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Llama 3.3 Nemotron Super 49B V1.5

Llama 3.3 Nemotron Super 49B V1.5 is a 49B-parameter NVIDIA language model optimized for English-centric reasoning and chat, with a long 128K+ context window and support for tool- and RAG-style agent workflows.

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What is Llama 3.3 Nemotron Super 49B V1.5?

Llama 3.3 Nemotron Super 49B V1.5 is a 49B-parameter reasoning and chat large language model from NVIDIA, derived from Meta’s Llama‑3.3‑70B‑Instruct and exposed via NVIDIA NIM and third‑party APIs. It is primarily used for complex reasoning tasks, long-context conversational agents, and retrieval‑augmented generation scenarios. It is also applied to coding, math and science assistance, and agentic workflows involving function or tool calling. The model belongs to NVIDIA’s Nemotron Super v1.5 family built on top of the Llama 3.3 series.

Providers

Different companies host the same model. OpenRouter routes your request to one of them based on the routing mode you pick — Balanced (price + speed), Nitro (fastest), or Exacto (highest tool-calling accuracy).

Provider Input Output Cache read /M Latency Throughput Uptime
NVIDIA ~$0.50 ~$0.50 — ~220ms ~40 tps 99.9%
AWS Bedrock (closest equivalent 70B family) ~$0.60 ~$0.60 — ~250ms ~35 tps 99.9%
Azure (closest equivalent Llama 3.1 70B) ~$0.65 ~$0.65 — ~260ms ~30 tps 99.9%

Try this model

Test Llama 3.3 Nemotron Super 49B V1.5 right here — free to start.

Llama 3.3 Nemotron Super 49B V1.5
Hi! Want to test the model?

Suggestions for your first prompt

Code snippet

Call the model through the OpenAI-compatible API.

python 
                                        from openai import OpenAI
                                            
                                            client = OpenAI(
                                                api_key="YOUR_API_KEY",
                                                base_url="https://inference.example.com/v1"
                                            )
                                            
                                            response = client.chat.completions.create(
                                                model="nvidia/llama-3-3-nemotron-super-49b-v1-5",
                                                messages=[
                                                    {
                                                        "role": "user",
                                                        "content": "Describe this image in one sentence."
                                                    }
                                                ],
                                            )
                                            
                                            print(response.to_json())
                                        
                                    
                                        {
                                                "model": "nvidia/llama-3-3-nemotron-super-49b-v1-5",
                                                "messages": [
                                                    {
                                                        "role": "user",
                                                        "content": "Describe this image in one sentence."
                                                    }
                                                ]
                                            }

5 Core Capabilities

  • Advanced Reasoning

    Performs complex step-by-step logical, mathematical, and scientific reasoning, benefiting from RLVR and other reasoning-focused post-training.

  • Agentic Workflows

    Supports agent-style applications, including Retrieval-Augmented Generation, tool calling, and long-context orchestration with up to 128K tokens.

  • Code Generation

    Generates, debugs, and explains source code across tasks, boosted by supervised fine-tuning on programming and software engineering datasets.

  • Instruction Following

    Provides aligned conversational responses tuned to human chat preferences, following instructions reliably in multi-turn assistant scenarios.

  • Domain Expertise

    Answers technical questions in math, science, and engineering domains, reflecting targeted supervised training and benchmarking on such tasks.

6 Most Valuable Use Cases

  • Code Generation Assistance
  • Enterprise Chatbots
  • Customer Support Automation
  • Knowledge Base Search
  • Contract Review Support
  • Business Document Summaries

Why Build on LLM.API?

One unified API. Every major model. Built-in reliability, cost control, and observability.

  • Intelligent Model Routing

    Automatically route each request to the optimal model and provider based on cost, latency, and quality—no client changes or custom logic required.

    One endpoint, smart routing.

  • Cost-Aware Orchestration

    Define per-project cost policies and let LLM.API pick the cheapest viable models while respecting quality and latency constraints, with detailed cost breakdowns per request.

    Optimize spend by design.

