leanstral-1-5Mistral AI
- endpoints
- 1
- regions
- 1
- api
- chat
- released
- May 2026
- eu routing
- available
capabilities 2/8
| # | flags | |||||||
|---|---|---|---|---|---|---|---|---|
| 1 | EU | 262K | free | free | free | 1.51s | zdr |
| provider | now | low | high | change |
|---|---|---|---|---|
| Mistral AI SAS | 1.51s | 1.58s | 5.03s | +6.3% |
median wait before the first token lands. this is the number a user feels, and it moves with provider load through the day.
Lines are steps: each sample is one week of traffic held flat, not a slide from the week before. Click a name in the key to drop it from the plot. Measured through Sep 12.
| eval | Score as a share of 100 | /100 |
|---|---|---|
| MMLU Pro | 49.1 | |
| GPQA Diamond | 34.9 | |
| LiveCodeBench | 9.9 | |
| Intelligence Index | 5.5 | |
| Humanity's Last Exam | 3.5 | |
| 5 evals | mean | 20.6 |
Scores from Artificial Analysis and public leaderboards, normalised to 100. Bar colour is the band, not the rank: green 80 and up, blue 55 and up, amber 30 and up. Benchmarks measure narrow skills, so test on your own workload before committing. Released 2023-12-11.
leanstral-1-5requesty routes this id across every provider serving leanstral-1-5, picking on price and health and failing over automatically. the id stays valid when a provider changes underneath it
Base url is https://router.requesty.ai/v1 for every id here. One key reaches the whole catalog.
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="leanstral-1-5", messages=[ {"role": "user", "content": "Explain quantum computing in one paragraph."}, ],) print(response.choices[0].message.content)
Change the base url, use your Requesty key, set the model to any id in the call pane. Existing OpenAI SDK code needs no other edit, and the same key reaches every other model in the catalog, so switching later is a one-parameter change. Browse all models
Leanstral 1.5 is an updated Lean 4 formal proof engineering model from Mistral AI, optimized for automated theorem proving and autoformalization. It has 119B total parameters with 6.5B active and supports a 256K token context window. It supports native function calling and structured output.
questions 6
Which providers serve leanstral-1-5?
How much does leanstral-1-5 cost?
Is leanstral-1-5 open weights?
What is the context window of leanstral-1-5?
How do I use leanstral-1-5 with the OpenAI SDK?
Should I call leanstral-1-5 by its managed id or a provider id?
more from mistral ai 6
| model | context | from /M | providers |
|---|---|---|---|
| devstral-latest | 256K | $0.44 | 1 |
| mistral-medium-3-5 | 262K | $1.65 | 1 |
| mistral-medium-latest | 131K | $0.44 | 1 |
| mistral-small-2603 | 256K | $0.17 | 1 |
| mistral-large-latest | 131K | $0.55 | 1 |
| codestral-latest | 131K | $0.33 | 1 |
route leanstral-1-5 through one endpoint
One key for 1 provider on this model and 600+ others. No markup on provider prices, automatic failover, caching built in. Weekly aggregates in the measured pane come from production traffic routed through Requesty, one line per provider on a shared axis. Methodology
