Mistral Small 4
mistral-small-2603mistral-small-4- 🆕 Unifies instruct, reasoning, and coding into one model
- 🧠 119B-parameter MoE, only ~6.5B active per token
- 🔧 128 experts, 4 active per token
- 📏 Native 256K-token context window
- 👁️ Accepts text and image input, text output
- 🎯 Configurable reasoning effort, toggle fast vs. thinking mode
- 🔒 Apache 2.0 license, open weights
- 💬 Native function calling and JSON output for agentic use
Mistral AI is a French artificial intelligence company headquartered in Paris, founded in 2023. The company focuses on developing large language models offered under both open-weight and proprietary licenses. Mistral AI has quickly risen to prominence in the AI landscape,…
Explore 1 more model by Mistral AI →Mistral Small 4, released March 16, 2026, is Mistral AI's open-weight hybrid model that consolidates three previously separate model families—Instruct, Reasoning (formerly Magistral), and Devstral coding—into a single checkpoint. It uses a Mixture-of-Experts architecture with 119 billion total parameters spread across 128 experts, activating only 4 experts (about 6.5 billion parameters) per token, giving it the inference profile of a much smaller dense model. It accepts text and image input, supports a 256K-token context window, and exposes a configurable reasoning-effort parameter that toggles between fast instant replies and a slower thinking mode.
This marks a substantial change from its same-family predecessor, Mistral Small 3.2 24B Instruct, a 24B dense instruction model from January 2026. Where Small 3.2 focused on text instruction following, Small 4 moves to a sparse MoE design, adds native vision, integrates dedicated reasoning and agentic-coding behavior, and expands deployment to enterprise-scale tasks.
Mistral distributes Small 4 under the Apache 2.0 license with multiple checkpoints, including an NVFP4 4-bit quantized version and a trained Eagle head for speculative decoding. Mistral positions it for chat assistants, coding, agentic workflows, and reasoning tasks, with native function calling and JSON output.
This About section is AI-generated from public sources via VeniceStats + Venice inference, with no human editing. It may contain inaccuracies.
Daily usage · Mistral Small 4 · Organic sellers
Top sellers · Mistral Small 4 · Organic
- Venice.ai Proxy94.5%
- ▲ Apex Ant5.2%
- Surplus-backed seller0.2%
- Make Karma0.0%
- Meridian AI0.0%
| Seller | Health | Rep↓ | Sales | Tokens | Routing | Input $/M | Cached $/M | Output $/M | Save |
|---|---|---|---|---|---|---|---|---|---|
| ▲ Apex Ant 0x73b4…e736 | healthy | 81.71 | $0.01 | 115.52k | #1 | $0.04 | $0.04 | $0.16 | −73% |
| Apex TEE Test 0xe672…7955 | — | 0.00 | — | — | gated | $0.0844 | $0.0844 | $0.3375 | −44% |
| antseed-neon-puma-944e 0x6650…944e | — | 0.00 | — | — | gated | $0.0639 | $0.0639not advertised | $0.2558 | −57% |
| Leftermute 0x388b…5389 | — | 0.00 | — | — | gated | $0.0379 | $0.0379not advertised | $0.1515 | −75% |
| Meridian AI 0x8c8c…06f5 | — | 0.00 | $0.00 | 230.00 | gated | $0.0304 | $0.0304not advertised | $0.1215 | −80% |
"Best price" and the seller table are live Antseed catalog data (advertised $/1M tokens — or $ per generated image for unit-billed image models — not settled amounts). Reputation = buyer trust score (0-100, the Antseed SDK's own formula). "Routing" = the SDK's default buyer routing (what the VPR desktop app ships with): a trust ≥ 60 gate on the effective reputation, then cheapest-first among routable sellers; live failover state (per-peer cooldowns) is buyer-side runtime and not included. Model knowledge (TLDR, provider, About) via the VeniceStats enrichment layer. Advertised catalog, not the model used in any specific purchase. "Usage on Antseed" counts only settlements whose buyers share the per-model split on-chain (metadata v2/v3, opt-in), so every usage figure is a lower bound.