MiniMax M2.1 leads, 20.9 against 7.6.
Same board, both measured
Reasoning
GPQA Diamond
No call
Only MiniMax M2.1 has a GPQA Diamond result, 78.5%.
Directional only
Math
AIME
No call
Only MiniMax M2.1 has a AIME result, 77.9%.
Directional only
Knowledge
MMLU Pro
No call
Only MiniMax M2.1 has a MMLU Pro result, 87.0%.
Directional only
Web design
Design Arena: websites
No call
Only MiniMax M2.1 has a Design Arena: websites result, 1201.
Directional only
Long documents
Listed context window
MiniMax M2.1
MiniMax M2.1 takes 205K tokens against 131K.
Listed by OpenRouter
Everyday cost
1K input + 500 output tokens
MiniMax M2.1
MiniMax M2.1 $0.0009, Mistral Large 2407 $0.0050 per turn.
List prices
Cost per solved task
One SWE-bench Verified task, solved
No call
Only MiniMax M2.1 has the number, $0.59.
Directional only
At a glance
- Coding index
- MiniMax M2.1 not benchmarked, Mistral Large 2407 not benchmarked.
- Widest category
- Math, 82.7 points apart on average over 2 shared benchmarks.
- Widest single gap
- AA Math Index: MiniMax M2.1 82.7 against Mistral Large 2407 0.0, 82.7 points apart on a 0 to 100 scale.
- Output price
- MiniMax M2.1 $1.20 against Mistral Large 2407 $6.00 per million output tokens, 5.0x apart.
- Context window
- MiniMax M2.1 takes 205K 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 | MiniMax M2.1 | Bars | Mistral Large 2407 | Ties |
|---|---|---|---|---|
| Coding | 1 | 0 | ||
| Intelligence | 1 | 0 | ||
| Agentic work | 1 | 0 | ||
| Reasoning | 2 | 0 | ||
| Math | 2 | 0 | ||
| Knowledge | 1 | 0 | ||
| General | 2 | 0 | ||
| All shared boards | 10 | 0 |
CodingLike-for-likeSix of these boards feed the Vibe Coding Index10
| Benchmark | MiniMax M2.1 | Mistral Large 2407 | Leader |
|---|---|---|---|
| LiveCodeBench (Vals AI) | 81.8% | Not measured | Only MiniMax M2.1 measured |
| SWE-bench Verified (Vals AI) | 74.8% | Not measured | Only MiniMax M2.1 measured |
| LiveCodeBench (Artificial Analysis run) | 81.0% | 26.7% | MiniMax M2.1 |
| Terminal-Bench Hard (Artificial Analysis run) | 28.8% | Not measured | Only MiniMax M2.1 measured |
| Terminal-Bench | 36.6% | Not measured | Only MiniMax M2.1 measured |
| ALE-Bench | 623.8 | Not measured | Only MiniMax M2.1 measured |
| Neither model has a result on the other 33 boards here | |||
IntelligenceLike-for-likePublic boards, shown as published, kept out of the index10
| Benchmark | MiniMax M2.1 | Mistral Large 2407 | Leader |
|---|---|---|---|
| AA Intelligence Index | 20.9 | 7.6 | MiniMax M2.1 |
| Neither model has a result on the other 1 board here | |||
Agentic workLike-for-likePublic boards, shown as published, kept out of the index10
| Benchmark | MiniMax M2.1 | Mistral Large 2407 | Leader |
|---|---|---|---|
| τ²-Bench Telecom | 85.4% | 33.0% | MiniMax M2.1 |
| Neither model has a result on the other 40 boards here | |||
ReasoningLike-for-likePublic boards, shown as published, kept out of the index20
| Benchmark | MiniMax M2.1 | Mistral Large 2407 | Leader |
|---|---|---|---|
| GPQA Diamond | 78.5% | Not measured | Only MiniMax M2.1 measured |
| GPQA Diamond (Artificial Analysis run) | 83.0% | 47.2% | MiniMax M2.1 |
| Humanity's Last Exam (Artificial Analysis run) | 23.2% | 2.9% | MiniMax M2.1 |
| GPQA Diamond (Epoch AI run) | Not measured | 49.0% | Only Mistral Large 2407 measured |
| SimpleBench | Not measured | 22.5% | Only Mistral Large 2407 measured |
| Neither model has a result on the other 17 boards here | |||
MathLike-for-likePublic boards, shown as published, kept out of the index20
| Benchmark | MiniMax M2.1 | Mistral Large 2407 | 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 | 0.0 | MiniMax M2.1 |
| AIME 2025 (Artificial Analysis run) | 82.7% | 0.0% | MiniMax M2.1 |
| OTIS Mock AIME | Not measured | 8.5% | Only Mistral Large 2407 measured |
| MATH level 5 | Not measured | 44.8% | Only Mistral Large 2407 measured |
