GLM 5.3 vs MiMo-V2.5-Pro
Updated October 21, 2026. 66 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 5.3 for coding: it scores 73.1 on the Vibe Coding Index against 40.0 for MiMo-V2.5-Pro.
Pick MiMo-V2.5-Pro when output tokens cost 2.3x less, or when a solved task costs $0.03 against $0.36.
- Shared results
- 66
- GLM 5.3 only
- 34
- MiMo-V2.5-Pro only
- 4
- 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 5.3
GLM 5.3 scores 73.1 against 40.0.
2 and 1 benchmarks behind the scores
Intelligence
AA Intelligence Index
GLM 5.3
GLM 5.3 leads, 44.8 against 26.0.
Same board, both measured
Agentic work
AA Agentic Index
GLM 5.3
GLM 5.3 leads, 53.1 against 21.3.
Same board, both measured
Reasoning
GPQA Diamond
GLM 5.3
GLM 5.3 leads, 88.1% against 82.6%.
Same board, both measured
Knowledge
MMLU Pro
GLM 5.3
GLM 5.3 leads, 86.8% against 84.6%.
Same board, both measured
Web design
Design Arena: websites
GLM 5.3
GLM 5.3 leads, 1295 against 1267.
Same board, both measured
General
Vals Index
No call
Only GLM 5.3 has a Vals Index result, 59.7%.
Directional only
Long documents
Listed context window
MiMo-V2.5-Pro
MiMo-V2.5-Pro takes 1.05M tokens against 1.05M.
Listed by OpenRouter
Everyday cost
1K input + 500 output tokens
MiMo-V2.5-Pro
GLM 5.3 $0.0008, MiMo-V2.5-Pro $0.0006 per turn.
List prices
Cost per solved task
One SWE-bench Verified task, solved
MiMo-V2.5-Pro
GLM 5.3 $0.36, MiMo-V2.5-Pro $0.03.
Published attempt cost and pass rate
At a glance
- Coding index
- GLM 5.3 73.1 from 2 benchmarks, MiMo-V2.5-Pro 40.0 from 1 benchmark.
- Widest category
- Agentic work, 21.7 points apart on average over 5 shared benchmarks.
- Widest single gap
- Vibe Code Bench (Vals AI): GLM 5.3 78.1% against MiMo-V2.5-Pro 34.1%, 44.0 points apart on a 0 to 100 scale.
- Output price
- GLM 5.3 $1.40 against MiMo-V2.5-Pro $0.609 per million output tokens, 2.3x apart.
- Context window
- GLM 5.3 takes 1.05M 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 | GLM 5.3 | Bars | MiMo-V2.5-Pro | Ties |
|---|---|---|---|---|
| Coding | 11 | 3 | ||
| Intelligence | 1 | 0 | ||
| Agentic work | 5 | 0 | ||
| Reasoning | 6 | 0 | ||
| Math | 3 | 0 | ||
| Knowledge | 5 | 3 | ||
| Web design | 8 | 0 | ||
| General | 18 | 2 | 1 tie | |
| All shared boards | 57 | 8 | 1 tie |
CodingLike-for-likeFour of these boards feed the Vibe Coding Index113
| Benchmark | GLM 5.3 | MiMo-V2.5-Pro | Leader |
|---|---|---|---|
| LiveBench coding | 82.8% | Not measured | Only GLM 5.3 measured |
| LMArena WebDev | 1621 | 1480 | GLM 5.3 |
| AA Coding Index | 74.8 | 60.2 | GLM 5.3 |
| LiveCodeBench (Vals AI) | 80.5% | 81.3% | MiMo-V2.5-Pro |
| SWE-bench Verified (Vals AI) | 95.4% | 74.0% | GLM 5.3 |
| Terminal-Bench 4 (Vals AI) | 38.9% | 0.5% | GLM 5.3 |
| Vibe Code Bench (Vals AI) | 78.1% | 34.1% | GLM 5.3 |
| Code migration (Vals AI) | 44.2% | 21.6% | GLM 5.3 |
| ProgramBench (Vals AI) | 1.5% | Not measured | Only GLM 5.3 measured |
| IOI (Vals AI) | 68.4% | Not measured | Only GLM 5.3 measured |
| Terminal-Bench 2.1 (Vals AI) | 71.5% | 57.3% | GLM 5.3 |
| 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% | Not measured | Only GLM 5.3 measured |
| SciCode | 59.0% | 50.6% | GLM 5.3 |
| Terminal-Bench Hard (Artificial Analysis run) | Not measured | 43.2% | Only MiMo-V2.5-Pro measured |
| 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 | 899.8 | GLM 5.3 |
| LMArena Text: coding | 1497 | 1501 | MiMo-V2.5-Pro |
| LMArena Text: software and IT | 1493 | 1495 | MiMo-V2.5-Pro |
| LMArena WebDev: React | 1641 | 1469 | GLM 5.3 |
| LMArena WebDev: HTML | 1595 | 1473 | GLM 5.3 |
| Neither model has a result on the other 14 boards here | |||
