GLM 5.3 vs MiMo-V2.6-Pro
GLM 5.3 leads MiMo-V2.6-Pro on coding, 70.6 to 65.4 on the Vibe Coding Index, and MiMo-V2.6-Pro costs 5.1x less per output token ($0.87 vs $4.40 per million). Updated October 21, 2026; 64 benchmarks have a result for both.
GLM 5.3 or MiMo-V2.6-Pro: which to pick
Pick GLM 5.3 for coding: it scores 70.6 on the Vibe Coding Index against 65.4 for MiMo-V2.6-Pro.
Pick MiMo-V2.6-Pro for output tokens 5.1x cheaper ($0.87 vs $4.40 per million) and a larger context window (1.05M against 1.05M tokens).
- Shared results
- 64
- GLM 5.3 only
- 36
- MiMo-V2.6-Pro only
- 9
- 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
GLM 5.3
GLM 5.3 scores 70.6 against 65.4.
6 and 4 benchmarks behind the scores
Intelligence
AA Intelligence Index
MiMo-V2.6-Pro
MiMo-V2.6-Pro leads, 46.3 against 44.8.
Same board, both measured
Agentic work
AA Agentic Index
No call
Only GLM 5.3 has a AA Agentic Index result, 53.1.
Directional only
Reasoning
GPQA Diamond
No call
Only GLM 5.3 has a GPQA Diamond result, 88.1%.
Directional only
Knowledge
MMLU Pro
No call
Only GLM 5.3 has a MMLU Pro result, 86.8%.
Directional only
Web design
Design Arena: websites
MiMo-V2.6-Pro
MiMo-V2.6-Pro leads, 1308 against 1295.
Same board, both measured
General
Vals Index
GLM 5.3
GLM 5.3 leads, 59.7% against 57.8%.
Same board, both measured
Long documents
Listed context window
MiMo-V2.6-Pro
MiMo-V2.6-Pro takes 1.05M tokens against 1.05M.
Listed by OpenRouter
Everyday cost
1K input + 500 output tokens
MiMo-V2.6-Pro
GLM 5.3 $0.0036, MiMo-V2.6-Pro $0.0009 per turn.
List prices
Cost per solved task
One SWE-bench Verified task, solved
No call
Only GLM 5.3 has the number, $0.36.
Directional only
At a glance
- Coding index
- GLM 5.3 70.6 from 6 benchmarks, MiMo-V2.6-Pro 65.4 from 4 benchmarks.
- Widest category
- Agentic work, 12.0 points apart on average over 11 shared benchmarks.
- Widest single gap
- LiveBench browser: GLM 5.3 0.0% against MiMo-V2.6-Pro 88.0%, 88.0 points apart on a 0 to 100 scale.
- Output price
- GLM 5.3 $4.40 against MiMo-V2.6-Pro $0.87 per million output tokens, 5.1x apart.
- Context window
- GLM 5.3 takes 1.05M 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 5.3 | Bars | MiMo-V2.6-Pro | Ties |
|---|---|---|---|---|
| Coding | 8 | 7 | ||
| Intelligence | 0 | 1 | ||
| Agentic work | 6 | 5 | ||
| Reasoning | 2 | 4 | ||
| Math | 0 | 3 | ||
| Knowledge | 0 | 5 | ||
| Web design | 1 | 6 | ||
| General | 4 | 12 | ||
| All shared boards | 21 | 43 |
CodingLike-for-likeSix of these boards feed the Vibe Coding Index87
| Benchmark | GLM 5.3 | MiMo-V2.6-Pro | Leader |
|---|---|---|---|
| LiveBench coding | 82.8% | 81.5% | GLM 5.3 |
| LMArena WebDev | 1621 | 1625 | MiMo-V2.6-Pro |
| AA Coding Index | 74.8 | Not measured | Only GLM 5.3 measured |
| LiveCodeBench (Vals AI) | 80.5% | Not measured | Only GLM 5.3 measured |
| SWE-bench Verified (Vals AI) | 95.4% | Not measured | Only GLM 5.3 measured |
| Terminal-Bench 4 (Vals AI) | 38.9% | 31.3% | GLM 5.3 |
| Vibe Code Bench (Vals AI) | 78.1% | 85.2% | MiMo-V2.6-Pro |
