DeepSeek V4.1 Flash vs GLM-5.2
DeepSeek V4.1 Flash 
GLM-5.2 Verdict
- GLM-5.2 (max) leads on quality: 63.9 vs 61.9.
- DeepSeek V4.1 Flash (max) is stronger in coding, composite, knowledge, long context, multimodal.
- GLM-5.2 (max) is stronger in agents & tools, reasoning, human preference, instruction following, maths.
- DeepSeek V4.1 Flash (max) is 8.2× cheaper ($0.262 vs $2.15 per 1M blended).
- DeepSeek V4.1 Flash (max) streams 3.0× faster (215 vs 71 tokens per second).
| Metric | DeepSeek V4.1 Flash (max) | GLM-5.2 (max) |
|---|---|---|
| BenchLeader Index | 61.9 | 63.9 |
| Agents & tools score | 59.6 | 63.8 |
| Coding score | 68.9 | 63.8 |
| Composite score | 74.5 | 72.1 |
| Knowledge score | 61.7 | 55.5 |
| Long context score | 68.5 | 65.6 |
| Multimodal score | 61.4 | – |
| Reasoning score | 69.5 | 72.9 |
| Human preference score | – | 67.2 |
| Instruction following score | – | 71.6 |
| Maths score | – | 59.0 |
| Blended price $/M | $0.262 | $2.15 |
| Output speed | 215 tok/s | 71 tok/s |
| Time to first answer | 10.5 s | 31.6 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% |
| WeirdML | – | 70.1% |
| ProofBench | – | 35.0% |
| LMArena Text | – | 1472 |
| LMArena Hard Prompts | – | 1493 |
| LMArena Coding | – | 1510 |
| LMArena WebDev | 1614 | 1592 |
| LMArena Agent | 4.9 | 4.4 |
| LiveBench | 81.1% | – |
| LiveBench Reasoning | 86.7% | – |
| LiveBench Coding | 80.0% | – |
| LiveBench Agentic Coding | 77.3% | – |
| LiveBench Mathematics | 93.3% | – |
| LiveBench Data Analysis | 79.3% | – |
| LiveBench Language | 81.2% | – |
| LiveBench Instruction Following | 70.0% | – |
| AA Intelligence Index | 39.5 | 34.0 |
| IFBench | – | 73.3% |
| AA-LCR | 84.0% | 78.3% |
| MMMU-Pro | 77.0% | – |
| AA-Omniscience | -5.3 | 4.4 |
| Terminal-Bench Hard | – | 50.8% |
| GPQA Diamond (AA) | – | 89.5% |
| Humanity's Last Exam (AA) | 39.3% | 41.1% |
| SciCode (AA) | 51.9% | 51.2% |
| τ²-Bench Telecom (AA) | – | 99.1% |
| Terminal-Bench 2.1 (Vals) | – | 67.8% |
| SWE-bench (Vals) | – | 82.8% |
| CritPt | 14.3% | 20.9% |
| GDPval (AA) | 56.6% | 45.3% |
| τ²-Bench Banking (AA) | – | 34.6% |
| 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 |
| LMArena Creative Writing | – | 1451 |
| LMArena Instruction Following | – | 1466 |
| LMArena Multi-turn | – | 1469 |
| LMArena Longer Queries | – | 1483 |
| 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.
DeepSeek V4.1 Flash vs GLM-5.2: questions
- Is DeepSeek V4.1 Flash better than GLM-5.2?
- GLM-5.2 (max) leads on quality: 63.9 vs 61.9. The BenchLeader Index combines every independent quality benchmark; GLM-5.2 (max) is ahead overall as of 2026-09-19, but check the category scores for your use.
- Is DeepSeek V4.1 Flash better than GLM-5.2 for coding?
- DeepSeek V4.1 Flash scores higher in coding (69 vs 64 on the category index, where 50 is average).
- Is DeepSeek V4.1 Flash better than GLM-5.2 for agentic tasks?
- GLM-5.2 scores higher in agentic tasks (64 vs 60 on the category index, where 50 is average).
- Which is cheaper, DeepSeek V4.1 Flash or GLM-5.2?
- DeepSeek V4.1 Flash is cheaper: $0.262 against $2.15 per million tokens, blended at three input tokens per output token.
- Which is faster, DeepSeek V4.1 Flash or GLM-5.2?
- DeepSeek V4.1 Flash streams faster: 215 against 71 output tokens per second.
- Which has the larger context window?
- Both accept 1M tokens of context.