Model comparison
GLM-5.3-Flash vs MiMo-V2-Pro
GLM-5.3-Flash is the stronger model overall, scoring 51.8 to 43.0 on the Noometry Index.
Last verified . 19 shared benchmarks.
Summary
- They share 19 benchmarks with published results for both. GLM-5.3-Flash scores higher in 8 categories and MiMo-V2-Pro in 0 categories; 8 gaps are clear of the uncertainty.
- The widest gap is in reasoning, where GLM-5.3-Flash leads 48.0 to 22.1.
- GLM-5.3-Flash is cheaper at $0.15 / $0.50 per million input/output tokens, against $0.43 / $0.87 for MiMo-V2-Pro.
- MiMo-V2-Pro accepts more context: 1.05M tokens versus 1M.
- GLM-5.3-Flash has downloadable open weights; the other is API-only.
Side by side
| GLM-5.3-Flash | MiMo-V2-Pro | |
|---|---|---|
| Provider | Z.ai (Zhipu) | Xiaomi |
| Noometry Index | 51.8 | 43.0 |
| Released | 2026-08-20 | 2026-03-18 |
| Weights | Open | Proprietary |
| 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 | 23 |
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Category by category
Coding GLM-5.3-Flash leads
GLM-5.3-Flash: 53.1 (#31), MiMo-V2-Pro: 43.8 (#83)
| Benchmark | GLM-5.3-Flash | MiMo-V2-Pro |
|---|---|---|
| LMArena WebDev | 1609 | 1433 |
| LMArena Coding | 1508 | 1476 |
| ALE-Bench | 303.55 | 785.17 |
| DeepSWE | 63.4% | — |
| FrontierCode | 31.8% | — |
| CursorBench | 36.8% | — |
| FrontierSWE | 18.1% | — |
| SciCode | 51.6% | — |
Agentic & Tool Use Not comparable
GLM-5.3-Flash: 34.2 (#47), MiMo-V2-Pro: —
| Benchmark | GLM-5.3-Flash | MiMo-V2-Pro |
|---|---|---|
| APEX-Agents | 52.8% | — |
| GDP.pdf | 14% | — |
Reasoning GLM-5.3-Flash leads
GLM-5.3-Flash: 48.0 (#42), MiMo-V2-Pro: 22.1 (#206)
| Benchmark | GLM-5.3-Flash | MiMo-V2-Pro |
|---|---|---|
| LMArena Hard Prompts | 1491 | 1457 |
| ARC-AGI-2 | 65.8% | — |
| NYT Connections (extended) | — | 25.8% |
| ARC-AGI-1 | 91% | — |
| CritPt | 15.4% | — |
| Chess Puzzles | 14% | — |
| Thematic Generalization | — | 45.9% |
| Mystery Game Puzzles | 8% | — |
| 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-Pro: 39.5 (#102)
| Benchmark | GLM-5.3-Flash | MiMo-V2-Pro |
|---|---|---|
| LMArena Math | 1500 | 1447 |
| FrontierMath (Tiers 1-3) | 55.8% | — |
| FrontierMath Tier 4 | 17.1% | — |
| OTIS Mock AIME 2024-2025 | 93.9% | — |
| ProofBench | 21% | — |
Knowledge GLM-5.3-Flash leads
GLM-5.3-Flash: 58.4 (#36), MiMo-V2-Pro: 41.4 (#111)
| Benchmark | GLM-5.3-Flash | MiMo-V2-Pro |
|---|---|---|
| LMArena Expert | 1513 | 1478 |
| GPQA Diamond | 90.2% | — |
Multimodal Not comparable
GLM-5.3-Flash: 42.8 (#27), MiMo-V2-Pro: —
| Benchmark | GLM-5.3-Flash | MiMo-V2-Pro |
|---|---|---|
| LMArena Vision | 1296 | — |
Multilingual GLM-5.3-Flash leads
GLM-5.3-Flash: 56.0 (#25), MiMo-V2-Pro: 52.7 (#81)
| Benchmark | GLM-5.3-Flash | MiMo-V2-Pro |
|---|---|---|
| LMArena Non-English | 1462 | 1416 |
| LMArena Chinese | 1527 | 1456 |
| LMArena French | 1496 | 1469 |
| LMArena German | 1470 | 1417 |
| LMArena Japanese | 1429 | 1366 |
| LMArena Korean | 1446 | 1400 |
| LMArena Russian | 1469 | 1427 |
| LMArena Spanish | 1471 | 1457 |
Instruction Following GLM-5.3-Flash leads
GLM-5.3-Flash: 77.5 (#20), MiMo-V2-Pro: 76.0 (#49)
| Benchmark | GLM-5.3-Flash | MiMo-V2-Pro |
|---|---|---|
| LMArena Instruction Following | 1478 | 1445 |
Long Context GLM-5.3-Flash leads
GLM-5.3-Flash: 45.4 (#39), MiMo-V2-Pro: 41.5 (#138)
| Benchmark | GLM-5.3-Flash | MiMo-V2-Pro |
|---|---|---|
| LMArena Longer Query | 1482 | 1455 |
| CL-bench | — | 15.7% |
| CL-bench Life | — | 6.9% |
Writing & Preference GLM-5.3-Flash leads
GLM-5.3-Flash: 65.3 (#50), MiMo-V2-Pro: 62.8 (#70)
| Benchmark | GLM-5.3-Flash | MiMo-V2-Pro |
|---|---|---|
| LMArena Text | 1471 | 1436 |
| LMArena Creative Writing | 1442 | 1415 |
| LMArena Multi-Turn | 1467 | 1456 |
Frequently asked questions
Is GLM-5.3-Flash better than MiMo-V2-Pro?
GLM-5.3-Flash is the stronger model overall, scoring 51.8 to 43.0 on the Noometry Index.
Which is cheaper, GLM-5.3-Flash or MiMo-V2-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-Pro lists at $0.43 and $0.87.
Is GLM-5.3-Flash or MiMo-V2-Pro better for coding?
GLM-5.3-Flash scores higher on coding benchmarks: 53.1 versus 43.8 in the Noometry coding category.
Which has the bigger context window?
MiMo-V2-Pro does, with 1.05M tokens against 1M.
How many benchmarks do GLM-5.3-Flash and MiMo-V2-Pro share?
19 benchmarks have published results for both models. GLM-5.3-Flash has 40 scored results on Noometry and MiMo-V2-Pro has 23.