Model comparison
GPT-5.4 nano vs Nvidia Llama 3.3 Nemotron Super 49b v1.5
GPT-5.4 nano is the stronger model overall, scoring 41.9 to 40.3 on the Noometry Index.
Last verified . 12 shared benchmarks.
NVIDIASummary
- They share 12 benchmarks with published results for both. GPT-5.4 nano scores higher in 7 categories and Nvidia Llama 3.3 Nemotron Super 49b v1.5 in 1 category; 8 gaps are clear of the uncertainty.
- The widest gap is in knowledge, where GPT-5.4 nano leads 41.9 to 36.7.
- Nvidia Llama 3.3 Nemotron Super 49b v1.5 is cheaper at $0.40 / $0.40 per million input/output tokens, against $0.20 / $1.25 for GPT-5.4 nano.
- GPT-5.4 nano accepts more context: 400K tokens versus 131K.
- Nvidia Llama 3.3 Nemotron Super 49b v1.5 has downloadable open weights; the other is API-only.
Side by side
| GPT-5.4 nano | Nvidia Llama 3.3 Nemotron Super 49b v1.5 | |
|---|---|---|
| Provider | OpenAI | NVIDIA |
| Noometry Index | 41.9 | 40.3 |
| Released | 2026-03-17 | 2025-07-25 |
| Weights | Proprietary | Open |
| Context window | 400K | 131K |
| Max output | 128K | 131K |
| Input $ / M tokens | $0.20 | $0.40 |
| Output $ / M tokens | $1.25 | $0.40 |
| Results tracked | 40 | 12 |
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Category by category
Coding GPT-5.4 nano leads
GPT-5.4 nano: 43.6 (#84), Nvidia Llama 3.3 Nemotron Super 49b v1.5: 39.8 (#154)
| Benchmark | GPT-5.4 nano | Nvidia Llama 3.3 Nemotron Super 49b v1.5 |
|---|---|---|
| LMArena Coding | 1405 | 1355 |
| SciCode | 46.9% | — |
| WeirdML | 49.2% | — |
| ALE-Bench | 1,005 | — |
Reasoning Nvidia Llama 3.3 Nemotron Super 49b v1.5 leads
GPT-5.4 nano: 23.7 (#173), Nvidia Llama 3.3 Nemotron Super 49b v1.5: 26.8 (#128)
| Benchmark | GPT-5.4 nano | Nvidia Llama 3.3 Nemotron Super 49b v1.5 |
|---|---|---|
| LMArena Hard Prompts | 1381 | 1336 |
| ARC-AGI-2 | 5.7% | — |
| Kagi LLM Benchmark | 39.7% | — |
| ARC-AGI-1 | 51.5% | — |
| CritPt | 9.3% | — |
| Chess Puzzles | 30% | — |
| Mystery Game Puzzles | 9% | — |
| DTBench | 80.3% | — |
| LMCA | 36.9% | — |
| Epoch Capabilities Index | 145.81 | — |
| ForecastBench | 57.3 | — |
Math GPT-5.4 nano leads
GPT-5.4 nano: 40.9 (#88), Nvidia Llama 3.3 Nemotron Super 49b v1.5: 38.2 (#141)
| Benchmark | GPT-5.4 nano | Nvidia Llama 3.3 Nemotron Super 49b v1.5 |
|---|---|---|
| LMArena Math | 1406 | 1392 |
| FrontierMath (Tiers 1-3) | 44.9% | — |
| FrontierMath Tier 4 | 12.2% | — |
| OTIS Mock AIME 2024-2025 | 87.8% | — |
| ProofBench | 5% | — |
| FrontierMath (Feb 2025 set) | 25.9% | — |
| FrontierMath Tier 4 (v1) | 6.3% | — |
Knowledge GPT-5.4 nano leads
GPT-5.4 nano: 41.9 (#103), Nvidia Llama 3.3 Nemotron Super 49b v1.5: 36.7 (#165)
