Model comparison
gpt-oss-20b vs Nvidia Llama 3.3 Nemotron Super 49b v1.5
Nvidia Llama 3.3 Nemotron Super 49b v1.5 is the stronger model overall, scoring 40.3 to 32.5 on the Noometry Index. gpt-oss-20b costs 11× less per token, which makes it the better buy when Nvidia Llama 3.3 Nemotron Super 49b v1.5's lead doesn't matter for your workload.
Last verified . 12 shared benchmarks.
NVIDIASummary
- They share 12 benchmarks with published results for both. gpt-oss-20b scores higher in 1 category and Nvidia Llama 3.3 Nemotron Super 49b v1.5 in 7 categories; 8 gaps are clear of the uncertainty.
- The widest gap is in writing & preference, where Nvidia Llama 3.3 Nemotron Super 49b v1.5 leads 53.1 to 35.5.
- gpt-oss-20b is cheaper at $0.018 / $0.09 per million input/output tokens, against $0.40 / $0.40 for Nvidia Llama 3.3 Nemotron Super 49b v1.5.
Side by side
| gpt-oss-20b | Nvidia Llama 3.3 Nemotron Super 49b v1.5 | |
|---|---|---|
| Provider | OpenAI | NVIDIA |
| Noometry Index | 32.5 | 40.3 |
| Released | 2025-08-05 | 2025-07-25 |
| Weights | Open | Open |
| Context window | 131K | 131K |
| Max output | 16K | 131K |
| Input $ / M tokens | $0.018 | $0.40 |
| Output $ / M tokens | $0.09 | $0.40 |
| Results tracked | 34 | 12 |
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Category by category
Coding Nvidia Llama 3.3 Nemotron Super 49b v1.5 leads
gpt-oss-20b: 37.6 (#192), Nvidia Llama 3.3 Nemotron Super 49b v1.5: 39.8 (#154)
| Benchmark | gpt-oss-20b | Nvidia Llama 3.3 Nemotron Super 49b v1.5 |
|---|---|---|
| LMArena Coding | 1306 | 1355 |
| SciCode | 34.4% | — |
| WeirdML | 40.9% | — |
| ALE-Bench | 566.05 | — |
Agentic & Tool Use Not comparable
gpt-oss-20b: 9.3 (#154), Nvidia Llama 3.3 Nemotron Super 49b v1.5: —
| Benchmark | gpt-oss-20b | Nvidia Llama 3.3 Nemotron Super 49b v1.5 |
|---|---|---|
| Terminal-Bench | 3.4% | — |
Reasoning Nvidia Llama 3.3 Nemotron Super 49b v1.5 leads
gpt-oss-20b: 19.3 (#261), Nvidia Llama 3.3 Nemotron Super 49b v1.5: 26.8 (#128)
| Benchmark | gpt-oss-20b | Nvidia Llama 3.3 Nemotron Super 49b v1.5 |
|---|---|---|
| LMArena Hard Prompts | 1274 | 1336 |
| Kagi LLM Benchmark | 53.2% | — |
| CritPt | 1.4% | — |
| Chess Puzzles | 4% | — |
| DTBench | 68% | — |
| LMCA | 14.5% | — |
| Epoch Capabilities Index | 137.82 | — |
Math gpt-oss-20b leads
gpt-oss-20b: 39.4 (#103), Nvidia Llama 3.3 Nemotron Super 49b v1.5: 38.2 (#141)
| Benchmark | gpt-oss-20b | Nvidia Llama 3.3 Nemotron Super 49b v1.5 |
|---|---|---|
| LMArena Math | 1317 | 1392 |
| OTIS Mock AIME 2024-2025 | 65.3% | — |
| Omni-MATH | 56.5% | — |
Knowledge Nvidia Llama 3.3 Nemotron Super 49b v1.5 leads
gpt-oss-20b: 34.6 (#195), Nvidia Llama 3.3 Nemotron Super 49b v1.5: 36.7 (#165)
