GLM 4.6 vs MiMo-V2.6-Pro
Updated October 21, 2026. 35 benchmarks have a result for both. Coding is ranked on the Vibe Coding Index; every other board is shown as published.
Decision reading
Pick MiMo-V2.6-Pro for coding: it scores 72.6 on the Vibe Coding Index against 52.2 for GLM 4.6.
GLM 4.6 is neither cheaper nor larger in context, and leads no benchmark both models were measured on.
- Shared results
- 35
- GLM 4.6 only
- 31
- MiMo-V2.6-Pro only
- 38
- Like-for-like categories
- 7 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
MiMo-V2.6-Pro
MiMo-V2.6-Pro scores 72.6 against 52.2.
2 benchmarks behind each score
Intelligence
AA Intelligence Index
MiMo-V2.6-Pro
MiMo-V2.6-Pro leads, 46.3 against 18.5.
Same board, both measured
Reasoning
GPQA Diamond
No call
Only GLM 4.6 has a GPQA Diamond result, 74.5%.
Directional only
Math
AIME
No call
Only GLM 4.6 has a AIME result, 92.7%.
Directional only
Knowledge
MMLU Pro
No call
Only GLM 4.6 has a MMLU Pro result, 82.2%.
Directional only
Web design
Design Arena: websites
MiMo-V2.6-Pro
MiMo-V2.6-Pro leads, 1308 against 1174.
Same board, both measured
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 205K.
Listed by OpenRouter
Everyday cost
1K input + 500 output tokens
MiMo-V2.6-Pro
GLM 4.6 $0.0016, MiMo-V2.6-Pro $0.0009 per turn.
List prices
At a glance
- Coding index
- GLM 4.6 52.2 from 2 benchmarks, MiMo-V2.6-Pro 72.6 from 2 benchmarks.
- Widest category
- Coding, 36.3 points apart on average over 6 shared benchmarks.
- Widest single gap
- Vibe Code Bench (Vals AI): GLM 4.6 3.1% against MiMo-V2.6-Pro 85.2%, 82.1 points apart on a 0 to 100 scale.
- Output price
- GLM 4.6 $2.20 against MiMo-V2.6-Pro $0.87 per million output tokens, 2.5x apart.
- Context window
- GLM 4.6 takes 205K 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 | GLM 4.6 | Bars | MiMo-V2.6-Pro | Ties |
|---|---|---|---|---|
| Coding | 0 | 6 | ||
| Intelligence | 0 | 1 | ||
| Reasoning | 0 | 4 | ||
| Math | 0 | 2 | ||
| Knowledge | 0 | 4 | ||
| Web design | 0 | 6 | ||
| General | 0 | 12 | ||
| All shared boards | 0 | 35 |
CodingLike-for-likeFour of these boards feed the Vibe Coding Index06
| Benchmark | GLM 4.6 | MiMo-V2.6-Pro | Leader |
|---|---|---|---|
| SWE-bench Verified | 68.2% | Not measured | Only GLM 4.6 measured |
| LiveBench coding | Not measured | 81.5% | Only MiMo-V2.6-Pro measured |
| LMArena WebDev | 1341 | 1625 | MiMo-V2.6-Pro |
| AA Coding Index | 45.8 | Not measured | Only GLM 4.6 measured |
| LiveCodeBench (Vals AI) | 81.0% | Not measured | Only GLM 4.6 measured |
| Terminal-Bench 4 (Vals AI) | Not measured | 31.3% | Only MiMo-V2.6-Pro measured |
| Vibe Code Bench (Vals AI) | 3.1% | 85.2% | MiMo-V2.6-Pro |
| 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 |
| LiveCodeBench (Artificial Analysis run) | 69.5% | Not measured | Only GLM 4.6 measured |
| SciCode | Not measured | 60.9% | Only MiMo-V2.6-Pro measured |
| Terminal-Bench Hard (Artificial Analysis run) | 28.8% | Not measured | Only GLM 4.6 measured |
| Terminal-Bench | 24.5% | Not measured | Only GLM 4.6 measured |
| ALE-Bench | 340.8 | 1158.1 | MiMo-V2.6-Pro |
| LMArena Text: coding | 1449 | 1533 | MiMo-V2.6-Pro |
| LMArena Text: software and IT | 1455 | 1528 | MiMo-V2.6-Pro |
