Mathstral Models by MistralAI
About
Mathstral is a family of large language models (LLMs) created by Mistral AI, focusing on mathematical reasoning and scientific exploration. Built on the Mistral 7B architecture, they inherit its efficient design and are fine-tuned specifically for STEM (Science, Technology, Engineering, and Mathematics) fields 124. These models are adept at handling complex, multi-step logical reasoning tasks and are available under the Apache 2.0 license, facilitating open-source collaboration 148. A prominent characteristic is their enhanced performance with increased inference-time computation, offering scalability for various applications 1. They provide leading reasoning abilities for their size category, as evidenced by industry-standard benchmarks 18. Notably, the Mathstral 7B v0.1 model supports a 32k context window, allowing it to tackle more extensive and intricate mathematical challenges 1. Model weights are accessible via Hugging Face, facilitating research and development efforts 1.
Current Variants
Use-when guidance is derived from seed capabilities, context, release, and replacement fields.
Use when the workload needs 32k context and 7B parameters.
| Model | Use when | Released | Signals | Status |
|---|---|---|---|---|
| Mathstral 7B | Use when the workload needs 32k context and 7B parameters. | 2024-07 | 32k context7B parameters | Current |
Release Timeline
1 release groupSpecifications(1 models)
| Model | Released | Context | Parameters |
|---|---|---|---|
| Mathstral 7B | 2024-07 | 32k | 7B |
Frequently Asked Questions
- What is Mathstral used for?
- Mathstral is used for mathematics and math-heavy prompts. The family description and listed model capabilities point to those workloads as the best fit.
- How does Mathstral compare to Ministral?
- Mathstral by MistralAI is strongest where you need mathematics, while Ministral by MistralAI is the closest related family to check for structured outputs. Mathstral has 1 listed variant and reaches up to 32k context, while Ministral reaches up to 32k context, so compare the specs and pricing tables before choosing a production model.
- Which Mathstral model should I use?
- If price is the main constraint, use the pricing table first because Mathstral does not have complete provider pricing in the local data. For the most capable/latest local choice, evaluate Mathstral 7B with 32k context.
