Qwen3.8 Max (0902) vs MiMo-V2.5
Qwen3.8 Max (0902) leads MiMo-V2.5 on coding, 62.4 to 50.0 on the Vibe Coding Index, and MiMo-V2.5 costs 17.9x less per output token ($0.336 vs $6.00 per million). Updated October 22, 2026; 74 benchmarks have a result for both.
Qwen3.8 Max (0902) or MiMo-V2.5: which to pick
Pick Qwen3.8 Max (0902) for coding: it scores 62.4 on the Vibe Coding Index against 50.0 for MiMo-V2.5.
Pick MiMo-V2.5 for output tokens 17.9x cheaper ($0.336 vs $6.00 per million) and a lower cost per solved task ($0.01 against $1.32).
- Shared results
- 74
- Qwen3.8 Max (0902) only
- 39
- MiMo-V2.5 only
- 6
- Like-for-like categories
- 8 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
Qwen3.8 Max (0902)
Qwen3.8 Max (0902) scores 62.4 against 50.0.
5 and 3 benchmarks behind the scores
Intelligence
AA Intelligence Index
Qwen3.8 Max (0902)
Qwen3.8 Max (0902) leads, 45.4 against 21.8.
Same board, both measured
Agentic work
AA Agentic Index
Qwen3.8 Max (0902)
Qwen3.8 Max (0902) leads, 56.0 against 15.8.
Same board, both measured
Reasoning
GPQA Diamond
Qwen3.8 Max (0902)
Qwen3.8 Max (0902) leads, 93.7% against 81.6%.
Same board, both measured
Knowledge
MMLU Pro
Qwen3.8 Max (0902)
Qwen3.8 Max (0902) leads, 88.6% against 82.9%.
Same board, both measured
Web design
Design Arena: websites
Qwen3.8 Max (0902)
Qwen3.8 Max (0902) leads, 1275 against 1265.
Same board, both measured
Long documents
Listed context window
MiMo-V2.5
MiMo-V2.5 takes 1.05M tokens against 1M.
Listed by OpenRouter
Everyday cost
1K input + 500 output tokens
MiMo-V2.5
Qwen3.8 Max (0902) $0.0050, MiMo-V2.5 $0.0003 per turn.
List prices
Cost per solved task
One SWE-bench Verified task, solved
MiMo-V2.5
Qwen3.8 Max (0902) $1.32, MiMo-V2.5 $0.01.
Published attempt cost and pass rate
At a glance
- Coding index
- Qwen3.8 Max (0902) 62.4 from 5 benchmarks, MiMo-V2.5 50.0 from 3 benchmarks.
- Widest category
- Agentic work, 24.4 points apart on average over 5 shared benchmarks.
- Widest single gap
- ProofBench: Qwen3.8 Max (0902) 58.0% against MiMo-V2.5 16.0%, 42.0 points apart on a 0 to 100 scale.
- Output price
- Qwen3.8 Max (0902) $6.00 against MiMo-V2.5 $0.336 per million output tokens, 17.9x apart.
