Qwen3 Next 80B A3B Thinking vs MiMo-V2.5-Pro
MiMo-V2.5-Pro leads Qwen3 Next 80B A3B Thinking on the Artificial Analysis Intelligence Index, 26.0 to 11.2, and MiMo-V2.5-Pro costs 1.3x less per output token ($0.9152 vs $1.20 per million). Updated October 22, 2026; 30 benchmarks have a result for both.
Qwen3 Next 80B A3B Thinking or MiMo-V2.5-Pro: which to pick
Only MiMo-V2.5-Pro has a coding score, 31.6 on the Vibe Coding Index; Qwen3 Next 80B A3B Thinking has none, so the two cannot be ranked on coding.
Compare them on price, context and the boards below.
- Shared results
- 30
- Qwen3 Next 80B A3B Thinking only
- 0
- MiMo-V2.5-Pro only
- 41
- 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-V2.5-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 11.2.
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 262K.
Listed by OpenRouter
Everyday cost
1K input + 500 output tokens
Qwen3 Next 80B A3B Thinking
Qwen3 Next 80B A3B Thinking $0.0008, 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
- Qwen3 Next 80B A3B Thinking not benchmarked, MiMo-V2.5-Pro 31.6 from 4 benchmarks.
- Widest category
- Coding, 26.0 points apart on average over 3 shared benchmarks.
- Widest single gap
- AA Coding Index: Qwen3 Next 80B A3B Thinking 17.4 against MiMo-V2.5-Pro 60.2, 42.8 points apart on a 0 to 100 scale.
- Output price
- Qwen3 Next 80B A3B Thinking $1.20 against MiMo-V2.5-Pro $0.9152 per million output tokens, 1.3x apart.
- Context window
- Qwen3 Next 80B A3B Thinking takes 262K 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 | Qwen3 Next 80B A3B Thinking | Bars | MiMo-V2.5-Pro | Ties |
|---|---|---|---|---|
| Coding | 0 | 3 | ||
| Intelligence | 0 | 1 | ||
| Reasoning | 0 | 3 | ||
| Math | 0 | 2 | ||
| Knowledge | 0 | 4 | ||
| General | 0 | 17 | ||
| All shared boards | 0 | 30 |
CodingLike-for-likeSix of these boards feed the Vibe Coding Index03
| Benchmark | Qwen3 Next 80B A3B Thinking | MiMo-V2.5-Pro | Leader |
|---|---|---|---|
| LMArena WebDev | Not measured | 1480 | Only MiMo-V2.5-Pro measured |
| AA Coding Index | 17.4 | 60.2 | MiMo-V2.5-Pro |
| 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 |
| 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 | 1392 | 1501 | MiMo-V2.5-Pro |
| LMArena Text: software and IT | 1393 | 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 28 boards here | |||
IntelligenceLike-for-likePublic boards, shown as published, kept out of the index01
| Benchmark | Qwen3 Next 80B A3B Thinking | MiMo-V2.5-Pro | Leader |
|---|---|---|---|
| AA Intelligence Index | 11.2 | 26.0 | MiMo-V2.5-Pro |
| Neither model has a result on the other 1 board here | |||
Agentic workDirectional onlyPublic boards, shown as published, kept out of the index6 boards, none shared
| Benchmark | Qwen3 Next 80B A3B Thinking | 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 | Not measured | 94.2% | Only MiMo-V2.5-Pro measured |
| Neither model has a result on the other 36 boards here | |||
ReasoningLike-for-likePublic boards, shown as published, kept out of the index03
| Benchmark | Qwen3 Next 80B A3B Thinking | MiMo-V2.5-Pro | Leader |
|---|---|---|---|
| GPQA Diamond | Not measured | 82.6% | Only MiMo-V2.5-Pro measured |
| GPQA Diamond (Artificial Analysis run) | Not measured | 86.6% | Only MiMo-V2.5-Pro measured |
| Humanity's Last Exam (Artificial Analysis run) | Not measured | 35.7% | Only MiMo-V2.5-Pro measured |
| LMArena Text: hard prompts | 1371 | 1486 | MiMo-V2.5-Pro |
| LMArena Text: expert | 1376 | 1501 | MiMo-V2.5-Pro |
| LMArena Text: hard prompts in English | 1384 | 1494 | MiMo-V2.5-Pro |
| Neither model has a result on the other 17 boards here | |||
MathLike-for-likePublic boards, shown as published, kept out of the index02
| Benchmark | Qwen3 Next 80B A3B Thinking | MiMo-V2.5-Pro | Leader |
|---|---|---|---|
| ProofBench | Not measured | 22.0% | Only MiMo-V2.5-Pro measured |
| LMArena Text: math | 1399 | 1481 | MiMo-V2.5-Pro |
