GLM 4.6V vs MiMo-V2.5
MiMo-V2.5 leads GLM 4.6V on the Artificial Analysis Intelligence Index, 21.8 to 11.2, and MiMo-V2.5 costs 2.7x less per output token ($0.336 vs $0.90 per million). Updated October 21, 2026; 34 benchmarks have a result for both.
GLM 4.6V or MiMo-V2.5: which to pick
Only MiMo-V2.5 has a coding score, 50.0 on the Vibe Coding Index; GLM 4.6V has none, so the two cannot be ranked on coding.
Compare them on price, context and the boards below.
- Shared results
- 34
- GLM 4.6V only
- 4
- MiMo-V2.5 only
- 46
- 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 has a coding score, so no call is made.
One side not measured
Intelligence
AA Intelligence Index
MiMo-V2.5
MiMo-V2.5 leads, 21.8 against 11.2.
Same board, both measured
Agentic work
AA Agentic Index
No call
Only MiMo-V2.5 has a AA Agentic Index result, 15.8.
Directional only
Reasoning
GPQA Diamond
No call
Only MiMo-V2.5 has a GPQA Diamond result, 81.6%.
Directional only
Knowledge
MMLU Pro
No call
Only MiMo-V2.5 has a MMLU Pro result, 82.9%.
Directional only
Web design
Design Arena: websites
No call
Only MiMo-V2.5 has a Design Arena: websites result, 1266.
Directional only
Long documents
Listed context window
MiMo-V2.5
MiMo-V2.5 takes 1.05M tokens against 131K.
Listed by OpenRouter
Everyday cost
1K input + 500 output tokens
MiMo-V2.5
GLM 4.6V $0.0008, MiMo-V2.5 $0.0003 per turn.
List prices
Cost per solved task
One SWE-bench Verified task, solved
No call
Only MiMo-V2.5 has the number, $0.01.
Directional only
At a glance
- Coding index
- GLM 4.6V not benchmarked, MiMo-V2.5 50.0 from 3 benchmarks.
- Widest category
- Agentic work, 59.1 points apart on average over 1 shared benchmark.
- Widest single gap
- τ²-Bench Telecom: GLM 4.6V 31.6% against MiMo-V2.5 90.6%, 59.1 points apart on a 0 to 100 scale.
- Output price
- GLM 4.6V $0.90 against MiMo-V2.5 $0.336 per million output tokens, 2.7x apart.
- Context window
- GLM 4.6V takes 131K 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 | GLM 4.6V | Bars | MiMo-V2.5 | Ties |
|---|---|---|---|---|
| Coding | 0 | 3 | ||
| Intelligence | 0 | 1 | ||
| Agentic work | 0 | 1 | ||
| Reasoning | 0 | 7 | ||
| Knowledge | 0 | 4 | ||
| General | 0 | 18 | ||
| All shared boards | 0 | 34 |
CodingLike-for-likeSix of these boards feed the Vibe Coding Index03
| Benchmark | GLM 4.6V | MiMo-V2.5 | Leader |
|---|---|---|---|
| LMArena WebDev | Not measured | 1438 | Only MiMo-V2.5 measured |
| AA Coding Index | Not measured | 56.8 | Only MiMo-V2.5 measured |
| LiveCodeBench (Vals AI) | Not measured | 81.5% | Only MiMo-V2.5 measured |
| SWE-bench Verified (Vals AI) | Not measured | 71.0% | Only MiMo-V2.5 measured |
| Vibe Code Bench (Vals AI) | Not measured | 42.2% | Only MiMo-V2.5 measured |
| Code migration (Vals AI) | Not measured | 14.2% | Only MiMo-V2.5 measured |
| Terminal-Bench 2.1 (Vals AI) | Not measured | 60.7% | Only MiMo-V2.5 measured |
| LiveCodeBench (Artificial Analysis run) | 41.1% | Not measured | Only GLM 4.6V measured |
| SciCode | Not measured | 43.9% | Only MiMo-V2.5 measured |
| Terminal-Bench Hard (Artificial Analysis run) | 14.4% | 41.7% | MiMo-V2.5 |
| ALE-Bench | Not measured | 514.0 | Only MiMo-V2.5 measured |
| LMArena Text: coding | 1389 | 1469 | MiMo-V2.5 |
| LMArena Text: software and IT | 1391 | 1463 | MiMo-V2.5 |
| LMArena WebDev: React | Not measured | 1425 | Only MiMo-V2.5 measured |
| LMArena WebDev: HTML | Not measured | 1429 | Only MiMo-V2.5 measured |
| Neither model has a result on the other 24 boards here | |||
IntelligenceLike-for-likePublic boards, shown as published, kept out of the index01
| Benchmark | GLM 4.6V | MiMo-V2.5 | Leader |
