Mistral Small 3 vs MiMo-V2.5-Pro
MiMo-V2.5-Pro leads Mistral Small 3 on the Artificial Analysis Intelligence Index, 26.0 to 6.7, and Mistral Small 3 costs 11.4x less per output token ($0.08 vs $0.9152 per million). Updated October 22, 2026; 32 benchmarks have a result for both.
Mistral Small 3 or MiMo-V2.5-Pro: which to pick
Only MiMo-V2.5-Pro has a coding score, 31.6 on the Vibe Coding Index; Mistral Small 3 has none, so the two cannot be ranked on coding.
Pick Mistral Small 3 for output tokens 11.4x cheaper ($0.08 vs $0.9152 per million).
- Shared results
- 32
- Mistral Small 3 only
- 4
- MiMo-V2.5-Pro only
- 38
- 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 MiMo-V2.5-Pro has a coding score, so no call is made.
One side not measured
Intelligence
AA Intelligence Index
MiMo-V2.5-Pro
MiMo-V2.5-Pro leads, 26.0 against 6.7.
Same board, both measured
Agentic work
AA Agentic Index
No call
Only MiMo-V2.5-Pro has a AA Agentic Index result, 21.3.
Directional only
Reasoning
GPQA Diamond
No call
Only MiMo-V2.5-Pro has a GPQA Diamond result, 82.6%.
Directional only
Knowledge
MMLU Pro
No call
Only MiMo-V2.5-Pro has a MMLU Pro result, 84.6%.
Directional only
Web design
Design Arena: websites
No call
Only MiMo-V2.5-Pro has a Design Arena: websites result, 1266.
Directional only
Long documents
Listed context window
MiMo-V2.5-Pro
MiMo-V2.5-Pro takes 1.05M tokens against 33K.
Listed by OpenRouter
Everyday cost
1K input + 500 output tokens
Mistral Small 3
Mistral Small 3 $0.0001, MiMo-V2.5-Pro $0.0009 per turn.
List prices
Cost per solved task
One SWE-bench Verified task, solved
No call
Only MiMo-V2.5-Pro has the number, $0.03.
Directional only
At a glance
- Coding index
- Mistral Small 3 not benchmarked, MiMo-V2.5-Pro 31.6 from 4 benchmarks.
- Widest category
- Agentic work, 74.6 points apart on average over 1 shared benchmark.
- Widest single gap
- Long context reasoning (AA-LCR): Mistral Small 3 0.0% against MiMo-V2.5-Pro 79.7%, 79.7 points apart on a 0 to 100 scale.
- Output price
- Mistral Small 3 $0.08 against MiMo-V2.5-Pro $0.9152 per million output tokens, 11.4x apart.
- Context window
- Mistral Small 3 takes 33K tokens, MiMo-V2.5-Pro 1.05M.
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 | Mistral Small 3 | Bars | MiMo-V2.5-Pro | Ties |
|---|---|---|---|---|
| Coding | 0 | 2 | ||
| Intelligence | 0 | 1 | ||
| Agentic work | 0 | 1 | ||
| Reasoning | 0 | 5 | ||
| Math | 0 | 2 | ||
| Knowledge | 0 | 4 | ||
| General | 0 | 17 | ||
| All shared boards | 0 | 32 |
CodingLike-for-likeSix of these boards feed the Vibe Coding Index02
| Benchmark | Mistral Small 3 | MiMo-V2.5-Pro | Leader |
|---|---|---|---|
| LMArena WebDev | Not measured | 1480 | Only MiMo-V2.5-Pro measured |
| AA Coding Index | Not measured | 60.2 | Only MiMo-V2.5-Pro measured |
| LiveCodeBench (Vals AI) | Not measured | 81.3% | Only MiMo-V2.5-Pro measured |
| SWE-bench Verified (Vals AI) | Not measured | 74.0% | Only MiMo-V2.5-Pro measured |
| Terminal-Bench 4 (Vals AI) | Not measured | 0.5% | Only MiMo-V2.5-Pro measured |
| Vibe Code Bench (Vals AI) | Not measured | 34.1% | Only MiMo-V2.5-Pro measured |
| Code migration (Vals AI) | Not measured | 21.6% | Only MiMo-V2.5-Pro measured |
| Terminal-Bench 2.1 (Vals AI) | Not measured | 57.3% | Only MiMo-V2.5-Pro measured |
| LiveCodeBench (Artificial Analysis run) | 25.2% | Not measured | Only Mistral Small 3 measured |
| SciCode | Not measured | 50.6% | Only MiMo-V2.5-Pro measured |
| Terminal-Bench Hard (Artificial Analysis run) | Not measured | 43.2% | Only MiMo-V2.5-Pro measured |
| ALE-Bench | Not measured | 899.8 | Only MiMo-V2.5-Pro measured |
| LMArena Text: coding | 1246 | 1501 | MiMo-V2.5-Pro |
| LMArena Text: software and IT | 1248 | 1495 | MiMo-V2.5-Pro |