| Benchmark | GPT-5.4 nano | Nvidia Llama 3.3 Nemotron Super 49b v1.5 |
|---|---|---|
| LMArena Expert | 1396 | 1330 |
| GPQA Diamond | 78.5% | — |
| SimpleQA Verified | 11.7% | — |
| Vectara Hallucination Rate | 3.1% | — |
Multimodal Not comparable
GPT-5.4 nano: 36.7 (#78), Nvidia Llama 3.3 Nemotron Super 49b v1.5: —
| Benchmark | GPT-5.4 nano | Nvidia Llama 3.3 Nemotron Super 49b v1.5 |
|---|---|---|
| LMArena Vision | 1196 | — |
Multilingual GPT-5.4 nano leads
GPT-5.4 nano: 48.6 (#140), Nvidia Llama 3.3 Nemotron Super 49b v1.5: 45.5 (#168)
| Benchmark | GPT-5.4 nano | Nvidia Llama 3.3 Nemotron Super 49b v1.5 |
|---|---|---|
| LMArena Non-English | 1359 | 1316 |
| LMArena Japanese | 1343 | 1300 |
| LMArena Russian | 1363 | 1332 |
| LMArena Chinese | 1392 | — |
| LMArena French | 1396 | — |
| LMArena German | 1367 | — |
| LMArena Korean | 1320 | — |
| LMArena Spanish | 1371 | — |
Instruction Following GPT-5.4 nano leads
GPT-5.4 nano: 71.9 (#144), Nvidia Llama 3.3 Nemotron Super 49b v1.5: 68.6 (#188)
| Benchmark | GPT-5.4 nano | Nvidia Llama 3.3 Nemotron Super 49b v1.5 |
|---|---|---|
| LMArena Instruction Following | 1362 | 1299 |
Long Context GPT-5.4 nano leads
GPT-5.4 nano: 41.6 (#137), Nvidia Llama 3.3 Nemotron Super 49b v1.5: 40.0 (#164)
| Benchmark | GPT-5.4 nano | Nvidia Llama 3.3 Nemotron Super 49b v1.5 |
|---|---|---|
| LMArena Longer Query | 1366 | 1315 |
Writing & Preference GPT-5.4 nano leads
GPT-5.4 nano: 55.7 (#142), Nvidia Llama 3.3 Nemotron Super 49b v1.5: 53.1 (#159)
| Benchmark | GPT-5.4 nano | Nvidia Llama 3.3 Nemotron Super 49b v1.5 |
|---|---|---|
| LMArena Text | 1372 | 1338 |
| LMArena Creative Writing | 1314 | 1307 |
| LMArena Multi-Turn | 1382 | 1334 |
Frequently asked questions
Is GPT-5.4 nano better than Nvidia Llama 3.3 Nemotron Super 49b v1.5?
GPT-5.4 nano is the stronger model overall, scoring 41.9 to 40.3 on the Noometry Index.
Which is cheaper, GPT-5.4 nano or Nvidia Llama 3.3 Nemotron Super 49b v1.5?
Nvidia Llama 3.3 Nemotron Super 49b v1.5 is cheaper. It lists at $0.40 per million input tokens and $0.40 per million output tokens; GPT-5.4 nano lists at $0.20 and $1.25.
Is GPT-5.4 nano or Nvidia Llama 3.3 Nemotron Super 49b v1.5 better for coding?
GPT-5.4 nano scores higher on coding benchmarks: 43.6 versus 39.8 in the Noometry coding category.
Which has the bigger context window?
GPT-5.4 nano does, with 400K tokens against 131K.
How many benchmarks do GPT-5.4 nano and Nvidia Llama 3.3 Nemotron Super 49b v1.5 share?
12 benchmarks have published results for both models. GPT-5.4 nano has 40 scored results on Noometry and Nvidia Llama 3.3 Nemotron Super 49b v1.5 has 12.