| LMArena Text: mathematical work | 1390 | 1491 | MiMo-V2.5-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 | Qwen3 Next 80B A3B Thinking | 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 |
| LMArena Text: science | 1384 | 1482 | MiMo-V2.5-Pro |
| LMArena Text: medicine and healthcare | 1392 | 1473 | MiMo-V2.5-Pro |
| LMArena Text: legal and government | 1364 | 1474 | MiMo-V2.5-Pro |
| LMArena Text: business and finance | 1360 | 1466 | MiMo-V2.5-Pro |
| Neither model has a result on the other 11 boards here | |||
Web designDirectional onlyPublic boards, shown as published, kept out of the index8 boards, none shared
| Benchmark | Qwen3 Next 80B A3B Thinking | 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 | 1182 | 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 | Qwen3 Next 80B A3B Thinking | MiMo-V2.5-Pro | Leader |
|---|---|---|---|
| MedScribe (Vals AI) | Not measured | 83.7% | Only MiMo-V2.5-Pro measured |
| IFBench | Not measured | 79.9% | Only MiMo-V2.5-Pro measured |
| Long context reasoning (AA-LCR) | Not measured | 79.7% | Only MiMo-V2.5-Pro measured |
| DTBench | Not measured | 84.5% | Only MiMo-V2.5-Pro measured |
| LMCA | Not measured | 29.5% | Only MiMo-V2.5-Pro measured |
| LMArena Text | 1368 | 1464 | MiMo-V2.5-Pro |
| LMArena Text: instruction following | 1344 | 1475 | MiMo-V2.5-Pro |
| LMArena Text: creative writing | 1314 | 1438 | MiMo-V2.5-Pro |
| LMArena Text: English | 1392 | 1477 | MiMo-V2.5-Pro |
| LMArena Text: non-English | 1342 | 1448 | MiMo-V2.5-Pro |
| LMArena Text: Chinese | 1407 | 1507 | MiMo-V2.5-Pro |
| LMArena Text: German | 1356 | 1459 | MiMo-V2.5-Pro |
| LMArena Text: Japanese | 1280 | 1410 | MiMo-V2.5-Pro |
| LMArena Text: Korean | 1310 | 1435 | MiMo-V2.5-Pro |
| LMArena Text: French | 1351 | 1487 | MiMo-V2.5-Pro |
| LMArena Text: Spanish | 1358 | 1469 | MiMo-V2.5-Pro |
| LMArena Text: Russian | 1337 | 1449 | MiMo-V2.5-Pro |
| LMArena Text: Polish | 1345 | 1464 | MiMo-V2.5-Pro |
| LMArena Text: multi-turn | 1346 | 1476 | MiMo-V2.5-Pro |
| LMArena Text: longer queries | 1353 | 1481 | MiMo-V2.5-Pro |
| LMArena Text: writing and language | 1330 | 1451 | MiMo-V2.5-Pro |
| LMArena Text: entertainment and media | 1312 | 1438 | MiMo-V2.5-Pro |
| EQ-Bench 4 | Not measured | 1208 | Only MiMo-V2.5-Pro measured |
| Neither model has a result on the other 25 boards here | |||
Image generationNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
| Benchmark | Qwen3 Next 80B A3B Thinking | 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
Qwen3 Next 80B A3B Thinking costs less
Repository review
50K input + 3K output tokens
Qwen3 Next 80B A3B Thinking costs less
Agent loop
200K input + 10K output tokens
Qwen3 Next 80B A3B Thinking 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 Next 80B A3B Thinking | Xiaomi MiMo-V2.5-Pro |
|---|---|---|
| Maker | Qwen | Xiaomi |
| Weights | Open weights | Open weights |
| Released | September 11, 2025 | April 22, 2026 |
| Context window | 262K | 1.05M |
| Input $/Mtok | $0.15 | $0.4575 |
| Output $/Mtok | $1.20 | $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 Qwen3 Next 80B A3B Thinking better than MiMo-V2.5-Pro for coding?
Only MiMo-V2.5-Pro has a coding score, 31.6 on the Vibe Coding Index; Qwen3 Next 80B A3B Thinking has none, so the two cannot be ranked on coding. Compare them on price, context and the boards below.
Which is cheaper, Qwen3 Next 80B A3B Thinking or MiMo-V2.5-Pro?
Qwen3 Next 80B A3B Thinking costs $0.15 per million input tokens and $1.20 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, Qwen3 Next 80B A3B Thinking or MiMo-V2.5-Pro?
MiMo-V2.5-Pro, with 1.05M tokens against 262K.
How many benchmarks is the Qwen3 Next 80B A3B Thinking vs MiMo-V2.5-Pro comparison based on?
30 benchmarks have a result for both models as of October 22, 2026. Qwen3 Next 80B A3B Thinking'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.