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Mistral AI: Mistral 7B

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Mistral 7B is Mistral's foundational 7.3B parameter open-source model under Apache 2.0, using sliding window attention and grouped-query attention. It outperforms Llama 2 13B on all benchmarks while being efficient enough for consumer hardware.

Context Window 33K

tokens

Max Output 33K

tokens

Input Cost $0.25

per million tokens

Output Cost $0.25

per million tokens

Input text

modalities

Tool Use Yes

 

Knowledge Cutoff Dec 2023

 

Release Date Sep 27, 2023

 

Code Example

Add AI to your app with the Puter.js AI API, no API keys or setup required.

// npm install @heyputer/puter.js
import { puter } from '@heyputer/puter.js';

puter.ai.chat("Explain quantum computing in simple terms").then(response => {
    document.body.innerHTML = response.message.content;
});
<html>
<body>
    <script src="https://js.puter.com/v2/"></script>
    <script>
        puter.ai.chat("Explain quantum computing in simple terms").then(response => {
            document.body.innerHTML = response.message.content;
        });
    </script>
</body>
</html>

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Mistral Large 4 is a multimodal sparse mixture-of-experts model from Mistral AI with 1 trillion total parameters and 49 billion active. It accepts text and image input and produces text output, and it supports tool calling and structured responses through the API. Mistral positions it for coding, cybersecurity, finance, manufacturing, and visual grounding. In Mistral's preview results it scores 62% on DeepSWE v1.1, 67% on FinWorkBench, and 73% on DIOR-RSVG, and it solves 93% of the 40 Cybench exercises. It is suited to developers building coding agents, security analysis tools, and enterprise workflows that need long context (1M tokens) and image understanding. Open weights are planned for release after the API preview.

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Mistral Medium 3.5

Mistral Medium 3.5 is a dense 128-billion-parameter multimodal model from Mistral AI that unifies instruction-following, reasoning, and coding into a single set of weights. It features a 256k-token context window, native function calling, structured JSON output, and vision capabilities via a custom-trained encoder that handles variable image sizes. A per-request reasoning_effort parameter lets you toggle between fast responses and deeper chain-of-thought processing, making the same model suitable for quick chat replies and complex agentic workflows. On benchmarks, it scores 77.6% on SWE-Bench Verified and 91.4% on τ³-Telecom. It replaces Mistral's previous Medium 3.1, Magistral, and Devstral 2 models. Priced at $1.50 per million input tokens and $7.50 per million output tokens, it's a strong fit for developers building tool-calling agents, long-horizon coding tasks, and multi-step automation pipelines.

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Mistral Medium 3.5

Mistral Medium 3.5 is a dense 128-billion-parameter multimodal model from Mistral AI that unifies instruction-following, reasoning, and coding into a single set of weights. This entry is Mistral's own dated direct-integration id for the same release available as mistralai/mistral-medium-3-5 through OpenRouter. It features a 256k-token context window, native function calling, structured JSON output, and vision capabilities via a custom-trained encoder that handles variable image sizes. A per-request reasoning_effort parameter lets you toggle between fast responses and deeper chain-of-thought processing, making the same model suitable for quick chat replies and complex agentic workflows. On benchmarks, it scores 77.6% on SWE-Bench Verified and 91.4% on τ³-Telecom. It replaces Mistral's previous Medium 3.1, Magistral, and Devstral 2 models. Priced at $1.50 per million input tokens and $7.50 per million output tokens, it's a strong fit for developers building tool-calling agents, long-horizon coding tasks, and multi-step automation pipelines.

Frequently Asked Questions

How do I use Mistral 7B?

Mistral 7B is no longer available through Puter.js. Explore other AI models for alternatives.

What is the pricing for Mistral 7B?
Mistral 7B costs $0.25 per 1M input tokens and $0.25 per 1M output tokens.
Price per 1M tokens
Input$0.25
Output$0.25
Who created Mistral 7B?

Mistral 7B was created by Mistral AI and released on Sep 27, 2023.

What is the context window of Mistral 7B?

Mistral 7B supports a context window of 33K tokens. For reference, that is roughly equivalent to 66 pages of text.

What is the max output length of Mistral 7B?

Mistral 7B can generate up to 33K tokens in a single response.

What is the knowledge cutoff of Mistral 7B?

Mistral 7B has a knowledge cutoff date of Dec 2023. This means the model was trained on data available up to that date.

What types of input can Mistral 7B process?

Mistral 7B accepts the following input types: text. It produces: text.

Does Mistral 7B support tool use (function calling)?

Yes, Mistral 7B supports tool use (function calling), allowing it to interact with external tools, APIs, and data sources as part of its response flow.

How does Mistral 7B perform on benchmarks?

Mistral 7B scores 5.0 on the Artificial Analysis Intelligence Index, outperforming 3% of tracked models.

Does it work with React / Vue / Vanilla JS / Node / etc.?

Yes — the Mistral 7B API works with any JavaScript framework, Node.js, or plain HTML through Puter.js. Just include the library and start building. See the documentation for more details.

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