Model comparison
gpt-oss-120b vs Llama-3.3-70B-Instruct
gpt-oss-120b is the stronger model overall, scoring 36.0 to 30.3 on the Noometry Index.
Last verified . 29 shared benchmarks.
MetaSummary
- They share 29 benchmarks with published results for both. gpt-oss-120b scores higher in 6 categories and Llama-3.3-70B-Instruct in 3 categories; 9 gaps are clear of the uncertainty.
- The widest gap is in math, where gpt-oss-120b leads 50.6 to 14.3.
- The biggest single-benchmark swing is OTIS Mock AIME 2024-2025: 88.9% for gpt-oss-120b and 5.1% for Llama-3.3-70B-Instruct.
- gpt-oss-120b is cheaper at $0.037 / $0.17 per million input/output tokens, against $0.10 / $0.32 for Llama-3.3-70B-Instruct.
- gpt-oss-120b accepts more context: 131K tokens versus 128K.
Side by side
| gpt-oss-120b | Llama-3.3-70B-Instruct | |
|---|---|---|
| Provider | OpenAI | Meta |
| Noometry Index | 36.0 | 30.3 |
| Released | 2025-08-05 | 2024-12-06 |
| Weights | Open | Open |
| Context window | 131K | 128K |
| Max output | 41K | 4K |
| Input $ / M tokens | $0.037 | $0.10 |
| Output $ / M tokens | $0.17 | $0.32 |
| Results tracked | 48 | 43 |
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Category by category
Coding gpt-oss-120b leads
gpt-oss-120b: 33.2 (#251), Llama-3.3-70B-Instruct: 30.6 (#284)
| Benchmark | gpt-oss-120b | Llama-3.3-70B-Instruct |
|---|---|---|
| SciCode | 36% | 26% |
| WeirdML | 48.2% | 14.4% |
| LMArena Coding | 1380 | 1268 |
| SWE-bench Verified (bash only) | 26% | — |
| Aider Polyglot | 41.8% | — |
| BigCodeBench Instruct | — | 46.9% |
| LiveBench Coding | — | 36.6% |
| BigCodeBench Complete | — | 57.5% |
| ALE-Bench | 575.62 | — |
| AlgoTune | 1.41 | — |
Agentic & Tool Use Llama-3.3-70B-Instruct leads
gpt-oss-120b: 12.1 (#145), Llama-3.3-70B-Instruct: 25.6 (#100)
| Benchmark | gpt-oss-120b | Llama-3.3-70B-Instruct |
|---|---|---|
| Terminal-Bench | 18.7% | — |
| APEX-Agents | 4.4% | — |
| Berkeley Function Calling Leaderboard | — | 31.9% |
| BALROG | — | 23% |
| METR Time Horizons | 56.6% | — |
| Vending-Bench 2 | -21.53 | — |
Reasoning gpt-oss-120b leads
gpt-oss-120b: 19.5 (#236), Llama-3.3-70B-Instruct: 13.7 (#325)
| Benchmark | gpt-oss-120b | Llama-3.3-70B-Instruct |
|---|---|---|
| SimpleBench | 22.1% | 19.9% |
| CritPt | 1.1% | 0% |
| LMArena Hard Prompts | 1364 | 1257 |
| DTBench | 76.3% | 59.5% |
| LMCA | 22.1% | 17.5% |
| Epoch Capabilities Index | 139.94 | 127.32 |
| Kagi LLM Benchmark | 58.6% | — |
| Chess Puzzles | 20% | — |
| LiveBench Reasoning | — | 50.8% |
| Mystery Game Puzzles | 2% | — |
| LiveBench Data Analysis | — | 49.5% |
| Surface Evolver Bench | 25% | — |
| ForecastBench | — | 58.6 |
| LiveBench | — | 50.2% |
Math gpt-oss-120b leads
gpt-oss-120b: 50.6 (#43), Llama-3.3-70B-Instruct: 14.3 (#292)
| Benchmark | gpt-oss-120b | Llama-3.3-70B-Instruct |
