Mistral AI

Mistral Small 3

Apache 2.0 licensed 24-billion-parameter model built for low latency and self-hosted deployment.

Workstation running a local language model in a dim office

Overview

Mistral Small 3 is a 24-billion-parameter open-weight model released under Apache 2.0 and optimised for latency. Mistral positions it against considerably larger models, and the 3.1 release adds vision understanding and a 128,000-token context window.

Typical use cases

  • Self-hosted assistants
  • Document understanding
  • On-premise agents

Deployment environment

Workstation or edge server with sufficient memory

Key specifications

Parameters
24 billion
Licence
Apache 2.0
Design goal
Latency-optimised, positioned against Llama 3.3 70B and Qwen 32B class models
Mistral Small 3.1
Adds vision understanding and a 128,000-token context window

Specifications taken from current manufacturer documentation and last checked on 2026-09-08.

Source: manufacturer documentation

Specifications are summarised from publicly published manufacturer material and are provided for orientation only. Always confirm current figures directly with the manufacturer before purchasing or designing in.

Terminal window showing a locally hosted language model responding on a dark screen
Ollama

Ollama

MIT-licensed runtime for pulling and serving open-weight models locally through a CLI and local API.

Type
Local model runtime and server
Licence
MIT
Interfaces
Command line and local HTTP API
Deployment
Workstation, edge server or on-premise host
Private assistants
Offline document analysis
Prototyping local inference
Terminal showing token generation from a local model
ggml.ai (llama.cpp)

llama.cpp

MIT-licensed C and C++ inference engine for open-weight language models, on CPU or GPU backends.

Type
LLM inference engine in C and C++
Licence
MIT
Backends
CPU plus CUDA, Metal and Vulkan
Deployment
Edge board, workstation or server, with or without a GPU
On-device assistants
Quantised model serving
Arm and Apple silicon inference
Laptop running a small language model locally
Microsoft

Microsoft Phi-4

MIT-licensed 14-billion-parameter small language model built for low-latency reasoning tasks.

Parameters
14 billion
Licence
MIT
Design goal
Low-latency scenarios with strong reasoning quality
Deployment
Workstation, edge server or capable AI PC
Local reasoning assistants
Private summarisation
Structured extraction