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Power up your code agents with a 128-billion-parameter model featuring a 256,000-token context window, which excels at coding, reasoning, and following instructions. This model requires only 4 GPUs to run and achieves a score of 77.6% on SWE-Bench Verified
5 is a high-performance 128-billion-parameter model designed specifically for complex developer workflows and autonomous code agents. It distinguishes itself with a substantial 256,000-token context window, allowing it to process extensive codebases or lengthy documentation in a single prompt.
A key technical advantage is its resource efficiency, requiring only 4 GPUs to run, which may appeal to teams looking to balance high-tier performance with infrastructure costs. 6% score on the SWE-Bench Verified benchmark, it demonstrates strong capabilities in software engineering tasks and logical reasoning.
Buyers should evaluate how this model integrates into their existing CI/CD pipelines and whether the instruction-following accuracy meets their specific production requirements. As a tool in the Developer & Data Science category, it targets those building sophisticated AI agents that need to maintain state over long interactions without losing coherence or context.
Compare Mistral Medium 3.5 with alternative Developer & Data Science Tools tools before choosing a product.
Take advantage of Anthropic’s AI model—more reliable, more honest, and four times less likely to overlook its own errors. Its fast mode delivers up to 2.5 times more tokens per second (at the same API rate as the previous version)
Tackle time-consuming and complex tasks by letting AI plan, use tools, check its work, and see tasks through to completion with minimal supervision. This model significantly improves coding, the use of virtual machines, online research, and knowledge work tasks
A Google AI model capable of handling complex tasks involving text, images, audio, or video. It can summarize lengthy sources, analyze data, and assist in coding end-to-end interfaces
This Mixture-of-Experts model, with approximately 1 trillion parameters, features a 1M-token context, virtually infinite Engram memory, and multimodal capabilities for text, images, and video. It aims to achieve performance scores comparable to Claude Opus while remaining significantly more affordable (to be released under the Apache 2.0 license)
OpenAI's most powerful frontier model directly controls your computer, processes 1 million tokens, and reasons about very long tasks. It is claimed to reduce errors by 33% and significantly improve code and document analysis
Take advantage of Anthropic’s AI model—more reliable, more honest, and four times less likely to overlook its own errors. Its fast mode delivers up to 2.5 times more tokens per second (at the same API rate as the previous version)
Tackle time-consuming and complex tasks by letting AI plan, use tools, check its work, and see tasks through to completion with minimal supervision. This model significantly improves coding, the use of virtual machines, online research, and knowledge work tasks
A Google AI model capable of handling complex tasks involving text, images, audio, or video. It can summarize lengthy sources, analyze data, and assist in coding end-to-end interfaces
This Mixture-of-Experts model, with approximately 1 trillion parameters, features a 1M-token context, virtually infinite Engram memory, and multimodal capabilities for text, images, and video. It aims to achieve performance scores comparable to Claude Opus while remaining significantly more affordable (to be released under the Apache 2.0 license)
OpenAI's most powerful frontier model directly controls your computer, processes 1 million tokens, and reasons about very long tasks. It is claimed to reduce errors by 33% and significantly improve code and document analysis
Mistral Medium 3.5 is listed as free on AIForest. Prospective users should check the official website for current limits, usage caps, and whether paid upgrades or enterprise support tiers are available. Infrastructure costs for hosting the 4-GPU requirement should be factored into the total cost of ownership.
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The 256,000-token context window allows Mistral Medium 3.5 to ingest entire project directories or extensive documentation in a single prompt. This capability reduces the need for complex RAG architectures for some tasks, though buyers should monitor for 'lost in the middle' issues and evaluate latency when the window is fully utilized.
Mistral Medium 3.5 is optimized to run on a 4-GPU configuration, which is efficient for a 128-billion-parameter model. While this lower hardware footprint is a competitive advantage, it still necessitates high-end compute resources. Organizations should evaluate their existing cloud or on-premise infrastructure costs before planning a full-scale deployment.
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