Model comparison
Claude Opus 4 vs GPT-5.6 Terra
GPT-5.6 Terra is the stronger model overall, scoring 59.2 to 43.1 on the Noometry Index.
Last verified . 30 shared benchmarks.
Summary
- They share 30 benchmarks with published results for both. Claude Opus 4 scores higher in 1 category and GPT-5.6 Terra in 9 categories; 9 gaps are clear of the uncertainty.
- The widest gap is in math, where GPT-5.6 Terra leads 81.6 to 42.0.
- The biggest single-benchmark swing is ARC-AGI-2: 8.6% for Claude Opus 4 and 83.9% for GPT-5.6 Terra.
- GPT-5.6 Terra is cheaper at $2 / $12 per million input/output tokens, against $15 / $75 for Claude Opus 4.
- GPT-5.6 Terra accepts more context: 1.05M tokens versus 200K.
Side by side
| Claude Opus 4 | GPT-5.6 Terra | |
|---|---|---|
| Provider | Anthropic | OpenAI |
| Noometry Index | 43.1 | 59.2 |
| Released | 2025-05-22 | 2026-07-09 |
| Weights | Proprietary | Proprietary |
| Context window | 200K | 1.05M |
| Max output | 32K | 128K |
| Input $ / M tokens | $15 | $2 |
| Output $ / M tokens | $75 | $12 |
| Results tracked | 56 | 52 |
Sponsored placements are available on pages like this one. Advertise on Noometry
Category by category
Coding GPT-5.6 Terra leads
Claude Opus 4: 47.2 (#62), GPT-5.6 Terra: 57.7 (#19)
| Benchmark | Claude Opus 4 | GPT-5.6 Terra |
|---|---|---|
| WeirdML | 43.7% | 78.3% |
| LMArena Coding | 1442 | 1484 |
| SWE-bench Verified | 70.7% | — |
| DeepSWE | — | 69.6% |
| FrontierCode | — | 41.3% |
| SWE-bench Verified (bash only) | 67.6% | — |
| Aider Polyglot | 72% | — |
| CursorBench | — | 41.3% |
| LMArena WebDev | — | 1522 |
| SciCode | — | 55% |
| GSO | 6.9% | — |
| ALE-Bench | — | 1,951 |
| AlgoTune | 1.33 | — |
Agentic & Tool Use GPT-5.6 Terra leads
Claude Opus 4: 34.8 (#42), GPT-5.6 Terra: 40.1 (#25)
| Benchmark | Claude Opus 4 | GPT-5.6 Terra |
|---|---|---|
| APEX-Agents | — | 58.2% |
| Cybench | 38% | — |
| DeepResearch Bench | 46.8% | — |
| BALROG | — | 53.2% |
| GDP.pdf | — | 24.7% |
| LMArena Search | 1127 | — |
| METR Time Horizons | 63.9% | — |
| Vending-Bench 2 | — | 7,343 |
Reasoning GPT-5.6 Terra leads
Claude Opus 4: 27.3 (#121), GPT-5.6 Terra: 60.7 (#21)
| Benchmark | Claude Opus 4 | GPT-5.6 Terra |
|---|---|---|
| ARC-AGI-2 | 8.6% | 83.9% |
| SimpleBench | 58.8% | 48.9% |
| Kagi LLM Benchmark | 74.3% | 51.3% |
| ARC-AGI-1 | 35.7% | 96.5% |
| CritPt | 0.3% | 30% |
| LMArena Hard Prompts | 1399 | 1468 |
| DTBench | 81.6% | 93.3% |
| LMCA | 37.4% | 55% |
| Epoch Capabilities Index | 142.67 | 159.62 |
| NYT Connections (extended) | — | 78.4% |
| Chess Puzzles | — | 54% |
| EnigmaEval | 5.6% | — |
| Mystery Game Puzzles | — | 35% |
| Surface Evolver Bench | — | 83.8% |
| ForecastBench | 61.1 | — |
Math GPT-5.6 Terra leads
Claude Opus 4: 42.0 (#86), GPT-5.6 Terra: 81.6 (#12)
| Benchmark | Claude Opus 4 | GPT-5.6 Terra |
|---|---|---|
| OTIS Mock AIME 2024-2025 | 64.4% | 99.7% |
| LMArena Math | 1390 | 1466 |
| FrontierMath (Tiers 1-3) | — | 86% |