- Context window
- Qwen3.8 Max (0902) takes 1M tokens, MiMo-V2.5 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 | Qwen3.8 Max (0902) | Bars | MiMo-V2.5 | Ties |
|---|---|---|---|---|
| Coding | 12 | 0 | ||
| Intelligence | 1 | 0 | ||
| Agentic work | 5 | 0 | ||
| Reasoning | 9 | 0 | ||
| Math | 3 | 0 | ||
| Knowledge | 10 | 0 | ||
| Web design | 7 | 0 | ||
| General | 27 | 0 | ||
| All shared boards | 74 | 0 |
CodingLike-for-likeSix of these boards feed the Vibe Coding Index120
| Benchmark | Qwen3.8 Max (0902) | MiMo-V2.5 | Leader |
|---|---|---|---|
| LiveBench coding | 60.3% | Not measured | Only Qwen3.8 Max (0902) measured |
| LMArena WebDev | 1674 | 1438 | Qwen3.8 Max (0902) |
| AA Coding Index | 76.2 | 56.8 | Qwen3.8 Max (0902) |
| LiveCodeBench (Vals AI) | 87.8% | 81.5% | Qwen3.8 Max (0902) |
| SWE-bench Verified (Vals AI) | 85.6% | 71.0% | Qwen3.8 Max (0902) |
| Terminal-Bench 4 (Vals AI) | 34.3% | Not measured | Only Qwen3.8 Max (0902) measured |
| Vibe Code Bench (Vals AI) | 64.7% | 42.2% | Qwen3.8 Max (0902) |
| Code migration (Vals AI) | 24.0% | 14.2% | Qwen3.8 Max (0902) |
| ProgramBench (Vals AI) | 0.0% | Not measured | Only Qwen3.8 Max (0902) measured |
| IOI (Vals AI) | 68.9% | Not measured | Only Qwen3.8 Max (0902) measured |
| Terminal-Bench 2.1 (Vals AI) | 67.4% | 60.7% | Qwen3.8 Max (0902) |
| SWE-bench Multimodal (Vals AI) | 42.5% | Not measured | Only Qwen3.8 Max (0902) measured |
| Vibe Code Bench 1-100 (Vals AI) | 12.8% | Not measured | Only Qwen3.8 Max (0902) measured |
| SkillsBench (Vals AI) | 42.0% | Not measured | Only Qwen3.8 Max (0902) measured |
| CyberBench (Vals AI) | 28.6% | Not measured | Only Qwen3.8 Max (0902) measured |
| SciCode | 53.2% | 43.9% | Qwen3.8 Max (0902) |
| Terminal-Bench Hard (Artificial Analysis run) | Not measured | 41.7% | Only MiMo-V2.5 measured |
| FrontierSWE | 17.8% | Not measured | Only Qwen3.8 Max (0902) measured |
| ALE-Bench | Not measured | 514.0 | Only MiMo-V2.5 measured |
| LMArena Text: coding | 1502 | 1469 | Qwen3.8 Max (0902) |
| LMArena Text: software and IT | 1498 | 1463 | Qwen3.8 Max (0902) |
| LMArena WebDev: React | 1692 | 1425 | Qwen3.8 Max (0902) |
| LMArena WebDev: HTML | 1649 | 1429 | Qwen3.8 Max (0902) |
| LMArena WebDev: from an image | 1637 | Not measured | Only Qwen3.8 Max (0902) measured |
| Neither model has a result on the other 15 boards here | |||
IntelligenceLike-for-likePublic boards, shown as published, kept out of the index10
| Benchmark | Qwen3.8 Max (0902) | MiMo-V2.5 | Leader |
|---|---|---|---|
| AA Intelligence Index | 45.4 | 21.8 | Qwen3.8 Max (0902) |
| Epoch Capabilities Index | 157.4 | Not measured | Only Qwen3.8 Max (0902) measured |
Agentic workLike-for-likePublic boards, shown as published, kept out of the index50
| Benchmark | Qwen3.8 Max (0902) | MiMo-V2.5 | Leader |
|---|---|---|---|
| AA Agentic Index | 56.0 | 15.8 | Qwen3.8 Max (0902) |
| Design Arena: agents building web apps | 1300 | Not measured | Only Qwen3.8 Max (0902) measured |
| Design Arena: agents building full-stack apps | 1305 | Not measured | Only Qwen3.8 Max (0902) measured |
