magistral-medium-2506:thinkingMagistral Medium 2506 (Thinking) by Mistral AI.
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from openai import OpenAI client = OpenAI( api_key="YOUR_API_KEY", base_url="https://api.apertis.ai/v1") response = client.chat.completions.create( model="magistral-medium-2506:thinking", messages=[ {"role": "user", "content": "Hello!"} ], max_tokens=1024, temperature=0.7) print(response.choices[0].message.content) # Optional: Enable context compression to reduce token usage# response = client.chat.completions.create(# model="magistral-medium-2506:thinking",# messages=[{"role": "user", "content": "Hello!"}],# extra_body={"compression": {"enabled": True, "model": "gpt-4.1-mini"}}# )modelmessagesmax_tokenstemperaturetop_pstreamtoolsreasoning_effortstream_optionsthinkingextra_bodyUse these namespaced identifiers in Cursor IDE to avoid conflicts with built-in models.
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See how this model compares to others from the same provider.
Mistral Small 4 is the latest release in the Mistral Small family, unifying capabilities from multiple flagship models into a single system. It integrates strong reasoning (Magistral), multimodal understanding (Pixtral), and agentic coding capabilities (Devstral), enabling a versatile, all-in-one model. Designed to handle complex analysis, software development, and visual tasks within the same workflow, Mistral Small 4 is well suited for integrated agentic applications and end-to-end problem solving across domains.
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Devstral 2 is Mistral AI's open-source, agentic coding model — a 123B dense transformer with a 256K context window. It can navigate and modify large codebases, coordinate multi-file changes, manage dependencies, and automatically detect and fix errors. Designed for modernization and large engineering tasks, it’s tunable for specific languages or enterprise code and is released under a modified MIT license.
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No observed failures in the current observation window