MiniMax M2 vs Mistral Small 3.1 24B
Mistral Small 3.1 24B costs 1.8x less per output token than MiniMax M2 ($0.555 vs $1.02 per million). Updated October 23, 2026; 26 benchmarks have a result for both.
MiniMax M2 or Mistral Small 3.1 24B: which to pick
Neither model has a coding score yet, so the two cannot be ranked on coding.
Pick MiniMax M2 for a larger context window (205K against 128K tokens).
- Shared results
- 26
- MiniMax M2 only
- 24
- Mistral Small 3.1 24B only
- 12
- Like-for-like categories
- 5 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
No call
Only MiniMax M2 has a AA Intelligence Index result, 18.6.
Directional only
Web design
Design Arena: websites
No call
Only MiniMax M2 has a Design Arena: websites result, 1143.
Directional only
Long documents
Listed context window
MiniMax M2
MiniMax M2 takes 205K tokens against 128K.
Listed by OpenRouter
Everyday cost
1K input + 500 output tokens
Mistral Small 3.1 24B
MiniMax M2 $0.0008, Mistral Small 3.1 24B $0.0006 per turn.
List prices
Cost per solved task
One SWE-bench Verified task, solved
No call
Only MiniMax M2 has the number, $0.702.
Directional only
At a glance
- Coding index
- MiniMax M2 not benchmarked, Mistral Small 3.1 24B not benchmarked.
- Widest category
- Math, 15.6 points apart on average over 2 shared benchmarks.
- Widest single gap
- LMArena Text: Chinese: MiniMax M2 1366 against Mistral Small 3.1 24B 1253, 18.9 points apart on a 0 to 100 scale.
- Output price
- MiniMax M2 $1.02 against Mistral Small 3.1 24B $0.555 per million output tokens, 1.8x apart.
- Context window
- MiniMax M2 takes 205K tokens, Mistral Small 3.1 24B 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 | MiniMax M2 | Bars | Mistral Small 3.1 24B | Ties |
|---|---|---|---|---|
| Coding | 2 | 0 | ||
| Reasoning | 3 | 0 | ||
| Math | 2 | 0 | ||
| Knowledge | 4 | 0 | ||
| General | 15 | 0 | ||
| All shared boards | 26 | 0 |
CodingLike-for-likeSix of these boards feed the Vibe Coding Index20
| Benchmark | MiniMax M2 | Mistral Small 3.1 24B | Leader |
|---|---|---|---|
| SWE-bench Verified | 61.0% | Not measured | Only MiniMax M2 measured |
| LMArena WebDev | 1297 | Not measured | Only MiniMax M2 measured |
| LiveCodeBench (Artificial Analysis run) | 82.6% | Not measured | Only MiniMax M2 measured |
| Terminal-Bench Hard (Artificial Analysis run) | 25.8% | Not measured | Only MiniMax M2 measured |
| Terminal-Bench | 30.0% | Not measured | Only MiniMax M2 measured |
| LMArena Text: coding | 1371 | 1309 | MiniMax M2 |
| LMArena Text: software and IT | 1373 | 1301 | MiniMax M2 |
| LMArena WebDev: HTML | 1306 | Not measured | Only MiniMax M2 measured |
| Neither model has a result on the other 35 boards here | |||
IntelligenceDirectional onlyPublic boards, shown as published, kept out of the index1 board, none shared
| Benchmark | MiniMax M2 | Mistral Small 3.1 24B | Leader |
|---|---|---|---|
| AA Intelligence Index | 18.6 | Not measured | Only MiniMax M2 measured |
| Neither model has a result on the other 1 board here | |||
Agentic workDirectional onlyPublic boards, shown as published, kept out of the index2 boards, none shared
| Benchmark | MiniMax M2 | Mistral Small 3.1 24B | Leader |
|---|---|---|---|
| τ²-Bench Telecom | 86.8% | Not measured | Only MiniMax M2 measured |
| Vending-Bench 2 | $161 | Not measured | Only MiniMax M2 measured |
