# GPT-5.6 Terra vs o3

> GPT-5.6 Terra is the stronger model overall, scoring 59.2 to 47.5 on the Noometry Index.

- Canonical page: https://noometry.com/compare/gpt-5-6-terra-vs-o3
- Last updated: 2026-10-10
- Shared benchmarks: 35

## Summary

- They share 35 benchmarks with published results for both. GPT-5.6 Terra scores higher in 9 categories and o3 in 1 category; 10 gaps are clear of the uncertainty.
- The widest gap is in math, where GPT-5.6 Terra leads 81.6 to 50.2.
- The biggest single-benchmark swing is ARC-AGI-2: 83.9% for GPT-5.6 Terra and 6.5% for o3.
- o3 is cheaper at $2 / $8 per million input/output tokens, against $2 / $12 for GPT-5.6 Terra.
- GPT-5.6 Terra accepts more context: 1.05M tokens versus 200K.

## Snapshot

| | GPT-5.6 Terra | o3 |
|---|---|---|
| Provider | OpenAI | OpenAI |
| Noometry Index | 59.2 | 47.5 |
| Rank | 17 | 61 |
| Context | 1.05M | 200K |
| Input $/M | $2 | $2 |
| Output $/M | $12 | $8 |
| Weights | Proprietary | Proprietary |

## Coding

- GPT-5.6 Terra: 57.7 (#19)
- o3: 46.8 (#64)

| Benchmark | GPT-5.6 Terra | o3 |
|---|---|---|
| WeirdML | 78.3% | 52.4% |
| LMArena Coding | 1484 | 1408 |
| ALE-Bench | 1,951 | 933.55 |
| SWE-bench Verified | — | 62.3% |
| DeepSWE | 69.6% | — |
| FrontierCode | 41.3% | — |
| SWE-bench Verified (bash only) | — | 58.4% |
| Aider Polyglot | — | 81.3% |
| CursorBench | 41.3% | — |
| LMArena WebDev | 1522 | — |
| SciCode | 55% | — |
| GSO | — | 8.8% |
| CadEval | — | 74% |

## Agentic & Tool Use

- GPT-5.6 Terra: 40.1 (#25)
- o3: 34.5 (#44)

| Benchmark | GPT-5.6 Terra | o3 |
|---|---|---|
| APEX-Agents | 58.2% | — |
| Berkeley Function Calling Leaderboard | — | 63% |
| GDPval | — | 30.8% |
| DeepResearch Bench | — | 45.2% |
| OSWorld | — | 23% |
| BALROG | 53.2% | — |
| GDP.pdf | 24.7% | — |
| LMArena Search | — | 1144 |
| METR Time Horizons | — | 65.4% |
| Vending-Bench 2 | 7,343 | — |

## Reasoning

- GPT-5.6 Terra: 60.7 (#21)
- o3: 32.0 (#78)

| Benchmark | GPT-5.6 Terra | o3 |
|---|---|---|
| ARC-AGI-2 | 83.9% | 6.5% |
| SimpleBench | 48.9% | 53.1% |
| Kagi LLM Benchmark | 51.3% | 67.6% |
| ARC-AGI-1 | 96.5% | 60.8% |
| CritPt | 30% | 1.4% |
| Chess Puzzles | 54% | 38% |
| LMArena Hard Prompts | 1468 | 1402 |
| Mystery Game Puzzles | 35% | 29% |
| DTBench | 93.3% | 84.8% |
| LMCA | 55% | 39.7% |
| Epoch Capabilities Index | 159.62 | 146.86 |
| NYT Connections (extended) | 78.4% | — |
| EnigmaEval | — | 13.1% |
| Surface Evolver Bench | 83.8% | — |
| ForecastBench | — | 62.5 |