| Code migration (Vals AI) | 44.2% | 43.0% | GLM 5.3 |
| ProgramBench (Vals AI) | 1.5% | 0.5% | GLM 5.3 |
| IOI (Vals AI) | 68.4% | 39.3% | GLM 5.3 |
| Terminal-Bench 2.1 (Vals AI) | 71.5% | 67.8% | GLM 5.3 |
| SWE-bench Multimodal (Vals AI) | Not measured | 41.3% | Only MiMo-V2.6-Pro measured |
| Vibe Code Bench 1-100 (Vals AI) | 20.0% | Not measured | Only GLM 5.3 measured |
| SkillsBench (Vals AI) | 47.5% | Not measured | Only GLM 5.3 measured |
| CyberBench (Vals AI) | 72.1% | 72.9% | MiMo-V2.6-Pro |
| SciCode | 59.0% | 60.9% | MiMo-V2.6-Pro |
| FrontierCode | 40.1% | Not measured | Only GLM 5.3 measured |
| FrontierSWE | 30.2% | Not measured | Only GLM 5.3 measured |
| WeirdML | 75.4% | Not measured | Only GLM 5.3 measured |
| ALE-Bench | 1317.4 | 1158.1 | GLM 5.3 |
| LMArena Text: coding | 1497 | 1533 | MiMo-V2.6-Pro |
| LMArena Text: software and IT | 1493 | 1528 | MiMo-V2.6-Pro |
| LMArena WebDev: React | 1641 | 1639 | GLM 5.3 |
| LMArena WebDev: HTML | 1595 | 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 14 boards here | |||
IntelligenceLike-for-likePublic boards, shown as published, kept out of the index01
| Benchmark | GLM 5.3 | MiMo-V2.6-Pro | Leader |
|---|---|---|---|
| AA Intelligence Index | 44.8 | 46.3 | MiMo-V2.6-Pro |
| Epoch Capabilities Index | 156.0 | Not measured | Only GLM 5.3 measured |
Agentic workLike-for-likePublic boards, shown as published, kept out of the index65
| Benchmark | GLM 5.3 | MiMo-V2.6-Pro | Leader |
|---|---|---|---|
| AA Agentic Index | 53.1 | Not measured | Only GLM 5.3 measured |
| Design Arena: agents building web apps | Not measured | 1290 | Only MiMo-V2.6-Pro measured |
| Design Arena: agents building mobile apps | 1194 | Not measured | Only GLM 5.3 measured |
| Design Arena: HTML slides | 1189 | Not measured | Only GLM 5.3 measured |
| Design Arena: PowerPoint slides | 1254 | Not measured | Only GLM 5.3 measured |
| LiveBench research | 73.2% | 76.7% | MiMo-V2.6-Pro |
| LiveBench browser | 0.0% | 88.0% | MiMo-V2.6-Pro |
| LiveBench long agentic | 35.9% | 47.5% | MiMo-V2.6-Pro |
| SREBench (Vals AI) | 9.2% | 3.0% | GLM 5.3 |
| Finance Agent (Vals AI) | 55.8% | 57.3% | MiMo-V2.6-Pro |
| Tax Agent Bench (Vals AI) | 39.7% | 32.4% | GLM 5.3 |
| Legal Research Bench (Vals AI) | 49.0% | 47.1% | GLM 5.3 |
| Harvey's Legal Agent Benchmark (Vals AI) | 14.6% | 13.8% | GLM 5.3 |
| Excel Modeling Benchmark (Vals AI) | 56.3% | 62.9% | MiMo-V2.6-Pro |
| Terminal-Bench-Science (Vals AI) | 5.7% | 2.9% | GLM 5.3 |
| APEX-Agents | 56.6% | 54.4% | GLM 5.3 |
| Vending-Bench 2 | $8,164 | Not measured | Only GLM 5.3 measured |
| Neither model has a result on the other 24 boards here | |||
ReasoningLike-for-likePublic boards, shown as published, kept out of the index24
| Benchmark | GLM 5.3 | MiMo-V2.6-Pro | Leader |
|---|---|---|---|
| GPQA Diamond | 88.1% | Not measured | Only GLM 5.3 measured |
| LiveBench reasoning | 85.8% | 79.6% | GLM 5.3 |
| MysteryMechanism (Vals AI) | 23.0% | 15.3% | GLM 5.3 |
