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Mistral

Model ● Covered in 6 stories Compare ⇄ + Follow

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Specifications

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Latest developments

Making LLMs more accurate by using all of their layers Google Research · 10 months ago · 23

Introducing the Together AI Batch API: Process Thousands of LLM Requests at 50% Lower Cost Together AI · 1 year ago · 15

Bringing serverless GPU inference to Hugging Face users Hugging Face Blog · 2 years ago · 2

Hugging Face Text Generation Inference available for AWS Inferentia2 Hugging Face Blog · 2 years ago · 10

Make LLM Fine-tuning 2x faster with Unsloth and 🤗 TRL Hugging Face Blog · 2 years ago · 20

2023, year of open LLMs Hugging Face Blog · 2 years ago · 38

Timeline

Month Quarter Year

September 2025

17 Sep Making LLMs more accurate by using all of their layers

June 2025

11 Jun Introducing the Together AI Batch API: Process Thousands of LLM Requests at 50% Lower Cost

April 2024

2 Apr Bringing serverless GPU inference to Hugging Face users

February 2024

1 Feb Hugging Face Text Generation Inference available for AWS Inferentia2

January 2024

10 Jan Make LLM Fine-tuning 2x faster with Unsloth and 🤗 TRL

December 2023

18 Dec 2023, year of open LLMs

Relationships

Products & technology

Together AI supplies this model · 1 source
Cloudflare deploys this model · 1 source
Unsloth supplies this model · 1 source

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TLDRocket 3 August 2026

The story of AI in August 2026 is one of consolidation meeting enforcement. Alibaba's Qwen3.8-Max, a 2.4-trillion-parameter mixture-of-experts model with a million-token context window, arrives as a direct challenge to OpenAI and Anthropic's frontier offerings—priced at $2 per million input tokens, it's positioning itself as the capable-and-cheaper alternative that China's AI push has long promised. But the more consequential development is structural: enterprises are finally getting the plumbing to actually deploy these models. June, backed by Salesforce's Marc Benioff, raised $20 million to automate AI integration into legacy systems, scanning corporate infrastructure and generating deployment workflows without expensive consultant fees. This solves what's become clear as the real bottleneck—not model capability, but implementation friction in messy, heterogeneous corporate environments.

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