Context Window
—
Input Price/1M
$0.60
Output Price/1M
$1.80
Parameters
—
Speed
53 tok/s
Latency (TTFT)
714ms
Llama 3.1 Nemotron Ultra 253B v1 (Reasoning) results on the main AI model evaluation benchmarks. Higher scores indicate better performance.
| Benchmark | Score | Maximum | Methodology |
|---|---|---|---|
| Terminal-Bench Hard | 2.0 | 100.0 | — |
| Benchmark | Score | Maximum | Methodology |
|---|---|---|---|
| LiveCodeBench | 64.0 | 100.0 | Artificial Analysis official API |
| SciCode | 35.0 | 100.0 | — |
| AA Coding Index | 13.1 | 100.0 | Artificial Analysis official API |
| Benchmark | Score | Maximum | Methodology |
|---|---|---|---|
| MMLU-Pro | 83.0 | 100.0 | — |
| Benchmark | Score | Maximum | Methodology |
|---|---|---|---|
| AA-LCR | 7.0 | 100.0 | — |
| Benchmark | Score | Maximum | Methodology |
|---|---|---|---|
| MATH-500 | 95.2 | 100.0 | Artificial Analysis official API |
| AIME 2025 | 64.0 | 100.0 | Artificial Analysis official API |
| AA Math Index | 63.7 | 100.0 | Artificial Analysis official API |
| Benchmark | Score | Maximum | Methodology |
|---|---|---|---|
| AA Intelligence Index | 15.0 | 100.0 | Artificial Analysis official API |
| Benchmark | Score | Maximum | Methodology |
|---|---|---|---|
| MMLU Pro | 82.5 | 100.0 | Artificial Analysis official API |
| GPQA Diamond | 73.0 | 100.0 | Artificial Analysis official API |
| IFBench | 38.0 | 100.0 | — |
| HLE | 8.0 | 100.0 | — |
| Benchmark | Score | Maximum | Methodology |
|---|---|---|---|
| Tau²-Bench | 11.0 | 100.0 | — |
Llama 3.1 Nemotron Ultra 253B v1 (Reasoning) is an AI model developed by NVIDIA, classified as a text model. It focuses on text processing and natural language generation. As a proprietary model, it is available via NVIDIA's cloud API.
Llama 3.1 Nemotron Ultra 253B v1 (Reasoning) is usage-based, priced at $0.6/1M input tokens and $1.8/1M output tokens. For context: 1 million tokens is approximately 750,000 words, or about 10 average-length books. At this aggressive price point, it is one of the most cost-effective options on the market, ideal for high-volume applications like chatbots, bulk document analysis, and automation.
Llama 3.1 Nemotron Ultra 253B v1 (Reasoning) was evaluated on 15 different benchmarks, covering categories like Agentic, Coding, Knowledge, Long Context, Math, overall, Reasoning, Tool Use. Results show exceptional performance across available evaluations.
It's important to note that benchmarks measure specific aspects and don't capture the full user experience. Factors like instruction adherence, behavior in long conversations, and real-world task quality vary significantly between models and aren't always reflected in standard scores.
Llama 3.1 Nemotron Ultra 253B v1 (Reasoning) specializes in text, offering advanced capabilities for creating and processing text content.
In the 2026 AI model ecosystem, Llama 3.1 Nemotron Ultra 253B v1 (Reasoning) competes directly with similarly capable models. NVIDIA competes in this segment against OpenAI, Anthropic, Google, and Meta. The choice between models depends on the specific use case, budget, latency requirements, and need for features like multimodality and tool calling.
For a detailed side-by-side comparison, use our comparison tool or check the overall model ranking.
Llama 3.1 Nemotron Ultra 253B v1 (Reasoning) is an AI model developed by NVIDIA. It is a text model.
Llama 3.1 Nemotron Ultra 253B v1 (Reasoning) costs $0.6/1M input tokens and $1.8/1M output tokens. For heavy usage (e.g., a chatbot handling 100k messages/month), costs can range from $10 to $1,000 depending on volume.
In available benchmarks, Llama 3.1 Nemotron Ultra 253B v1 (Reasoning) scored: Terminal-Bench Hard: 2/100, LiveCodeBench: 64/100, SciCode: 35/100. See the full table above for a detailed comparison.
No, Llama 3.1 Nemotron Ultra 253B v1 (Reasoning) is a proprietary model from NVIDIA. It is available via cloud API. For open source alternatives, check our open source model ranking.
Llama 3.1 Nemotron Ultra 253B v1 (Reasoning) excels at general-purpose language tasks.
Last updated: June 01, 2026 • View methodology →