MiniMax M1 vs MiMo-V2.6-Pro
MiMo-V2.6-Pro leads MiniMax M1 on the Artificial Analysis Intelligence Index, 46.3 to 11.7, and MiMo-V2.6-Pro costs 2.5x less per output token ($0.87 vs $2.20 per million). Updated October 22, 2026; 23 benchmarks have a result for both.
MiniMax M1 or MiMo-V2.6-Pro: which to pick
Only MiMo-V2.6-Pro has a coding score, 65.4 on the Vibe Coding Index; MiniMax M1 has none, so the two cannot be ranked on coding.
Compare them on price, context and the boards below.
- Shared results
- 23
- MiniMax M1 only
- 6
- MiMo-V2.6-Pro only
- 50
- Like-for-like categories
- 6 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-V2.6-Pro has a coding score, so no call is made.
One side not measured
Intelligence
AA Intelligence Index
MiMo-V2.6-Pro
MiMo-V2.6-Pro leads, 46.3 against 11.7.
Same board, both measured
Web design
Design Arena: websites
No call
Only MiMo-V2.6-Pro has a Design Arena: websites result, 1307.
Directional only
General
Vals Index
No call
Only MiMo-V2.6-Pro has a Vals Index result, 57.8%.
Directional only
Long documents
Listed context window
MiMo-V2.6-Pro
MiMo-V2.6-Pro takes 1.05M tokens against 1M.
Listed by OpenRouter
Everyday cost
1K input + 500 output tokens
MiMo-V2.6-Pro
MiniMax M1 $0.0015, MiMo-V2.6-Pro $0.0009 per turn.
List prices
At a glance
- Coding index
- MiniMax M1 not benchmarked, MiMo-V2.6-Pro 65.4 from 4 benchmarks.
- Widest category
- Intelligence, 34.6 points apart on average over 1 shared benchmark.
- Widest single gap
- LMArena Text: expert: MiniMax M1 1317 against MiMo-V2.6-Pro 1541, 37.4 points apart on a 0 to 100 scale.
- Output price
- MiniMax M1 $2.20 against MiMo-V2.6-Pro $0.87 per million output tokens, 2.5x apart.
- Context window
- MiniMax M1 takes 1M tokens, MiMo-V2.6-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 | MiniMax M1 | Bars | MiMo-V2.6-Pro | Ties |
|---|---|---|---|---|
| Coding | 0 | 2 | ||
| Intelligence | 0 | 1 | ||
| Reasoning | 0 | 3 | ||
| Math | 0 | 2 | ||
| Knowledge | 0 | 4 | ||
| General | 0 | 11 | ||
| All shared boards | 0 | 23 |
CodingLike-for-likeSix of these boards feed the Vibe Coding Index02
| Benchmark | MiniMax M1 | MiMo-V2.6-Pro | Leader |
|---|---|---|---|
| LiveBench coding | Not measured | 81.5% | Only MiMo-V2.6-Pro measured |
| LMArena WebDev | Not measured | 1625 | Only MiMo-V2.6-Pro measured |
| Terminal-Bench 4 (Vals AI) | Not measured | 31.3% | Only MiMo-V2.6-Pro measured |
| Vibe Code Bench (Vals AI) | Not measured | 85.2% | Only MiMo-V2.6-Pro measured |
| Code migration (Vals AI) | Not measured | 43.0% | Only MiMo-V2.6-Pro measured |
| ProgramBench (Vals AI) | Not measured | 0.5% | Only MiMo-V2.6-Pro measured |
| IOI (Vals AI) | Not measured | 39.3% | Only MiMo-V2.6-Pro measured |
| Terminal-Bench 2.1 (Vals AI) | Not measured | 67.8% | Only MiMo-V2.6-Pro measured |
| SWE-bench Multimodal (Vals AI) | Not measured | 41.3% | Only MiMo-V2.6-Pro measured |
| CyberBench (Vals AI) | Not measured | 72.9% | Only MiMo-V2.6-Pro measured |
| SciCode | Not measured | 60.9% | Only MiMo-V2.6-Pro measured |
| ALE-Bench | Not measured | 1158.1 | Only MiMo-V2.6-Pro measured |
| LMArena Text: coding | 1359 | 1533 | MiMo-V2.6-Pro |
| LMArena Text: software and IT | 1359 | 1528 | MiMo-V2.6-Pro |
| LMArena WebDev: React | Not measured | 1639 | Only MiMo-V2.6-Pro measured |
| LMArena WebDev: HTML | Not measured | 1616 | Only MiMo-V2.6-Pro measured |
| LMArena WebDev: from an image | Not measured | 1598 | Only MiMo-V2.6-Pro measured |
| Neither model has a result on the other 26 boards here | |||
IntelligenceLike-for-likePublic boards, shown as published, kept out of the index01
| Benchmark | MiniMax M1 | MiMo-V2.6-Pro | Leader |
|---|---|---|---|
| AA Intelligence Index | 11.7 | 46.3 | MiMo-V2.6-Pro |
