Mistral 3 Review: Benchmarks, API Pricing, and How to Run the New Edge Models Locally

Introduction

Mistral AI just dropped a massive update. They didn’t just release a model. They released an entire ecosystem. Mistral 3 is here and it is a comprehensive lineup covering everything from edge devices to frontier-class reasoning.

For a while now the open-weight community has been waiting for a true successor to the “Nemo” class, that sweet spot between tiny models that hallucinate and massive models that require a second mortgage to host. The new Mistral 3 14B seems to be exactly that “Goldilocks” model. But the release goes deeper. We have a new flagship frontier model, a revamped API structure, and edge models that actually understand images.

We are going to look at the hard numbers. We will break down the benchmarks to see if the hype holds up against DeepSeek and Qwen. We will look at the Mistral API pricing to see if it makes sense for your business. Finally, I will walk you through exactly how to run Mistral locally on your own hardware.

1. What is Mistral 3? Understanding the New Family

To understand Mistral 3, you have to separate the frontier from the edge. Mistral has split the philosophy into two distinct product lines that share the same DNA.

1.1 Mistral Large 3 (675B)

This is the big gun. Mistral Large 3 is a sparse Mixture-of-Experts (MoE) model. It has 675 billion total parameters but only activates 41 billion per token. This is a critical architectural choice. It allows the model to have the knowledge base of a giant but the inference speed of a much smaller model. It is designed to rival GPT-4o and Claude 3.5 Sonnet in complex reasoning and enterprise tasks. It is not something you run on a Macbook Air.

1.2 The Ministral Series (3B, 8B, 14B)

This is where things get interesting for developers. The Ministral series represents the “edge” models. Unlike the Large variant these are dense models. They are not MoEs. They are multimodal native which means they have vision support baked in from pre-training. They are designed specifically for local use, high throughput, and low latency. The Mistral 3 14B in particular is targeting the high-performance local assistant niche.

2. Mistral 3 Benchmarks: How It Stacks Up

Benchmarks are often marketing fluff but in the open-weight arena they give us a necessary baseline. We need to see how Mistral 3 handles logic, math, and coding tasks compared to the current heavyweights.

2.1 Mistral Large 3 vs. DeepSeek & Kimi

There was some noise on Reddit about Mistral 3 being “dead on arrival” because of DeepSeek V3. That is a premature take. While DeepSeek might edge it out in raw logic speed, Mistral Large 3 holds its own in multimodal tasks and multilingual capabilities across 40+ languages. Here is the data comparing the flagship models:

You will notice Mistral Large 3 wins on general knowledge (MMMLU) and expert reasoning (GPQA-Diamond). It falls slightly behind on coding tasks compared to Kimi but stays competitive.

2.2 Ministral 14B Performance

The real excitement is in the smaller sizes. The Mistral 3 14B model is posting numbers that we used to only see in 70B+ models a year ago. It is beating Google’s Gemma 3 12B and Alibaba’s Qwen 3 14B in key reasoning metrics.

The standout statistic here is the AIME25 score. Mistral 3 14B hits 85.0. That is significantly higher than the Qwen 3 “Thinking” variant at 73.7. If you are building local agents that need to plan and reason without hallucinating steps this is your new default model.

3. Mistral API Pricing Breakdown

For businesses the decision usually comes down to cost per token. Mistral has been aggressive here. They are targeting the B2B market that wants GPT-4 performance without the OpenAI lock-in. Here is the current Mistral API pricing structure:

The API costs are very competitive. At $0.50 per million input tokens Mistral 3 is undercutting many legacy models while offering a massive 256k context window. This makes it viable for RAG (Retrieval Augmented Generation) applications where you need to stuff entire documents into the context.

4. Mistral vs. DeepSeek vs. Qwen: Which Should You Choose?

The ecosystem is crowded. We have Mistral AI models. We have DeepSeek. We have Qwen. Choosing one depends entirely on your constraints.

4.1 For Logic & Math

If your primary use case is raw number crunching or solving logic puzzles DeepSeek V3 retains the crown for pure reasoning speed. Its architecture is heavily optimized for this specific vertical.

4.2 For Creative Writing & RP

This is where Mistral 3 shines. The 14B model specifically has a “personality.” It feels less robotic than Qwen. If you are generating prose, marketing copy, or running roleplay scenarios locally, Mistral 3 flows better. It feels more human.

