GPT-5.6 Terra vs Qwen3.8-Flash-Next
GPT-5.6 Terra 
Qwen3.8-Flash-Next Verdict
- GPT-5.6 Terra (max) leads on quality: 65.0 vs 61.6.
- GPT-5.6 Terra (max) is stronger in composite, instruction following, long context, maths, multimodal, reasoning.
- Qwen3.8-Flash-Next is stronger in agents & tools, coding, knowledge.
- Qwen3.8-Flash-Next is 20× cheaper ($0.230 vs $4.50 per 1M blended).
- GPT-5.6 Terra (max) streams 1.5× faster (84 vs 55 tokens per second).
| Metric | GPT-5.6 Terra (max) | Qwen3.8-Flash-Next |
|---|---|---|
| BenchLeader Index | 65.0 | 61.6 |
| Agents & tools score | 59.0 | 65.8 |
| Coding score | 67.2 | 71.2 |
| Composite score | 71.5 | 67.4 |
| Instruction following score | 69.8 | – |
| Knowledge score | 58.8 | 59.6 |
| Long context score | 68.0 | 66.3 |
| Maths score | 71.5 | – |
| Multimodal score | 65.3 | 64.3 |
| Reasoning score | 72.3 | 63.4 |
| Blended price $/M | $4.50 | $0.230 |
| Output speed | 84 tok/s | 55 tok/s |
| Time to first answer | 220.9 s | 39.0 s |
| Context window | 1.1M | 256k |
| GPQA Diamond | 93.3% | – |
| FrontierMath Tiers 1–3 | 86.0% | – |
| FrontierMath Tier 4 | 70.7% | – |
| OTIS Mock AIME | 99.7% | – |
| SimpleQA Verified | 43.2% | – |
| Terminal-Bench | 21.5% | – |
| SciCode | 53.9% | – |
| LMArena WebDev | – | 1635 |
| LMArena Agent | – | -0.1 |
| LiveBench | 77.9% | 76.2% |
| LiveBench Reasoning | 90.6% | 87.4% |
| LiveBench Coding | 78.3% | 72.5% |
| LiveBench Agentic Coding | 55.0% | 61.6% |
| LiveBench Mathematics | 94.9% | 85.8% |
| LiveBench Data Analysis | 79.3% | 74.2% |
| LiveBench Language | 82.9% | 74.6% |
| LiveBench Instruction Following | 64.6% | 77.1% |
| AA Intelligence Index | 42.3 | 39.9 |
| IFBench | 71.2% | – |
| AA-LCR | 83.0% | 79.7% |
| MMMU-Pro | 80.7% | 79.8% |
| AA-Omniscience | 0.1 | -9.7 |
| Terminal-Bench Hard | 57.6% | – |
| GPQA Diamond (AA) | 92.5% | 92.3% |
| Humanity's Last Exam (AA) | 42.9% | 38.0% |
| SciCode (AA) | 55.0% | 50.6% |
| τ²-Bench Telecom (AA) | 86.3% | – |
| IOI | 87.6% | – |
| LegalBench | 85.1% | – |
| Terminal-Bench 2.1 (Vals) | 77.5% | – |
| SWE-bench (Vals) | 95.4% | – |
| Vals Index | 59.6 | – |
| ARC-AGI-1 | 96.5% | – |
| ARC-AGI-2 | 83.9% | – |
| ARC-AGI-3 | 0.8% | – |
| CritPt | 30.0% | 11.1% |
| GDPval (AA) | 48.9% | 57.4% |
| τ²-Bench Banking (AA) | 40.2% | 45.4% |
| ITBench SRE (AA) | 51.0% | – |
| APEX-Agents (AA) | 38.9% | – |
| Code Migration | 47.8% | – |
| Excel Modeling Benchmark | 66.2% | – |
| Finance Agent v2 | 54.4% | – |
| Harvey's Legal Agent Benchmark | 0.8% | – |
| Legal Research Bench | 41.4% | – |
| MMMU-Pro (Vals) | 86.5% | – |
| ProgramBench | 0.5% | – |
| SAGE | 47.0% | – |
| SkillsBench | 58.9% | – |
| Tax Agent Bench | 65.2% | – |
| Terminal-Bench 4.0 (Vals) | 26.3% | – |
| Terminal-Bench Science | 10.0% | – |
| Vibe Code Bench 1-100 | 14.8% | – |
| Vibe Code Bench v1.1 | 74.6% | – |
| Chess Puzzles | 54.0% | – |
| Mystery Game Puzzles | 35.0% | – |
| DeepSWE | 69.6% | – |
| LMCA | 52.2% | – |
| DTBench | 92.5% | – |
| CursorBench | 64.9% | – |
| ALE-Bench | 1951.4 | – |
Data as of 2026-09-19. Best configuration of each model; every score links to its source on the model pages.
GPT-5.6 Terra vs Qwen3.8-Flash-Next: questions
- Is GPT-5.6 Terra better than Qwen3.8-Flash-Next?
- GPT-5.6 Terra (max) leads on quality: 65.0 vs 61.6. The BenchLeader Index combines every independent quality benchmark; GPT-5.6 Terra (max) is ahead overall as of 2026-09-19, but check the category scores for your use.
- Is GPT-5.6 Terra better than Qwen3.8-Flash-Next for coding?
- Qwen3.8-Flash-Next scores higher in coding (71 vs 67 on the category index, where 50 is average).
- Is GPT-5.6 Terra better than Qwen3.8-Flash-Next for agentic tasks?
- Qwen3.8-Flash-Next scores higher in agentic tasks (66 vs 59 on the category index, where 50 is average).
- Which is cheaper, GPT-5.6 Terra or Qwen3.8-Flash-Next?
- Qwen3.8-Flash-Next is cheaper: $0.230 against $4.50 per million tokens, blended at three input tokens per output token.
- Which is faster, GPT-5.6 Terra or Qwen3.8-Flash-Next?
- GPT-5.6 Terra streams faster: 84 against 55 output tokens per second.
- Which has the larger context window?
- GPT-5.6 Terra accepts more context: 1.1M against 256k tokens.