Model comparison
GPT-5.6 Terra vs o3-mini
GPT-5.6 Terra is the stronger model overall, scoring 59.1 to 36.7 on the Noometry Index. o3-mini costs 2.3× less per token, which makes it the better buy when GPT-5.6 Terra's lead doesn't matter for your workload.
Last verified . 33 shared benchmarks.
Summary
- They share 33 benchmarks with published results for both. GPT-5.6 Terra scores higher in 9 categories and o3-mini in 0 categories; 9 gaps are clear of the uncertainty.
- The widest gap is in math, where GPT-5.6 Terra leads 81.5 to 27.6.
- The biggest single-benchmark swing is ARC-AGI-2: 83.9% for GPT-5.6 Terra and 3% for o3-mini.
- o3-mini is cheaper at $1.10 / $4.40 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.
Side by side
| GPT-5.6 Terra | o3-mini | |
|---|---|---|
| Provider | OpenAI | OpenAI |
| Noometry Index | 59.1 | 36.7 |
| Released | 2026-07-09 | 2024-12-20 |
| Weights | Proprietary | Proprietary |
| Context window | 1.05M | 200K |
| Max output | 128K | 100K |
| Input $ / M tokens | $2 | $1.10 |
| Output $ / M tokens | $12 | $4.40 |
| Results tracked | 52 | 51 |
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Category by category
Coding GPT-5.6 Terra leads
GPT-5.6 Terra: 57.1 (#16), o3-mini: 41.4 (#116)
| Benchmark | GPT-5.6 Terra | o3-mini |
|---|---|---|
| SciCode | 55% | 39.8% |
| WeirdML | 78.3% | 43.7% |
| LMArena Coding | 1484 | 1378 |
| DeepSWE | 69.6% | — |
| FrontierCode | 41.3% | — |
| Aider Polyglot | — | 60.4% |
| CursorBench | 41.3% | — |
| LMArena WebDev | 1520 | — |
| GSO | — | 1.3% |
| LiveBench Coding | — | 82.7% |
| CadEval | — | 54% |
| ALE-Bench | 1,951 | — |
Agentic & Tool Use GPT-5.6 Terra leads
GPT-5.6 Terra: 40.2 (#23), o3-mini: 29.3 (#79)
| Benchmark | GPT-5.6 Terra | o3-mini |
|---|---|---|
| APEX-Agents | 58.2% | — |
| Cybench | — | 22.5% |
| BALROG | 53.2% | — |
| GDP.pdf | 24.7% | — |
| Vending-Bench 2 | 7,343 | — |
Reasoning GPT-5.6 Terra leads
GPT-5.6 Terra: 60.6 (#19), o3-mini: 16.3 (#295)
| Benchmark | GPT-5.6 Terra | o3-mini |
|---|---|---|
| ARC-AGI-2 | 83.9% | 3% |
| SimpleBench | 48.9% | 22.8% |
| ARC-AGI-1 | 96.5% | 34.5% |
| CritPt | 30% | 0.3% |
| Chess Puzzles | 54% | 17% |
| LMArena Hard Prompts | 1467 | 1366 |
| Mystery Game Puzzles | 35% | 7% |
| DTBench | 93.3% | 68.8% |
| LMCA | 55% | 19% |
| Epoch Capabilities Index | 159.31 | 140.35 |
| Kagi LLM Benchmark | 51.3% | — |
| NYT Connections (extended) | 78.4% | — |
| LiveBench Reasoning | — | 89.6% |
| LiveBench Data Analysis | — | 70.6% |
| Surface Evolver Bench | 83.8% | — |
| ForecastBench | — | 59.6 |
| LiveBench | — | 75.9% |
Math GPT-5.6 Terra leads
GPT-5.6 Terra: 81.5 (#10), o3-mini: 27.6 (#236)
| Benchmark | GPT-5.6 Terra | o3-mini |
|---|---|---|
| FrontierMath (Tiers 1-3) | 86% | 18.6% |
| FrontierMath Tier 4 | 70.7% | 0% |
