Codestral vs. Nemotron 3.5 Bolt Comparison


Codestral Mistral AI	Nemotron 3.5 Bolt NVIDIA	+	+
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About We introduce Codestral, our first-ever code model. Codestral is an open-weight generative AI model explicitly designed for code generation tasks. It helps developers write and interact with code through a shared instruction and completion API endpoint. As it masters code and English, it can be used to design advanced AI applications for software developers. Codestral is trained on a diverse dataset of 80+ programming languages, including the most popular ones, such as Python, Java, C, C++, JavaScript, and Bash. It also performs well on more specific ones like Swift and Fortran. This broad language base ensures Codestral can assist developers in various coding environments and projects.	About NVIDIA Nemotron 3.5 Bolt is a family of open artificial intelligence models designed for local AI agents, autonomous task execution, and conversational applications. The family includes Nemotron 3.5 Bolt 74B-A9B, a 74-billion-parameter mixture-of-experts model with approximately 9 billion active parameters, and Nemotron 3.5 Bolt E4B, a compact model optimized for conversational agents. The 74B-A9B variant uses native 2-bit quantization to operate within approximately 24GB of memory on compatible NVIDIA hardware. Its agentic capabilities support web research, software development, code debugging, and multistep task automation without requiring cloud-based inference. The E4B variant targets local voice assistants, interactive characters, and embedded applications with a 4GB memory footprint. Nemotron 3.5 Bolt supports deployment on NVIDIA RTX systems, DGX Spark, and other compatible hardware, giving developers greater control over inference, application data, and local AI workflows.
Platforms Supported Windows Supported Mac Supported Linux Supported Cloud Supported On-Premises Supported iPhone Not Supported iPad Not Supported Android Not Supported Chromebook Not Supported	Platforms Supported Windows Not Supported Mac Not Supported Linux Supported Cloud Supported On-Premises Supported iPhone Not Supported iPad Not Supported Android Not Supported Chromebook Not Supported
Audience Developers interested in a large language model for coding	Audience AI developers, software engineers, machine learning researchers, game developers, robotics engineers, enterprise IT teams, and organizations building local autonomous agents, coding assistants, conversational AI systems, and privacy-conscious applications on NVIDIA hardware
Support Phone Support Not Supported 24/7 Live Support Not Supported Online Not Supported	Support Phone Support Not Supported 24/7 Live Support Not Supported Online Not Supported
API Offers API Supported	API Offers API Supported
Screenshots and Videos View more images or videos	Screenshots and Videos View more images or videos
Pricing Free Open source Free Version Supported Free Trial Not Supported	Pricing Free Free Version Supported Free Trial Not Supported
Reviews/Ratings Overall 0.0 / 5 ease 0.0 / 5 features 0.0 / 5 design 0.0 / 5 support 0.0 / 5 This software hasn't been reviewed yet. Be the first to provide a review: Review this Software	Reviews/Ratings Overall 5.0 / 5 features 5.0 / 5 Read all reviews
	Pros & Cons from Real Users Pros What I like most is that I can run a genuinely capable agent model locally without needing datacenter hardware. The 74B-A9B version fits in 24GB of memory, which makes it realistic for high-end RTX systems and DGX Spark. It is also surprisingly useful for real agent work. I can use it for research, coding, debugging, writing, and multi-step tasks while keeping everything on my own machine instead of pushing code or sensitive data to a cloud API. The performance-to-memory tradeoff is probably the biggest win. NVIDIA says Bolt 74B-A9B is the #1 open model in its class on PinchBench and #2 on BrowseComp, so it is not just small enough to run locally — it is actually competitive at agentic tasks. Cons The main downside is that 24GB is still a meaningful hardware requirement, and local inference will not always match the raw capability of the biggest cloud frontier models.
Training Documentation Supported Webinars Not Supported Live Online Not Supported In Person Not Supported	Training Documentation Supported Webinars Not Supported Live Online Not Supported In Person Not Supported
Company Information Mistral AI Founded: 2023 France mistral.ai/	Company Information NVIDIA Founded: 1993 United States nvidia.com
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Categories AI Code Generators Supported AI Code Refactoring Supported AI Coding Assistants Supported AI Coding Models Supported AI Models Supported Foundation Models Supported Large Language Models Supported Open-Weight AI Models Supported	Categories AI Coding Models Supported AI Models Supported AI Reasoning Models Supported Foundation Models Supported Large Language Models Supported Multimodal Models Supported Open-Weight AI Models Supported

Integrations Airtrain Supported AnythingLLM Supported Clojure Supported F# Supported GMTech Supported Go Supported Groq Supported Hugging Face Transformers Not Supported JavaScript Supported Kiin Supported Langflow Supported Mistral Code Supported Mistral Vibe Supported NexalAI Supported PI Prompts Supported PostgresML Supported Python Supported Ragas Supported promptmate.io Supported Show More Integrations View All 102 Integrations	Integrations Airtrain Not Supported AnythingLLM Not Supported Clojure Not Supported F# Not Supported GMTech Not Supported Go Not Supported Groq Not Supported Hugging Face Transformers Supported JavaScript Not Supported Kiin Not Supported Langflow Not Supported Mistral Code Not Supported Mistral Vibe Not Supported NexalAI Not Supported PI Prompts Not Supported PostgresML Not Supported Python Not Supported Ragas Not Supported promptmate.io Not Supported Show More Integrations View All 5 Integrations
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