| Neither model has a result on the other 1 board here | |||
Agentic workDirectional onlyPublic boards, shown as published, kept out of the index12 boards, none shared
| Benchmark | MiniMax M1 | MiMo-V2.6-Pro | Leader |
|---|---|---|---|
| Design Arena: agents building web apps | Not measured | 1290 | Only MiMo-V2.6-Pro measured |
| LiveBench research | Not measured | 76.7% | Only MiMo-V2.6-Pro measured |
| LiveBench browser | Not measured | 88.0% | Only MiMo-V2.6-Pro measured |
| LiveBench long agentic | Not measured | 47.5% | Only MiMo-V2.6-Pro measured |
| SREBench (Vals AI) | Not measured | 3.0% | Only MiMo-V2.6-Pro measured |
| Finance Agent (Vals AI) | Not measured | 57.3% | Only MiMo-V2.6-Pro measured |
| Tax Agent Bench (Vals AI) | Not measured | 32.4% | Only MiMo-V2.6-Pro measured |
| Legal Research Bench (Vals AI) | Not measured | 47.1% | Only MiMo-V2.6-Pro measured |
| Harvey's Legal Agent Benchmark (Vals AI) | Not measured | 13.8% | Only MiMo-V2.6-Pro measured |
| Excel Modeling Benchmark (Vals AI) | Not measured | 62.9% | Only MiMo-V2.6-Pro measured |
| Terminal-Bench-Science (Vals AI) | Not measured | 2.9% | Only MiMo-V2.6-Pro measured |
| APEX-Agents | Not measured | 54.4% | Only MiMo-V2.6-Pro measured |
| Neither model has a result on the other 30 boards here | |||
ReasoningLike-for-likePublic boards, shown as published, kept out of the index03
| Benchmark | MiniMax M1 | MiMo-V2.6-Pro | Leader |
|---|---|---|---|
| LiveBench reasoning | Not measured | 79.6% | Only MiMo-V2.6-Pro measured |
| MysteryMechanism (Vals AI) | Not measured | 15.3% | Only MiMo-V2.6-Pro measured |
| Humanity's Last Exam (Artificial Analysis run) | Not measured | 49.4% | Only MiMo-V2.6-Pro measured |
| LMArena Text: hard prompts | 1339 | 1511 | MiMo-V2.6-Pro |
| LMArena Text: expert | 1317 | 1541 | MiMo-V2.6-Pro |
| LMArena Text: hard prompts in English | 1353 | 1520 | MiMo-V2.6-Pro |
| LMArena Vision: diagrams | Not measured | 1281 | Only MiMo-V2.6-Pro measured |
| LMArena Vision: homework | Not measured | 1264 | Only MiMo-V2.6-Pro measured |
| Neither model has a result on the other 15 boards here | |||
MathLike-for-likePublic boards, shown as published, kept out of the index02
| Benchmark | MiniMax M1 | MiMo-V2.6-Pro | Leader |
|---|---|---|---|
| ProofBench | Not measured | 70.0% | Only MiMo-V2.6-Pro measured |
| LMArena Text: math | 1361 | 1500 | MiMo-V2.6-Pro |
| LMArena Text: mathematical work | 1363 | 1511 | MiMo-V2.6-Pro |
| Neither model has a result on the other 11 boards here | |||
KnowledgeLike-for-likePublic boards, shown as published, kept out of the index04
| Benchmark | MiniMax M1 | MiMo-V2.6-Pro | Leader |
|---|---|---|---|
| MedCode (Vals AI) | Not measured | 45.0% | Only MiMo-V2.6-Pro measured |
| LMArena Text: science | 1354 | 1506 | MiMo-V2.6-Pro |
| LMArena Text: medicine and healthcare | 1355 | 1497 | MiMo-V2.6-Pro |
| LMArena Text: legal and government | 1337 | 1485 | MiMo-V2.6-Pro |
| LMArena Text: business and finance | 1319 | 1468 | MiMo-V2.6-Pro |
| 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 M1 | MiMo-V2.6-Pro | Leader |
|---|---|---|---|
| Design Arena: websites | Not measured | 1307 | Only MiMo-V2.6-Pro measured |
| Design Arena: UI components | Not measured | 1337 | Only MiMo-V2.6-Pro measured |
| Design Arena: game dev | Not measured | 1313 | Only MiMo-V2.6-Pro measured |
| Design Arena: data viz | Not measured | 1337 | Only MiMo-V2.6-Pro measured |
| Design Arena: 3D | Not measured | 1329 | Only MiMo-V2.6-Pro measured |
| Design Arena: all code categories | Not measured | 1316 | Only MiMo-V2.6-Pro measured |
| LiveBench design | Not measured | 78.5% | Only MiMo-V2.6-Pro measured |
| Neither model has a result on the other 2 boards here | |||
