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4 NEW Mistral 3 Models!!

It’s a common belief that Large Language Models (LLMs) like GPT-4 are rapidly mastering the art of coding, on a clear trajectory to surpass human developers. We see benchmarks and leaderboards that…

4 min read

Mistral’s Major Comeback: Full Analysis of Mistral Large 3 & Ministral

Official news article: https://mistral.ai/news/mistral-3

Mistral has officially broken its silence with a massive release that directly challenges the narrative that it has fallen behind in the AI arms race. After five months of quiet—a lifetime in AI development—the European lab has dropped four new models: the frontier-class Mistral Large 3 and a new family of efficient edge models called Ministral.

[0:00.000] This release is more than just an update; it is a strategic response to the surging dominance of Chinese open-weight models (like Qwen) and the closed-source US giants. Mistral is reasserting itself not just as a regional European champion, but as a top-tier global competitor.

Mistral Large 3: The New Frontier MoE

[0:59.000] The headline act is Mistral Large 3, a massive Mixture of Experts (MoE) model. It features a total of 675 billion parameters, with 41 billion active parameters during inference.

“This is a 675 billion parameter model with 41 billion parameters active.”

This architecture marks a return to the heavy-hitting MoE strategy that put Mistral on the map. Notably, the active parameter count (41B) is significantly higher than recent efficiency-focused trends from competitors like GPT-4o or Qwen, suggesting a focus on raw capability and density over pure inference minimization.

The Ministral Family: Dominating the Edge

[2:00.000] Alongside the flagship, Mistral introduced the Ministral 3 series, a set of three dense models (3B, 8B, and 14B) designed to replace the older “Mistral Small” line. A major advantage for developers is that Mistral has released reasoning versions even for these smaller sizes, democratizing advanced cognitive chains-of-thought for edge devices and local applications.

Benchmarking the Heavyweight

[2:41.000] In terms of performance, Mistral Large 3 trades blows with top-tier models like DeepSeek V3 and the Kimi k2. The comparison landscape has shifted; Mistral is no longer benchmarking against mid-weights like Claude Haiku but is aiming for the heavy hitters.

[3:24.000] On the LLM Arena (Chatbot Arena) leaderboard, the model currently sits around position 28 globally. While this might seem modest against the absolute peak of closed-source models, the context changes largely when looking at open weights.

[3:40.000] When filtering for Apache 2 / Open Source licenses, Mistral Large 3 rockets to the top echelons, beating out several Qwen 2.5 variants and sitting neck-and-neck with the largest Qwen MoE. This solidifies it as one of the most powerful truly open models available for enterprise use.

Detailed Small Model Comparisons

[5:56.000] The video provides a deep dive into how the Ministral models stack up against the competition.

  • Ministral 3B: Competes aggressively with the Gemma 4B class and Qwen’s smaller entries, effectively punching above its weight class.
  • Ministral 8B: Holds its ground against the crowded 7B-9B market.
  • Ministral 14B: While it trails the Qwen 14B slightly in general knowledge benchmarks, it appears to outperform competitors in instruction following, making it a potentially better candidate for chat applications and agentic workflows.

The speaker notes that while Qwen models are excellent, the release cycle suggests we might see a “Qwen 3” soon, whereas Mistral is offering fresh, usable models right now.

The Shift to Private Benchmarks

One of the most insightful takeaways from the video comes from recent interviews with heads of AI at major enterprises. The reliance on public leaderboards is fading.

“They don’t really care about the public benchmarks anymore… having your own benchmarks for the particular app or use case that you’re using the models in.”

Large companies are now building infrastructure to swap models in and out rapidly to test against their own internal datasets. By releasing the base models (not just instruct versions), Mistral empowers these organizations to perform valid fine-tuning experiments and domain-specific evaluations that pre-finetuned chat models often obscure.

Availability: GGUF, Base, and Reasoning

[7:48.000] Mistral has aggressively lowered the barrier to entry with this launch.

  • Base Models: Available for all sizes, crucial for researchers and fine-tuners.
  • Instruct Models: Ready-to-use chat versions.
  • Reasoning Models: Available for the Ministral line.
  • GGUF Formats: Official quantized versions were released immediately, allowing users to run these models on consumer hardware (via tools like LM Studio or Ollama) on day one.

Final Verdict: Is Mistral Still Relevant?

[8:07.000] Mistral is definitely still relevant, countering the idea that it is merely a “European regulatory play.” While they may not have the infinite budget of Google or OpenAI, they are producing solid, competitive open models that offer a distinct alternative to the Chinese ecosystem.

Crucially, the video teases one final upcoming release: a Mistral Large 3 Reasoning model. This unreleased variant is expected to compete directly with “thinking” models like the Kimi k2 and could be the final piece of the puzzle to cement Mistral’s position in the top tier of AI labs.