| LMArena WebDev: React | Not measured | 1469 | Only MiMo-V2.5-Pro measured |
| LMArena WebDev: HTML | Not measured | 1473 | Only MiMo-V2.5-Pro measured |
| Neither model has a result on the other 23 boards here | |||
IntelligenceLike-for-likePublic boards, shown as published, kept out of the index01
| Benchmark | Mistral Small 3 | MiMo-V2.5-Pro | Leader |
|---|---|---|---|
| AA Intelligence Index | 6.7 | 26.0 | MiMo-V2.5-Pro |
| Neither model has a result on the other 1 board here | |||
Agentic workLike-for-likePublic boards, shown as published, kept out of the index01
| Benchmark | Mistral Small 3 | MiMo-V2.5-Pro | Leader |
|---|---|---|---|
| AA Agentic Index | Not measured | 21.3 | Only MiMo-V2.5-Pro measured |
| Finance Agent (Vals AI) | Not measured | 41.5% | Only MiMo-V2.5-Pro measured |
| Tax Agent Bench (Vals AI) | Not measured | 11.5% | Only MiMo-V2.5-Pro measured |
| Legal Research Bench (Vals AI) | Not measured | 15.9% | Only MiMo-V2.5-Pro measured |
| Excel Modeling Benchmark (Vals AI) | Not measured | 55.2% | Only MiMo-V2.5-Pro measured |
| τ²-Bench Telecom | 19.6% | 94.2% | MiMo-V2.5-Pro |
| Neither model has a result on the other 35 boards here | |||
ReasoningLike-for-likePublic boards, shown as published, kept out of the index05
| Benchmark | Mistral Small 3 | MiMo-V2.5-Pro | Leader |
|---|---|---|---|
| GPQA Diamond | Not measured | 82.6% | Only MiMo-V2.5-Pro measured |
| GPQA Diamond (Artificial Analysis run) | 46.2% | 86.6% | MiMo-V2.5-Pro |
| Humanity's Last Exam (Artificial Analysis run) | 3.8% | 35.7% | MiMo-V2.5-Pro |
| LMArena Text: hard prompts | 1233 | 1486 | MiMo-V2.5-Pro |
| LMArena Text: expert | 1202 | 1501 | MiMo-V2.5-Pro |
| LMArena Text: hard prompts in English | 1243 | 1494 | MiMo-V2.5-Pro |
| Neither model has a result on the other 16 boards here | |||
MathLike-for-likePublic boards, shown as published, kept out of the index02
| Benchmark | Mistral Small 3 | MiMo-V2.5-Pro | Leader |
|---|---|---|---|
| AA Math Index | 4.3 | Not measured | Only Mistral Small 3 measured |
| AIME 2025 (Artificial Analysis run) | 4.3% | Not measured | Only Mistral Small 3 measured |
| ProofBench | Not measured | 22.0% | Only MiMo-V2.5-Pro measured |
| LMArena Text: math | 1240 | 1481 | MiMo-V2.5-Pro |
| LMArena Text: mathematical work | 1243 | 1491 | MiMo-V2.5-Pro |
| Neither model has a result on the other 8 boards here | |||
KnowledgeLike-for-likePublic boards, shown as published, kept out of the index04
| Benchmark | Mistral Small 3 | MiMo-V2.5-Pro | Leader |
|---|---|---|---|
| MMLU Pro | Not measured | 84.6% | Only MiMo-V2.5-Pro measured |
| LegalBench (Vals AI) | Not measured | 77.1% | Only MiMo-V2.5-Pro measured |
| CorpFin (Vals AI) | Not measured | 61.4% | Only MiMo-V2.5-Pro measured |
| TaxEval (Vals AI) | Not measured | 73.8% | Only MiMo-V2.5-Pro measured |
| MedCode (Vals AI) | Not measured | 32.5% | Only MiMo-V2.5-Pro measured |
| MMLU Pro (Artificial Analysis run) | 65.2% | Not measured | Only Mistral Small 3 measured |
| LMArena Text: science | 1235 | 1482 | MiMo-V2.5-Pro |
| LMArena Text: medicine and healthcare | 1215 | 1473 | MiMo-V2.5-Pro |
| LMArena Text: legal and government | 1219 | 1474 | MiMo-V2.5-Pro |
| LMArena Text: business and finance | 1205 | 1466 | MiMo-V2.5-Pro |
| Neither model has a result on the other 8 boards here | |||
Web designDirectional onlyPublic boards, shown as published, kept out of the index8 boards, none shared
| Benchmark | Mistral Small 3 | MiMo-V2.5-Pro | Leader |
|---|---|---|---|
| Design Arena: websites | Not measured | 1266 | Only MiMo-V2.5-Pro measured |
| Design Arena: UI components | Not measured | 1246 | Only MiMo-V2.5-Pro measured |
| Design Arena: game dev | Not measured | 1261 | Only MiMo-V2.5-Pro measured |
| Design Arena: data viz | Not measured | 1257 | Only MiMo-V2.5-Pro measured |