|---|---|---|---|
| AA Intelligence Index | 11.2 | 21.8 | MiMo-V2.5 |
| 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 | GLM 4.6V | MiMo-V2.5 | Leader |
|---|---|---|---|
| AA Agentic Index | Not measured | 15.8 | Only MiMo-V2.5 measured |
| Finance Agent (Vals AI) | Not measured | 36.7% | Only MiMo-V2.5 measured |
| Tax Agent Bench (Vals AI) | Not measured | 7.8% | Only MiMo-V2.5 measured |
| Legal Research Bench (Vals AI) | Not measured | 9.1% | Only MiMo-V2.5 measured |
| Excel Modeling Benchmark (Vals AI) | Not measured | 55.1% | Only MiMo-V2.5 measured |
| τ²-Bench Telecom | 31.6% | 90.6% | MiMo-V2.5 |
| Neither model has a result on the other 35 boards here | |||
ReasoningLike-for-likePublic boards, shown as published, kept out of the index07
| Benchmark | GLM 4.6V | MiMo-V2.5 | Leader |
|---|---|---|---|
| GPQA Diamond | Not measured | 81.6% | Only MiMo-V2.5 measured |
| MMMU Pro (Vals AI) | Not measured | 80.0% | Only MiMo-V2.5 measured |
| GPQA Diamond (Artificial Analysis run) | 71.9% | 84.9% | MiMo-V2.5 |
| Humanity's Last Exam (Artificial Analysis run) | 9.6% | 27.2% | MiMo-V2.5 |
| LMArena Text: hard prompts | 1367 | 1450 | MiMo-V2.5 |
| LMArena Text: expert | 1371 | 1459 | MiMo-V2.5 |
| LMArena Text: hard prompts in English | 1391 | 1461 | MiMo-V2.5 |
| LMArena Vision: diagrams | 1182 | 1269 | MiMo-V2.5 |
| LMArena Vision: homework | 1155 | 1262 | MiMo-V2.5 |
| Neither model has a result on the other 13 boards here | |||
MathDirectional onlyPublic boards, shown as published, kept out of the index5 boards, none shared
| Benchmark | GLM 4.6V | MiMo-V2.5 | Leader |
|---|---|---|---|
| AA Math Index | 85.3 | Not measured | Only GLM 4.6V measured |
| AIME 2025 (Artificial Analysis run) | 85.3% | Not measured | Only GLM 4.6V measured |
| ProofBench | Not measured | 16.0% | Only MiMo-V2.5 measured |
| LMArena Text: math | Not measured | 1436 | Only MiMo-V2.5 measured |
| LMArena Text: mathematical work | Not measured | 1451 | Only MiMo-V2.5 measured |
| Neither model has a result on the other 8 boards here | |||
KnowledgeLike-for-likePublic boards, shown as published, kept out of the index04
| Benchmark | GLM 4.6V | MiMo-V2.5 | Leader |
|---|---|---|---|
| MMLU Pro | Not measured | 82.9% | Only MiMo-V2.5 measured |
| LegalBench (Vals AI) | Not measured | 78.9% | Only MiMo-V2.5 measured |
| CorpFin (Vals AI) | Not measured | 59.9% | Only MiMo-V2.5 measured |
| TaxEval (Vals AI) | Not measured | 71.8% | Only MiMo-V2.5 measured |
| MortgageTax (Vals AI) | Not measured | 59.3% | Only MiMo-V2.5 measured |
| MedCode (Vals AI) | Not measured | 31.9% | Only MiMo-V2.5 measured |
| MMLU Pro (Artificial Analysis run) | 79.9% | Not measured | Only GLM 4.6V measured |
| LMArena Text: science | 1389 | 1440 | MiMo-V2.5 |
| LMArena Text: medicine and healthcare | 1389 | 1432 | MiMo-V2.5 |
| LMArena Text: legal and government | 1393 | 1425 | MiMo-V2.5 |
| LMArena Text: business and finance | 1359 | 1432 | MiMo-V2.5 |
| Neither model has a result on the other 7 boards here | |||
Web designDirectional onlyPublic boards, shown as published, kept out of the index8 boards, none shared
| Benchmark | GLM 4.6V | MiMo-V2.5 | Leader |
|---|---|---|---|
| Design Arena: websites | Not measured | 1266 | Only MiMo-V2.5 measured |
| Design Arena: UI components | Not measured | 1258 | Only MiMo-V2.5 measured |
| Design Arena: game dev | Not measured | 1249 | Only MiMo-V2.5 measured |
| Design Arena: data viz | Not measured | 1249 | Only MiMo-V2.5 measured |
| Design Arena: 3D | Not measured | 1216 | Only MiMo-V2.5 measured |
| Design Arena: all code categories | Not measured | 1260 | Only MiMo-V2.5 measured |
