GLM 4.6 vs MiMo-V2.5
Updated October 21, 2026. 53 benchmarks have a result for both. Coding is ranked on the Vibe Coding Index; every other board is shown as published.
Decision reading
Pick GLM 4.6 for coding: it scores 52.2 on the Vibe Coding Index against 34.0 for MiMo-V2.5.
Pick MiMo-V2.5 when output tokens cost 9.2x less, or when the context window is larger, 1.05M against 205K tokens.
- Shared results
- 53
- GLM 4.6 only
- 13
- MiMo-V2.5 only
- 27
- Like-for-like categories
- 8 of 9
Shared results can support a head-to-head reading. Results only one model has describe coverage, not superiority.
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, four public coding benchmarks
GLM 4.6
GLM 4.6 scores 52.2 against 34.0.
2 and 1 benchmarks behind the scores
Intelligence
AA Intelligence Index
MiMo-V2.5
MiMo-V2.5 leads, 21.8 against 18.5.
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
MiMo-V2.5
MiMo-V2.5 leads, 81.6% against 74.5%.
Same board, both measured
Math
AIME
No call
Only GLM 4.6 has a AIME result, 92.7%.
Directional only
Knowledge
MMLU Pro
MiMo-V2.5
MiMo-V2.5 leads, 82.9% against 82.2%.
Same board, both measured
Web design
Design Arena: websites
MiMo-V2.5
MiMo-V2.5 leads, 1266 against 1174.
Same board, both measured
Long documents
Listed context window
MiMo-V2.5
MiMo-V2.5 takes 1.05M tokens against 205K.
Listed by OpenRouter
Everyday cost
1K input + 500 output tokens
MiMo-V2.5
GLM 4.6 $0.0016, MiMo-V2.5 $0.0002 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 4.6 52.2 from 2 benchmarks, MiMo-V2.5 34.0 from 1 benchmark.
- Widest category
- Agentic work, 13.7 points apart on average over 1 shared benchmark.
- Widest single gap
- Vibe Code Bench (Vals AI): GLM 4.6 3.1% against MiMo-V2.5 42.2%, 39.1 points apart on a 0 to 100 scale.
- Output price
- GLM 4.6 $2.20 against MiMo-V2.5 $0.238 per million output tokens, 9.2x apart.
- Context window
- GLM 4.6 takes 205K 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 4.6 | Bars | MiMo-V2.5 | Ties |
|---|---|---|---|---|
| Coding | 0 | 9 | ||
| Intelligence | 0 | 1 | ||
| Agentic work | 0 | 1 | ||
| Reasoning | 0 | 6 | ||
| Math | 0 | 2 | ||
| Knowledge | 4 | 4 | ||
| Web design | 0 | 7 | ||
| General | 14 | 5 | ||
| All shared boards | 18 | 35 |
CodingLike-for-likeFour of these boards feed the Vibe Coding Index09
| Benchmark | GLM 4.6 | MiMo-V2.5 | Leader |
|---|---|---|---|
| SWE-bench Verified | 68.2% | Not measured | Only GLM 4.6 measured |
| LMArena WebDev | 1341 | 1438 | MiMo-V2.5 |
| AA Coding Index | 45.8 | 56.8 | MiMo-V2.5 |
| LiveCodeBench (Vals AI) | 81.0% | 81.5% | MiMo-V2.5 |
| SWE-bench Verified (Vals AI) | Not measured | 71.0% | Only MiMo-V2.5 measured |
| Vibe Code Bench (Vals AI) | 3.1% | 42.2% | MiMo-V2.5 |
| 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) | 69.5% | Not measured | Only GLM 4.6 measured |
| SciCode | Not measured | 43.9% | Only MiMo-V2.5 measured |
| Terminal-Bench Hard (Artificial Analysis run) | 28.8% | 41.7% | MiMo-V2.5 |
| Terminal-Bench | 24.5% | Not measured | Only GLM 4.6 measured |
| ALE-Bench | 340.8 | 514.0 | MiMo-V2.5 |
| LMArena Text: coding | 1449 | 1469 | MiMo-V2.5 |
| LMArena Text: software and IT | 1455 | 1463 | MiMo-V2.5 |
| LMArena WebDev: React | Not measured | 1425 | Only MiMo-V2.5 measured |
| LMArena WebDev: HTML | 1350 | 1429 | MiMo-V2.5 |
| Neither model has a result on the other 21 boards here | |||
