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open-mistral-7b

Mistral AI SAS/🇪🇺 EU/chat

OpenMistral-7B (officially Mistral 7B) is a highly efficient, 7.3-billion parameter open-weight language model released by Mistral AI. Praised for its excellent performance-to-size ratio, it frequently outperforms much larger proprietary and open-source models while being light enough to run on standard consumer hardware. More
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Which id to call

mistral/open-mistral-7b

This exact deployment on Mistral AI SAS, with no routing and no failover. Send it as the model field.

Input /1M

$0.28

Mistral AI SAS

Output /1M

$0.28

1.0x input

Context

33K

tokens

Added

Sep 2023

chat

Capabilities 2/8

VisionReasoningTool callingCachingWeb searchJSON schemaComputer useImage generation

Provider rates

What open-mistral-7b costs

Provider prices per 1M tokens, updated September 22, 2026.more

These are the upstream provider rates. Pay as you go adds 5%, or 0% if you bring your own keys, and there is no per-request fee. Prompt caching and routing change what you pay against these rates, not the rates themselves.

Input /1M

$0.28

Output /1M

$0.28

Cache write /1M

-

Cache read /1M

-

What a workload costs

100K input + 10K output
$0.0302
1M input + 100K output
$0.30
10M input + 1M output
$3.02

At the rates above. This endpoint does not offer prompt caching, so repeated context is billed as new input every time.

OpenAI compatible

Call it in three lines

Change the base url, use your Requesty key, set the model to mistral/open-mistral-7b. Existing OpenAI SDK code needs no other edit.

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from openai import OpenAI client = OpenAI( api_key="YOUR_REQUESTY_API_KEY", base_url="https://router.requesty.ai/v1", ) response = client.chat.completions.create( model="mistral/open-mistral-7b", messages=[ {"role": "user", "content": "Explain quantum computing in one paragraph."}, ], ) print(response.choices[0].message.content)

Reference

Specs and data terms

What the catalog reports for this deployment, and what the provider does with the traffic.

Context window33K tokens
Max output-
API typechat
AddedSep 2023
Model id
Data retentionNone
Used for trainingNo
Served from🇪🇺 EU

Released 2023-12-11

Benchmark scores

Benchmarks measure the model, not this endpoint, so they are the same wherever these weights are served.

GPQA Diamondreasoning
34.9%

Graduate-level physics, chemistry & biology questions designed to resist Googling.

Intelligence Indexreasoning
5.5%

Artificial Analysis Intelligence Index: a composite of multiple evaluations measuring overall model capability.

Scores from official model cards, Artificial Analysis and public leaderboards. They measure specific skills and do not capture every aspect of model quality, so test on your own workload.

Same provider

More from Mistral AI SAS

Newest first, on the same provider and the same key.

Reference

open-mistral-7b questions

How much does open-mistral-7b cost?

open-mistral-7b is priced at $0.28 per million input tokens and $0.28 per million output tokens when accessed via Requesty. Those are the upstream provider rates: pay as you go adds 5% on top, or 0% if you bring your own provider keys.

What is the context window of open-mistral-7b?

open-mistral-7b has a context window of 33K tokens. That's roughly 44 words of input you can fit in a single prompt.

How does open-mistral-7b perform on benchmarks?

open-mistral-7b scores 49.1% on MMLU Pro, 34.9% on GPQA Diamond, 9.9% on LiveCodeBench. See the full benchmark chart above for results across MMLU Pro, GPQA Diamond, SWE-Bench Verified, HumanEval, MATH, AIME, MMMU, and LiveBench.

What can open-mistral-7b do?

open-mistral-7b supports tool calling, structured outputs (JSON schema). You can call it through any OpenAI-compatible client by pointing base_url to Requesty.

How do I use open-mistral-7b with the OpenAI SDK?

Install the OpenAI SDK, set base_url to "https://router.requesty.ai/v1", set your API key to your Requesty key, and set the model to "mistral/open-mistral-7b". The Quickstart above shows Python, JavaScript and cURL snippets.

Can I run open-mistral-7b through Requesty?

Yes. open-mistral-7b runs through Requesty's OpenAI-compatible API, served from Mistral AI SAS. You do not host the model yourself: point base_url at Requesty, set the model to "mistral/open-mistral-7b", and requests are routed to the upstream provider with automatic failover. The same key gives you 600+ other models too.

Call open-mistral-7b through one endpoint

One key for this endpoint and 600+ other models. No markup on provider prices, automatic failover, caching built in.