GLM 5.3 vs MiMo-V2.6-Pro
GLM 5.3 
MiMo-V2.6-Pro Verdict
- GLM 5.3 (max) and MiMo-V2.6-Pro are level on quality (65.2 vs 64.3).
- GLM 5.3 (max) is stronger in agents & tools, coding, human preference, maths.
- MiMo-V2.6-Pro is stronger in composite, knowledge, long context, reasoning.
- MiMo-V2.6-Pro is 3.9× cheaper ($0.548 vs $2.15 per 1M blended).
| Metric | GLM 5.3 (max) | MiMo-V2.6-Pro |
|---|---|---|
| BenchLeader Index | 65.2 | 64.3 |
| Agents & tools score | 61.8 | 55.1 |
| Coding score | 63.6 | – |
| Composite score | 82.9 | 84.8 |
| Human preference score | 68.6 | – |
| Knowledge score | 59.3 | 66.8 |
| Long context score | 65.6 | 69.1 |
| Maths score | 62.7 | – |
| Reasoning score | 70.4 | 88.8 |
| Blended price $/M | $2.15 | $0.548 |
| Output speed | 61 tok/s | 54 tok/s |
| Time to first answer | 36.4 s | 39.8 s |
| Context window | 1M | 1.0M |
| GPQA Diamond | 90.9% | – |
| FrontierMath Tiers 1–3 | 68.8% | – |
| FrontierMath Tier 4 | 29.3% | – |
| OTIS Mock AIME | 91.1% | – |
| SimpleQA Verified | 41.0% | – |
| Terminal-Bench | 41.8% | – |
| SciCode | 59.0% | – |
| WeirdML | 75.4% | – |
| FrontierCode | 40.1% | – |
| ProofBench | 49.0% | – |
| LMArena Text | 1483 | – |
| LMArena Hard Prompts | 1507 | – |
| LMArena Coding | 1524 | – |
| LMArena WebDev | 1620 | – |
| LMArena Agent | 3.1 | – |
| AA Intelligence Index | 44.8 | 46.3 |
| AA-LCR | 79.7% | 86.3% |
| AA-Omniscience | 14.3 | 8.4 |
| GPQA Diamond (AA) | 91.7% | – |
| Humanity's Last Exam (AA) | 42.3% | 49.4% |
| SciCode (AA) | 59.0% | 60.9% |
| LiveCodeBench | 80.5% | – |
| MMLU-Pro | 86.8% | – |
| IOI | 68.4% | – |
| LegalBench | 84.8% | – |
| TaxEval | 72.4% | – |
| Terminal-Bench 2.1 (Vals) | 71.5% | 67.8% |
| SWE-bench (Vals) | 95.4% | – |
| GPQA Diamond (Vals) | 88.1% | – |
| Vals Index | 57.0 | 59.7 |
| CritPt | 19.1% | 26.6% |
| GDPval (AA) | 57.3% | 58.7% |
| τ²-Bench Banking (AA) | 50.3% | – |
| Code Migration | 44.2% | 43.0% |
| Excel Modeling Benchmark | 56.3% | 62.9% |
| Finance Agent v2 | 55.8% | 58.3% |
| Harvey's Legal Agent Benchmark | 8.3% | 10.8% |
| Legal Research Bench | 49.0% | 47.1% |
| MedCode | 42.9% | – |
| MedScribe | 88.8% | – |
| MysteryMechanism | 23.0% | – |
| ProgramBench | 1.5% | – |
| Public Benefits Bench | 68.5% | – |
| SkillsBench | 47.5% | – |
| Tax Agent Bench | 73.1% | – |
| Terminal-Bench 4.0 (Vals) | 25.3% | – |
| Terminal-Bench Science | 4.3% | 2.9% |
| Vibe Code Bench 1-100 | 20.0% | – |
| Vibe Code Bench v1.1 | 78.1% | 85.2% |
| FORTRESS | 28.2% | – |
| LMArena Maths | 1504 | – |
| LMArena Creative Writing | 1462 | – |
| LMArena Instruction Following | 1480 | – |
| LMArena Multi-turn | 1493 | – |
| LMArena Longer Queries | 1492 | – |
| Chess Puzzles | 21.0% | – |
| Mystery Game Puzzles | 33.0% | – |
| DeepSWE | 69.0% | – |
| FrontierSWE | 30.2% | – |
| Terminal-Bench 4.0 (AA) | 41.9% | 34.9% |
| Terminal-Bench 2.1 (AA) | 83.9% | – |
| AutomationBench | 62.2% | 58.6% |
| GDP.pdf | 11.2% | 19.2% |
| MLCR | 48.3% | – |
| EnterpriseOps-Gym | 36.4% | – |
| AA-Omniscience: accuracy | 33.9% | 34.9% |
| AA-Omniscience: non-hallucination | 70.5% | 59.4% |
| AA-Briefcase | 1525 | 1522 |
| AA Openness Index | 33.3 | – |
Data as of 2026-09-23. Best configuration of each model; every score links to its source on the model pages.
GLM 5.3 vs MiMo-V2.6-Pro: questions
- Is GLM 5.3 better than MiMo-V2.6-Pro?
- GLM 5.3 (max) and MiMo-V2.6-Pro are level on quality (65.2 vs 64.3). The BenchLeader Index combines every independent quality benchmark; GLM 5.3 (max) is ahead overall as of 2026-09-23, but check the category scores for your use.
- Is GLM 5.3 better than MiMo-V2.6-Pro for agentic tasks?
- GLM 5.3 scores higher in agentic tasks (62 vs 55 on the category index, where 50 is average).
- Which is cheaper, GLM 5.3 or MiMo-V2.6-Pro?
- MiMo-V2.6-Pro is cheaper: $0.548 against $2.15 per million tokens, blended at three input tokens per output token.
- Which is faster, GLM 5.3 or MiMo-V2.6-Pro?
- GLM 5.3 streams faster: 61 against 54 output tokens per second.
- Which has the larger context window?
- MiMo-V2.6-Pro accepts more context: 1.0M against 1M tokens.