DeepSeek V4 1 Flash 1017
At a glance
- Price per 1M tokens (input)
- $0.30
- Price per 1M tokens (output)
- $1.20
- Context
- 1,048,576 tokens
- Free access
- yes (see below)
Refreshed daily; data verified October 30, 2026. Published October 30, 2026.
DeepSeek V4 1 Flash 1017 by Mistral AI is built for teams that need a fast, efficient model for routine work and straightforward automation. It handles general language tasks, simple coding help, and structured data extraction well. Tool calling lets the model connect to external functions such as databases or APIs, while step-by-step reasoning helps it break down multi-part problems before answering. Mistral AI designs this line to balance speed and cost for production deployments rather than maximize raw capability.
Specifications & pricing
| Input (per 1M tokens) | $0.30 |
|---|---|
| Output (per 1M tokens) | $1.20 |
| Cache read (per 1M tokens) | $0.007 |
| Context window | 1,048,576 tokens |
| Max output | 131,072 tokens |
| Capabilities | tool calling, reasoning |
LiteLLM community dataset (MIT), verified October 30, 2026. Official Mistral AI pricing.
What DeepSeek V4 1 Flash 1017 would cost on your workload — run it through the cost calculator →
Where to try DeepSeek V4 1 Flash 1017 for free
- Mistral AI offers a free chat — Le Chat (free plan). A vendor's free chat may run a different model from the same family — the exact model is not guaranteed.
Frequently asked questions
What is this model good for?+
It suits customer support triage, content drafting, basic code assistance, and extracting information from documents into structured formats. Tool calling enables it to trigger actions in other systems, and step-by-step reasoning improves reliability on tasks with multiple stages.
How do I get started?+
Access the model through Mistral AI's platform or API, send prompts in plain language, and define any external functions you want it to call. Start with simple prompts, test tool definitions in a sandbox, and move to production once responses meet your quality bar.
How does it differ from sibling models in the line?+
This variant prioritizes speed and efficiency for high-volume, lower-complexity work. Other models in the family are larger and better suited to deep reasoning, complex coding, or very long documents, but they require more compute and respond more slowly.
What are its limitations?+
It is not intended for highly specialized domains such as medical diagnosis or legal analysis without human review. Step-by-step reasoning helps, but it can still miss subtle details or produce incorrect facts, so important outputs should be verified.
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