Mistral-Small-3.1-24B-Instruct-2503 is a 24 billion parameter instruction-tuned model from Mistral AI, building on Mistral Small 3. It features state-of-the-art vision understanding and an enhanced 128k token context window, while maintaining strong text performance. This model excels in both text and vision tasks, offering advanced reasoning, multilingual support, and agentic capabilities with native function calling and JSON output. It is optimized for fast-response conversational agents, local inference, programming, math reasoning, and long document understanding.
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mistralai/Mistral-Small-3.1-24B-Instruct-2503Most commonly used values from Featherless users
temperature
This setting influences the sampling randomness. Lower values make the model more deterministic; higher values introduce randomness. Zero is greedy sampling.
top_p
This setting controls the cumulative probability of considered top tokens. Must be in (0, 1]. Set to 1 to consider all tokens.
top_k
This limits the number of top tokens to consider. Set to -1 to consider all tokens.
frequency_penalty
This setting penalizes new tokens based on their frequency in the generated text. Values > 0 encourage new tokens; < 0 encourages repetition.
presence_penalty
This setting penalizes new tokens based on their presence in the generated text so far. Values > 0 encourage new tokens; < 0 encourages repetition.
repetition_penalty
This setting penalizes new tokens based on their appearance in the prompt and generated text. Values > 1 encourage new tokens; < 1 encourages repetition.
min_p
This setting representing the minimum probability for a token to be considered relative to the most likely token. Must be in [0, 1]. Set to 0 to disable.