---
title: "Mistral 3"
type: model
id: "mistral-3"
provider: "Mistral AI"
model_type: "open-source"
release_date: "2025-12"
description: "Mistral's flagship model suite with Apache 2.0 license. A European alternative focused on enterprise compliance, adopting a DeepSeek-style MoE architecture for efficiency."
last_updated: "2026-04-10"
context_window: "128K tokens"
website: "https://mistral.ai"
license: "Apache 2.0"
modality:
- "text"
tags:
- "mistral ai"
- "open-source"
- "text"
pricing:
input: "$2.00 / 1M tokens"
output: "$6.00 / 1M tokens"
benchmarks:
reasoning: 86
coding: 87
math: 84
writing: 86
multilingual: 92
speed: 78
parameters: "675B total (41B active)"
hardware_requirements: "8x A100 80GB (FP16); multi-GPU setup required"
best_for:
- "European compliance"
- "Multilingual applications"
- "Enterprise deployment"
- "Code generation"
---
# Mistral 3
The model you pick when European compliance isn't optional. Mistral 3 ships under Apache 2.0 from a Paris-based company, making it the cleanest open-source option for organizations navigating EU AI Act requirements and GDPR data sovereignty. The 92/100 multilingual score -- second only to Qwen 3 -- reflects genuine strength across European languages.
The DeepSeek-style MoE architecture (675B total, 41B active) keeps Mistral 3 efficient, and the benchmark profile is solid if unspectacular: 87/100 coding, 86/100 reasoning, 86/100 writing. At $2/$6 per million tokens via Mistral's API, the pricing is competitive with Gemini 3.1 Pro. Self-hosting requires a serious multi-GPU setup (8x A100), but the Apache 2.0 license gives you complete freedom in how you deploy.
The challenge is that Mistral 3 doesn't clearly lead in any single dimension except European compliance. DeepSeek V3.2 offers similar capability at a fraction of the API cost. Qwen 3 beats it on multilingual, math, and coding. Llama 4 Maverick has a far larger community. Mistral's ecosystem is growing but remains smaller than its competitors'.
**When to pick something else:** If European compliance isn't a factor, DeepSeek V3.2 delivers comparable quality at $0.27/$1.10 -- nearly 10x cheaper. For the strongest open-source coding, Qwen 3 (90/100) or MiniMax M2.7 (95/100) pull ahead. Mistral 3's value proposition is clearest for European enterprises that need a local, compliant, well-rounded model under a permissive license.
Mistral 3
The model you pick when European compliance isn't optional. Mistral 3 ships under Apache 2.0 from a Paris-based company, making it the cleanest open-source option for organizations navigating EU AI Act requirements and GDPR data sovereignty. The 92/100 multilingual score -- second only to Qwen 3 -- reflects genuine strength across European languages.
The DeepSeek-style MoE architecture (675B total, 41B active) keeps Mistral 3 efficient, and the benchmark profile is solid if unspectacular: 87/100 coding, 86/100 reasoning, 86/100 writing. At $2/$6 per million tokens via Mistral's API, the pricing is competitive with Gemini 3.1 Pro. Self-hosting requires a serious multi-GPU setup (8x A100), but the Apache 2.0 license gives you complete freedom in how you deploy.
The challenge is that Mistral 3 doesn't clearly lead in any single dimension except European compliance. DeepSeek V3.2 offers similar capability at a fraction of the API cost. Qwen 3 beats it on multilingual, math, and coding. Llama 4 Maverick has a far larger community. Mistral's ecosystem is growing but remains smaller than its competitors'.
When to pick something else: If European compliance isn't a factor, DeepSeek V3.2 delivers comparable quality at $0.27/$1.10 -- nearly 10x cheaper. For the strongest open-source coding, Qwen 3 (90/100) or MiniMax M2.7 (95/100) pull ahead. Mistral 3's value proposition is clearest for European enterprises that need a local, compliant, well-rounded model under a permissive license.