Model comparison
GPT-5.6 Terra vs gpt-oss-20b
GPT-5.6 Terra is the stronger model overall, scoring 59.2 to 32.5 on the Noometry Index. gpt-oss-20b costs 125× less per token, which makes it the better buy when GPT-5.6 Terra's lead doesn't matter for your workload.
Last verified . 28 shared benchmarks.
Summary
- They share 28 benchmarks with published results for both. GPT-5.6 Terra scores higher in 9 categories and gpt-oss-20b in 0 categories; 9 gaps are clear of the uncertainty.
- The widest gap is in math, where GPT-5.6 Terra leads 81.6 to 39.4.
- The biggest single-benchmark swing is Chess Puzzles: 54% for GPT-5.6 Terra and 4% for gpt-oss-20b.
- gpt-oss-20b is cheaper at $0.018 / $0.09 per million input/output tokens, against $2 / $12 for GPT-5.6 Terra.
- GPT-5.6 Terra accepts more context: 1.05M tokens versus 131K.
- gpt-oss-20b has downloadable open weights; the other is API-only.
Side by side
| GPT-5.6 Terra | gpt-oss-20b | |
|---|---|---|
| Provider | OpenAI | OpenAI |
| Noometry Index | 59.2 | 32.5 |
| Released | 2026-07-09 | 2025-08-05 |
| Weights | Proprietary | Open |
| Context window | 1.05M | 131K |
| Max output | 128K | 16K |
| Input $ / M tokens | $2 | $0.018 |
| Output $ / M tokens | $12 | $0.09 |
| Results tracked | 52 | 34 |
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Category by category
Coding GPT-5.6 Terra leads
GPT-5.6 Terra: 57.7 (#19), gpt-oss-20b: 37.6 (#192)
| Benchmark | GPT-5.6 Terra | gpt-oss-20b |
|---|---|---|
| SciCode | 55% | 34.4% |
| WeirdML | 78.3% | 40.9% |
| LMArena Coding | 1484 | 1306 |
| ALE-Bench | 1,951 | 566.05 |
| DeepSWE | 69.6% | — |
| FrontierCode | 41.3% | — |
| CursorBench | 41.3% | — |
| LMArena WebDev | 1522 | — |
Agentic & Tool Use GPT-5.6 Terra leads
GPT-5.6 Terra: 40.1 (#25), gpt-oss-20b: 9.3 (#154)
| Benchmark | GPT-5.6 Terra | gpt-oss-20b |
|---|---|---|
| Terminal-Bench | — | 3.4% |
| APEX-Agents | 58.2% | — |
| BALROG | 53.2% | — |
| GDP.pdf | 24.7% | — |
| Vending-Bench 2 | 7,343 | — |
Reasoning GPT-5.6 Terra leads
GPT-5.6 Terra: 60.7 (#21), gpt-oss-20b: 19.3 (#261)
| Benchmark | GPT-5.6 Terra | gpt-oss-20b |
|---|---|---|
| Kagi LLM Benchmark | 51.3% | 53.2% |
| CritPt | 30% | 1.4% |
| Chess Puzzles | 54% | 4% |
| LMArena Hard Prompts | 1468 | 1274 |
| DTBench | 93.3% | 68% |
| LMCA | 55% | 14.5% |
| Epoch Capabilities Index | 159.62 | 137.82 |
| ARC-AGI-2 | 83.9% | — |
| SimpleBench | 48.9% | — |
| NYT Connections (extended) | 78.4% | — |
| ARC-AGI-1 | 96.5% | — |
| Mystery Game Puzzles | 35% | — |
| Surface Evolver Bench | 83.8% | — |
Math GPT-5.6 Terra leads
GPT-5.6 Terra: 81.6 (#12), gpt-oss-20b: 39.4 (#103)
| Benchmark | GPT-5.6 Terra | gpt-oss-20b |
|---|---|---|
| OTIS Mock AIME 2024-2025 | 99.7% | 65.3% |
| LMArena Math | 1466 | 1317 |
| FrontierMath (Tiers 1-3) | 86% | — |
| FrontierMath Tier 4 | 70.7% | — |
| ProofBench | 74% | — |
| Omni-MATH | — | 56.5% |
Knowledge GPT-5.6 Terra leads
GPT-5.6 Terra: 61.2 (#30), gpt-oss-20b: 34.6 (#195)
| Benchmark | GPT-5.6 Terra | gpt-oss-20b |
|---|---|---|
| GPQA Diamond | 93.3% | 60.8% |
| LMArena Expert | 1492 | 1258 |
| SimpleQA Verified | 43.2% | — |
| MMLU-Pro | — | 74% |
| GPQA (HELM) | — | 59.4% |
Multimodal Not comparable
GPT-5.6 Terra: 47.3 (#11), gpt-oss-20b: —
| Benchmark | GPT-5.6 Terra | gpt-oss-20b |
|---|---|---|
| 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: 54.4 (#44), gpt-oss-20b: 42.2 (#197)
| Benchmark | GPT-5.6 Terra | gpt-oss-20b |
|---|---|---|
| LMArena Non-English | 1439 | 1268 |
| LMArena Chinese | 1513 | 1314 |
| LMArena German | 1460 | 1255 |
| LMArena Japanese | 1457 | 1244 |
| LMArena Korean | 1425 | 1236 |
| LMArena Russian | 1450 | 1278 |
| LMArena Spanish | 1448 | 1267 |
| LMArena French | 1471 | — |
Instruction Following GPT-5.6 Terra leads
GPT-5.6 Terra: 76.4 (#40), gpt-oss-20b: 61.8 (#240)
| Benchmark | GPT-5.6 Terra | gpt-oss-20b |
|---|---|---|
| LMArena Instruction Following | 1454 | 1236 |
| IFEval | — | 73.2% |
Long Context GPT-5.6 Terra leads
GPT-5.6 Terra: 44.4 (#68), gpt-oss-20b: 37.9 (#209)
| Benchmark | GPT-5.6 Terra | gpt-oss-20b |
|---|---|---|
| LMArena Longer Query | 1451 | 1250 |
Writing & Preference GPT-5.6 Terra leads
GPT-5.6 Terra: 70.2 (#23), gpt-oss-20b: 35.5 (#265)
| Benchmark | GPT-5.6 Terra | gpt-oss-20b |
|---|---|---|
| LMArena Text | 1447 | 1287 |
| LMArena Creative Writing | 1410 | 1201 |
| EQ-Bench Creative Writing | 1855 | 666 |
| LMArena Multi-Turn | 1449 | 1268 |
| WildBench | — | 73.7% |
| EQ-Bench 4 | 1234 | — |
Frequently asked questions
Is GPT-5.6 Terra better than gpt-oss-20b?
GPT-5.6 Terra is the stronger model overall, scoring 59.2 to 32.5 on the Noometry Index. gpt-oss-20b costs 125× 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 gpt-oss-20b?
gpt-oss-20b is cheaper. It lists at $0.018 per million input tokens and $0.09 per million output tokens; GPT-5.6 Terra lists at $2 and $12.
Is GPT-5.6 Terra or gpt-oss-20b better for coding?
GPT-5.6 Terra scores higher on coding benchmarks: 57.7 versus 37.6 in the Noometry coding category.
Which has the bigger context window?
GPT-5.6 Terra does, with 1.05M tokens against 131K.
How many benchmarks do GPT-5.6 Terra and gpt-oss-20b share?
28 benchmarks have published results for both models. GPT-5.6 Terra has 52 scored results on Noometry and gpt-oss-20b has 34.