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Blended $/1M across tracked versions of this line.
Typical 3:1 output-to-input mix, per 1M tokens
Price as of 2026-04-28 · Source: legacy_model_catalog
Mixed text, image, audio and document workloads that benefit from one model across modalities.
Mistral-Small-3.2-24B-Instruct-2506 is an updated 24B parameter model from Mistral optimized for instruction following, repetition reduction, and improved function calling. Compared to the 3.1 release, version 3.2 sig...
mistral-small-3.2-24b-instruct is a Multimodal model from Mistral AI (US). HotON.ai tracks it at $0.08 per 1M input tokens and $0.20 per 1M output tokens, with a 128K-token context window. Its composite efficiency score is 89/100 at an estimated $0.000 per successful task.
mistral-small-3.2-24b-instruct is tracked at $0.08 per 1M input tokens and $0.20 per 1M output tokens. A typical 3:1 output-to-input workload blends to roughly $0.17 per 1M tokens. Figures are illustrative demo data.
Mixed text, image, audio and document workloads that benefit from one model across modalities.
mistral-small-3.2-24b-instruct supports up to a 128K-token context window — large enough for long documents and extended conversations in a single request.
Within the HotON.ai tracked set, mistral-small-3.2-24b-instruct is cheaper than 85% of models on input price and ranks #200 of 535 by overall efficiency.
Yes — qwen3.5-9b is a lower-cost option at $0.15 per 1M output tokens, while still covering similar Multimodal use cases. Compare them side by side on HotON.ai.
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HotON.ai — mistral-small-3.2-24b-instruct (Mistral AI): $0.08/1M input, $0.20/1M output, as of 2026-04-28. https://hoton.ai/en/models/mistralai-mistral-small-3-2-24b-instructPricing is real (via the TestKey catalog, updated daily). Quality (Arena Elo) is real where the model is ranked on LMArena. Efficiency is a modeled composite of real price and context.