| LMArena WebDev: React | Not measured | 1639 | Only MiMo-V2.6-Pro measured |
| LMArena WebDev: HTML | 1350 | 1616 | MiMo-V2.6-Pro |
| LMArena WebDev: from an image | Not measured | 1598 | Only MiMo-V2.6-Pro measured |
| Neither model has a result on the other 15 boards here | |||
IntelligenceLike-for-likePublic boards, shown as published, kept out of the index01
| Benchmark | GLM 4.6 | MiMo-V2.6-Pro | Leader |
|---|---|---|---|
| AA Intelligence Index | 18.5 | 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 index17 boards, none shared
| Benchmark | GLM 4.6 | MiMo-V2.6-Pro | Leader |
|---|---|---|---|
| Design Arena: agents building web apps | Not measured | 1291 | Only MiMo-V2.6-Pro 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 |
| 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 |
| τ²-Bench Telecom | 76.9% | Not measured | Only GLM 4.6 measured |
| APEX-Agents | Not measured | 54.4% | Only MiMo-V2.6-Pro measured |
| Neither model has a result on the other 22 boards here | |||
ReasoningLike-for-likePublic boards, shown as published, kept out of the index04
| Benchmark | GLM 4.6 | MiMo-V2.6-Pro | Leader |
|---|---|---|---|
| GPQA Diamond | 74.5% | Not measured | Only GLM 4.6 measured |
| 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 |
| GPQA Diamond (Artificial Analysis run) | 78.0% | Not measured | Only GLM 4.6 measured |
| Humanity's Last Exam (Artificial Analysis run) | 14.5% | 49.4% | MiMo-V2.6-Pro |
| LMArena Text: hard prompts | 1440 | 1511 | MiMo-V2.6-Pro |
| LMArena Text: expert | 1432 | 1541 | MiMo-V2.6-Pro |
| LMArena Text: hard prompts in English | 1445 | 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 11 boards here | |||
MathLike-for-likePublic boards, shown as published, kept out of the index02
| Benchmark | GLM 4.6 | MiMo-V2.6-Pro | 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 | 70.0% | Only MiMo-V2.6-Pro measured |
| LMArena Text: math | 1432 | 1500 | MiMo-V2.6-Pro |
| LMArena Text: mathematical work | 1447 | 1511 | MiMo-V2.6-Pro |
| Neither model has a result on the other 6 boards here | |||
KnowledgeLike-for-likePublic boards, shown as published, kept out of the index04
| Benchmark | GLM 4.6 | MiMo-V2.6-Pro | Leader |
|---|---|---|---|
| MMLU Pro | 82.2% | Not measured | Only GLM 4.6 measured |
| LegalBench (Vals AI) | 79.6% | Not measured | Only GLM 4.6 measured |
| CorpFin (Vals AI) | 56.8% | Not measured | Only GLM 4.6 measured |
| TaxEval (Vals AI) | 66.2% | Not measured | Only GLM 4.6 measured |
| MedQA (Vals AI) | 92.2% | Not measured | Only GLM 4.6 measured |
| MedCode (Vals AI) | Not measured | 45.0% | Only MiMo-V2.6-Pro measured |
| MMLU Pro (Artificial Analysis run) | 82.9% | Not measured | Only GLM 4.6 measured |
| LMArena Text: science | 1456 | 1506 | MiMo-V2.6-Pro |
| LMArena Text: medicine and healthcare | 1455 | 1497 | MiMo-V2.6-Pro |
| LMArena Text: legal and government | 1448 | 1485 | MiMo-V2.6-Pro |
| LMArena Text: business and finance | 1416 | 1468 | MiMo-V2.6-Pro |
| Neither model has a result on the other 6 boards here | |||
Web designLike-for-likePublic boards, shown as published, kept out of the index06
| Benchmark | GLM 4.6 | MiMo-V2.6-Pro | Leader |
|---|---|---|---|
| Design Arena: websites | 1174 | 1308 | MiMo-V2.6-Pro |
| Design Arena: UI components | 1147 | 1338 | MiMo-V2.6-Pro |