IntelligenceLike-for-likePublic boards, shown as published, kept out of the index10
| Benchmark | GLM 5.3 | MiMo-V2.5-Pro | Leader |
|---|---|---|---|
| AA Intelligence Index | 44.8 | 26.0 | GLM 5.3 |
| Epoch Capabilities Index | 156.0 | Not measured | Only GLM 5.3 measured |
Agentic workLike-for-likePublic boards, shown as published, kept out of the index50
| Benchmark | GLM 5.3 | MiMo-V2.5-Pro | Leader |
|---|---|---|---|
| AA Agentic Index | 53.1 | 21.3 | GLM 5.3 |
| 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 | 1255 | Not measured | Only GLM 5.3 measured |
| LiveBench research | 73.2% | Not measured | Only GLM 5.3 measured |
| LiveBench browser | 0.0% | Not measured | Only GLM 5.3 measured |
| LiveBench long agentic | 35.9% | Not measured | Only GLM 5.3 measured |
| SREBench (Vals AI) | 9.2% | Not measured | Only GLM 5.3 measured |
| Finance Agent (Vals AI) | 55.8% | 41.5% | GLM 5.3 |
| Tax Agent Bench (Vals AI) | 39.7% | 11.5% | GLM 5.3 |
| Legal Research Bench (Vals AI) | 49.0% | 15.9% | GLM 5.3 |
| Harvey's Legal Agent Benchmark (Vals AI) | 14.6% | Not measured | Only GLM 5.3 measured |
| Excel Modeling Benchmark (Vals AI) | 56.3% | 55.2% | GLM 5.3 |
| Terminal-Bench-Science (Vals AI) | 5.7% | Not measured | Only GLM 5.3 measured |
| τ²-Bench Telecom | Not measured | 94.2% | Only MiMo-V2.5-Pro measured |
| APEX-Agents | 56.6% | Not measured | Only GLM 5.3 measured |
| Vending-Bench 2 | $8,164 | Not measured | Only GLM 5.3 measured |
| Neither model has a result on the other 22 boards here | |||
ReasoningLike-for-likePublic boards, shown as published, kept out of the index60
| Benchmark | GLM 5.3 | MiMo-V2.5-Pro | Leader |
|---|---|---|---|
| GPQA Diamond | 88.1% | 82.6% | GLM 5.3 |
| LiveBench reasoning | 85.8% | Not measured | Only GLM 5.3 measured |
| MysteryMechanism (Vals AI) | 23.0% | Not measured | Only GLM 5.3 measured |
| GPQA Diamond (Artificial Analysis run) | 91.7% | 86.6% | GLM 5.3 |
| Humanity's Last Exam (Artificial Analysis run) | 42.3% | 35.7% | GLM 5.3 |
| 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 | 1486 | GLM 5.3 |
| LMArena Text: expert | 1514 | 1501 | GLM 5.3 |
| LMArena Text: hard prompts in English | 1496 | 1494 | GLM 5.3 |
| Neither model has a result on the other 10 boards here | |||
MathLike-for-likePublic boards, shown as published, kept out of the index30
| Benchmark | GLM 5.3 | MiMo-V2.5-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% | 22.0% | GLM 5.3 |
| LMArena Text: math | 1495 | 1481 | GLM 5.3 |
| LMArena Text: mathematical work | 1497 | 1491 | GLM 5.3 |
| Neither model has a result on the other 7 boards here | |||
KnowledgeLike-for-likePublic boards, shown as published, kept out of the index53
| Benchmark | GLM 5.3 | MiMo-V2.5-Pro | Leader |
|---|---|---|---|
| MMLU Pro | 86.8% | 84.6% | GLM 5.3 |
| LegalBench (Vals AI) | 84.8% | 77.1% | GLM 5.3 |
| CorpFin (Vals AI) | Not measured | 61.4% | Only MiMo-V2.5-Pro measured |
| TaxEval (Vals AI) | 72.4% | 73.8% | MiMo-V2.5-Pro |
| MedCode (Vals AI) | 42.9% | 32.5% | GLM 5.3 |
| SimpleQA Verified | 41.0% | Not measured | Only GLM 5.3 measured |
| LMArena Text: science | 1498 | 1482 | GLM 5.3 |
| LMArena Text: medicine and healthcare | 1481 | 1473 | GLM 5.3 |
| LMArena Text: legal and government | 1473 | 1474 | MiMo-V2.5-Pro |
| LMArena Text: business and finance | 1459 | 1466 | MiMo-V2.5-Pro |
| Neither model has a result on the other 7 boards here | |||
Web designLike-for-likePublic boards, shown as published, kept out of the index80
| Benchmark | GLM 5.3 | MiMo-V2.5-Pro | Leader |
|---|---|---|---|
| Design Arena: websites | 1295 | 1267 | GLM 5.3 |
| Design Arena: UI components | 1314 | 1248 | GLM 5.3 |
