Select an endpoint and copy a working example for this model.
from openai import OpenAI client = OpenAI( api_key="YOUR_API_KEY", base_url="https://api.apertis.ai/v1") response = client.chat.completions.create( model="codestral-2501", 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="codestral-2501",# 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.
See how this model compares to others from the same provider.
See how this model compares to others from the same provider.
Voxtral Small is an upgraded version of Mistral Small 3 that adds advanced audio understanding while preserving strong text performance. It handles speech transcription, translation, and audio comprehension, with audio input billed per million seconds.
Mistral Small Creative is an experimental lightweight model focused on creative writing and storytelling. It excels at narrative generation, roleplay, character dialogue, and general instruction-following for conversational agents.
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.
Mistral Medium 3.1 is an enterprise-grade update to Mistral Medium 3, delivering near–frontier reasoning and multimodal performance at much lower cost (up to 8× cheaper than large models). It excels in coding, STEM, and enterprise workflows, supports hybrid and on-prem deployments, and offers accuracy competitive with larger models like Claude Sonnet and Llama Maverick while remaining easy to integrate across cloud environments.
Initialized observational baseline with no recorded failures