Model comparison
GPT-4 Turbo 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 30.5 on the Noometry Index.
Last verified . 12 shared benchmarks.
NVIDIASummary
- They share 12 benchmarks with published results for both. GPT-4 Turbo scores higher in 0 categories and Nvidia Llama 3.3 Nemotron Super 49b v1.5 in 8 categories; 8 gaps are clear of the uncertainty.
- The widest gap is in math, where Nvidia Llama 3.3 Nemotron Super 49b v1.5 leads 38.2 to 9.0.
- Nvidia Llama 3.3 Nemotron Super 49b v1.5 is cheaper at $0.40 / $0.40 per million input/output tokens, against $10 / $30 for GPT-4 Turbo.
- Nvidia Llama 3.3 Nemotron Super 49b v1.5 accepts more context: 131K tokens versus 128K.
- Nvidia Llama 3.3 Nemotron Super 49b v1.5 has downloadable open weights; the other is API-only.
Side by side
| GPT-4 Turbo | Nvidia Llama 3.3 Nemotron Super 49b v1.5 | |
|---|---|---|
| Provider | OpenAI | NVIDIA |
| Noometry Index | 30.5 | 40.3 |
| Released | 2023-11-06 | 2025-07-25 |
| Weights | Proprietary | Open |
| Context window | 128K | 131K |
| Max output | 4K | 131K |
| Input $ / M tokens | $10 | $0.40 |
| Output $ / M tokens | $30 | $0.40 |
| Results tracked | 36 | 12 |
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Category by category
Coding Nvidia Llama 3.3 Nemotron Super 49b v1.5 leads
GPT-4 Turbo: 33.8 (#249), Nvidia Llama 3.3 Nemotron Super 49b v1.5: 39.8 (#154)
| Benchmark | GPT-4 Turbo | Nvidia Llama 3.3 Nemotron Super 49b v1.5 |
|---|---|---|
| LMArena Coding | 1268 | 1355 |
| WeirdML | 18% | — |
| BigCodeBench Instruct | 48.2% | — |
| BigCodeBench Complete | 58.2% | — |
| HumanEval+ | 86.6% | — |
| MBPP+ | 73.3% | — |
Agentic & Tool Use Not comparable
GPT-4 Turbo: —, Nvidia Llama 3.3 Nemotron Super 49b v1.5: —
| Benchmark | GPT-4 Turbo | Nvidia Llama 3.3 Nemotron Super 49b v1.5 |
|---|---|---|
| METR Time Horizons | 36.7% | — |
Reasoning Nvidia Llama 3.3 Nemotron Super 49b v1.5 leads
GPT-4 Turbo: 15.3 (#317), Nvidia Llama 3.3 Nemotron Super 49b v1.5: 26.8 (#128)
| Benchmark | GPT-4 Turbo | Nvidia Llama 3.3 Nemotron Super 49b v1.5 |
|---|---|---|
| LMArena Hard Prompts | 1251 | 1336 |
| SimpleBench | 25.1% | — |
| Chess Puzzles | 6% | — |
| DTBench | 61.6% | — |
| LMCA | 9.8% | — |
| Epoch Capabilities Index | 127.25 | — |
| ForecastBench | 59.4 | — |
Math Nvidia Llama 3.3 Nemotron Super 49b v1.5 leads
GPT-4 Turbo: 9.0 (#322), Nvidia Llama 3.3 Nemotron Super 49b v1.5: 38.2 (#141)
| Benchmark | GPT-4 Turbo | Nvidia Llama 3.3 Nemotron Super 49b v1.5 |
|---|---|---|
| LMArena Math | 1272 | 1392 |
| FrontierMath (Tiers 1-3) | 0.7% | — |
| OTIS Mock AIME 2024-2025 | 6.7% | — |
| MATH Level 5 | 46.7% | — |
Knowledge Nvidia Llama 3.3 Nemotron Super 49b v1.5 leads
GPT-4 Turbo: 24.3 (#268), Nvidia Llama 3.3 Nemotron Super 49b v1.5: 36.7 (#165)
| Benchmark | GPT-4 Turbo | Nvidia Llama 3.3 Nemotron Super 49b v1.5 |
|---|---|---|
| LMArena Expert | 1223 | 1330 |
| GPQA Diamond | 46.6% | — |
| Confabulations | 28.4% | — |
| MMLU | 81.3% | — |
Multimodal Not comparable
GPT-4 Turbo: 30.6 (#110), Nvidia Llama 3.3 Nemotron Super 49b v1.5: —
| Benchmark | GPT-4 Turbo | Nvidia Llama 3.3 Nemotron Super 49b v1.5 |
|---|---|---|
| LMArena Vision | 1090 | — |
Multilingual Nvidia Llama 3.3 Nemotron Super 49b v1.5 leads
GPT-4 Turbo: 40.5 (#216), Nvidia Llama 3.3 Nemotron Super 49b v1.5: 45.5 (#168)
| Benchmark | GPT-4 Turbo | Nvidia Llama 3.3 Nemotron Super 49b v1.5 |
|---|---|---|
| LMArena Non-English | 1245 | 1316 |
| LMArena Japanese | 1194 | 1300 |
| LMArena Russian | 1259 | 1332 |
| LMArena Chinese | 1242 | — |
| LMArena French | 1276 | — |
| LMArena German | 1259 | — |
| LMArena Korean | 1187 | — |
| LMArena Spanish | 1260 | — |
Instruction Following Nvidia Llama 3.3 Nemotron Super 49b v1.5 leads
GPT-4 Turbo: 65.8 (#216), Nvidia Llama 3.3 Nemotron Super 49b v1.5: 68.6 (#188)
| Benchmark | GPT-4 Turbo | Nvidia Llama 3.3 Nemotron Super 49b v1.5 |
|---|---|---|
| LMArena Instruction Following | 1249 | 1299 |
Long Context Nvidia Llama 3.3 Nemotron Super 49b v1.5 leads
GPT-4 Turbo: 38.0 (#206), Nvidia Llama 3.3 Nemotron Super 49b v1.5: 40.0 (#164)
| Benchmark | GPT-4 Turbo | Nvidia Llama 3.3 Nemotron Super 49b v1.5 |
|---|---|---|
| LMArena Longer Query | 1254 | 1315 |
Writing & Preference Nvidia Llama 3.3 Nemotron Super 49b v1.5 leads
GPT-4 Turbo: 47.7 (#206), Nvidia Llama 3.3 Nemotron Super 49b v1.5: 53.1 (#159)
| Benchmark | GPT-4 Turbo | Nvidia Llama 3.3 Nemotron Super 49b v1.5 |
|---|---|---|
| LMArena Text | 1272 | 1338 |
| LMArena Creative Writing | 1269 | 1307 |
| LMArena Multi-Turn | 1267 | 1334 |
Frequently asked questions
Is GPT-4 Turbo 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 30.5 on the Noometry Index.
Which is cheaper, GPT-4 Turbo 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-4 Turbo lists at $10 and $30.
Is GPT-4 Turbo 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 33.8 in the Noometry coding category.
Which has the bigger context window?
Nvidia Llama 3.3 Nemotron Super 49b v1.5 does, with 131K tokens against 128K.
How many benchmarks do GPT-4 Turbo and Nvidia Llama 3.3 Nemotron Super 49b v1.5 share?
12 benchmarks have published results for both models. GPT-4 Turbo has 36 scored results on Noometry and Nvidia Llama 3.3 Nemotron Super 49b v1.5 has 12.