| Neither model has a result on the other 40 boards here | |||
ReasoningLike-for-likePublic boards, shown as published, kept out of the index30
| Benchmark | MiniMax M2 | Mistral Small 3.1 24B | Leader |
|---|---|---|---|
| GPQA Diamond (Artificial Analysis run) | 77.7% | Not measured | Only MiniMax M2 measured |
| Humanity's Last Exam (Artificial Analysis run) | 13.7% | Not measured | Only MiniMax M2 measured |
| LMArena Text: hard prompts | 1357 | 1278 | MiniMax M2 |
| LMArena Text: expert | 1338 | 1257 | MiniMax M2 |
| LMArena Text: hard prompts in English | 1376 | 1290 | MiniMax M2 |
| LMArena Vision: diagrams | Not measured | 1119 | Only Mistral Small 3.1 24B measured |
| LMArena Vision: homework | Not measured | 1158 | Only Mistral Small 3.1 24B measured |
| Kagi LLM Benchmark | 57.8% | Not measured | Only MiniMax M2 measured |
| Neither model has a result on the other 15 boards here | |||
MathLike-for-likePublic boards, shown as published, kept out of the index20
| Benchmark | MiniMax M2 | Mistral Small 3.1 24B | Leader |
|---|---|---|---|
| AA Math Index | 78.3 | Not measured | Only MiniMax M2 measured |
| AIME 2025 (Artificial Analysis run) | 78.3% | Not measured | Only MiniMax M2 measured |
| LMArena Text: math | 1351 | 1261 | MiniMax M2 |
| LMArena Text: mathematical work | 1368 | 1271 | MiniMax M2 |
| Neither model has a result on the other 10 boards here | |||
KnowledgeLike-for-likePublic boards, shown as published, kept out of the index40
| Benchmark | MiniMax M2 | Mistral Small 3.1 24B | Leader |
|---|---|---|---|
| MMLU Pro (Artificial Analysis run) | 82.0% | Not measured | Only MiniMax M2 measured |
| LMArena Text: science | 1349 | 1265 | MiniMax M2 |
| LMArena Text: medicine and healthcare | 1367 | 1267 | MiniMax M2 |
| LMArena Text: legal and government | 1349 | 1270 | MiniMax M2 |
| LMArena Text: business and finance | 1336 | 1257 | MiniMax M2 |
| Neither model has a result on the other 15 boards here | |||
Web designDirectional onlyPublic boards, shown as published, kept out of the index7 boards, none shared
| Benchmark | MiniMax M2 | Mistral Small 3.1 24B | Leader |
|---|---|---|---|
| Design Arena: websites | 1143 | Not measured | Only MiniMax M2 measured |
| Design Arena: UI components | 1134 | Not measured | Only MiniMax M2 measured |
| Design Arena: game dev | 1118 | Not measured | Only MiniMax M2 measured |
| Design Arena: data viz | 1140 | Not measured | Only MiniMax M2 measured |
| Design Arena: 3D | 1098 | Not measured | Only MiniMax M2 measured |
| Design Arena: all code categories | 1135 | Not measured | Only MiniMax M2 measured |
| Design Arena: SVG | 1100 | Not measured | Only MiniMax M2 measured |
| Neither model has a result on the other 2 boards here | |||
GeneralLike-for-likePublic boards, shown as published, kept out of the index150
| Benchmark | MiniMax M2 | Mistral Small 3.1 24B | Leader |
|---|---|---|---|
| IFBench | 72.3% | Not measured | Only MiniMax M2 measured |
| Long context reasoning (AA-LCR) | 64.3% | Not measured | Only MiniMax M2 measured |
| LMArena Text | 1340 | 1277 | MiniMax M2 |
| LMArena Text: instruction following | 1329 | 1263 | MiniMax M2 |
| LMArena Text: creative writing | 1287 | 1252 | MiniMax M2 |
| LMArena Vision | Not measured | 1136 | Only Mistral Small 3.1 24B measured |
