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Model releases

New models released, opened and updated: flagship launches, open weights, and performance and price changes as they happen.

135 picksLast 30 days 72 itemsCollected 252 items

New

Picks archive · page 7

3/17Tue
  1. Mistral AI78

    Mistral releases Mistral Small 4, unifying reasoning, multimodal, and coding capabilities

    Mistral AI has released Mistral Small 4, the next major version of the Mistral Small family and its first model to combine Magistral's reasoning, Pixtral's multimodal, and Devstral's agentic-coding capabilities in one model. It is licensed under Apache 2.0.

    Why it matters: Mistral has combined reasoning, multimodal, and coding capabilities in one open small model, with adjustable reasoning effort and deployment requirements to help assess implementation costs.

  2. Mistral AI60

    Mistral releases Leanstral, an open-source coding agent for Lean 4

    Mistral has released Leanstral, which it calls the first open-source coding agent for Lean 4. It uses a sparse architecture with 6B active parameters; weights are available under Apache 2.0, and the agent is integrated into Mistral Vibe and the free `labs-leanstral-2603` API endpoint.

    Why it matters: Mistral has open-sourced a Lean 4 coding agent and reports cost and score comparisons with Claude models and open models, helping readers assess the value of formal-verification approaches.

2/5Thu
12/17Wed
  1. Mistral AI65

    Mistral releases OCR 3, with a 74% win rate over its predecessor on forms and handwriting

    Mistral AI has released Mistral OCR 3, reporting a 74% overall win rate against Mistral OCR 2 on forms, scanned documents, complex tables, and handwriting. The company says it outperforms both enterprise document-processing and AI-native OCR solutions in accuracy.

    Why it matters: The official post compares win rates with the previous version on forms, handwriting, and tables and gives pricing per thousand pages, helping readers assess document-parsing costs and use cases.

12/9Tue
  1. Mistral AI82

    Mistral releases Devstral 2 coding models and Mistral Vibe CLI

    Mistral AI has released the next-generation Devstral 2 coding-model family, comprising Devstral 2 at 123B and Devstral Small 2 at 24B. Both are open-source, under modified MIT and Apache 2.0 licenses respectively.

    Why it matters: The article reports the two open coding models' sizes, licenses, SWE-bench results, and head-to-head comparisons with closed models, helping readers assess the current level of open coding agents.

12/3Wed
  1. Mistral AI82

    Mistral releases Mistral 3 family, from 675B Mistral Large 3 to 3B, 8B, and 14B Ministral 3

    Mistral has released the Mistral 3 family: dense small models at 3B, 8B, and 14B, plus Mistral Large 3, a sparse MoE model with 41B active and 675B total parameters. All are open-source under Apache 2.0.

    Why it matters: Mistral has released Apache-2.0 weights ranging from 3B to 675B, giving readers a view of open-model options from edge devices to frontier scale.

7/15Tue
  1. Mistral AI78

    Mistral releases Voxtral speech-understanding models in open 24B and 3B sizes

    Mistral has released Voxtral speech-understanding models in 24B and 3B sizes, both open-source under Apache 2.0 and available through an API. They support a 32K-token context, can transcribe up to 30 minutes of audio or understand up to 40 minutes, and provide built-in question answering and summarization, automatic multilingual detection, and speech-triggered function calls. Their text capabilities are inherited from Mistral Small 3.1.

    Why it matters: Mistral's open speech-understanding models come in two sizes, under Apache 2.0, with API pricing disclosed, helping readers assess transcription and audio-understanding costs.

7/11Fri
6/10Tue
5/28Wed
  1. Mistral AI62

    Mistral releases Codestral Embed, its first code-embedding model

    Mistral has released Codestral Embed, its first embedding model designed for code. It outperforms Voyage Code 3, Cohere Embed v4.0, and OpenAI's large embedding model on real-world code-retrieval tasks.

    Why it matters: Mistral's first code-embedding model offers adjustable dimensions and precision, helping readers weigh retrieval quality against storage costs.

5/21Wed
5/7Wed
  1. Mistral AI72

    Mistral AI releases Mistral Medium 3 for low-cost enterprise deployment

    Mistral AI has released Mistral Medium 3, which it says reaches at least 90% of Claude Sonnet 3.7's performance on several benchmarks. Pricing is $0.40 per million input tokens and $2 per million output tokens.

    Why it matters: The official release gives Mistral Medium 3's pricing, comparison model, and deployment requirements, helping readers assess the cost of enterprise self-hosting.

3/17Mon
  1. Mistral AI71

    Mistral releases Mistral Small 3.1 with multimodal support and 128K context

    Mistral AI has released Mistral Small 3.1, improving text performance and multimodal understanding over Mistral Small 3. It extends the context window to 128K tokens, runs at 150 tokens per second, and is open-source under Apache 2.0.

    Why it matters: Mistral reports comparisons for a small model across text, multimodal, and long-context tasks, helping readers assess on-device deployment options.

2/17Mon
  1. Mistral AI62

    Mistral releases regional language model Mistral Saba

    Mistral AI has released its first regional language model, Mistral Saba, with 24B parameters. Trained on Middle Eastern and South Asian data, it supports Arabic and several Indic languages and performs particularly well in Tamil and other South Indian languages.

    Why it matters: Mistral's first regional model has 24B parameters but is positioned against models five times larger and can run locally on a single GPU, offering a new option for Middle Eastern and South Asian markets.

1/30Thu
  1. Mistral AI78

    Mistral AI releases Mistral Small 3, an open 24B model under Apache 2.0

    Mistral AI has released Mistral Small 3, a 24B-parameter model optimized for low latency and open-sourced under Apache 2.0. It provides both pretrained and instruction-tuned checkpoints.

    Why it matters: A 24B model under Apache 2.0 is positioned against models three times its size, with quantization details for single-GPU local deployment to help readers assess its fit.