  • Resilient Fallback Flows

    Survive provider outages and rate limits with automatic cross-provider retries and degradations, so production traffic keeps flowing without redeploys.

    Stay online, automatically.

  • Deep LLM Observability

    Trace every call across providers with unified logs, metrics, and structured events, making it easy to debug prompts, audit behavior, and tune performance.

    See every token hop.

  • Task-Level Abstractions

    Describe tasks—chat, generation, tools, RAG—once, and let LLM.API translate them into provider-specific calls while enforcing schemas and contracts.

    Think tasks, not vendors.

  • High-Throughput Batch Workloads

    Run large-scale batch jobs across providers with concurrency control, automatic retries, and progress tracking, ideal for labeling, embedding, and offline inference pipelines.

    Scale batches without pain.

When to Use — When NOT to Use

Use it if...

  • You need an open-weight LLM that can be self-hosted on NVIDIA infrastructure efficiently.
  • You need strong general-purpose chat, coding, and analysis without frontier-level model costs.
  • Your use case involves on-premises or VPC deployment where data locality and control matter.
  • Your use case involves fine-tuning or LoRA adaptation on domain-specific enterprise data.
  • You need solid English capabilities for agents, RAG pipelines, and internal developer tools.
  • Your use case involves leveraging NVIDIA NIM or CUDA-optimized inference for better throughput.

Avoid if...

  • You need state-of-the-art reasoning and benchmark performance rivaling the very top proprietary models.
  • Your workload requires extremely low-latency mobile inference on edge devices with limited compute.
  • You need heavy multilingual coverage or non-English excellence across many low-resource languages.
  • Your workload requires built-in image, audio, or multimodal understanding beyond pure text.
  • You need turnkey fully-managed SaaS APIs without managing NVIDIA GPU infrastructure or deployment stacks.
  • Your workload requires tight integration with another provider’s proprietary ecosystem or toolchain exclusively.

Frequently Asked Questions

  • What is Llama 3.3 Nemotron Super 49B V1.5?

    Llama 3.3 Nemotron Super 49B V1.5 is an NVIDIA large language model optimized for high‑quality code, reasoning, and general assistant workloads.

  • What is Llama 3.3 Nemotron Super 49B V1.5 best suited for?

    It is best suited for complex software engineering assistance, multi-step reasoning, and general-purpose chat where response quality matters more than minimal latency.

  • What is the context window of Llama 3.3 Nemotron Super 49B V1.5?

    Llama 3.3 Nemotron Super 49B V1.5 supports a 32K token context window for combining prompts, system messages, and conversation history.

  • What modalities does Llama 3.3 Nemotron Super 49B V1.5 support on LLM.API?

    On LLM.API, Llama 3.3 Nemotron Super 49B V1.5 is available as a text-only model for natural language and code generation.

  • How is Llama 3.3 Nemotron Super 49B V1.5 priced on LLM.API?

    Pricing is usage-based per 1,000 tokens for input and output, with exact rates defined in the LLM.API NVIDIA model pricing table.

  • How fast is Llama 3.3 Nemotron Super 49B V1.5 in terms of latency?

    Latency depends on prompt length and load, but LLM.API streams tokens so first tokens generally appear within a few hundred milliseconds to a couple seconds.

  • How do I call Llama 3.3 Nemotron Super 49B V1.5 via the LLM.API?

    Use the standard LLM.API chat or completions endpoint and set the model field to the exact Llama 3.3 Nemotron Super 49B V1.5 identifier.

  • How does Llama 3.3 Nemotron Super 49B V1.5 compare to similar 30–70B models?

    It typically offers stronger reasoning and coding quality than smaller open models, with higher cost and latency than compact 7–14B alternatives.

  • Does Llama 3.3 Nemotron Super 49B V1.5 support function calling or tool usage?

    If enabled by LLM.API, you can use it with the platform’s standardized tool-calling schema, otherwise treat it as a pure text generator.

  • What limitations does Llama 3.3 Nemotron Super 49B V1.5 have?

    It can hallucinate facts, lacks real-time knowledge, does not access external APIs or databases by itself, and may reflect training-data biases.

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