# gpt-oss-20b vs o3

> o3 is the stronger model overall, scoring 47.5 to 32.5 on the Noometry Index. gpt-oss-20b costs 97× less per token, which makes it the better buy when o3's lead doesn't matter for your workload.

- Canonical page: https://noometry.com/compare/gpt-oss-20b-vs-o3
- Last updated: 2026-10-11
- Shared benchmarks: 32

## Summary

- They share 32 benchmarks with published results for both. gpt-oss-20b scores higher in 0 categories and o3 in 9 categories; 9 gaps are clear of the uncertainty.
- The widest gap is in writing & preference, where o3 leads 63.5 to 35.5.
- The biggest single-benchmark swing is Chess Puzzles: 4% for gpt-oss-20b and 38% for o3.
- gpt-oss-20b is cheaper at $0.018 / $0.09 per million input/output tokens, against $2 / $8 for o3.
- o3 accepts more context: 200K tokens versus 131K.
- gpt-oss-20b has downloadable open weights; the other is API-only.

## Snapshot

| | gpt-oss-20b | o3 |
|---|---|---|
| Provider | OpenAI | OpenAI |
| Noometry Index | 32.5 | 47.5 |
| Rank | 255 | 61 |
| Context | 131K | 200K |
| Input $/M | $0.018 | $2 |
| Output $/M | $0.09 | $8 |
| Weights | Open | Proprietary |

## Coding

- gpt-oss-20b: 37.6 (#192)
- o3: 46.8 (#64)

| Benchmark | gpt-oss-20b | o3 |
|---|---|---|
| WeirdML | 40.9% | 52.4% |
| LMArena Coding | 1306 | 1408 |
| ALE-Bench | 566.05 | 933.55 |
| SWE-bench Verified | — | 62.3% |
| SWE-bench Verified (bash only) | — | 58.4% |
| Aider Polyglot | — | 81.3% |
| SciCode | 34.4% | — |
| GSO | — | 8.8% |
| CadEval | — | 74% |

## Agentic & Tool Use

- gpt-oss-20b: 9.3 (#154)
- o3: 34.5 (#44)

| Benchmark | gpt-oss-20b | o3 |
|---|---|---|
| Terminal-Bench | 3.4% | — |
| Berkeley Function Calling Leaderboard | — | 63% |
| GDPval | — | 30.8% |
| DeepResearch Bench | — | 45.2% |
| OSWorld | — | 23% |
| LMArena Search | — | 1144 |
| METR Time Horizons | — | 65.4% |

## Reasoning

- gpt-oss-20b: 19.3 (#261)
- o3: 32.0 (#78)

| Benchmark | gpt-oss-20b | o3 |
|---|---|---|
| Kagi LLM Benchmark | 53.2% | 67.6% |
| CritPt | 1.4% | 1.4% |
| Chess Puzzles | 4% | 38% |
| LMArena Hard Prompts | 1274 | 1402 |
| DTBench | 68% | 84.8% |
| LMCA | 14.5% | 39.7% |
| Epoch Capabilities Index | 137.82 | 146.86 |
| ARC-AGI-2 | — | 6.5% |
| SimpleBench | — | 53.1% |
| ARC-AGI-1 | — | 60.8% |
| EnigmaEval | — | 13.1% |
| Mystery Game Puzzles | — | 29% |
| ForecastBench | — | 62.5 |

## Math

- gpt-oss-20b: 39.4 (#103)
- o3: 50.2 (#58)

| Benchmark | gpt-oss-20b | o3 |
|---|---|---|
| OTIS Mock AIME 2024-2025 | 65.3% | 84.4% |
| Omni-MATH | 56.5% | 71.4% |
| LMArena Math | 1317 | 1426 |
| FrontierMath (Tiers 1-3) | — | 33.3% |
| MATH Level 5 | — | 97.8% |
| FrontierMath (Feb 2025 set) | — | 18.7% |
| FrontierMath Tier 4 (v1) | — | 2.1% |

## Knowledge

- gpt-oss-20b: 34.6 (#195)
- o3: 54.6 (#52)

| Benchmark | gpt-oss-20b | o3 |
|---|---|---|
| GPQA Diamond | 60.8% | 81.8% |
| MMLU-Pro | 74% | 85.9% |
| GPQA (HELM) | 59.4% | 75.3% |
| LMArena Expert | 1258 | 1402 |
| Humanity's Last Exam | — | 20.3% |
| SimpleQA Verified | — | 49.4% |
| Confabulations | — | 14.4% |

## Multimodal

- gpt-oss-20b: —
- o3: 41.4 (#36)

| Benchmark | gpt-oss-20b | o3 |
|---|---|---|
| LMArena Vision | — | 1214 |
| GeoBench | — | 74% |
| VPCT | — | 52% |

## Multilingual

- gpt-oss-20b: 42.2 (#197)
- o3: 51.7 (#105)

| Benchmark | gpt-oss-20b | o3 |
|---|---|---|
| LMArena Non-English | 1268 | 1401 |
| LMArena Chinese | 1314 | 1437 |
| LMArena German | 1255 | 1420 |
| LMArena Japanese | 1244 | 1403 |
| LMArena Korean | 1236 | 1370 |
| LMArena Russian | 1278 | 1406 |
| LMArena Spanish | 1267 | 1395 |
| LMArena French | — | 1430 |

## Instruction Following

- gpt-oss-20b: 61.8 (#240)
- o3: 72.8 (#127)

| Benchmark | gpt-oss-20b | o3 |
|---|---|---|
| IFEval | 73.2% | 86.9% |
| LMArena Instruction Following | 1236 | 1368 |

## Long Context

- gpt-oss-20b: 37.9 (#209)
- o3: 53.3 (#6)

| Benchmark | gpt-oss-20b | o3 |
|---|---|---|
| LMArena Longer Query | 1250 | 1372 |
| Fiction.LiveBench | — | 88.9% |
| CL-bench | — | 17.8% |

## Writing & Preference

- gpt-oss-20b: 35.5 (#265)
- o3: 63.5 (#64)

| Benchmark | gpt-oss-20b | o3 |
|---|---|---|
| LMArena Text | 1287 | 1410 |
| LMArena Creative Writing | 1201 | 1359 |
| EQ-Bench Creative Writing | 666 | 1676 |
| WildBench | 73.7% | 86.1% |
| LMArena Multi-Turn | 1268 | 1405 |
| Short-Story Creative Writing | — | 83.9% |

## FAQ

### Is gpt-oss-20b better than o3?

o3 is the stronger model overall, scoring 47.5 to 32.5 on the Noometry Index. gpt-oss-20b costs 97× less per token, which makes it the better buy when o3's lead doesn't matter for your workload.

### Which is cheaper, gpt-oss-20b or o3?

gpt-oss-20b is cheaper. It lists at $0.018 per million input tokens and $0.09 per million output tokens; o3 lists at $2 and $8.

### Is gpt-oss-20b or o3 better for coding?

o3 scores higher on coding benchmarks: 46.8 versus 37.6 in the Noometry coding category.

### Which has the bigger context window?

o3 does, with 200K tokens against 131K.

### How many benchmarks do gpt-oss-20b and o3 share?

32 benchmarks have published results for both models. gpt-oss-20b has 34 scored results on Noometry and o3 has 63.