GLM-5.3 vs Qwen3.8-Flash-Next
GLM-5.3 
Qwen3.8-Flash-Next Verdict
- GLM-5.3 (max) leads on quality: 65.8 vs 61.6.
- GLM-5.3 (max) is stronger in composite, human preference, maths, reasoning.
- Qwen3.8-Flash-Next is stronger in agents & tools, coding, multimodal.
- Qwen3.8-Flash-Next is 9.3× cheaper ($0.230 vs $2.15 per 1M blended).
| Metric | GLM-5.3 (max) | Qwen3.8-Flash-Next |
|---|---|---|
| BenchLeader Index | 65.8 | 61.6 |
| Agents & tools score | 62.0 | 65.8 |
| Coding score | 64.4 | 71.2 |
| Composite score | 85.9 | 67.4 |
| Human preference score | 68.5 | – |
| Knowledge score | 59.6 | 59.6 |
| Long context score | 66.3 | 66.3 |
| Maths score | 62.7 | – |
| Reasoning score | 71.6 | 63.4 |
| Multimodal score | – | 64.3 |
| Blended price $/M | $2.15 | $0.230 |
| Output speed | 63 tok/s | 55 tok/s |
| Time to first answer | 34.6 s | 39.0 s |
| Context window | 1M | 256k |
| 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 | 56.5% | – |
| WeirdML | 75.4% | – |
| ProofBench | 49.0% | – |
| LMArena Text | 1483 | – |
| LMArena Hard Prompts | 1507 | – |
| LMArena Coding | 1524 | – |
| LMArena WebDev | 1614 | 1635 |
| LMArena Agent | 3.1 | -0.1 |
| LiveBench | – | 76.2% |
| LiveBench Reasoning | – | 87.4% |
| LiveBench Coding | – | 72.5% |
| LiveBench Agentic Coding | – | 61.6% |
| LiveBench Mathematics | – | 85.8% |
| LiveBench Data Analysis | – | 74.2% |
| LiveBench Language | – | 74.6% |
| LiveBench Instruction Following | – | 77.1% |
| AA Intelligence Index | 44.9 | 39.9 |
| AA-LCR | 79.7% | 79.7% |
| MMMU-Pro | – | 79.8% |
| AA-Omniscience | 14.3 | -9.7 |
| GPQA Diamond (AA) | 91.7% | 92.3% |
| Humanity's Last Exam (AA) | 42.3% | 38.0% |
| SciCode (AA) | 59.0% | 50.6% |
| LiveCodeBench | 80.5% | – |
| MMLU-Pro | 86.8% | – |
| IOI | 68.4% | – |
| LegalBench | 84.8% | – |
| TaxEval | 72.4% | – |
| Terminal-Bench 2.1 (Vals) | 71.5% | – |
| SWE-bench (Vals) | 95.4% | – |
| GPQA Diamond (Vals) | 88.1% | – |
| Vals Index | 57.0 | – |
| CritPt | 19.1% | 11.1% |
| GDPval (AA) | 56.7% | 57.4% |
| τ²-Bench Banking (AA) | 50.3% | 45.4% |
| Code Migration | 44.2% | – |
| Excel Modeling Benchmark | 56.3% | – |
| Finance Agent v2 | 55.8% | – |
| Harvey's Legal Agent Benchmark | 8.3% | – |
| Legal Research Bench | 49.0% | – |
| 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% | – |
| Vibe Code Bench 1-100 | 20.0% | – |
| Vibe Code Bench v1.1 | 78.1% | – |
| 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% | – |
Data as of 2026-09-19. Best configuration of each model; every score links to its source on the model pages.
GLM-5.3 vs Qwen3.8-Flash-Next: questions
- Is GLM-5.3 better than Qwen3.8-Flash-Next?
- GLM-5.3 (max) leads on quality: 65.8 vs 61.6. The BenchLeader Index combines every independent quality benchmark; GLM-5.3 (max) is ahead overall as of 2026-09-19, but check the category scores for your use.
- Is GLM-5.3 better than Qwen3.8-Flash-Next for coding?
- Qwen3.8-Flash-Next scores higher in coding (71 vs 64 on the category index, where 50 is average).
- Is GLM-5.3 better than Qwen3.8-Flash-Next for agentic tasks?
- Qwen3.8-Flash-Next scores higher in agentic tasks (66 vs 62 on the category index, where 50 is average).
- Which is cheaper, GLM-5.3 or Qwen3.8-Flash-Next?
- Qwen3.8-Flash-Next is cheaper: $0.230 against $2.15 per million tokens, blended at three input tokens per output token.
- Which is faster, GLM-5.3 or Qwen3.8-Flash-Next?
- GLM-5.3 streams faster: 63 against 55 output tokens per second.
- Which has the larger context window?
- GLM-5.3 accepts more context: 1M against 256k tokens.