Model comparison
GLM-5.3-Flash vs MiMo-V2.5-Pro
GLM-5.3-Flash is the stronger model overall, scoring 51.8 to 45.2 on the Noometry Index.
Last verified . 22 shared benchmarks.
Summary
- They share 22 benchmarks with published results for both. GLM-5.3-Flash scores higher in 6 categories and MiMo-V2.5-Pro in 2 categories; 4 gaps are clear of the uncertainty.
- The widest gap is in reasoning, where GLM-5.3-Flash leads 48.0 to 26.8.
- The biggest single-benchmark swing is CritPt: 15.4% for GLM-5.3-Flash and 4% for MiMo-V2.5-Pro.
- GLM-5.3-Flash is cheaper at $0.15 / $0.50 per million input/output tokens, against $0.43 / $0.87 for MiMo-V2.5-Pro.
- MiMo-V2.5-Pro accepts more context: 1.05M tokens versus 1M.
Side by side
| GLM-5.3-Flash | MiMo-V2.5-Pro | |
|---|---|---|
| Provider | Z.ai (Zhipu) | Xiaomi |
| Noometry Index | 51.8 | 45.2 |
| Released | 2026-08-20 | 2026-04-22 |
| Weights | Open | Open |
| Context window | 1M | 1.05M |
| Max output | 131K | 131K |
| Input $ / M tokens | $0.15 | $0.43 |
| Output $ / M tokens | $0.50 | $0.87 |
| Results tracked | 40 | 27 |
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Category by category
Coding GLM-5.3-Flash leads
GLM-5.3-Flash: 53.1 (#31), MiMo-V2.5-Pro: 47.4 (#60)
| Benchmark | GLM-5.3-Flash | MiMo-V2.5-Pro |
|---|---|---|
| LMArena WebDev | 1609 | 1479 |
| SciCode | 51.6% | 50.2% |
| LMArena Coding | 1508 | 1503 |
| ALE-Bench | 303.55 | 899.8 |
| DeepSWE | 63.4% | — |
| FrontierCode | 31.8% | — |
| CursorBench | 36.8% | — |
| FrontierSWE | 18.1% | — |
Agentic & Tool Use Not comparable
GLM-5.3-Flash: 34.2 (#47), MiMo-V2.5-Pro: —
| Benchmark | GLM-5.3-Flash | MiMo-V2.5-Pro |
|---|---|---|
| APEX-Agents | 52.8% | — |
| GDP.pdf | 14% | — |
Reasoning GLM-5.3-Flash leads
GLM-5.3-Flash: 48.0 (#42), MiMo-V2.5-Pro: 26.8 (#130)
| Benchmark | GLM-5.3-Flash | MiMo-V2.5-Pro |
|---|---|---|
| CritPt | 15.4% | 4% |
| LMArena Hard Prompts | 1491 | 1488 |
| ARC-AGI-2 | 65.8% | — |
| NYT Connections (extended) | — | 34.4% |
| ARC-AGI-1 | 91% | — |
| Chess Puzzles | 14% | — |
| Mystery Game Puzzles | 8% | — |
| DTBench | — | 84.5% |
| LMCA | — | 29.5% |
| Surface Evolver Bench | 52.5% | — |
| Bench to the Future 3 | 0.15 | — |
| Epoch Capabilities Index | 151.88 | — |
Math GLM-5.3-Flash leads
GLM-5.3-Flash: 53.3 (#47), MiMo-V2.5-Pro: 40.0 (#96)
| Benchmark | GLM-5.3-Flash | MiMo-V2.5-Pro |
|---|---|---|
| ProofBench | 21% | 22% |
| LMArena Math | 1500 | 1481 |
| FrontierMath (Tiers 1-3) | 55.8% | — |
| FrontierMath Tier 4 | 17.1% | — |
| OTIS Mock AIME 2024-2025 | 93.9% | — |
Knowledge GLM-5.3-Flash leads
GLM-5.3-Flash: 58.4 (#36), MiMo-V2.5-Pro: 42.2 (#98)
| Benchmark | GLM-5.3-Flash | MiMo-V2.5-Pro |
|---|---|---|
| LMArena Expert | 1513 | 1503 |
| GPQA Diamond | 90.2% | — |
Multimodal Not comparable
GLM-5.3-Flash: 42.8 (#27), MiMo-V2.5-Pro: —
| Benchmark | GLM-5.3-Flash | MiMo-V2.5-Pro |
|---|---|---|
| LMArena Vision | 1296 | — |
Multilingual Too close to call
GLM-5.3-Flash: 56.0 (#25), MiMo-V2.5-Pro: 55.1 (#34)
| Benchmark | GLM-5.3-Flash | MiMo-V2.5-Pro |
|---|---|---|
| LMArena Non-English | 1462 | 1449 |
| LMArena Chinese | 1527 | 1507 |
| LMArena French | 1496 | 1488 |
| LMArena German | 1470 | 1458 |
| LMArena Japanese | 1429 | 1412 |
| LMArena Korean | 1446 | 1437 |
| LMArena Russian | 1469 | 1450 |
| LMArena Spanish | 1471 | 1471 |
Instruction Following Too close to call
GLM-5.3-Flash: 77.5 (#20), MiMo-V2.5-Pro: 77.5 (#21)
| Benchmark | GLM-5.3-Flash | MiMo-V2.5-Pro |
|---|---|---|
| LMArena Instruction Following | 1478 | 1477 |
Long Context Too close to call
GLM-5.3-Flash: 45.4 (#39), MiMo-V2.5-Pro: 45.4 (#37)
| Benchmark | GLM-5.3-Flash | MiMo-V2.5-Pro |
|---|---|---|
| LMArena Longer Query | 1482 | 1483 |
Writing & Preference Too close to call
GLM-5.3-Flash: 65.3 (#50), MiMo-V2.5-Pro: 65.3 (#49)
| Benchmark | GLM-5.3-Flash | MiMo-V2.5-Pro |
|---|---|---|
| LMArena Text | 1471 | 1465 |
| LMArena Creative Writing | 1442 | 1440 |
| LMArena Multi-Turn | 1467 | 1477 |
| EQ-Bench Creative Writing | — | 1493 |
| EQ-Bench 4 | — | 1208 |
Frequently asked questions
Is GLM-5.3-Flash better than MiMo-V2.5-Pro?
GLM-5.3-Flash is the stronger model overall, scoring 51.8 to 45.2 on the Noometry Index.
Which is cheaper, GLM-5.3-Flash or MiMo-V2.5-Pro?
GLM-5.3-Flash is cheaper. It lists at $0.15 per million input tokens and $0.50 per million output tokens; MiMo-V2.5-Pro lists at $0.43 and $0.87.
Is GLM-5.3-Flash or MiMo-V2.5-Pro better for coding?
GLM-5.3-Flash scores higher on coding benchmarks: 53.1 versus 47.4 in the Noometry coding category.
Which has the bigger context window?
MiMo-V2.5-Pro does, with 1.05M tokens against 1M.
How many benchmarks do GLM-5.3-Flash and MiMo-V2.5-Pro share?
22 benchmarks have published results for both models. GLM-5.3-Flash has 40 scored results on Noometry and MiMo-V2.5-Pro has 27.