| Benchmark | gpt-oss-20b | Nvidia Llama 3.3 Nemotron Super 49b v1.5 |
|---|---|---|
| LMArena Expert | 1258 | 1330 |
| GPQA Diamond | 60.8% | — |
| MMLU-Pro | 74% | — |
| GPQA (HELM) | 59.4% | — |
Multilingual Nvidia Llama 3.3 Nemotron Super 49b v1.5 leads
gpt-oss-20b: 42.2 (#197), Nvidia Llama 3.3 Nemotron Super 49b v1.5: 45.5 (#168)
| Benchmark | gpt-oss-20b | Nvidia Llama 3.3 Nemotron Super 49b v1.5 |
|---|---|---|
| LMArena Non-English | 1268 | 1316 |
| LMArena Japanese | 1244 | 1300 |
| LMArena Russian | 1278 | 1332 |
| LMArena Chinese | 1314 | — |
| LMArena German | 1255 | — |
| LMArena Korean | 1236 | — |
| LMArena Spanish | 1267 | — |
Instruction Following Nvidia Llama 3.3 Nemotron Super 49b v1.5 leads
gpt-oss-20b: 61.8 (#240), Nvidia Llama 3.3 Nemotron Super 49b v1.5: 68.6 (#188)
| Benchmark | gpt-oss-20b | Nvidia Llama 3.3 Nemotron Super 49b v1.5 |
|---|---|---|
| LMArena Instruction Following | 1236 | 1299 |
| IFEval | 73.2% | — |
Long Context Nvidia Llama 3.3 Nemotron Super 49b v1.5 leads
gpt-oss-20b: 37.9 (#209), Nvidia Llama 3.3 Nemotron Super 49b v1.5: 40.0 (#164)
| Benchmark | gpt-oss-20b | Nvidia Llama 3.3 Nemotron Super 49b v1.5 |
|---|---|---|
| LMArena Longer Query | 1250 | 1315 |
Writing & Preference Nvidia Llama 3.3 Nemotron Super 49b v1.5 leads
gpt-oss-20b: 35.5 (#265), Nvidia Llama 3.3 Nemotron Super 49b v1.5: 53.1 (#159)
| Benchmark | gpt-oss-20b | Nvidia Llama 3.3 Nemotron Super 49b v1.5 |
|---|---|---|
| LMArena Text | 1287 | 1338 |
| LMArena Creative Writing | 1201 | 1307 |
| LMArena Multi-Turn | 1268 | 1334 |
| EQ-Bench Creative Writing | 666 | — |
| WildBench | 73.7% | — |
Frequently asked questions
Is gpt-oss-20b better than Nvidia Llama 3.3 Nemotron Super 49b v1.5?
Nvidia Llama 3.3 Nemotron Super 49b v1.5 is the stronger model overall, scoring 40.3 to 32.5 on the Noometry Index. gpt-oss-20b costs 11× less per token, which makes it the better buy when Nvidia Llama 3.3 Nemotron Super 49b v1.5's lead doesn't matter for your workload.
Which is cheaper, gpt-oss-20b or Nvidia Llama 3.3 Nemotron Super 49b v1.5?
gpt-oss-20b is cheaper. It lists at $0.018 per million input tokens and $0.09 per million output tokens; Nvidia Llama 3.3 Nemotron Super 49b v1.5 lists at $0.40 and $0.40.
Is gpt-oss-20b or Nvidia Llama 3.3 Nemotron Super 49b v1.5 better for coding?
Nvidia Llama 3.3 Nemotron Super 49b v1.5 scores higher on coding benchmarks: 39.8 versus 37.6 in the Noometry coding category.
Which has the bigger context window?
Both accept 131K tokens.
How many benchmarks do gpt-oss-20b and Nvidia Llama 3.3 Nemotron Super 49b v1.5 share?
12 benchmarks have published results for both models. gpt-oss-20b has 34 scored results on Noometry and Nvidia Llama 3.3 Nemotron Super 49b v1.5 has 12.