IntelligenceLike-for-likePublic boards, shown as published, kept out of the index01
| Benchmark | GLM 4.6 | MiMo-V2.5 | Leader |
|---|---|---|---|
| AA Intelligence Index | 18.5 | 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 4.6 | MiMo-V2.5 | Leader |
|---|---|---|---|
| AA Agentic Index | Not measured | 15.8 | Only MiMo-V2.5 measured |
| Design Arena: agents building full-stack apps | 1029 | Not measured | Only GLM 4.6 measured |
| Design Arena: agents building mobile apps | 1096 | Not measured | Only GLM 4.6 measured |
| Design Arena: agents building Android apps | 1090 | Not measured | Only GLM 4.6 measured |
| Design Arena: agents building Godot games | 1180 | Not measured | Only GLM 4.6 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 | 76.9% | 90.6% | MiMo-V2.5 |
| Neither model has a result on the other 29 boards here | |||
ReasoningLike-for-likePublic boards, shown as published, kept out of the index06
| Benchmark | GLM 4.6 | MiMo-V2.5 | Leader |
|---|---|---|---|
| GPQA Diamond | 74.5% | 81.6% | MiMo-V2.5 |
| MMMU Pro (Vals AI) | Not measured | 80.0% | Only MiMo-V2.5 measured |
| GPQA Diamond (Artificial Analysis run) | 78.0% | 84.9% | MiMo-V2.5 |
| Humanity's Last Exam (Artificial Analysis run) | 14.5% | 27.2% | MiMo-V2.5 |
| LMArena Text: hard prompts | 1440 | 1450 | MiMo-V2.5 |
| LMArena Text: expert | 1432 | 1459 | MiMo-V2.5 |
| LMArena Text: hard prompts in English | 1445 | 1461 | MiMo-V2.5 |
| LMArena Vision: diagrams | Not measured | 1269 | Only MiMo-V2.5 measured |
| LMArena Vision: homework | Not measured | 1262 | Only MiMo-V2.5 measured |
| Neither model has a result on the other 12 boards here | |||
MathLike-for-likePublic boards, shown as published, kept out of the index02
| Benchmark | GLM 4.6 | MiMo-V2.5 | Leader |
|---|---|---|---|
| AIME | 92.7% | Not measured | Only GLM 4.6 measured |
| MGSM (Vals AI) | 89.8% | Not measured | Only GLM 4.6 measured |
| AA Math Index | 86.0 | Not measured | Only GLM 4.6 measured |
| AIME 2025 (Artificial Analysis run) | 86.0% | Not measured | Only GLM 4.6 measured |
| ProofBench | Not measured | 16.0% | Only MiMo-V2.5 measured |
| LMArena Text: math | 1432 | 1436 | MiMo-V2.5 |
| LMArena Text: mathematical work | 1447 | 1451 | MiMo-V2.5 |
| Neither model has a result on the other 6 boards here | |||
KnowledgeLike-for-likePublic boards, shown as published, kept out of the index44
| Benchmark | GLM 4.6 | MiMo-V2.5 | Leader |
|---|---|---|---|
| MMLU Pro | 82.2% | 82.9% | MiMo-V2.5 |
| LegalBench (Vals AI) | 79.6% | 78.9% | GLM 4.6 |
| CorpFin (Vals AI) | 56.8% | 59.9% | MiMo-V2.5 |
| TaxEval (Vals AI) | 66.2% | 71.8% | MiMo-V2.5 |
| MortgageTax (Vals AI) | Not measured | 59.3% | Only MiMo-V2.5 measured |
| MedQA (Vals AI) | 92.2% | Not measured | Only GLM 4.6 measured |
| MedCode (Vals AI) | Not measured | 31.9% | Only MiMo-V2.5 measured |
| MMLU Pro (Artificial Analysis run) | 82.9% | Not measured | Only GLM 4.6 measured |
| LMArena Text: science | 1456 | 1440 | GLM 4.6 |
| LMArena Text: medicine and healthcare | 1455 | 1432 | GLM 4.6 |
| LMArena Text: legal and government | 1448 | 1425 | GLM 4.6 |
| LMArena Text: business and finance | 1416 | 1432 | MiMo-V2.5 |
| Neither model has a result on the other 5 boards here | |||
Web designLike-for-likePublic boards, shown as published, kept out of the index07
| Benchmark | GLM 4.6 | MiMo-V2.5 | Leader |
|---|---|---|---|