4.3 For Privacy & Sovereignty

This is the Mistral vs DeepSeek tie-breaker. Mistral is an EU company. For enterprise clients concerned about data sovereignty or avoiding US/China data bias Mistral is the safest bet. It is the top choice for GDPR compliance.

5. Hardware Requirements: Can You Run It?

This is the question every developer asks first. Can I run Mistral 3 on my gaming rig or do I need to rent H100s?

5.1 Ministral 3B

This is the tiny giant. It runs on modern phones. It runs on a Raspberry Pi 5. If you have a laptop with any dedicated GPU or even a modern M-series Mac you are good to go.

5.2 Ministral 8B

This is the standard lightweight class. You can comfortably run this on 8GB VRAM. If you have an NVIDIA RTX 3070 or 4060 you will get blazing fast token speeds.

5.3 Ministral 14B

This requires a bit more heft. You need about 12-16GB VRAM for a decent quantization (Q4 or Q5). This is the perfect workload for an RTX 3060 12GB or the 4070 Ti Super. It fits nicely on a Mac with 16GB unified memory provided you close your Chrome tabs.

5.4 Mistral Large 3

Do not try this at home. This is an enterprise cluster model. You need H100s or a massive multi-GPU rig. For 99% of us this is an API-only model.

6. How to Run Mistral 3 Locally (Step-by-Step)

If you have the hardware let’s get Mistral 3 running. We will look at the three most common methods.

6.1 Method 1: Ollama (Easiest)

Ollama is the standard for local inference now. It is clean, fast, and handles the backend complexity for you.

• Install Ollama: Go to ollama.com and download the installer for your OS.
• Pull the Model: Open your terminal and run the command for the size you want.

To run the 14B instruct model:

To run the smaller 8B model:

Ollama handles the quantization and GPU offloading automatically. You will be chatting with Mistral 3 in seconds.

6.2 Method 2: LM Studio (GUI)

If you prefer a graphical interface over a terminal LM Studio is excellent.

• Download LM Studio.
• In the search bar type “Mistral 3” or “Ministral”.
• Look for the quantization that fits your VRAM (usually Q4_K_M or Q5_K_M).
• Click download.
• Select the model in the top bar and start chatting.

This method is great if you want to tweak parameters like temperature and system prompts visually.

6.3 Method 3: vLLM (For Developers)

If you are building an application and need high throughput how to run Mistral locally changes slightly. You want vLLM. Mistral has worked with NVIDIA to support the new NVFP4 format which drastically improves throughput on newer cards.

You will need a Linux environment with CUDA 12.1+ installed.

This exposes an OpenAI-compatible API server on your local machine that you can plug directly into your code.

7. Agentic Capabilities & Tool Calling

We are moving past simple “chatbot” interactions. Mistral 3 was trained with agentic workflows in mind. In the “Le Chat” interface there is a new “Agent Mode.”

This isn’t just a UI trick. The underlying model has been fine-tuned to handle function calling more reliably. If you are building a coding agent that needs to search the web, write a file, and then execute that file, Mistral 3 handles the multi-step logic better than previous iterations. It might trade a tiny bit of raw chat speed for accuracy here but that is a trade you want to make when you are letting an AI run code on your machine.

8. Is Ministral 14B the New “Mistral Nemo”?

There has been a gap in the market since the 12B Mistral Nemo released. It was good but it was aging. Mistral 3 14B is the direct replacement.

It sits in the exact same hardware tier, accessible to high-end consumers, but offers significantly better reasoning. It also adds native vision support which Nemo lacked. If you have been using Nemo as your daily driver for local tasks it is time to upgrade. The 14B model is smarter, sees images, and follows complex instructions with higher fidelity.

9. Conclusion: The Open Weight Winner?

Mistral 3 is a statement. It proves that open weights are not just catching up to closed source. In some specific verticals they are creating their own lane.

The Mistral 3 14B model is likely going to become the default choice for local development in 2025. It balances size and intelligence perfectly. The Mistral API pricing makes the large frontier model accessible for businesses that need to scale. And for those of us who care about privacy, having a European alternative to the US/China duopoly is vital.

Download the 14B model. Spin it up in Ollama. Throw your hardest logic puzzles at it. The benchmarks look great on paper but seeing it run on your own silicon is where the real proof lies.

Next Step: I can generate a specific Python script for you that uses the Mistral API to build a simple local RAG (Retrieval Augmented Generation) system if you want to test the new context window. Would you like me to do that?