Model comparison
GPT-6 Astra vs gpt-oss-120b
GPT-6 Astra is the stronger model overall, scoring 70.3 to 36.0 on the Noometry Index. gpt-oss-120b costs 285× less per token, which makes it the better buy when GPT-6 Astra's lead doesn't matter for your workload.
Last verified . 29 shared benchmarks.
Summary
- They share 29 benchmarks with published results for both. GPT-6 Astra scores higher in 9 categories and gpt-oss-120b in 0 categories; 9 gaps are clear of the uncertainty.
- The widest gap is in reasoning, where GPT-6 Astra leads 85.1 to 19.5.
- The biggest single-benchmark swing is Mystery Game Puzzles: 84% for GPT-6 Astra and 2% for gpt-oss-120b.
- gpt-oss-120b is cheaper at $0.037 / $0.17 per million input/output tokens, against $10 / $50 for GPT-6 Astra.
- GPT-6 Astra accepts more context: 1.05M tokens versus 131K.
- gpt-oss-120b has downloadable open weights; the other is API-only.
Side by side
| GPT-6 Astra | gpt-oss-120b | |
|---|---|---|
| Provider | OpenAI | OpenAI |
| Noometry Index | 70.3 | 36.0 |
| Released | 2026-09-03 | 2025-08-05 |
| Weights | Proprietary | Open |
| Context window | 1.05M | 131K |
| Max output | 128K | 41K |
| Input $ / M tokens | $10 | $0.037 |
| Output $ / M tokens | $50 | $0.17 |
| Results tracked | 51 | 48 |
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Category by category
Coding GPT-6 Astra leads
GPT-6 Astra: 70.5 (#3), gpt-oss-120b: 33.2 (#251)
| Benchmark | GPT-6 Astra | gpt-oss-120b |
|---|---|---|
| SciCode | 56.5% | 36% |
| WeirdML | 93.6% | 48.2% |
| LMArena Coding | 1488 | 1380 |
| ALE-Bench | 2,951 | 575.62 |
| DeepSWE | 74.1% | — |
| FrontierCode | 53.3% | — |
| SWE-bench Verified (bash only) | — | 26% |
| Aider Polyglot | — | 41.8% |
| LMArena WebDev | 1792 | — |
| FrontierSWE | 65.5% | — |
| MirrorCode | 46.7% | — |
| AlgoTune | — | 1.41 |
Agentic & Tool Use GPT-6 Astra leads
GPT-6 Astra: 52.3 (#3), gpt-oss-120b: 12.1 (#145)
| Benchmark | GPT-6 Astra | gpt-oss-120b |
|---|---|---|
| APEX-Agents | 64.7% | 4.4% |
| Vending-Bench 2 | 15,515 | -21.53 |
| Terminal-Bench | — | 18.7% |
| Remote Labor Index | 20.8% | — |
| BALROG | 68.3% | — |
| METR Time Horizons | — | 56.6% |
Reasoning GPT-6 Astra leads
GPT-6 Astra: 85.1 (#1), gpt-oss-120b: 19.5 (#236)
| Benchmark | GPT-6 Astra | gpt-oss-120b |
|---|---|---|
| CritPt | 31.7% | 1.1% |
| Chess Puzzles | 72% | 20% |
| LMArena Hard Prompts | 1464 | 1364 |
| Mystery Game Puzzles | 84% | 2% |
| DTBench | 97.3% | 76.3% |
| LMCA | 64.4% | 22.1% |
| Epoch Capabilities Index | 166.6 | 139.94 |
| ARC-AGI-2 | 95% | — |
| SimpleBench | — | 22.1% |
| Kagi LLM Benchmark | — | 58.6% |
| NYT Connections (extended) | 98.1% | — |
| ARC-AGI-1 | 98.5% | — |
| EBR-Bench | 76.2% | — |
| Surface Evolver Bench | — | 25% |
| Bench to the Future 3 | 0.14 | — |
Math GPT-6 Astra leads
GPT-6 Astra: 93.4 (#1), gpt-oss-120b: 50.6 (#43)
| Benchmark | GPT-6 Astra | gpt-oss-120b |
|---|---|---|
| OTIS Mock AIME 2024-2025 | 100% | 88.9% |
