Claude Opus 5.5 vs GPT-5.6 Terra
Claude Opus 5.5 
GPT-5.6 Terra Verdict
- Claude Opus 5.5 (thinking) leads on quality: 70.7 vs 64.8.
- Claude Opus 5.5 (thinking) is stronger in composite, knowledge, long context, multimodal, reasoning.
- GPT-5.6 Terra (max) is stronger in agents & tools, coding, instruction following, maths.
- GPT-5.6 Terra (max) is 1.8× cheaper ($4.50 vs $8.00 per 1M blended).
- GPT-5.6 Terra (max) streams 1.8× faster (86 vs 48 tokens per second).
| Metric | Claude Opus 5.5 (thinking) | GPT-5.6 Terra (max) |
|---|---|---|
| BenchLeader Index | 70.7 | 64.8 |
| Composite score | 95.0 | 70.3 |
| Knowledge score | 85.0 | 58.4 |
| Long context score | 68.5 | 67.7 |
| Multimodal score | 71.9 | 64.7 |
| Reasoning score | 95.0 | 72.3 |
| Agents & tools score | – | 59.0 |
| Coding score | – | 67.5 |
| Instruction following score | – | 69.8 |
| Maths score | – | 71.3 |
| Blended price $/M | $8.00 | $4.50 |
| Output speed | 48 tok/s | 86 tok/s |
| Time to first answer | 4.2 s | 221.5 s |
| Context window | 1M | 1.1M |
| 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 | – | 55.0% |
| APEX-Agents | – | 58.2% |
| LiveBench | – | 77.9% |
| LiveBench Reasoning | – | 90.6% |
| LiveBench Coding | – | 78.3% |
| LiveBench Agentic Coding | – | 55.0% |
| LiveBench Mathematics | – | 94.9% |
| LiveBench Data Analysis | – | 79.3% |
| LiveBench Language | – | 82.9% |
| LiveBench Instruction Following | – | 64.6% |
| AA Intelligence Index | 57.6 | 42.1 |
| IFBench | – | 71.2% |
| AA-LCR | 84.7% | 83.0% |
| MMMU-Pro | 87.7% | 80.7% |
| AA-Omniscience | 46.4 | 0.1 |
| Terminal-Bench Hard | – | 57.6% |
| GPQA Diamond (AA) | – | 92.5% |
| Humanity's Last Exam (AA) | 61.4% | 42.9% |
| SciCode (AA) | 66.9% | 55.0% |
| 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 | 31.7% | 30.0% |
| GDPval (AA) | 67.3% | 46.6% |
| τ²-Bench Banking (AA) | – | 40.2% |
| 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% |
| BALROG | – | 53.2% |
| DeepSWE | – | 69.6% |
| LMCA | – | 55.0% |
| DTBench | – | 93.3% |
| CursorBench | – | 41.3% |
| ALE-Bench | – | 1951.4 |
| Terminal-Bench 4.0 (AA) | 59.6% | 35.4% |
| Terminal-Bench 2.1 (AA) | – | 88.0% |
| AutomationBench | 69.5% | 59.6% |
| GDP.pdf | 26.2% | 24.0% |
| MLCR | – | 31.7% |
| Harvey LAB | 91.2% | 85.2% |
| EnterpriseOps-Gym | – | 38.5% |
| AA-Omniscience: accuracy | 66.2% | 46.8% |
| AA-Omniscience: non-hallucination | 41.4% | 12.1% |
| AA-Briefcase | 1822 | 1336 |
Data as of 2026-09-22. Best configuration of each model; every score links to its source on the model pages.
Claude Opus 5.5 vs GPT-5.6 Terra: questions
- Is Claude Opus 5.5 better than GPT-5.6 Terra?
- Claude Opus 5.5 (thinking) leads on quality: 70.7 vs 64.8. The BenchLeader Index combines every independent quality benchmark; Claude Opus 5.5 (thinking) is ahead overall as of 2026-09-22, but check the category scores for your use.
- Which is cheaper, Claude Opus 5.5 or GPT-5.6 Terra?
- GPT-5.6 Terra is cheaper: $4.50 against $8.00 per million tokens, blended at three input tokens per output token.
- Which is faster, Claude Opus 5.5 or GPT-5.6 Terra?
- GPT-5.6 Terra streams faster: 86 against 48 output tokens per second.
- Which has the larger context window?
- GPT-5.6 Terra accepts more context: 1.1M against 1M tokens.