## Math

- GPT-5.6 Terra: 81.6 (#12)
- o3: 50.2 (#58)

| Benchmark | GPT-5.6 Terra | o3 |
|---|---|---|
| FrontierMath (Tiers 1-3) | 86% | 33.3% |
| OTIS Mock AIME 2024-2025 | 99.7% | 84.4% |
| LMArena Math | 1466 | 1426 |
| FrontierMath Tier 4 | 70.7% | — |
| ProofBench | 74% | — |
| Omni-MATH | — | 71.4% |
| MATH Level 5 | — | 97.8% |
| FrontierMath (Feb 2025 set) | — | 18.7% |
| FrontierMath Tier 4 (v1) | — | 2.1% |

## Knowledge

- GPT-5.6 Terra: 61.2 (#30)
- o3: 54.6 (#52)

| Benchmark | GPT-5.6 Terra | o3 |
|---|---|---|
| GPQA Diamond | 93.3% | 81.8% |
| SimpleQA Verified | 43.2% | 49.4% |
| LMArena Expert | 1492 | 1402 |
| Humanity's Last Exam | — | 20.3% |
| MMLU-Pro | — | 85.9% |
| Confabulations | — | 14.4% |
| GPQA (HELM) | — | 75.3% |

## Multimodal

- GPT-5.6 Terra: 47.3 (#11)
- o3: 41.4 (#36)

| Benchmark | GPT-5.6 Terra | o3 |
|---|---|---|
| LMArena Vision | 1271 | 1214 |
| GeoBench | — | 74% |
| VPCT | — | 52% |
| Blueprint-Bench 2 | 30.8% | — |
| Furniture Assembly | 54.2% | — |
| LMArena Document | 1472 | — |

## Multilingual

- GPT-5.6 Terra: 54.4 (#44)
- o3: 51.7 (#105)

| Benchmark | GPT-5.6 Terra | o3 |
|---|---|---|
| LMArena Non-English | 1439 | 1401 |
| LMArena Chinese | 1513 | 1437 |
| LMArena French | 1471 | 1430 |
| LMArena German | 1460 | 1420 |
| LMArena Japanese | 1457 | 1403 |
| LMArena Korean | 1425 | 1370 |
| LMArena Russian | 1450 | 1406 |
| LMArena Spanish | 1448 | 1395 |

## Instruction Following

- GPT-5.6 Terra: 76.4 (#40)
- o3: 72.8 (#127)

| Benchmark | GPT-5.6 Terra | o3 |
|---|---|---|
| LMArena Instruction Following | 1454 | 1368 |
| IFEval | — | 86.9% |

## Long Context

- GPT-5.6 Terra: 44.4 (#68)
- o3: 53.3 (#6)

| Benchmark | GPT-5.6 Terra | o3 |
|---|---|---|
| LMArena Longer Query | 1451 | 1372 |
| Fiction.LiveBench | — | 88.9% |
| CL-bench | — | 17.8% |

## Writing & Preference

- GPT-5.6 Terra: 70.2 (#23)
- o3: 63.5 (#64)

| Benchmark | GPT-5.6 Terra | o3 |
|---|---|---|
| LMArena Text | 1447 | 1410 |
| LMArena Creative Writing | 1410 | 1359 |
| EQ-Bench Creative Writing | 1855 | 1676 |
| LMArena Multi-Turn | 1449 | 1405 |
| Short-Story Creative Writing | — | 83.9% |
| WildBench | — | 86.1% |
| EQ-Bench 4 | 1234 | — |

## FAQ

### Is GPT-5.6 Terra better than o3?

GPT-5.6 Terra is the stronger model overall, scoring 59.2 to 47.5 on the Noometry Index.

### Which is cheaper, GPT-5.6 Terra or o3?

o3 is cheaper. It lists at $2 per million input tokens and $8 per million output tokens; GPT-5.6 Terra lists at $2 and $12.

### Is GPT-5.6 Terra or o3 better for coding?

GPT-5.6 Terra scores higher on coding benchmarks: 57.7 versus 46.8 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 o3 share?

35 benchmarks have published results for both models. GPT-5.6 Terra has 52 scored results on Noometry and o3 has 63.