| Design Arena: agents building mobile apps | 1224 | Not measured | Only Qwen3.8 Max (0902) measured |
| Design Arena: agents building Android apps | 1196 | Not measured | Only Qwen3.8 Max (0902) measured |
| Design Arena: agents building Godot games | 1234 | Not measured | Only Qwen3.8 Max (0902) measured |
| Design Arena: PowerPoint slides | 1233 | Not measured | Only Qwen3.8 Max (0902) measured |
| Design Arena: spreadsheets | 967 | Not measured | Only Qwen3.8 Max (0902) measured |
| LiveBench research | 69.9% | Not measured | Only Qwen3.8 Max (0902) measured |
| LiveBench browser | 32.0% | Not measured | Only Qwen3.8 Max (0902) measured |
| LiveBench long agentic | 42.3% | Not measured | Only Qwen3.8 Max (0902) measured |
| Finance Agent (Vals AI) | 50.6% | 36.7% | Qwen3.8 Max (0902) |
| Tax Agent Bench (Vals AI) | 32.1% | 7.8% | Qwen3.8 Max (0902) |
| Legal Research Bench (Vals AI) | 47.6% | 9.1% | Qwen3.8 Max (0902) |
| Excel Modeling Benchmark (Vals AI) | 60.1% | 55.1% | Qwen3.8 Max (0902) |
| Terminal-Bench-Science (Vals AI) | 12.9% | Not measured | Only Qwen3.8 Max (0902) measured |
| τ²-Bench Telecom | Not measured | 90.6% | Only MiMo-V2.5 measured |
| APEX-Agents | 63.3% | Not measured | Only Qwen3.8 Max (0902) measured |
| LMArena Search | 1261 | Not measured | Only Qwen3.8 Max (0902) measured |
| Neither model has a result on the other 22 boards here | |||
ReasoningLike-for-likePublic boards, shown as published, kept out of the index90
| Benchmark | Qwen3.8 Max (0902) | MiMo-V2.5 | Leader |
|---|---|---|---|
| GPQA Diamond | 93.7% | 81.6% | Qwen3.8 Max (0902) |
| LiveBench reasoning | 88.2% | Not measured | Only Qwen3.8 Max (0902) measured |
| MysteryMechanism (Vals AI) | 23.9% | Not measured | Only Qwen3.8 Max (0902) measured |
| MMMU Pro (Vals AI) | 88.0% | 80.0% | Qwen3.8 Max (0902) |
| GPQA Diamond (Artificial Analysis run) | 92.8% | 84.9% | Qwen3.8 Max (0902) |
| Humanity's Last Exam (Artificial Analysis run) | 43.1% | 27.2% | Qwen3.8 Max (0902) |
| GPQA Diamond (Epoch AI run) | 92.7% | Not measured | Only Qwen3.8 Max (0902) measured |
| Chess puzzles | 46.0% | Not measured | Only Qwen3.8 Max (0902) measured |
| Mystery game puzzles | 38.0% | Not measured | Only Qwen3.8 Max (0902) measured |
| LMArena Text: hard prompts | 1496 | 1450 | Qwen3.8 Max (0902) |
| LMArena Text: expert | 1507 | 1459 | Qwen3.8 Max (0902) |
| LMArena Text: hard prompts in English | 1503 | 1461 | Qwen3.8 Max (0902) |
| LMArena Vision: diagrams | 1323 | 1269 | Qwen3.8 Max (0902) |
| LMArena Vision: homework | 1336 | 1262 | Qwen3.8 Max (0902) |
| Neither model has a result on the other 8 boards here | |||
MathLike-for-likePublic boards, shown as published, kept out of the index30
| Benchmark | Qwen3.8 Max (0902) | MiMo-V2.5 | Leader |
|---|---|---|---|
| OTIS Mock AIME | 100.0% | Not measured | Only Qwen3.8 Max (0902) measured |
| FrontierMath, tiers 1 to 3 | 74.7% | Not measured | Only Qwen3.8 Max (0902) measured |
| FrontierMath, tier 4 | 46.3% | Not measured | Only Qwen3.8 Max (0902) measured |
| ProofBench | 58.0% | 16.0% | Qwen3.8 Max (0902) |
