Specifications
Context window33K tokens
Max output—
API typechat
AddedFeb 24, 2024
Model IDmistral/open-mistral-7b
Data retentionYes (30 days)
Used for trainingNo
Provider location🇪🇺 EU
Privacy policyMistral Privacy Policy→
Benchmarks
MMLU Proknowledge
60.1%Massive Multitask Language Understanding across 57 academic subjects.
Scores are sourced from official model cards, Artificial Analysis, and public leaderboards. Benchmarks measure specific skills and do not capture every aspect of model quality — always test on your own workload.
Pricing
Input / 1M
$0.25
Output / 1M
$0.25
Cache write
—
Cache read
—
Estimated cost
100K input + 10K output$0.0275
1M input + 100K output$0.28
10M input + 1M output$2.75
Requesty charges exactly what the upstream provider charges — no markup, no per-request fees. Prompt caching and smart routing can reduce effective cost by 30-80%.
Quickstart
Drop-in compatible with the OpenAI SDK. Change the base URL, swap in your Requesty API key, and set the model to mistral/open-mistral-7b.
123456789101112131415from 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)
Other Mistral AI SAS models
Frequently asked questions
How much does open-mistral-7b cost?
open-mistral-7b is priced at $0.25 per million input tokens and $0.25 per million output tokens when accessed via Requesty. Requesty charges exactly what the upstream provider charges — we don't add markup.
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 60.1% on MMLU Pro, 30.5% on HumanEval, 12.7% on MATH. 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. 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.
Access open-mistral-7b through Requesty
One API key, 400+ models, OpenAI-compatible. No markup on provider prices, automatic failover, and smart caching built-in.