Llama 3.3 Nemotron Super 49B V1.5

Llama 3.3 Nemotron Super 49B V1.5 by NVIDIA excels in the Coding category, where it ranks #146 with a composite score of 61/100. Llama-3.3-Nemotron-Super-49B-v1.5 is a 49B-parameter, English-centric reasoning/chat model derived from Meta’s Llama-3.3-70B-Instruct with a 128K context. It’s post-trained for agentic workflows (RAG, tool calling) via SFT across math, code, science, and... It is particularly strong in areas highlighted by its top benchmark performance and adoption metrics, making it suitable for both individual developers and enterprise teams looking for a reliable coding solution.

Llama 3.3 Nemotron Super 49B V1.5 is priced at $0.10 per million input tokens and $0.40 per million output tokens (USD). Contact the provider for volume discounts and enterprise pricing. Pricing is competitive within the coding category and reflects the model's quality-to-cost ratio.

In the Coding category, Llama 3.3 Nemotron Super 49B V1.5 holds rank #146 out of 341 models tracked. Its quality rank is #146 and adoption rank is #146. You can use our comparison tool at /compare to see detailed side-by-side metrics with specific alternatives. Key differentiators include its composite scoring across benchmarks, community sentiment, and real-world adoption rates.

Llama 3.3 Nemotron Super 49B V1.5 has been evaluated across 6 different signals. Its strongest areas include Capabilities (67/100), Benchmarks (59/100), Pricing (100/100). These scores are derived from industry-standard benchmarks, community ratings, and real-world performance metrics. The composite score of 61/100 reflects a weighted combination of all tracked signals.

Llama 3.3 Nemotron Super 49B V1.5 is a paid model, though some providers may offer trial credits or limited free tiers for evaluation. Check NVIDIA's website for current free tier availability and promotional offers.

Llama 3.3 Nemotron Super 49B V1.5 supports a 131K token context window (131,072 tokens total). That translates to roughly 98,304 words in a single prompt. This is large enough to process entire codebases, research papers, or long conversation histories in one shot.

Llama 3.3 Nemotron Super 49B V1.5 can generate up to 16K output tokens (16,384 tokens) per response. That is roughly 12,288 words. This is enough for generating complete code files, detailed reports, or long-form content in a single response.

Llama 3.3 Nemotron Super 49B V1.5 supports function/tool calling, structured JSON output, extended reasoning/chain-of-thought, streaming responses. Function calling lets you integrate it with external APIs and tools programmatically. These capabilities determine which workflows and integrations the model can handle natively.

Yes, Llama 3.3 Nemotron Super 49B V1.5 is an open-source model. You can download the weights, run it locally, fine-tune it for your use case, or deploy it on your own infrastructure. Many cloud providers also offer hosted versions if you prefer not to manage the infrastructure yourself. Self-hosting gives you full control over data privacy and eliminates per-token API costs.

Llama 3.3 Nemotron Super 49B V1.5 was developed by NVIDIA. It was released on October 10, 2025. You can access it through NVIDIA's API or download the model weights directly. Check our provider page for all models from NVIDIA and how they compare against each other.

Pick Llama 3.3 Nemotron Super 49B V1.5 when you need a solid balance of cost and capability for everyday development tasks, content generation, and standard API integrations. If your task is straightforward text completion or classification, a cheaper model might give you 90% of the quality at a fraction of the price. Run a quick benchmark on your actual use case before committing.

You can access Llama 3.3 Nemotron Super 49B V1.5 through NVIDIA's API using standard HTTP requests or their official SDK. Most providers support OpenAI-compatible endpoints, so switching between models often requires changing just the model name in your API call. Streaming is supported for real-time token-by-token output. For production use, implement proper error handling, rate limiting, and cost monitoring.