| Design Arena: 3D | Not measured | 1243 | Only MiMo-V2.5-Pro measured |
| Design Arena: all code categories | Not measured | 1265 | Only MiMo-V2.5-Pro measured |
| Design Arena: SVG | Not measured | 1181 | Only MiMo-V2.5-Pro measured |
| Design Arena: ASCII art | Not measured | 1156 | Only MiMo-V2.5-Pro measured |
| Neither model has a result on the other 1 board here | |||
GeneralLike-for-likePublic boards, shown as published, kept out of the index017
| Benchmark | Mistral Small 3 | MiMo-V2.5-Pro | Leader |
|---|---|---|---|
| MedScribe (Vals AI) | Not measured | 83.7% | Only MiMo-V2.5-Pro measured |
| IFBench | 26.4% | 79.9% | MiMo-V2.5-Pro |
| Long context reasoning (AA-LCR) | 0.0% | 79.7% | MiMo-V2.5-Pro |
| DTBench | Not measured | 84.5% | Only MiMo-V2.5-Pro measured |
| LMCA | Not measured | 29.5% | Only MiMo-V2.5-Pro measured |
| LMArena Text | 1234 | 1464 | MiMo-V2.5-Pro |
| LMArena Text: instruction following | 1214 | 1475 | MiMo-V2.5-Pro |
| LMArena Text: creative writing | 1194 | 1438 | MiMo-V2.5-Pro |
| LMArena Text: English | 1256 | 1477 | MiMo-V2.5-Pro |
| LMArena Text: non-English | 1198 | 1448 | MiMo-V2.5-Pro |
| LMArena Text: Chinese | 1204 | 1507 | MiMo-V2.5-Pro |
| LMArena Text: German | 1211 | 1459 | MiMo-V2.5-Pro |
| LMArena Text: Japanese | 1111 | 1410 | MiMo-V2.5-Pro |
| LMArena Text: Korean | 1188 | 1435 | MiMo-V2.5-Pro |
| LMArena Text: French | 1204 | 1487 | MiMo-V2.5-Pro |
| LMArena Text: Spanish | Not measured | 1469 | Only MiMo-V2.5-Pro measured |
| LMArena Text: Russian | 1216 | 1449 | MiMo-V2.5-Pro |
| LMArena Text: Polish | Not measured | 1464 | Only MiMo-V2.5-Pro measured |
| LMArena Text: multi-turn | 1217 | 1476 | MiMo-V2.5-Pro |
| LMArena Text: longer queries | 1246 | 1481 | MiMo-V2.5-Pro |
| LMArena Text: writing and language | 1214 | 1451 | MiMo-V2.5-Pro |
| LMArena Text: entertainment and media | 1184 | 1438 | MiMo-V2.5-Pro |
| Neither model has a result on the other 24 boards here | |||
Image generationNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
| Benchmark | Mistral Small 3 | MiMo-V2.5-Pro | 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 costs less
Repository review
50K input + 3K output tokens
Mistral Small 3 costs less
Agent loop
200K input + 10K output tokens
Mistral Small 3 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 | Mistral Small 3 | Xiaomi MiMo-V2.5-Pro |
|---|---|---|
| Maker | Mistral AI | Xiaomi |
| Weights | Open weights | Open weights |
| Released | January 30, 2025 | April 22, 2026 |
| Context window | 33K | 1.05M |
| Input $/Mtok | $0.05 | $0.4575 |
| Output $/Mtok | $0.08 | $0.9152 |
| Coding index | Not benchmarked | 31.6 |
| Independent measurements behind it | 0 | 4 |
| Cost per solved task | Not computable | $0.03 |
| Source | OpenRouter listing | OpenRouter listing |
Questions
Is Mistral Small 3 better than MiMo-V2.5-Pro for coding?
Only MiMo-V2.5-Pro has a coding score, 31.6 on the Vibe Coding Index; Mistral Small 3 has none, so the two cannot be ranked on coding. Pick Mistral Small 3 for output tokens 11.4x cheaper ($0.08 vs $0.9152 per million).
Which is cheaper, Mistral Small 3 or MiMo-V2.5-Pro?
Mistral Small 3 costs $0.05 per million input tokens and $0.08 per million output tokens; MiMo-V2.5-Pro costs $0.4575 and $0.9152. Cost per solved task is known for MiMo-V2.5-Pro only.
Which has the bigger context window, Mistral Small 3 or MiMo-V2.5-Pro?
MiMo-V2.5-Pro, with 1.05M tokens against 33K.
How many benchmarks is the Mistral Small 3 vs MiMo-V2.5-Pro comparison based on?
32 benchmarks have a result for both models as of October 22, 2026. Mistral Small 3's coding index rests on 0 independent benchmarks and MiMo-V2.5-Pro's on 4. Where only one model has a result, the page says so instead of naming a winner.