Model comparison
gpt-oss-120b vs Llama 3.2 3B
gpt-oss-120b is the stronger model overall, scoring 36.0 to 28.4 on the Noometry Index.
Last verified . 14 shared benchmarks.
MetaSummary
- They share 14 benchmarks with published results for both. gpt-oss-120b scores higher in 6 categories and Llama 3.2 3B in 3 categories; 8 gaps are clear of the uncertainty.
- The widest gap is in writing & preference, where gpt-oss-120b leads 46.7 to 24.9.
- gpt-oss-120b is cheaper at $0.037 / $0.17 per million input/output tokens, against $0.05 / $0.33 for Llama 3.2 3B.
Side by side
| gpt-oss-120b | Llama 3.2 3B | |
|---|---|---|
| Provider | OpenAI | Meta |
| Noometry Index | 36.0 | 28.4 |
| Released | 2025-08-05 | 2024-09-24 |
| Weights | Open | Open |
| Context window | 131K | 131K |
| Max output | 41K | 118K |
| Input $ / M tokens | $0.037 | $0.05 |
| Output $ / M tokens | $0.17 | $0.33 |
| Results tracked | 48 | 18 |
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Category by category
Coding gpt-oss-120b leads
gpt-oss-120b: 33.2 (#251), Llama 3.2 3B: 27.1 (#314)
| Benchmark | gpt-oss-120b | Llama 3.2 3B |
|---|---|---|
| LMArena Coding | 1380 | 1098 |
| SWE-bench Verified (bash only) | 26% | — |
| Aider Polyglot | 41.8% | — |
| SciCode | 36% | — |
| WeirdML | 48.2% | — |
| BigCodeBench Instruct | — | 23.4% |
| BigCodeBench Complete | — | 28.3% |
| ALE-Bench | 575.62 | — |
| AlgoTune | 1.41 | — |
Agentic & Tool Use Llama 3.2 3B leads
gpt-oss-120b: 12.1 (#145), Llama 3.2 3B: 19.9 (#135)
| Benchmark | gpt-oss-120b | Llama 3.2 3B |
|---|---|---|
| Terminal-Bench | 18.7% | — |
| APEX-Agents | 4.4% | — |
| Berkeley Function Calling Leaderboard | — | 21.9% |
| BALROG | — | 10.1% |
| METR Time Horizons | 56.6% | — |
| Vending-Bench 2 | -21.53 | — |
Reasoning Too close to call
gpt-oss-120b: 19.5 (#236), Llama 3.2 3B: 20.1 (#225)
| Benchmark | gpt-oss-120b | Llama 3.2 3B |
|---|---|---|
| LMArena Hard Prompts | 1364 | 1095 |
| SimpleBench | 22.1% | — |
| Kagi LLM Benchmark | 58.6% | — |
| CritPt | 1.1% | — |
| Chess Puzzles | 20% | — |
| Mystery Game Puzzles | 2% | — |
| DTBench | 76.3% | — |
| LMCA | 22.1% | — |
| Surface Evolver Bench | 25% | — |
| Epoch Capabilities Index | 139.94 | — |
Math gpt-oss-120b leads
gpt-oss-120b: 50.6 (#43), Llama 3.2 3B: 29.9 (#213)
| Benchmark | gpt-oss-120b | Llama 3.2 3B |
|---|---|---|
| LMArena Math | 1388 | 1126 |
| OTIS Mock AIME 2024-2025 | 88.9% | — |
| Omni-MATH | 68.8% | — |
Knowledge gpt-oss-120b leads
gpt-oss-120b: 42.3 (#89), Llama 3.2 3B: 29.1 (#232)
| Benchmark | gpt-oss-120b | Llama 3.2 3B |
|---|---|---|
| LMArena Expert | 1356 | 1090 |
| GPQA Diamond | 75.8% | — |
| MMLU-Pro | 79.5% | — |
| Confabulations | 15.7% | — |
| Vectara Hallucination Rate | 14.2% | — |
| GPQA (HELM) | 68.4% | — |
Multilingual gpt-oss-120b leads
gpt-oss-120b: 48.2 (#142), Llama 3.2 3B: 26.4 (#276)
| Benchmark | gpt-oss-120b | Llama 3.2 3B |
|---|---|---|
| LMArena Non-English | 1350 | 1019 |
| LMArena Chinese | 1384 | 1017 |
| LMArena German | 1355 | 1056 |
| LMArena Russian | 1343 | 949 |
| LMArena French | 1370 | — |
| LMArena Japanese | 1332 | — |
| LMArena Korean | 1283 | — |
| LMArena Spanish | 1388 | — |
Instruction Following gpt-oss-120b leads
gpt-oss-120b: 69.5 (#168), Llama 3.2 3B: 56.3 (#270)
| Benchmark | gpt-oss-120b | Llama 3.2 3B |
|---|---|---|
| LMArena Instruction Following | 1318 | 1089 |
| IFEval | 83.6% | — |
Long Context Llama 3.2 3B leads
gpt-oss-120b: 31.4 (#273), Llama 3.2 3B: 33.4 (#256)
| Benchmark | gpt-oss-120b | Llama 3.2 3B |
|---|---|---|
| LMArena Longer Query | 1319 | 1101 |
| Fiction.LiveBench | 44.4% | — |
Writing & Preference gpt-oss-120b leads
gpt-oss-120b: 46.7 (#213), Llama 3.2 3B: 24.9 (#302)
| Benchmark | gpt-oss-120b | Llama 3.2 3B |
|---|---|---|
| LMArena Text | 1366 | 1110 |
| LMArena Creative Writing | 1274 | 1094 |
| EQ-Bench Creative Writing | 961 | 595 |
| LMArena Multi-Turn | 1340 | 1105 |
| Short-Story Creative Writing | 77.1% | — |
| WildBench | 84.5% | — |
Frequently asked questions
Is gpt-oss-120b better than Llama 3.2 3B?
gpt-oss-120b is the stronger model overall, scoring 36.0 to 28.4 on the Noometry Index.
Which is cheaper, gpt-oss-120b or Llama 3.2 3B?
gpt-oss-120b is cheaper. It lists at $0.037 per million input tokens and $0.17 per million output tokens; Llama 3.2 3B lists at $0.05 and $0.33.
Is gpt-oss-120b or Llama 3.2 3B better for coding?
gpt-oss-120b scores higher on coding benchmarks: 33.2 versus 27.1 in the Noometry coding category.
Which has the bigger context window?
Both accept 131K tokens.
How many benchmarks do gpt-oss-120b and Llama 3.2 3B share?
14 benchmarks have published results for both models. gpt-oss-120b has 48 scored results on Noometry and Llama 3.2 3B has 18.