| GPQA Diamond (Artificial Analysis run) | 91.7% | Not measured | Only GLM 5.3 measured |
| Humanity's Last Exam (Artificial Analysis run) | 42.3% | 49.4% | MiMo-V2.6-Pro |
| GPQA Diamond (Epoch AI run) | 90.9% | Not measured | Only GLM 5.3 measured |
| Chess puzzles | 37.0% | Not measured | Only GLM 5.3 measured |
| Mystery game puzzles | 33.0% | Not measured | Only GLM 5.3 measured |
| LMArena Text: hard prompts | 1490 | 1511 | MiMo-V2.6-Pro |
| LMArena Text: expert | 1514 | 1541 | MiMo-V2.6-Pro |
| LMArena Text: hard prompts in English | 1496 | 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 9 boards here | |||
MathLike-for-likePublic boards, shown as published, kept out of the index03
| Benchmark | GLM 5.3 | MiMo-V2.6-Pro | Leader |
|---|---|---|---|
| OTIS Mock AIME | 91.1% | Not measured | Only GLM 5.3 measured |
| FrontierMath, tiers 1 to 3 | 68.8% | Not measured | Only GLM 5.3 measured |
| FrontierMath, tier 4 | 29.3% | Not measured | Only GLM 5.3 measured |
| ProofBench | 49.0% | 70.0% | MiMo-V2.6-Pro |
| LMArena Text: math | 1495 | 1500 | MiMo-V2.6-Pro |
| LMArena Text: mathematical work | 1497 | 1511 | MiMo-V2.6-Pro |
| Neither model has a result on the other 7 boards here | |||
KnowledgeLike-for-likePublic boards, shown as published, kept out of the index05
| Benchmark | GLM 5.3 | MiMo-V2.6-Pro | Leader |
|---|---|---|---|
| MMLU Pro | 86.8% | Not measured | Only GLM 5.3 measured |
| LegalBench (Vals AI) | 84.8% | Not measured | Only GLM 5.3 measured |
| TaxEval (Vals AI) | 72.4% | Not measured | Only GLM 5.3 measured |
| MedCode (Vals AI) | 42.9% | 45.0% | MiMo-V2.6-Pro |
| SimpleQA Verified | 41.0% | Not measured | Only GLM 5.3 measured |
| LMArena Text: science | 1498 | 1506 | MiMo-V2.6-Pro |
| LMArena Text: medicine and healthcare | 1481 | 1497 | MiMo-V2.6-Pro |
| LMArena Text: legal and government | 1473 | 1485 | MiMo-V2.6-Pro |
| LMArena Text: business and finance | 1459 | 1468 | MiMo-V2.6-Pro |
| Neither model has a result on the other 9 boards here | |||
Web designLike-for-likePublic boards, shown as published, kept out of the index16
| Benchmark | GLM 5.3 | MiMo-V2.6-Pro | Leader |
|---|---|---|---|
| Design Arena: websites | 1295 | 1308 | MiMo-V2.6-Pro |
| Design Arena: UI components | 1314 | 1339 | MiMo-V2.6-Pro |
| Design Arena: game dev | 1311 | 1313 | MiMo-V2.6-Pro |
| Design Arena: data viz | 1259 | 1336 | MiMo-V2.6-Pro |
| Design Arena: 3D | 1332 | 1331 | GLM 5.3 |
| Design Arena: all code categories | 1304 | 1316 | MiMo-V2.6-Pro |
| Design Arena: SVG | 1293 | Not measured | Only GLM 5.3 measured |
| Design Arena: ASCII art | 1213 | Not measured | Only GLM 5.3 measured |
| LiveBench design | 77.3% | 78.5% | MiMo-V2.6-Pro |
GeneralLike-for-likePublic boards, shown as published, kept out of the index412
| Benchmark | GLM 5.3 | MiMo-V2.6-Pro | Leader |
|---|---|---|---|
| Vals Index | 59.7% | 57.8% | GLM 5.3 |
| LiveBench data analysis | 70.2% | 72.3% | MiMo-V2.6-Pro |
| Public Benefits Bench (Vals AI) | 68.5% | 68.9% | MiMo-V2.6-Pro |
