Model comparison
GPT-5.6 Terra vs o1
GPT-5.6 Terra is the stronger model overall, scoring 59.1 to 40.6 on the Noometry Index.
Last verified . 29 shared benchmarks.
Summary
- They share 29 benchmarks with published results for both. GPT-5.6 Terra scores higher in 9 categories and o1 in 1 category; 10 gaps are clear of the uncertainty.
- The widest gap is in math, where GPT-5.6 Terra leads 81.5 to 34.9.
- The biggest single-benchmark swing is FrontierMath (Tiers 1-3): 86% for GPT-5.6 Terra and 14.7% for o1.
- GPT-5.6 Terra is cheaper at $2 / $12 per million input/output tokens, against $15 / $60 for o1.
- GPT-5.6 Terra accepts more context: 1.05M tokens versus 200K.
Side by side
| GPT-5.6 Terra | o1 | |
|---|---|---|
| Provider | OpenAI | OpenAI |
| Noometry Index | 59.1 | 40.6 |
| Released | 2026-07-09 | 2024-09-12 |
| Weights | Proprietary | Proprietary |
| Context window | 1.05M | 200K |
| Max output | 128K | 100K |
| Input $ / M tokens | $2 | $15 |
| Output $ / M tokens | $12 | $60 |
| Results tracked | 52 | 52 |
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Category by category
Coding GPT-5.6 Terra leads
GPT-5.6 Terra: 57.1 (#16), o1: 45.7 (#64)
| Benchmark | GPT-5.6 Terra | o1 |
|---|---|---|
| WeirdML | 78.3% | 47.6% |
| LMArena Coding | 1484 | 1367 |
| DeepSWE | 69.6% | — |
| FrontierCode | 41.3% | — |
| Aider Polyglot | — | 61.7% |
| CursorBench | 41.3% | — |
| LMArena WebDev | 1520 | — |
| SciCode | 55% | — |
| LiveBench Coding | — | 69.7% |
| CadEval | — | 56% |
| ALE-Bench | 1,951 | — |
| HumanEval+ | — | 89% |
| MBPP+ | — | 80.2% |
Agentic & Tool Use GPT-5.6 Terra leads
GPT-5.6 Terra: 40.2 (#23), o1: 24.3 (#111)
| Benchmark | GPT-5.6 Terra | o1 |
|---|---|---|
| APEX-Agents | 58.2% | — |
| Cybench | — | 10% |
| BALROG | 53.2% | — |
| GDP.pdf | 24.7% | — |
| METR Time Horizons | — | 51.1% |
| Vending-Bench 2 | 7,343 | — |
Reasoning GPT-5.6 Terra leads
GPT-5.6 Terra: 60.6 (#19), o1: 27.5 (#103)
| Benchmark | GPT-5.6 Terra | o1 |
|---|---|---|
| SimpleBench | 48.9% | 41.7% |
| ARC-AGI-1 | 96.5% | 30.7% |
| Chess Puzzles | 54% | 15% |
| LMArena Hard Prompts | 1467 | 1372 |
| DTBench | 93.3% | 74.7% |
| LMCA | 55% | 22.3% |
| Epoch Capabilities Index | 159.31 | 141.92 |
| ARC-AGI-2 | 83.9% | — |
| Kagi LLM Benchmark | 51.3% | — |
| NYT Connections (extended) | 78.4% | — |
| CritPt | 30% | — |
| EnigmaEval | — | 5.7% |
| LiveBench Reasoning | — | 91.6% |
| Mystery Game Puzzles | 35% | — |
| LiveBench Data Analysis | — | 65.5% |
| Surface Evolver Bench | 83.8% | — |
| LiveBench | — | 75.7% |
Math GPT-5.6 Terra leads
GPT-5.6 Terra: 81.5 (#10), o1: 34.9 (#150)
| Benchmark | GPT-5.6 Terra | o1 |
|---|---|---|
| FrontierMath (Tiers 1-3) | 86% | 14.7% |