| Design Arena: game dev | 1311 | 1261 | GLM 5.3 |
| Design Arena: data viz | 1260 | 1257 | GLM 5.3 |
| Design Arena: 3D | 1332 | 1244 | GLM 5.3 |
| Design Arena: all code categories | 1304 | 1266 | GLM 5.3 |
| Design Arena: SVG | 1292 | 1181 | GLM 5.3 |
| Design Arena: ASCII art | 1213 | 1157 | GLM 5.3 |
| LiveBench design | 77.3% | Not measured | Only GLM 5.3 measured |
GeneralLike-for-likePublic boards, shown as published, kept out of the index182
| Benchmark | GLM 5.3 | MiMo-V2.5-Pro | Leader |
|---|---|---|---|
| Vals Index | 59.7% | Not measured | Only GLM 5.3 measured |
| LiveBench data analysis | 70.2% | Not measured | Only GLM 5.3 measured |
| Public Benefits Bench (Vals AI) | 68.5% | Not measured | Only GLM 5.3 measured |
| MedScribe (Vals AI) | 88.8% | 83.7% | GLM 5.3 |
| IFBench | Not measured | 79.9% | Only MiMo-V2.5-Pro measured |
| Long context reasoning (AA-LCR) | 79.7% | 79.7% | Tie |
| DTBench | 87.7% | 84.5% | GLM 5.3 |
| LMCA | 55.5% | 29.5% | GLM 5.3 |
| LMArena Text | 1474 | 1464 | GLM 5.3 |
| LMArena Text: instruction following | 1479 | 1475 | GLM 5.3 |
| LMArena Text: creative writing | 1458 | 1438 | GLM 5.3 |
| LMArena Text: English | 1482 | 1477 | GLM 5.3 |
| LMArena Text: non-English | 1459 | 1448 | GLM 5.3 |
| LMArena Text: Chinese | 1532 | 1507 | GLM 5.3 |
| LMArena Text: German | 1488 | 1459 | GLM 5.3 |
| LMArena Text: Japanese | 1460 | 1410 | GLM 5.3 |
| LMArena Text: Korean | 1460 | 1435 | GLM 5.3 |
| LMArena Text: French | 1503 | 1487 | GLM 5.3 |
| LMArena Text: Spanish | 1462 | 1469 | MiMo-V2.5-Pro |
| LMArena Text: Russian | 1469 | 1449 | GLM 5.3 |
| LMArena Text: Polish | 1466 | 1464 | GLM 5.3 |
| LMArena Text: multi-turn | 1472 | 1476 | MiMo-V2.5-Pro |
| LMArena Text: longer queries | 1483 | 1481 | GLM 5.3 |
| LMArena Text: writing and language | 1469 | 1451 | GLM 5.3 |
| LMArena Text: entertainment and media | 1447 | 1438 | GLM 5.3 |
| Neither model has a result on the other 21 boards here | |||
Image generationNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
| Benchmark | GLM 5.3 | 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
MiMo-V2.5-Pro costs less
Repository review
50K input + 3K output tokens
GLM 5.3 costs less
Agent loop
200K input + 10K output tokens
GLM 5.3 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-V2.5-Pro |
|---|---|---|
| Maker | Z.ai | Xiaomi |
| Weights | Open weights | Open weights |
| Released | August 18, 2026 | April 22, 2026 |
| Context window | 1.05M | 1.05M |
| Input $/Mtok | $0.079 | $0.3045 |
| Output $/Mtok | $1.40 | $0.609 |
| Coding index | 73.1 | 40.0 |
| Independent measurements behind it | 2 | 1 |
| Cost per solved task | $0.36 | $0.03 |
| Source | OpenRouter listing | OpenRouter listing |
Questions
Is GLM 5.3 better than MiMo-V2.5-Pro for coding?
Pick GLM 5.3 for coding: it scores 73.1 on the Vibe Coding Index against 40.0 for MiMo-V2.5-Pro. Pick MiMo-V2.5-Pro when output tokens cost 2.3x less, or when a solved task costs $0.03 against $0.36.
Which is cheaper, GLM 5.3 or MiMo-V2.5-Pro?
GLM 5.3 costs $0.079 per million input tokens and $1.40 per million output tokens; MiMo-V2.5-Pro costs $0.3045 and $0.609. Per solved SWE-bench Verified task, GLM 5.3 costs $0.36 and MiMo-V2.5-Pro $0.03.
Which has the bigger context window, GLM 5.3 or MiMo-V2.5-Pro?
MiMo-V2.5-Pro, with 1.05M tokens against 1.05M.
How many benchmarks is the GLM 5.3 vs MiMo-V2.5-Pro comparison based on?
66 benchmarks have a result for both models as of October 21, 2026. GLM 5.3's coding index rests on 2 independent benchmarks and MiMo-V2.5-Pro's on 1. Where only one model has a result, the page says so instead of naming a winner.