| SAGE (Vals AI) | Not measured | 45.0% | Only MiMo-V2.6-Pro measured |
| MedScribe (Vals AI) | 88.8% | 88.3% | GLM 5.3 |
| Long context reasoning (AA-LCR) | 79.7% | 86.3% | MiMo-V2.6-Pro |
| DTBench | 87.7% | Not measured | Only GLM 5.3 measured |
| LMCA | 55.5% | Not measured | Only GLM 5.3 measured |
| LMArena Text | 1474 | 1491 | MiMo-V2.6-Pro |
| LMArena Text: instruction following | 1479 | 1491 | MiMo-V2.6-Pro |
| LMArena Text: creative writing | 1458 | 1466 | MiMo-V2.6-Pro |
| LMArena Vision | Not measured | 1263 | Only MiMo-V2.6-Pro measured |
| LMArena Text: English | 1482 | 1504 | MiMo-V2.6-Pro |
| LMArena Text: non-English | 1459 | 1474 | MiMo-V2.6-Pro |
| LMArena Text: Chinese | 1532 | 1529 | GLM 5.3 |
| LMArena Text: German | 1488 | Not measured | Only GLM 5.3 measured |
| LMArena Text: Japanese | 1460 | Not measured | Only GLM 5.3 measured |
| LMArena Text: Korean | 1460 | Not measured | Only GLM 5.3 measured |
| LMArena Text: French | 1503 | Not measured | Only GLM 5.3 measured |
| LMArena Text: Spanish | 1462 | Not measured | Only GLM 5.3 measured |
| LMArena Text: Russian | 1469 | 1479 | MiMo-V2.6-Pro |
| LMArena Text: Polish | 1466 | Not measured | Only GLM 5.3 measured |
| LMArena Text: multi-turn | 1472 | 1466 | GLM 5.3 |
| LMArena Text: longer queries | 1483 | 1501 | MiMo-V2.6-Pro |
| LMArena Text: writing and language | 1469 | 1470 | MiMo-V2.6-Pro |
| LMArena Text: entertainment and media | 1447 | 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 18 boards here | |||
Image generationNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
| Benchmark | GLM 5.3 | 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 5.3 | Xiaomi MiMo-V2.6-Pro |
|---|---|---|
| Maker | Z.ai | Xiaomi |
| Weights | Open weights | Open weights |
| Released | August 18, 2026 | September 21, 2026 |
| Context window | 1.05M | 1.05M |
| Input $/Mtok | $1.40 | $0.435 |
| Output $/Mtok | $4.40 | $0.87 |
| Coding index | 70.6 | 65.4 |
| Independent measurements behind it | 6 | 4 |
| Cost per solved task | $0.36 | Not computable |
| Source | OpenRouter listing | OpenRouter listing |
Questions
Is GLM 5.3 better than MiMo-V2.6-Pro for coding?
Pick GLM 5.3 for coding: it scores 70.6 on the Vibe Coding Index against 65.4 for MiMo-V2.6-Pro. Pick MiMo-V2.6-Pro for output tokens 5.1x cheaper ($0.87 vs $4.40 per million) and a larger context window (1.05M against 1.05M tokens).
Which is cheaper, GLM 5.3 or MiMo-V2.6-Pro?
GLM 5.3 costs $1.40 per million input tokens and $4.40 per million output tokens; MiMo-V2.6-Pro costs $0.435 and $0.87. Cost per solved task is known for GLM 5.3 only.
Which has the bigger context window, GLM 5.3 or MiMo-V2.6-Pro?
MiMo-V2.6-Pro, with 1.05M tokens against 1.05M.
How many benchmarks is the GLM 5.3 vs MiMo-V2.6-Pro comparison based on?
64 benchmarks have a result for both models as of October 21, 2026. GLM 5.3's coding index rests on 6 independent benchmarks and MiMo-V2.6-Pro's on 4. Where only one model has a result, the page says so instead of naming a winner.