| Design Arena: websites | 1174 | 1266 | MiMo-V2.5 |
| Design Arena: UI components | 1147 | 1257 | MiMo-V2.5 |
| Design Arena: game dev | 1151 | 1249 | MiMo-V2.5 |
| Design Arena: data viz | 1161 | 1249 | MiMo-V2.5 |
| Design Arena: 3D | 1129 | 1216 | MiMo-V2.5 |
| Design Arena: all code categories | 1166 | 1260 | MiMo-V2.5 |
| Design Arena: SVG | 1108 | 1178 | MiMo-V2.5 |
| Design Arena: ASCII art | Not measured | 1141 | 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 index145
| Benchmark | GLM 4.6 | 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 | 43.4% | 67.1% | MiMo-V2.5 |
| Long context reasoning (AA-LCR) | 54.0% | 73.0% | MiMo-V2.5 |
| LMArena Text | 1440 | 1428 | GLM 4.6 |
| LMArena Text: instruction following | 1410 | 1435 | MiMo-V2.5 |
| LMArena Text: creative writing | 1412 | 1393 | GLM 4.6 |
| LMArena Vision | Not measured | 1247 | Only MiMo-V2.5 measured |
| LMArena Text: English | 1449 | 1447 | GLM 4.6 |
| LMArena Text: non-English | 1426 | 1405 | GLM 4.6 |
| LMArena Text: Chinese | 1499 | 1469 | GLM 4.6 |
| LMArena Text: German | 1447 | 1421 | GLM 4.6 |
| LMArena Text: Japanese | 1393 | 1306 | GLM 4.6 |
| LMArena Text: Korean | 1400 | 1361 | GLM 4.6 |
| LMArena Text: French | 1459 | 1448 | GLM 4.6 |
| LMArena Text: Spanish | 1436 | 1414 | GLM 4.6 |
| LMArena Text: Russian | 1418 | 1396 | GLM 4.6 |
| LMArena Text: Polish | 1447 | 1413 | GLM 4.6 |
| LMArena Text: multi-turn | 1427 | 1445 | MiMo-V2.5 |
| LMArena Text: longer queries | 1422 | 1445 | MiMo-V2.5 |
| LMArena Text: writing and language | 1415 | 1409 | GLM 4.6 |
| LMArena Text: entertainment and media | 1409 | 1399 | GLM 4.6 |
| LMArena Vision: English | Not measured | 1259 | Only MiMo-V2.5 measured |
| LMArena Vision: Chinese | Not measured | 1326 | Only MiMo-V2.5 measured |
| LMArena Vision: reading text | Not measured | 1256 | Only MiMo-V2.5 measured |
| 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 | Not measured | 1230 | Only MiMo-V2.5 measured |
| 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 4.6 | 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 4.6 | Xiaomi MiMo-V2.5 |
|---|---|---|
| Maker | Z.ai | Xiaomi |
| Weights | Open weights | Open weights |
| Released | September 30, 2025 | April 22, 2026 |
| Context window | 205K | 1.05M |
| Input $/Mtok | $0.55 | $0.119 |
| Output $/Mtok | $2.20 | $0.238 |
| Coding index | 52.2 | 34.0 |
| Independent measurements behind it | 2 | 1 |
| Cost per solved task | Not computable | $0.01 |
| Source | OpenRouter listing | OpenRouter listing |
Questions
Is GLM 4.6 better than MiMo-V2.5 for coding?
Pick GLM 4.6 for coding: it scores 52.2 on the Vibe Coding Index against 34.0 for MiMo-V2.5. Pick MiMo-V2.5 when output tokens cost 9.2x less, or when the context window is larger, 1.05M against 205K tokens.
Which is cheaper, GLM 4.6 or MiMo-V2.5?
GLM 4.6 costs $0.55 per million input tokens and $2.20 per million output tokens; MiMo-V2.5 costs $0.119 and $0.238. Cost per solved task is known for MiMo-V2.5 only.
Which has the bigger context window, GLM 4.6 or MiMo-V2.5?
MiMo-V2.5, with 1.05M tokens against 205K.
How many benchmarks is the GLM 4.6 vs MiMo-V2.5 comparison based on?
53 benchmarks have a result for both models as of October 21, 2026. GLM 4.6's coding index rests on 2 independent benchmarks and MiMo-V2.5's on 1. Where only one model has a result, the page says so instead of naming a winner.