Mistral Small 4 by OpenRouter | AI model information

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Model details

Mistral Small 4

Mistral Small 4 represents a deliberate consolidation effort, merging the formerly separate Mistral Instruct, Magistral reasoning, and Devstral coding lines into a single hybrid model rather than asking developers to route between specialists. The architectural backbone is a 119B-parameter mixture-of-experts design with 128 experts and 4 active per token, yielding roughly 6.5B activated parameters per forward pass. A 262k token context window, multimodal input handling for text and images, and configurable reasoning effort per request make it a flexible general-purpose system that can switch between quick conversational replies and more deliberate analytical responses without changing weights or endpoints.

For practitioners, the practical appeal lies in deployment efficiency and openness. Mistral reports a 40% reduction in end-to-end completion latency and roughly 3x higher requests-per-second throughput compared to Mistral Small 3 in optimized serving setups, and the team ships companion artifacts — a trained Eagle speculative-decoding head and an NVFP4 4-bit float checkpoint — that let operators trade memory for speed without retraining. The model fits well as a single backbone for production assistants that need chat, image understanding, tool-augmented reasoning, and lightweight code generation on the same API call, particularly for teams that want an open-weights alternative to comparable dense frontier models and value the ability to self-host quantized variants for cost control.

OpenRoutermistralai/mistral-small-2603mistral-small

Open provider page Provider docs

Quick Info

Provider: OpenRouter
Model key: mistralai/mistral-small-2603
Release date: Mar 16, 2026
Last updated: Mar 16, 2026
Knowledge cutoff: 2025-06
Input modalities
Output modalities
Capabilities

Cost

Input token cost: $0.15
Output token cost: $0.60

Limits

Output tokens: 209,715 tokens
Context window: 262,144 tokens

Latest news about Mistral Small 4

OpenRouter

CoverageRelease Notes

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

OpenRouter

Coverage

Mistral AI has released Mistral Small 4, combining fast text responses, logical reasoning, and image processing in one model.

OpenRouter

Coverage

Pi's developer integration page for Mistral Small 4 confirms it is served via OpenRouter's OpenAI-compatible completions endpoint at https://openrouter.ai/api/v1, with a 262,144-token context window, 4,096 max output tokens, and reasoning support enabled. Modalities are listed as text and image input, matching the prim The page enumerates effective compatibility flags that matter for tool-use integrations: supportsStore is true, supportsDeveloperRole is false, supportsReasoningEffort is true, supportsUsageInStreaming is true, maxTokensField maps to max completion tokens, requiresToolResultName is false, requiresAssistantAfterToolResu

OpenRouter

Official sourceBenchmark

The OpenRouter model page for Mistral Small 4 (mistralai/mistral-small-2603) documents it as the next major release in the Mistral Small family, unifying Magistral reasoning, Pixtral multimodal understanding, and Devstral agentic coding into a single system capable of complex analysis, software development, and visual The same listing provides a price-history chart and per-provider token-share and cache-hit-rate breakdowns, showing that the Mistral provider currently handles 92.8% of token share with a 43.1% cache-hit rate, while Venice handles 7.0% at 24.8% cache hits and Mistral (US) only 0.2% share at 9.8% cache hits. Throughput