| LMArena Math | 1463 | 1388 |
| FrontierMath (Tiers 1-3) | 93.7% | — |
| FrontierMath Tier 4 | 97.6% | — |
| ProofBench | 99% | — |
| Omni-MATH | — | 68.8% |
| FrontierMath Erdős | 2.9% | — |
Knowledge GPT-6 Astra leads
GPT-6 Astra: 75.3 (#1), gpt-oss-120b: 42.3 (#89)
| Benchmark | GPT-6 Astra | gpt-oss-120b |
|---|---|---|
| GPQA Diamond | 95.8% | 75.8% |
| Vectara Hallucination Rate | 8.7% | 14.2% |
| LMArena Expert | 1486 | 1356 |
| Humanity's Last Exam | 54.8% | — |
| SimpleQA Verified | 75.6% | — |
| MMLU-Pro | — | 79.5% |
| Confabulations | — | 15.7% |
| GPQA (HELM) | — | 68.4% |
Multimodal Not comparable
GPT-6 Astra: 55.1 (#3), gpt-oss-120b: —
| Benchmark | GPT-6 Astra | gpt-oss-120b |
|---|---|---|
| LMArena Vision | 1280 | — |
| Blueprint-Bench 2 | 49.7% | — |
| Furniture Assembly | 80% | — |
| LMArena Document | 1468 | — |
Multilingual GPT-6 Astra leads
GPT-6 Astra: 54.0 (#53), gpt-oss-120b: 48.2 (#142)
| Benchmark | GPT-6 Astra | gpt-oss-120b |
|---|---|---|
| LMArena Non-English | 1431 | 1350 |
| LMArena Chinese | 1495 | 1384 |
| LMArena French | 1467 | 1370 |
| LMArena Russian | 1434 | 1343 |
| LMArena Spanish | 1412 | 1388 |
| LMArena German | — | 1355 |
| LMArena Japanese | — | 1332 |
| LMArena Korean | — | 1283 |
Instruction Following GPT-6 Astra leads
GPT-6 Astra: 76.6 (#37), gpt-oss-120b: 69.5 (#168)
| Benchmark | GPT-6 Astra | gpt-oss-120b |
|---|---|---|
| LMArena Instruction Following | 1454 | 1318 |
| IFEval | — | 83.6% |
Long Context GPT-6 Astra leads
GPT-6 Astra: 44.7 (#57), gpt-oss-120b: 31.4 (#273)
| Benchmark | GPT-6 Astra | gpt-oss-120b |
|---|---|---|
| LMArena Longer Query | 1460 | 1319 |
| Fiction.LiveBench | — | 44.4% |
Writing & Preference GPT-6 Astra leads
GPT-6 Astra: 75.7 (#7), gpt-oss-120b: 46.7 (#213)
| Benchmark | GPT-6 Astra | gpt-oss-120b |
|---|---|---|
| LMArena Text | 1443 | 1366 |
| LMArena Creative Writing | 1423 | 1274 |
| EQ-Bench Creative Writing | 2173 | 961 |
| LMArena Multi-Turn | 1457 | 1340 |
| Short-Story Creative Writing | — | 77.1% |
| WildBench | — | 84.5% |
Frequently asked questions
Is GPT-6 Astra better than gpt-oss-120b?
GPT-6 Astra is the stronger model overall, scoring 70.3 to 36.0 on the Noometry Index. gpt-oss-120b costs 285× less per token, which makes it the better buy when GPT-6 Astra's lead doesn't matter for your workload.
Which is cheaper, GPT-6 Astra or gpt-oss-120b?
gpt-oss-120b is cheaper. It lists at $0.037 per million input tokens and $0.17 per million output tokens; GPT-6 Astra lists at $10 and $50.
Is GPT-6 Astra or gpt-oss-120b better for coding?
GPT-6 Astra scores higher on coding benchmarks: 70.5 versus 33.2 in the Noometry coding category.
Which has the bigger context window?
GPT-6 Astra does, with 1.05M tokens against 131K.
How many benchmarks do GPT-6 Astra and gpt-oss-120b share?
29 benchmarks have published results for both models. GPT-6 Astra has 51 scored results on Noometry and gpt-oss-120b has 48.