GLM-5.2 vs GLM-5.3-Flash
GLM-5.2 Verdict
- GLM-5.2 (max) and GLM-5.3-Flash are level on quality (63.9 vs 63.9).
- GLM-5.2 (max) is stronger in composite, instruction following, reasoning.
- GLM-5.3-Flash is stronger in agents & tools, coding, human preference, knowledge, long context, maths, multimodal.
- GLM-5.3-Flash is 18× cheaper ($0.119 vs $2.15 per 1M blended).
- GLM-5.3-Flash streams 1.4× faster (98 vs 71 tokens per second).
| Metric | GLM-5.2 (max) | GLM-5.3-Flash |
|---|---|---|
| BenchLeader Index | 63.9 | 63.9 |
| Agents & tools score | 63.8 | 68.0 |
| Coding score | 63.8 | 64.0 |
| Composite score | 72.1 | 61.7 |
| Human preference score | 67.2 | 67.6 |
| Instruction following score | 71.6 | – |
| Knowledge score | 55.5 | 67.8 |
| Long context score | 65.6 | 66.5 |
| Maths score | 59.0 | 71.0 |
| Reasoning score | 72.9 | 70.3 |
| Multimodal score | – | 65.3 |
| Blended price $/M | $2.15 | $0.119 |
| Output speed | 71 tok/s | 98 tok/s |
| Time to first answer | 31.6 s | 22.9 s |
| Context window | 1M | 1M |
| GPQA Diamond | 91.9% | – |
| FrontierMath Tiers 1–3 | 59.2% | – |
| FrontierMath Tier 4 | 29.3% | – |
| OTIS Mock AIME | 86.4% | – |
| SWE-bench Verified (Epoch) | 78.7% | – |
| SimpleQA Verified | 34.2% | – |
| SciCode | 50.5% | 46.1% |
| WeirdML | 70.1% | – |
| ProofBench | 35.0% | – |
| Epoch Capabilities Index | – | 151.4 |
| LMArena Text | 1472 | 1475 |
| LMArena Hard Prompts | 1493 | 1498 |
| LMArena Coding | 1510 | 1525 |
| LMArena WebDev | 1592 | 1607 |
| LMArena Vision | – | 1299 |
| LMArena Agent | 4.4 | 1.1 |
| LiveBench | – | 71.6% |
| LiveBench Reasoning | – | 77.6% |
| LiveBench Coding | – | 79.0% |
| LiveBench Agentic Coding | – | 56.8% |
| LiveBench Mathematics | – | 81.2% |
| LiveBench Data Analysis | – | 76.4% |
| LiveBench Language | – | 77.3% |
| LiveBench Instruction Following | – | 52.8% |
| AA Intelligence Index | 34.0 | 41.9 |
| IFBench | 73.3% | – |
| AA-LCR | 78.3% | 80.0% |
| AA-Omniscience | 4.4 | 7.5 |
| Terminal-Bench Hard | 50.8% | – |
| GPQA Diamond (AA) | 89.5% | 91.2% |
| Humanity's Last Exam (AA) | 41.1% | 39.9% |
| SciCode (AA) | 51.2% | 51.6% |
| τ²-Bench Telecom (AA) | 99.1% | – |
| Terminal-Bench 2.1 (Vals) | 67.8% | – |
| SWE-bench (Vals) | 82.8% | – |
| CritPt | 20.9% | 15.4% |
| GDPval (AA) | 45.3% | 57.8% |
| τ²-Bench Banking (AA) | 34.6% | 47.2% |
| ITBench SRE (AA) | 42.7% | – |
| APEX-Agents (AA) | 33.7% | – |
| Code Migration | 37.9% | – |
| Harvey's Legal Agent Benchmark | 7.1% | – |
| Legal Research Bench | 31.3% | – |
| ProgramBench | 0.5% | – |
| SkillsBench | 45.1% | – |
| SREBench | 0.0% | – |
| Vibe Code Bench v1.1 | 64.0% | – |
| LMArena Maths | 1480 | 1513 |
| LMArena Creative Writing | 1451 | 1435 |
| LMArena Instruction Following | 1466 | 1471 |
| LMArena Multi-turn | 1469 | 1475 |
| LMArena Longer Queries | 1483 | 1476 |
| Chess Puzzles | 21.0% | – |
| EBR-bench | 9.5% | – |
| PostTrainBench | 31.7% | – |
| DeepSWE | 43.8% | – |
| LMCA | 45.8% | – |
| DTBench | 93.6% | – |
| CursorBench | 55.0% | – |
| ALE-Bench | 1010.2 | – |
Data as of 2026-09-19. Best configuration of each model; every score links to its source on the model pages.
GLM-5.2 vs GLM-5.3-Flash: questions
- Is GLM-5.2 better than GLM-5.3-Flash?
- GLM-5.2 (max) and GLM-5.3-Flash are level on quality (63.9 vs 63.9). The BenchLeader Index combines every independent quality benchmark; GLM-5.3-Flash is ahead overall as of 2026-09-19, but check the category scores for your use.
- Is GLM-5.2 better than GLM-5.3-Flash for coding?
- GLM-5.3-Flash scores higher in coding (64 vs 64 on the category index, where 50 is average).
- Is GLM-5.2 better than GLM-5.3-Flash for agentic tasks?
- GLM-5.3-Flash scores higher in agentic tasks (68 vs 64 on the category index, where 50 is average).
- Which is cheaper, GLM-5.2 or GLM-5.3-Flash?
- GLM-5.3-Flash is cheaper: $0.119 against $2.15 per million tokens, blended at three input tokens per output token.
- Which is faster, GLM-5.2 or GLM-5.3-Flash?
- GLM-5.3-Flash streams faster: 98 against 71 output tokens per second.
- Which has the larger context window?
- Both accept 1M tokens of context.