| LMArena Text: math | 1501 | 1436 | Qwen3.8 Max (0902) |
| LMArena Text: mathematical work | 1491 | 1451 | Qwen3.8 Max (0902) |
| Neither model has a result on the other 7 boards here | |||
KnowledgeLike-for-likePublic boards, shown as published, kept out of the index100
| Benchmark | Qwen3.8 Max (0902) | MiMo-V2.5 | Leader |
|---|---|---|---|
| MMLU Pro | 88.6% | 82.9% | Qwen3.8 Max (0902) |
| LegalBench (Vals AI) | 83.6% | 78.9% | Qwen3.8 Max (0902) |
| CorpFin (Vals AI) | 65.8% | 59.9% | Qwen3.8 Max (0902) |
| TaxEval (Vals AI) | 75.5% | 71.8% | Qwen3.8 Max (0902) |
| MortgageTax (Vals AI) | 64.0% | 59.3% | Qwen3.8 Max (0902) |
| MedCode (Vals AI) | 40.7% | 31.9% | Qwen3.8 Max (0902) |
| SimpleQA Verified | 47.3% | Not measured | Only Qwen3.8 Max (0902) measured |
| LMArena Text: science | 1512 | 1440 | Qwen3.8 Max (0902) |
| LMArena Text: medicine and healthcare | 1503 | 1432 | Qwen3.8 Max (0902) |
| LMArena Text: legal and government | 1492 | 1425 | Qwen3.8 Max (0902) |
| LMArena Text: business and finance | 1480 | 1432 | Qwen3.8 Max (0902) |
| Neither model has a result on the other 7 boards here | |||
Web designLike-for-likePublic boards, shown as published, kept out of the index70
| Benchmark | Qwen3.8 Max (0902) | MiMo-V2.5 | Leader |
|---|---|---|---|
| Design Arena: websites | 1275 | 1265 | Qwen3.8 Max (0902) |
| Design Arena: UI components | 1338 | 1255 | Qwen3.8 Max (0902) |
| Design Arena: game dev | 1312 | 1249 | Qwen3.8 Max (0902) |
| Design Arena: data viz | 1281 | 1250 | Qwen3.8 Max (0902) |
| Design Arena: 3D | 1336 | 1215 | Qwen3.8 Max (0902) |
| Design Arena: all code categories | 1293 | 1260 | Qwen3.8 Max (0902) |
| Design Arena: SVG | Not measured | 1179 | Only MiMo-V2.5 measured |
| Design Arena: ASCII art | 1200 | 1140 | Qwen3.8 Max (0902) |
| LiveBench design | 78.6% | Not measured | Only Qwen3.8 Max (0902) measured |
GeneralLike-for-likePublic boards, shown as published, kept out of the index270
| Benchmark | Qwen3.8 Max (0902) | MiMo-V2.5 | Leader |
|---|---|---|---|
| LiveBench data analysis | 78.4% | Not measured | Only Qwen3.8 Max (0902) measured |
| Public Benefits Bench (Vals AI) | 67.1% | Not measured | Only Qwen3.8 Max (0902) measured |
| SAGE (Vals AI) | 51.2% | 43.3% | Qwen3.8 Max (0902) |
| MedScribe (Vals AI) | 85.0% | 72.2% | Qwen3.8 Max (0902) |
| Vals Multimodal Index | 65.4% | 52.8% | Qwen3.8 Max (0902) |
| IFBench | Not measured | 67.1% | Only MiMo-V2.5 measured |
| Long context reasoning (AA-LCR) | 80.3% | 73.0% | Qwen3.8 Max (0902) |
| DTBench | 92.0% | Not measured | Only Qwen3.8 Max (0902) measured |
| LMCA | 46.2% | Not measured | Only Qwen3.8 Max (0902) measured |
| Furniture assembly | 20.0% | Not measured | Only Qwen3.8 Max (0902) measured |
| LMArena Text | 1483 | 1428 | Qwen3.8 Max (0902) |
| LMArena Text: instruction following | 1481 | 1435 | Qwen3.8 Max (0902) |
| LMArena Text: creative writing | 1477 | 1393 | Qwen3.8 Max (0902) |
| LMArena Vision | 1313 | 1247 | Qwen3.8 Max (0902) |
| LMArena Text: English | 1491 | 1447 | Qwen3.8 Max (0902) |
| LMArena Text: non-English | 1473 | 1405 | Qwen3.8 Max (0902) |