|---|---|---|
| OTIS Mock AIME 2024-2025 | 88.9% | 5.1% |
| LMArena Math | 1388 | 1268 |
| Omni-MATH | 68.8% | — |
| LiveBench Math | — | 42.2% |
| MATH Level 5 | — | 41.6% |
Knowledge gpt-oss-120b leads
gpt-oss-120b: 42.3 (#89), Llama-3.3-70B-Instruct: 30.5 (#220)
| Benchmark | gpt-oss-120b | Llama-3.3-70B-Instruct |
|---|---|---|
| GPQA Diamond | 75.8% | 47.4% |
| Confabulations | 15.7% | 22.8% |
| Vectara Hallucination Rate | 14.2% | 4.1% |
| LMArena Expert | 1356 | 1225 |
| MMLU-Pro | 79.5% | — |
| GPQA (HELM) | 68.4% | — |
| MMLU | — | 86.3% |
Multilingual gpt-oss-120b leads
gpt-oss-120b: 48.2 (#142), Llama-3.3-70B-Instruct: 40.1 (#215)
| Benchmark | gpt-oss-120b | Llama-3.3-70B-Instruct |
|---|---|---|
| LMArena Non-English | 1350 | 1236 |
| LMArena Chinese | 1384 | 1217 |
| LMArena French | 1370 | 1280 |
| LMArena German | 1355 | 1251 |
| LMArena Japanese | 1332 | 1149 |
| LMArena Korean | 1283 | 1142 |
| LMArena Russian | 1343 | 1252 |
| LMArena Spanish | 1388 | 1269 |
Instruction Following Llama-3.3-70B-Instruct leads
gpt-oss-120b: 69.5 (#168), Llama-3.3-70B-Instruct: 71.3 (#152)
| Benchmark | gpt-oss-120b | Llama-3.3-70B-Instruct |
|---|---|---|
| LMArena Instruction Following | 1318 | 1242 |
| LiveBench Instruction Following | — | 82.7% |
| IFEval | 83.6% | — |
Long Context gpt-oss-120b leads
gpt-oss-120b: 31.4 (#273), Llama-3.3-70B-Instruct: 26.4 (#290)
| Benchmark | gpt-oss-120b | Llama-3.3-70B-Instruct |
|---|---|---|
| Fiction.LiveBench | 44.4% | 33.3% |
| LMArena Longer Query | 1319 | 1256 |
Writing & Preference Llama-3.3-70B-Instruct leads
gpt-oss-120b: 46.7 (#213), Llama-3.3-70B-Instruct: 47.8 (#203)
| Benchmark | gpt-oss-120b | Llama-3.3-70B-Instruct |
|---|---|---|
| LMArena Text | 1366 | 1274 |
| LMArena Creative Writing | 1274 | 1250 |
| LMArena Multi-Turn | 1340 | 1279 |
| Short-Story Creative Writing | 77.1% | — |
| EQ-Bench Creative Writing | 961 | — |
| WildBench | 84.5% | — |
| LiveBench Language | — | 39.2% |
Frequently asked questions
Is gpt-oss-120b better than Llama-3.3-70B-Instruct?
gpt-oss-120b is the stronger model overall, scoring 36.0 to 30.3 on the Noometry Index.
Which is cheaper, gpt-oss-120b or Llama-3.3-70B-Instruct?
gpt-oss-120b is cheaper. It lists at $0.037 per million input tokens and $0.17 per million output tokens; Llama-3.3-70B-Instruct lists at $0.10 and $0.32.
Is gpt-oss-120b or Llama-3.3-70B-Instruct better for coding?
gpt-oss-120b scores higher on coding benchmarks: 33.2 versus 30.6 in the Noometry coding category.
Which has the bigger context window?
gpt-oss-120b does, with 131K tokens against 128K.
How many benchmarks do gpt-oss-120b and Llama-3.3-70B-Instruct share?
29 benchmarks have published results for both models. gpt-oss-120b has 48 scored results on Noometry and Llama-3.3-70B-Instruct has 43.