| OTIS Mock AIME 2024-2025 | 99.7% | 76.9% |
| LMArena Math | 1463 | 1396 |
| ProofBench | 74% | — |
| LiveBench Math | — | 77.3% |
| MATH Level 5 | — | 96.5% |
| FrontierMath (Feb 2025 set) | — | 12.4% |
| FrontierMath Tier 4 (v1) | — | 4.2% |
Knowledge GPT-5.6 Terra leads
GPT-5.6 Terra: 61.3 (#27), o3-mini: 38.3 (#135)
| Benchmark | GPT-5.6 Terra | o3-mini |
|---|---|---|
| GPQA Diamond | 93.3% | 77% |
| SimpleQA Verified | 43.2% | 15.3% |
| LMArena Expert | 1492 | 1364 |
| Confabulations | — | 17.9% |
Multimodal Not comparable
GPT-5.6 Terra: 47.5 (#10), o3-mini: —
| Benchmark | GPT-5.6 Terra | o3-mini |
|---|---|---|
| LMArena Vision | 1271 | — |
| Blueprint-Bench 2 | 30.8% | — |
| Furniture Assembly | 54.2% | — |
| LMArena Document | 1472 | — |
Multilingual GPT-5.6 Terra leads
GPT-5.6 Terra: 54.5 (#42), o3-mini: 45.9 (#159)
| Benchmark | GPT-5.6 Terra | o3-mini |
|---|---|---|
| LMArena Non-English | 1439 | 1319 |
| LMArena Chinese | 1514 | 1379 |
| LMArena French | 1476 | 1333 |
| LMArena German | 1462 | 1303 |
| LMArena Japanese | 1446 | 1286 |
| LMArena Korean | 1420 | 1314 |
| LMArena Russian | 1450 | 1304 |
| LMArena Spanish | 1455 | 1321 |
Instruction Following GPT-5.6 Terra leads
GPT-5.6 Terra: 76.6 (#38), o3-mini: 75.3 (#67)
| Benchmark | GPT-5.6 Terra | o3-mini |
|---|---|---|
| LMArena Instruction Following | 1453 | 1338 |
| LiveBench Instruction Following | — | 84.4% |
Long Context GPT-5.6 Terra leads
GPT-5.6 Terra: 44.4 (#64), o3-mini: 33.8 (#251)
| Benchmark | GPT-5.6 Terra | o3-mini |
|---|---|---|
| LMArena Longer Query | 1452 | 1343 |
| Fiction.LiveBench | — | 50% |
Writing & Preference GPT-5.6 Terra leads
GPT-5.6 Terra: 70.4 (#21), o3-mini: 50.4 (#177)
| Benchmark | GPT-5.6 Terra | o3-mini |
|---|---|---|
| LMArena Text | 1446 | 1337 |
| LMArena Creative Writing | 1411 | 1286 |
| LMArena Multi-Turn | 1451 | 1320 |
| Short-Story Creative Writing | — | 61.7% |
| EQ-Bench Creative Writing | 1855 | — |
| EQ-Bench 4 | 1234 | — |
| LiveBench Language | — | 50.7% |
Frequently asked questions
Is GPT-5.6 Terra better than o3-mini?
GPT-5.6 Terra is the stronger model overall, scoring 59.1 to 36.7 on the Noometry Index. o3-mini costs 2.3× less per token, which makes it the better buy when GPT-5.6 Terra's lead doesn't matter for your workload.
Which is cheaper, GPT-5.6 Terra or o3-mini?
o3-mini is cheaper. It lists at $1.10 per million input tokens and $4.40 per million output tokens; GPT-5.6 Terra lists at $2 and $12.
Is GPT-5.6 Terra or o3-mini better for coding?
GPT-5.6 Terra scores higher on coding benchmarks: 57.1 versus 41.4 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-mini share?
33 benchmarks have published results for both models. GPT-5.6 Terra has 52 scored results on Noometry and o3-mini has 51.