| LMArena Text: Chinese | 1539 | 1469 | Qwen3.8 Max (0902) |
| LMArena Text: German | 1478 | 1421 | Qwen3.8 Max (0902) |
| LMArena Text: Japanese | 1473 | 1306 | Qwen3.8 Max (0902) |
| LMArena Text: Korean | 1460 | 1361 | Qwen3.8 Max (0902) |
| LMArena Text: French | 1498 | 1448 | Qwen3.8 Max (0902) |
| LMArena Text: Spanish | 1489 | 1414 | Qwen3.8 Max (0902) |
| LMArena Text: Russian | 1482 | 1396 | Qwen3.8 Max (0902) |
| LMArena Text: Polish | 1471 | 1413 | Qwen3.8 Max (0902) |
| LMArena Text: multi-turn | 1489 | 1445 | Qwen3.8 Max (0902) |
| LMArena Text: longer queries | 1488 | 1445 | Qwen3.8 Max (0902) |
| LMArena Text: writing and language | 1477 | 1409 | Qwen3.8 Max (0902) |
| LMArena Text: entertainment and media | 1464 | 1399 | Qwen3.8 Max (0902) |
| LMArena Vision: English | 1311 | 1259 | Qwen3.8 Max (0902) |
| LMArena Vision: Chinese | 1358 | 1326 | Qwen3.8 Max (0902) |
| LMArena Vision: reading text | 1322 | 1256 | Qwen3.8 Max (0902) |
| LMArena Vision: humor | 1304 | 1226 | Qwen3.8 Max (0902) |
| LMArena Vision: recognizing things | Not measured | 1189 | Only MiMo-V2.5 measured |
| LMArena Vision: creative writing | 1312 | 1230 | Qwen3.8 Max (0902) |
| Neither model has a result on the other 12 boards here | |||
Image generationNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
| Benchmark | Qwen3.8 Max (0902) | MiMo-V2.5 | 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
MiMo-V2.5 costs less
Repository review
50K input + 3K output tokens
MiMo-V2.5 costs less
Agent loop
200K input + 10K output tokens
MiMo-V2.5 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 | Qwen3.8 Max (0902) | Xiaomi MiMo-V2.5 |
|---|---|---|
| Maker | Qwen | Xiaomi |
| Weights | No public weights listed | Open weights |
| Released | September 3, 2026 | April 22, 2026 |
| Context window | 1M | 1.05M |
| Input $/Mtok | $2.00 | $0.168 |
| Output $/Mtok | $6.00 | $0.336 |
| Coding index | 62.4 | 50.0 |
| Independent measurements behind it | 5 | 3 |
| Cost per solved task | $1.32 | $0.01 |
| Source | OpenRouter listing | OpenRouter listing |
Questions
Is Qwen3.8 Max (0902) better than MiMo-V2.5 for coding?
Pick Qwen3.8 Max (0902) for coding: it scores 62.4 on the Vibe Coding Index against 50.0 for MiMo-V2.5. Pick MiMo-V2.5 for output tokens 17.9x cheaper ($0.336 vs $6.00 per million) and a lower cost per solved task ($0.01 against $1.32).
Which is cheaper, Qwen3.8 Max (0902) or MiMo-V2.5?
Qwen3.8 Max (0902) costs $2.00 per million input tokens and $6.00 per million output tokens; MiMo-V2.5 costs $0.168 and $0.336. Per solved SWE-bench Verified task, Qwen3.8 Max (0902) costs $1.32 and MiMo-V2.5 $0.01.
Which has the bigger context window, Qwen3.8 Max (0902) or MiMo-V2.5?
MiMo-V2.5, with 1.05M tokens against 1M.
How many benchmarks is the Qwen3.8 Max (0902) vs MiMo-V2.5 comparison based on?
74 benchmarks have a result for both models as of October 22, 2026. Qwen3.8 Max (0902)'s coding index rests on 5 independent benchmarks and MiMo-V2.5's on 3. Where only one model has a result, the page says so instead of naming a winner.