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Model details
Ministral 8B (latest)
Ministral 8B is Mistral AI's flagship edge model in the sub-10B category, designed to bring frontier-level intelligence to resource-constrained environments. The model features a specialized interleaved sliding-window attention pattern that enables faster and memory-efficient inference, making it practical for on-device deployment where compute and memory are limited. It was introduced alongside Ministral 3B as part of what Mistral calls "les Ministraux"—a family built to set new standards in knowledge, commonsense reasoning, and function-calling within compact model sizes. The design intent centers on enabling privacy-first applications such as on-device translation, internet-less smart assistants, local analytics, and autonomous robotics without relying on cloud connectivity.
The model carries forward the innovation legacy of Mistral 7B, which originally sparked widespread independent AI development when it launched. Ministral 8B can operate as an efficient intermediary within larger AI pipelines, handling input parsing, task routing, and API calls based on user intent across extended contexts. It is fully open-weight, allowing teams to fine-tune it for specialized task workers or unique domain requirements. When paired with larger models like Mistral Large, it becomes a cost-effective and low-latency function-calling layer in multi-step agentic workflows, enabling sophisticated automation without the overhead of running larger models for every interaction.
Quick Info
Powered by- Provider
- Vercel AI Gateway
- Model key
- mistral/ministral-8b
- Release date
- Oct 1, 2024
- Last updated
- Oct 4, 2024
- Knowledge cutoff
- 2024-10
- Input modalities
- Output modalities
- Capabilities
Cost
- Input token cost
- $0.10
- Output token cost
- $0.10
Limits
- Output tokens
- 128,000 tokens
- Context window
- 128,000 tokens
Transparent token rates
Compare Ministral 8B (latest) pricing
Rates are shown per one million tokens. Combined means one million input plus one million output tokens.
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