- Developers & Engineers: Deploy in agent-focused applications requiring long-context reasoning, tools, or code.
- Researchers & Data Scientists: Benchmark and explore model capabilities across extensive language, code, and reasoning tasks.
- Enterprises & Cloud Providers: Integrate into large-scale inference engines using NVIDIA hardware.
- Educators & Students: Utilize for advanced chain-of-thought, math problem solving, and programming support.
- Multilingual Application Builders: Fine-tune or use it in multiple languages at scale.
How to Use Mistral Nemotron?
- Access via NVIDIA NIM API: Available for inference on GPUs like Hopper with TensorRT-LLM or vLLM.
- Supply Long Prompts: Supports the full 128K-token context for rich reasoning and instruction chaining.
- Call Functions & Tools: Leverage built-in instruction-following capabilities for sophisticated agentic workflows.
- Perform in Inference-Optimized Environments: Use on H100/Hopper GPUs with NVIDIA’s optimized runtime.
- Monitor Benchmark Scores: Check benchmark metrics to ensure quality and accuracy for your use case.
- Ultra-Long Context: Handles up to 128K tokens—far beyond typical LLM limits.
- Inference-Optimized: Runs efficiently via TensorRT-LLM and vLLM on NVIDIA GPUs.
- Agent-Ready Design: Built for function calling and tool-based, multi-step operations.
- Commercially Ready: Licensed and deployable for enterprise use via NVIDIA API.
- Agent-Ready Design: Built for function calling and tool-based, multi-step operations.
- Commercially Ready: Licensed and deployable for enterprise use via NVIDIA API.
- Support for 128K‑token context suits long document and code workflows
- Optimized for high-throughput NVIDIA inference stacks
- Excellent coding, math, and reasoning performance
- Multilingual capabilities across Asian and European languages
- Function-calling and instruction-following tailored for agents
- Still in preview—access requires NVIDIA’s trial API terms
- Large memory requirements—Hopper/H100 GPUs needed
- Moderately lower code-score on LiveCodeBench compared to specialized models.
API only
$0.15/$0.15 per 1M tokens
$0.15 per 1M output token
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Editorial Note
This page was researched and written by the ATB Editorial Team. Our team researches each AI tool by reviewing its official website, testing features, exploring real use cases, and considering user feedback. Every page is fact-checked and regularly updated to ensure the information stays accurate, neutral, and useful for our readers.
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