| Design Arena: game dev | 1151 | 1314 | MiMo-V2.6-Pro |
| Design Arena: data viz | 1161 | 1337 | MiMo-V2.6-Pro |
| Design Arena: 3D | 1129 | 1330 | MiMo-V2.6-Pro |
| Design Arena: all code categories | 1166 | 1317 | MiMo-V2.6-Pro |
| Design Arena: SVG | 1108 | Not measured | Only GLM 4.6 measured |
| LiveBench design | Not measured | 78.5% | Only MiMo-V2.6-Pro measured |
| Neither model has a result on the other 1 board here | |||
GeneralLike-for-likePublic boards, shown as published, kept out of the index012
| Benchmark | GLM 4.6 | 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 |
| IFBench | 43.4% | Not measured | Only GLM 4.6 measured |
| Long context reasoning (AA-LCR) | 54.0% | 86.3% | MiMo-V2.6-Pro |
| LMArena Text | 1440 | 1491 | MiMo-V2.6-Pro |
| LMArena Text: instruction following | 1410 | 1491 | MiMo-V2.6-Pro |
| LMArena Text: creative writing | 1412 | 1466 | MiMo-V2.6-Pro |
| LMArena Vision | Not measured | 1263 | Only MiMo-V2.6-Pro measured |
| LMArena Text: English | 1449 | 1504 | MiMo-V2.6-Pro |
| LMArena Text: non-English | 1426 | 1474 | MiMo-V2.6-Pro |
| LMArena Text: Chinese | 1499 | 1529 | MiMo-V2.6-Pro |
| LMArena Text: German | 1447 | Not measured | Only GLM 4.6 measured |
| LMArena Text: Japanese | 1393 | Not measured | Only GLM 4.6 measured |
| LMArena Text: Korean | 1400 | Not measured | Only GLM 4.6 measured |
| LMArena Text: French | 1459 | Not measured | Only GLM 4.6 measured |
| LMArena Text: Spanish | 1436 | Not measured | Only GLM 4.6 measured |
| LMArena Text: Russian | 1418 | 1479 | MiMo-V2.6-Pro |
| LMArena Text: Polish | 1447 | Not measured | Only GLM 4.6 measured |
| LMArena Text: multi-turn | 1427 | 1466 | MiMo-V2.6-Pro |
| LMArena Text: longer queries | 1422 | 1501 | MiMo-V2.6-Pro |
| LMArena Text: writing and language | 1415 | 1470 | MiMo-V2.6-Pro |
| LMArena Text: entertainment and media | 1409 | 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 19 boards here | |||
Image generationNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
| Benchmark | GLM 4.6 | 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 | Z.ai GLM 4.6 | Xiaomi MiMo-V2.6-Pro |
|---|---|---|
| Maker | Z.ai | Xiaomi |
| Weights | Open weights | Open weights |
| Released | September 30, 2025 | September 21, 2026 |
| Context window | 205K | 1.05M |
| Input $/Mtok | $0.55 | $0.435 |
| Output $/Mtok | $2.20 | $0.87 |
| Coding index | 52.2 | 72.6 |
| Independent measurements behind it | 2 | 2 |
| Cost per solved task | Not computable | Not computable |
| Source | OpenRouter listing | OpenRouter listing |
Questions
Is GLM 4.6 better than MiMo-V2.6-Pro for coding?
Pick MiMo-V2.6-Pro for coding: it scores 72.6 on the Vibe Coding Index against 52.2 for GLM 4.6. GLM 4.6 is neither cheaper nor larger in context, and leads no benchmark both models were measured on.
Which is cheaper, GLM 4.6 or MiMo-V2.6-Pro?
GLM 4.6 costs $0.55 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, GLM 4.6 or MiMo-V2.6-Pro?
MiMo-V2.6-Pro, with 1.05M tokens against 205K.
How many benchmarks is the GLM 4.6 vs MiMo-V2.6-Pro comparison based on?
35 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-V2.6-Pro's on 2. Where only one model has a result, the page says so instead of naming a winner.