| OTIS Mock AIME 2024-2025 | 99.7% | 73.3% |
| LMArena Math | 1463 | 1388 |
| FrontierMath Tier 4 | 70.7% | — |
| ProofBench | 74% | — |
| LiveBench Math | — | 80.3% |
| MATH Level 5 | — | 94.7% |
| FrontierMath (Feb 2025 set) | — | 9.3% |
Knowledge GPT-5.6 Terra leads
GPT-5.6 Terra: 61.3 (#27), o1: 41.5 (#101)
| Benchmark | GPT-5.6 Terra | o1 |
|---|---|---|
| GPQA Diamond | 93.3% | 76.8% |
| SimpleQA Verified | 43.2% | 41.1% |
| LMArena Expert | 1492 | 1362 |
| Humanity's Last Exam | — | 8% |
| Confabulations | — | 11.7% |
Multimodal GPT-5.6 Terra leads
GPT-5.6 Terra: 47.5 (#10), o1: 33.6 (#87)
| Benchmark | GPT-5.6 Terra | o1 |
|---|---|---|
| LMArena Vision | 1271 | 1168 |
| GeoBench | — | 80% |
| VPCT | — | 37% |
| Blueprint-Bench 2 | 30.8% | — |
| Furniture Assembly | 54.2% | — |
| LMArena Document | 1472 | — |
| SpatialViz-Bench | — | 41.4% |
Multilingual GPT-5.6 Terra leads
GPT-5.6 Terra: 54.5 (#42), o1: 48.8 (#137)
| Benchmark | GPT-5.6 Terra | o1 |
|---|---|---|
| LMArena Non-English | 1439 | 1359 |
| LMArena Chinese | 1514 | 1394 |
| LMArena French | 1476 | 1343 |
| LMArena German | 1462 | 1337 |
| LMArena Japanese | 1446 | 1346 |
| LMArena Korean | 1420 | 1396 |
| LMArena Russian | 1450 | 1356 |
| LMArena Spanish | 1455 | 1345 |
Instruction Following GPT-5.6 Terra leads
GPT-5.6 Terra: 76.6 (#38), o1: 75.0 (#82)
| Benchmark | GPT-5.6 Terra | o1 |
|---|---|---|
| LMArena Instruction Following | 1453 | 1368 |
| LiveBench Instruction Following | — | 81.5% |
Long Context o1 leads
GPT-5.6 Terra: 44.4 (#64), o1: 50.3 (#9)
| Benchmark | GPT-5.6 Terra | o1 |
|---|---|---|
| LMArena Longer Query | 1452 | 1378 |
| Fiction.LiveBench | — | 83.3% |
Writing & Preference GPT-5.6 Terra leads
GPT-5.6 Terra: 70.4 (#21), o1: 55.8 (#139)
| Benchmark | GPT-5.6 Terra | o1 |
|---|---|---|
| LMArena Text | 1446 | 1366 |
| LMArena Creative Writing | 1411 | 1347 |
| LMArena Multi-Turn | 1451 | 1368 |
| Short-Story Creative Writing | — | 70.2% |
| EQ-Bench Creative Writing | 1855 | — |
| EQ-Bench 4 | 1234 | — |
| LiveBench Language | — | 65.4% |
Frequently asked questions
Is GPT-5.6 Terra better than o1?
GPT-5.6 Terra is the stronger model overall, scoring 59.1 to 40.6 on the Noometry Index.
Which is cheaper, GPT-5.6 Terra or o1?
GPT-5.6 Terra is cheaper. It lists at $2 per million input tokens and $12 per million output tokens; o1 lists at $15 and $60.
Is GPT-5.6 Terra or o1 better for coding?
GPT-5.6 Terra scores higher on coding benchmarks: 57.1 versus 45.7 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 GPT-5.6 Terra and o1 share?
29 benchmarks have published results for both models. GPT-5.6 Terra has 52 scored results on Noometry and o1 has 52.