| Neither model has a result on the other 6 boards here | |||
KnowledgeLike-for-likePublic boards, shown as published, kept out of the index10
| Benchmark | MiniMax M2.1 | Mistral Large 2407 | Leader |
|---|---|---|---|
| MMLU Pro | 87.0% | Not measured | Only MiniMax M2.1 measured |
| LegalBench (Vals AI) | 75.5% | Not measured | Only MiniMax M2.1 measured |
| CaseLaw (Vals AI) | 55.8% | Not measured | Only MiniMax M2.1 measured |
| CorpFin (Vals AI) | 58.9% | Not measured | Only MiniMax M2.1 measured |
| TaxEval (Vals AI) | 66.3% | Not measured | Only MiniMax M2.1 measured |
| MedQA (Vals AI) | 91.2% | Not measured | Only MiniMax M2.1 measured |
| MedCode (Vals AI) | 34.1% | Not measured | Only MiniMax M2.1 measured |
| MMLU Pro (Artificial Analysis run) | 87.5% | 68.3% | MiniMax M2.1 |
| MMLU | Not measured | 80.0% | Only Mistral Large 2407 measured |
| Neither model has a result on the other 9 boards here | |||
Web designDirectional onlyPublic boards, shown as published, kept out of the index7 boards, none shared
| Benchmark | MiniMax M2.1 | Mistral Large 2407 | Leader |
|---|---|---|---|
| Design Arena: websites | 1201 | Not measured | Only MiniMax M2.1 measured |
| Design Arena: UI components | 1220 | Not measured | Only MiniMax M2.1 measured |
| Design Arena: game dev | 1134 | Not measured | Only MiniMax M2.1 measured |
| Design Arena: data viz | 1206 | Not measured | Only MiniMax M2.1 measured |
| Design Arena: 3D | 1164 | Not measured | Only MiniMax M2.1 measured |
| Design Arena: all code categories | 1190 | Not measured | Only MiniMax M2.1 measured |
| Design Arena: SVG | 1131 | Not measured | Only MiniMax M2.1 measured |
| Neither model has a result on the other 2 boards here | |||
GeneralLike-for-likePublic boards, shown as published, kept out of the index20
| Benchmark | MiniMax M2.1 | Mistral Large 2407 | Leader |
|---|---|---|---|
| MedScribe (Vals AI) | 80.8% | Not measured | Only MiniMax M2.1 measured |
| IFBench | 69.9% | 31.6% | MiniMax M2.1 |
| Long context reasoning (AA-LCR) | 67.7% | 2.0% | MiniMax M2.1 |
| DTBench | Not measured | 61.2% | Only Mistral Large 2407 measured |
| Creative writing (Lech Mazur) | Not measured | 69.0% | Only Mistral Large 2407 measured |
| Neither model has a result on the other 41 boards here | |||
Image generationNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
| Benchmark | MiniMax M2.1 | 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
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 Large 2407 |
|---|---|---|
| Maker | MiniMax | Mistral AI |
| Weights | Open weights | No public weights listed |
| Released | December 23, 2025 | November 19, 2024 |
| Context window | 205K | 131K |
| Input $/Mtok | $0.30 | $2.00 |
| Output $/Mtok | $1.20 | $6.00 |
| Coding index | Not benchmarked | Not benchmarked |
| Independent measurements behind it | 1 | 0 |
| Cost per solved task | $0.59 | Not computable |
| Source | OpenRouter listing | OpenRouter listing |
Questions
Is MiniMax M2.1 better than Mistral Large 2407 for coding?
Neither model has a coding score yet, so the two cannot be ranked on coding. Pick MiniMax M2.1 for output tokens 5.0x cheaper ($1.20 vs $6.00 per million) and a larger context window (205K against 131K tokens).
Which is cheaper, MiniMax M2.1 or Mistral Large 2407?
MiniMax M2.1 costs $0.30 per million input tokens and $1.20 per million output tokens; Mistral Large 2407 costs $2.00 and $6.00. Cost per solved task is known for MiniMax M2.1 only.
Which has the bigger context window, MiniMax M2.1 or Mistral Large 2407?
MiniMax M2.1, with 205K tokens against 131K.
How many benchmarks is the MiniMax M2.1 vs Mistral Large 2407 comparison based on?
10 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 Large 2407's on 0. Where only one model has a result, the page says so instead of naming a winner.