GeneralLike-for-likePublic boards, shown as published, kept out of the index011
| Benchmark | MiniMax M1 | MiMo-V2.6-Pro | Leader |
|---|---|---|---|
| Vals Index | Not measured | 57.8% | Only MiMo-V2.6-Pro measured |
| LiveBench data analysis | Not measured | 72.3% | Only MiMo-V2.6-Pro measured |
| Public Benefits Bench (Vals AI) | Not measured | 68.9% | Only MiMo-V2.6-Pro measured |
| SAGE (Vals AI) | Not measured | 45.0% | Only MiMo-V2.6-Pro measured |
| MedScribe (Vals AI) | Not measured | 88.3% | Only MiMo-V2.6-Pro measured |
| Long context reasoning (AA-LCR) | Not measured | 86.3% | Only MiMo-V2.6-Pro measured |
| LMArena Text | 1343 | 1491 | MiMo-V2.6-Pro |
| LMArena Text: instruction following | 1312 | 1491 | MiMo-V2.6-Pro |
| LMArena Text: creative writing | 1297 | 1466 | MiMo-V2.6-Pro |
| LMArena Vision | Not measured | 1263 | Only MiMo-V2.6-Pro measured |
| LMArena Text: English | 1363 | 1504 | MiMo-V2.6-Pro |
| LMArena Text: non-English | 1319 | 1474 | MiMo-V2.6-Pro |
| LMArena Text: Chinese | 1360 | 1529 | MiMo-V2.6-Pro |
| LMArena Text: German | 1350 | Not measured | Only MiniMax M1 measured |
| LMArena Text: Japanese | 1217 | Not measured | Only MiniMax M1 measured |
| LMArena Text: Korean | 1266 | Not measured | Only MiniMax M1 measured |
| LMArena Text: French | 1370 | Not measured | Only MiniMax M1 measured |
| LMArena Text: Spanish | 1353 | Not measured | Only MiniMax M1 measured |
| LMArena Text: Russian | 1329 | 1479 | MiMo-V2.6-Pro |
| LMArena Text: Polish | 1329 | Not measured | Only MiniMax M1 measured |
| LMArena Text: multi-turn | 1335 | 1466 | MiMo-V2.6-Pro |
| LMArena Text: longer queries | 1326 | 1501 | MiMo-V2.6-Pro |
| LMArena Text: writing and language | 1311 | 1470 | MiMo-V2.6-Pro |
| LMArena Text: entertainment and media | 1303 | 1465 | MiMo-V2.6-Pro |
| LMArena Vision: English | Not measured | 1268 | Only MiMo-V2.6-Pro measured |
| LMArena Vision: reading text | Not measured | 1270 | Only MiMo-V2.6-Pro measured |
| Neither model has a result on the other 22 boards here | |||
Image generationNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
| Benchmark | MiniMax M1 | MiMo-V2.6-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
MiMo-V2.6-Pro costs less
Repository review
50K input + 3K output tokens
MiMo-V2.6-Pro costs less
Agent loop
200K input + 10K output tokens
MiMo-V2.6-Pro 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 M1 | Xiaomi MiMo-V2.6-Pro |
|---|---|---|
| Maker | MiniMax | Xiaomi |
| Weights | No public weights listed | Open weights |
| Released | June 17, 2025 | September 21, 2026 |
| Context window | 1M | 1.05M |
| Input $/Mtok | $0.40 | $0.435 |
| Output $/Mtok | $2.20 | $0.87 |
| Coding index | Not benchmarked | 65.4 |
| Independent measurements behind it | 0 | 4 |
| Cost per solved task | Not computable | Not computable |
| Source | OpenRouter listing | OpenRouter listing |
Questions
Is MiniMax M1 better than MiMo-V2.6-Pro for coding?
Only MiMo-V2.6-Pro has a coding score, 65.4 on the Vibe Coding Index; MiniMax M1 has none, so the two cannot be ranked on coding. Compare them on price, context and the boards below.
Which is cheaper, MiniMax M1 or MiMo-V2.6-Pro?
MiniMax M1 costs $0.40 per million input tokens and $2.20 per million output tokens; MiMo-V2.6-Pro costs $0.435 and $0.87. Cost per solved task is known for neither of them.
Which has the bigger context window, MiniMax M1 or MiMo-V2.6-Pro?
MiMo-V2.6-Pro, with 1.05M tokens against 1M.
How many benchmarks is the MiniMax M1 vs MiMo-V2.6-Pro comparison based on?
23 benchmarks have a result for both models as of October 22, 2026. MiniMax M1's coding index rests on 0 independent benchmarks and MiMo-V2.6-Pro's on 4. Where only one model has a result, the page says so instead of naming a winner.