| Design Arena: SVG | Not measured | 1178 | Only MiMo-V2.5 measured |
| Design Arena: ASCII art | Not measured | 1140 | Only MiMo-V2.5 measured |
| Neither model has a result on the other 1 board here | |||
GeneralLike-for-likePublic boards, shown as published, kept out of the index018
| Benchmark | GLM 4.6V | MiMo-V2.5 | Leader |
|---|---|---|---|
| SAGE (Vals AI) | Not measured | 43.3% | Only MiMo-V2.5 measured |
| MedScribe (Vals AI) | Not measured | 72.2% | Only MiMo-V2.5 measured |
| Vals Multimodal Index | Not measured | 52.8% | Only MiMo-V2.5 measured |
| IFBench | 30.1% | 67.1% | MiMo-V2.5 |
| Long context reasoning (AA-LCR) | 48.7% | 73.0% | MiMo-V2.5 |
| LMArena Text | 1376 | 1428 | MiMo-V2.5 |
| LMArena Text: instruction following | 1350 | 1435 | MiMo-V2.5 |
| LMArena Text: creative writing | 1347 | 1393 | MiMo-V2.5 |
| LMArena Vision | 1164 | 1247 | MiMo-V2.5 |
| LMArena Text: English | 1395 | 1447 | MiMo-V2.5 |
| LMArena Text: non-English | 1358 | 1405 | MiMo-V2.5 |
| LMArena Text: Chinese | 1425 | 1469 | MiMo-V2.5 |
| LMArena Text: German | Not measured | 1421 | Only MiMo-V2.5 measured |
| LMArena Text: Japanese | Not measured | 1306 | Only MiMo-V2.5 measured |
| LMArena Text: Korean | Not measured | 1361 | Only MiMo-V2.5 measured |
| LMArena Text: French | Not measured | 1448 | Only MiMo-V2.5 measured |
| LMArena Text: Spanish | Not measured | 1414 | Only MiMo-V2.5 measured |
| LMArena Text: Russian | 1339 | 1396 | MiMo-V2.5 |
| LMArena Text: Polish | Not measured | 1413 | Only MiMo-V2.5 measured |
| LMArena Text: multi-turn | 1359 | 1445 | MiMo-V2.5 |
| LMArena Text: longer queries | 1357 | 1445 | MiMo-V2.5 |
| LMArena Text: writing and language | 1347 | 1409 | MiMo-V2.5 |
| LMArena Text: entertainment and media | 1343 | 1399 | MiMo-V2.5 |
| LMArena Vision: English | 1179 | 1259 | MiMo-V2.5 |
| LMArena Vision: Chinese | 1217 | 1326 | MiMo-V2.5 |
| LMArena Vision: reading text | 1171 | 1256 | MiMo-V2.5 |
| LMArena Vision: humor | Not measured | 1226 | Only MiMo-V2.5 measured |
| LMArena Vision: recognizing things | Not measured | 1189 | Only MiMo-V2.5 measured |
| LMArena Vision: creative writing | 1113 | 1230 | MiMo-V2.5 |
| Neither model has a result on the other 17 boards here | |||
Image generationNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
| Benchmark | GLM 4.6V | 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 | Z.ai GLM 4.6V | Xiaomi MiMo-V2.5 |
|---|---|---|
| Maker | Z.ai | Xiaomi |
| Weights | Open weights | Open weights |
| Released | December 8, 2025 | April 22, 2026 |
| Context window | 131K | 1.05M |
| Input $/Mtok | $0.30 | $0.168 |
| Output $/Mtok | $0.90 | $0.336 |
| Coding index | Not benchmarked | 50.0 |
| Independent measurements behind it | 0 | 3 |
| Cost per solved task | Not computable | $0.01 |
| Source | OpenRouter listing | OpenRouter listing |
Questions
Is GLM 4.6V better than MiMo-V2.5 for coding?
Only MiMo-V2.5 has a coding score, 50.0 on the Vibe Coding Index; GLM 4.6V has none, so the two cannot be ranked on coding. Compare them on price, context and the boards below.
Which is cheaper, GLM 4.6V or MiMo-V2.5?
GLM 4.6V costs $0.30 per million input tokens and $0.90 per million output tokens; MiMo-V2.5 costs $0.168 and $0.336. Cost per solved task is known for MiMo-V2.5 only.
Which has the bigger context window, GLM 4.6V or MiMo-V2.5?
MiMo-V2.5, with 1.05M tokens against 131K.
How many benchmarks is the GLM 4.6V vs MiMo-V2.5 comparison based on?
34 benchmarks have a result for both models as of October 21, 2026. GLM 4.6V's coding index rests on 0 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.