| LMArena Text: English | 1364 | 1295 | MiniMax M2 |
| LMArena Text: non-English | 1313 | 1255 | MiniMax M2 |
| LMArena Text: Chinese | 1366 | 1253 | MiniMax M2 |
| LMArena Text: German | 1354 | 1266 | MiniMax M2 |
| LMArena Text: Japanese | Not measured | 1208 | Only Mistral Small 3.1 24B measured |
| LMArena Text: Korean | Not measured | 1206 | Only Mistral Small 3.1 24B measured |
| LMArena Text: French | 1336 | 1273 | MiniMax M2 |
| LMArena Text: Spanish | 1326 | 1283 | MiniMax M2 |
| LMArena Text: Russian | 1330 | 1263 | MiniMax M2 |
| LMArena Text: Polish | 1311 | 1242 | MiniMax M2 |
| LMArena Text: multi-turn | 1361 | 1270 | MiniMax M2 |
| LMArena Text: longer queries | 1331 | 1298 | MiniMax M2 |
| LMArena Text: writing and language | 1295 | 1269 | MiniMax M2 |
| LMArena Text: entertainment and media | 1298 | 1250 | MiniMax M2 |
| LMArena Vision: English | Not measured | 1157 | Only Mistral Small 3.1 24B measured |
| LMArena Vision: Chinese | Not measured | 1155 | Only Mistral Small 3.1 24B measured |
| LMArena Vision: reading text | Not measured | 1132 | Only Mistral Small 3.1 24B measured |
| LMArena Vision: humor | Not measured | 1142 | Only Mistral Small 3.1 24B measured |
| LMArena Vision: captioning | Not measured | 1153 | Only Mistral Small 3.1 24B measured |
| LMArena Vision: recognizing things | Not measured | 1155 | Only Mistral Small 3.1 24B measured |
| LMArena Vision: creative writing | Not measured | 1123 | Only Mistral Small 3.1 24B measured |
| Neither model has a result on the other 21 boards here | |||
Image generationNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
| Benchmark | MiniMax M2 | Mistral Small 3.1 24B | 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
Mistral Small 3.1 24B costs less
Repository review
50K input + 3K output tokens
MiniMax M2 costs less
Agent loop
200K input + 10K output tokens
MiniMax M2 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 | Mistral Small 3.1 24B |
|---|---|---|
| Maker | MiniMax | Mistral AI |
| Weights | Open weights | Open weights |
| Released | October 23, 2025 | March 17, 2025 |
| Context window | 205K | 128K |
| Input $/Mtok | $0.255 | $0.351 |
| Output $/Mtok | $1.02 | $0.555 |
| Coding index | Not benchmarked | Not benchmarked |
| Independent measurements behind it | 1 | 0 |
| Cost per solved task | $0.702 | Not computable |
| Source | OpenRouter listing | OpenRouter listing |
Questions
Is MiniMax M2 better than Mistral Small 3.1 24B for coding?
Neither model has a coding score yet, so the two cannot be ranked on coding. Pick MiniMax M2 for a larger context window (205K against 128K tokens).
Which is cheaper, MiniMax M2 or Mistral Small 3.1 24B?
MiniMax M2 costs $0.255 per million input tokens and $1.02 per million output tokens; Mistral Small 3.1 24B costs $0.351 and $0.555. Cost per solved task is known for MiniMax M2 only.
Which has the bigger context window, MiniMax M2 or Mistral Small 3.1 24B?
MiniMax M2, with 205K tokens against 128K.
How many benchmarks is the MiniMax M2 vs Mistral Small 3.1 24B comparison based on?
26 benchmarks have a result for both models as of October 23, 2026. MiniMax M2's coding index rests on 1 independent benchmark and Mistral Small 3.1 24B's on 0. Where only one model has a result, the page says so instead of naming a winner.