Mistral AI Releases Mistral Small 4: A 119B-Parameter MoE Model that Unifies Instruct, Reasoning, and Multimodal Workloads

Mistral AI has released

Mistral Small 4

, a new model in the Mistral Small family designed to consolidate several previously separate capabilities into a single deployment target. Mistral team describes Small 4 as its first model to combine the roles associated with

Mistral Small

for instruction following,

Magistral

for reasoning,

Pixtral

for multimodal understanding, and

Devstral

for agentic coding. The result is a single model that can operate as a general assistant, a reasoning model, and a multimodal system without requiring model switching across workflows.

Architecture: 128 Experts, Sparse Activation

Architecturally, Mistral Small 4 is a

Mixture-of-Experts (MoE)

model with

128 experts

and

4 active experts per token

. The model has

119B total parameters

, with

6B active parameters per token

, or

8B including embedding and output layers

.

Long Context and Multimodal Support

The model supports a

256k context window

, which is a meaningful jump for practical engineering use cases. Long-context capacity matters less as a marketing number and more as an operational simplifier: it reduces the need for aggressive chunking, retrieval orchestration, and context pruning in tasks such as long-document analysis, codebase exploration, multi-file reasoning, and agentic workflows. Mistral positions the model for

general chat, coding, agentic tasks, and complex reasoning

, with

text and image inputs

and

text output

. That places Small 4 in the increasingly important category of general-purpose models that are expected to handle both language-heavy and visually grounded enterprise tasks under one API surface.

Configurable Reasoning at Inference Time

A more important product decision than the raw parameter count is the introduction of

configurable reasoning effort

. Small 4 exposes a per-request

reasoning_effort

parameter that allows developers to trade latency for deeper test-time reasoning. In the official documentation,

reasoning_effort="none"

is described as producing fast responses with a chat style equivalent to

Mistral Small 3.2

, while

reasoning_effort="high"

is intended for more deliberate, step-by-step reasoning with verbosity comparable to earlier

Magistral

models. This changes the deployment pattern. Instead of routing between one fast model and one reasoning model, dev teams can keep a single model in service and vary inference behavior at request time. That is cleaner from a systems perspective and easier to manage in products where only a subset of queries actually need expensive reasoning.

Performance Claims and Throughput Positioning

Mistral team also emphasizes inference efficiency. Small 4 delivers a

40% reduction in end-to-end completion time

in a latency-optimized setup and

3x more requests per second

in a throughput-optimized setup, both measured

against Mistral Small 3

. Mistral is not presenting Small 4 as just a larger reasoning model, but as a system aimed at improving the economics of deployment under real serving loads.

Benchmark Results and Output Efficiency

On reasoning benchmarks, Mistral’s release focuses on both quality and output efficiency. The Mistral’s research team reports that

Mistral Small 4 with reasoning

matches or exceeds

GPT-OSS 120B

across

AA LCR

,

LiveCodeBench

, and

AIME 2025

, while generating shorter outputs. In the numbers published by Mistral, Small 4 scores

0.72 on AA LCR with 1.6K characters

, while Qwen models require

5.8K to 6.1K characters

for comparable performance. On

LiveCodeBench

, Mistral team states that Small 4 outperforms GPT-OSS 120B while producing

20% less output

. These are company-published results, but they highlight a more practical metric than benchmark score alone:

performance per generated token

. For production workloads, shorter outputs can directly reduce latency, inference cost, and downstream parsing overhead.

Deployment Details

For self-hosting, Mistral gives specific infrastructure guidance. The company lists a minimum deployment target of

4x NVIDIA HGX H100

,

2x NVIDIA HGX H200

, or

1x NVIDIA DGX B200

, with larger configurations recommended for best performance. The model card on HuggingFace lists support across

vLLM

,

llama.cpp

,

SGLang

, and

Transformers

, though some paths are marked

work in progress

, and

vLLM

is the recommended option. Mistral team also provides a custom Docker image and notes that fixes related to tool calling and reasoning parsing are still being upstreamed. That is useful detail for engineering teams because it clarifies that support exists, but some pieces are still stabilizing in the broader open-source serving stack.

Key Takeaways

• 
  
  One unified model:
  
  Mistral Small 4 combines instruct, reasoning, multimodal, and agentic coding capabilities in one model.
  


• 
  
  Sparse MoE design:
  
  It uses
  
  128 experts
  
  with
  
  4 active experts per token
  
  , targeting better efficiency than dense models of similar total size.
  


• 
  
  Long-context support:
  
  The model supports a
  
  256k context window
  
  and accepts
  
  text and image inputs
  
  with text output.
  


• 
  
  Reasoning is configurable:
  
  Developers can adjust
  
  
reasoning_effort

  
  at inference time instead of routing between separate fast and reasoning models.
  


• 
  
  Open deployment focus:
  
  It is released under
  
  Apache 2.0
  
  and supports serving through stacks such as
  
  vLLM
  
  , with multiple checkpoint variants on Hugging Face.
  

Check out


Model Card on HF


and


Technical details

.

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