| FrontierMath Tier 4 | — | 70.7% |
| ProofBench | — | 74% |
| Omni-MATH | 61.6% | — |
| MATH Level 5 | 85% | — |
| FrontierMath (Feb 2025 set) | 4.5% | — |
| FrontierMath Tier 4 (v1) | 4.2% | — |
Knowledge GPT-5.6 Terra leads
Claude Opus 4: 44.0 (#88), GPT-5.6 Terra: 61.2 (#30)
| Benchmark | Claude Opus 4 | GPT-5.6 Terra |
|---|---|---|
| GPQA Diamond | 76.3% | 93.3% |
| LMArena Expert | 1386 | 1492 |
| Humanity's Last Exam | 10.7% | — |
| SimpleQA Verified | — | 43.2% |
| MMLU-Pro | 87.5% | — |
| Confabulations | 15.9% | — |
| Vectara Hallucination Rate | 12% | — |
| GPQA (HELM) | 70.8% | — |
Multimodal GPT-5.6 Terra leads
Claude Opus 4: 31.5 (#106), GPT-5.6 Terra: 47.3 (#11)
| Benchmark | Claude Opus 4 | GPT-5.6 Terra |
|---|---|---|
| LMArena Vision | 1192 | 1271 |
| GeoBench | 49% | — |
| VPCT | 38% | — |
| Blueprint-Bench 2 | — | 30.8% |
| Furniture Assembly | — | 54.2% |
| LMArena Document | — | 1472 |
Multilingual GPT-5.6 Terra leads
Claude Opus 4: 48.8 (#138), GPT-5.6 Terra: 54.4 (#44)
| Benchmark | Claude Opus 4 | GPT-5.6 Terra |
|---|---|---|
| LMArena Non-English | 1362 | 1439 |
| LMArena Chinese | 1386 | 1513 |
| LMArena French | 1372 | 1471 |
| LMArena German | 1391 | 1460 |
| LMArena Japanese | 1331 | 1457 |
| LMArena Korean | 1321 | 1425 |
| LMArena Russian | 1392 | 1450 |
| LMArena Spanish | 1389 | 1448 |
Instruction Following Too close to call
Claude Opus 4: 77.1 (#28), GPT-5.6 Terra: 76.4 (#40)
| Benchmark | Claude Opus 4 | GPT-5.6 Terra |
|---|---|---|
| LMArena Instruction Following | 1406 | 1454 |
| IFEval | 91.8% | — |
Long Context GPT-5.6 Terra leads
Claude Opus 4: 39.6 (#172), GPT-5.6 Terra: 44.4 (#68)
| Benchmark | Claude Opus 4 | GPT-5.6 Terra |
|---|---|---|
| LMArena Longer Query | 1422 | 1451 |
| Fiction.LiveBench | 61.1% | — |
Writing & Preference GPT-5.6 Terra leads
Claude Opus 4: 61.2 (#89), GPT-5.6 Terra: 70.2 (#23)
| Benchmark | Claude Opus 4 | GPT-5.6 Terra |
|---|---|---|
| LMArena Text | 1377 | 1447 |
| LMArena Creative Writing | 1387 | 1410 |
| EQ-Bench Creative Writing | 1580 | 1855 |
| LMArena Multi-Turn | 1396 | 1449 |
| Short-Story Creative Writing | 83.6% | — |
| WildBench | 85.2% | — |
| EQ-Bench 4 | — | 1234 |
Frequently asked questions
Is Claude Opus 4 better than GPT-5.6 Terra?
GPT-5.6 Terra is the stronger model overall, scoring 59.2 to 43.1 on the Noometry Index.
Which is cheaper, Claude Opus 4 or GPT-5.6 Terra?
GPT-5.6 Terra is cheaper. It lists at $2 per million input tokens and $12 per million output tokens; Claude Opus 4 lists at $15 and $75.
Is Claude Opus 4 or GPT-5.6 Terra better for coding?
GPT-5.6 Terra scores higher on coding benchmarks: 57.7 versus 47.2 in the Noometry coding category.
Which has the bigger context window?
GPT-5.6 Terra does, with 1.05M tokens against 200K.
How many benchmarks do Claude Opus 4 and GPT-5.6 Terra share?
30 benchmarks have published results for both models. Claude Opus 4 has 56 scored results on Noometry and GPT-5.6 Terra has 52.