New evals are nice because they show who’s really ahead & pushing the frontier versus who’s chasing.

Today we announced MAI-Thinking-1, a strong generalist and reasoning LLM built from the ground up without distilling third-party models. 97% on AIME 2025; 53% on SWE-Bench Pro; preferred by human raters over Sonnet 4.6 (blind side-by-side). Tech report: microsoft.ai/wp-content/uplo…

Seven new models launching at Build: let’s go! Reasoning. Code. Image. Transcribe. Voice. Built from scratch on a clean data lineage, designed for efficiency, working seamlessly as a family of models Thread 🧵 #MSBuild

big congrats to the microsoft AI team on MAI-Thinking-1! this is the kind of thoughtful post-training the field needs more of - focused on what actually matters to users excited to see a new frontier model in the race 😎 microsoft.ai/news/introducin…

Meet our third @MicrosoftAI model: MAI-Image-1 #9 on LMArena, striking an impressive balance of generation speed and quality Excited to keep refining climbing the leaderboard from here! We're just getting started. microsoft.ai/news/introducin…

This was an amazing week at ⁦@MicrosoftAI⁩ !! We released MAI 1 preview and a taste of MAI Voice. I’m super happy with this team - only about 100 people and already shipping in ⁦@lmarena_ai⁩ in less than a year. Strong support. More soon. Thanks for feedback!

hello, world!

Interesting

SOTA just got way cheaper

Quite interesting to see how some models generalize dramatically better!

Just shipped a few updates 1. Gemini 2.5 Pro to try for free on gemini.google.com in the model drop down. Advanced has higher limits. 2. Canvas with 2.5 Pro in Advanced. Our best coding model yet. We had so much fun building demos internally, can't wait to see what y'all come up with!

Gemini 2.5 Pro is SOTA on pretty much everything

WE HAVE A NEW BEST MODEL IN THE WORLD! GEMINI 2.5 IS #1 ON LIVEBENCH

BREAKING: Gemini 2.5 Pro is now #1 on the Arena leaderboard - the largest score jump ever ( 40 pts vs Grok-3/GPT-4.5)! 🏆 Tested under codename "nebula"🌌, Gemini 2.5 Pro ranked #1🥇 across ALL categories and UNIQUELY #1 in Math, Creative Writing, Instruction Following, Longer Query, and Multi-Turn! Massive congrats to @GoogleDeepMind for this incredible Arena milestone! 🙌 More highlights in thread👇

cook or die

If you're fine-tuning LLMs, Gemma 3 is the new 👑 and it's not close. Gemma 3 trounces Qwen/Llama models at every size! - Gemma 3 4B beats 7B/8B competition - Gemma 3 27B matches 70B competiton Vision benchmarks coming soon!

Wow, quite impressive for a 27B model!

We replaced GPT-4o with Gemini-2.0 Flash for Bot9, reducing our costs by about 20× with no visible loss in accuracy. This change was implemented on a highly complex support agent that makes 32 tool calls. I was seriously not expecting this. At the application layer, it also made us one of the top 10 apps built with Gemini worldwide — and the only one from India in the list. - Data source : Openrouter.

1/ After residency at Mass General Hospital, I reported to Atlanta to meet my fellow CDC Epidemic Intelligence Service Officers. I have never felt so intimidated by my peers The best and the brightest, they were star clinicians, had served in disaster zones; MD/PhDs and MSF.

We have to take the LLMs to school. When you open any textbook, you'll see three major types of information: 1. Background information / exposition. The meat of the textbook that explains concepts. As you attend over it, your brain is training on that data. This is equivalent to pretraining, where the model is reading the internet and accumulating background knowledge. 2. Worked problems with solutions. These are concrete examples of how an expert solves problems. They are demonstrations to be imitated. This is equivalent to supervised finetuning, where the model is finetuning on "ideal responses" for an Assistant, written by humans. 3. Practice problems. These are prompts to the student, usually without the solution, but always with the final answer. There are usually many, many of these at the end of each chapter. They are prompting the student to learn by trial & error - they have to try a bunch of stuff to get to the right answer. This is equivalent to reinforcement learning. We've subjected LLMs to a ton of 1 and 2, but 3 is a nascent, emerging frontier. When we're creating datasets for LLMs, it's no different from writing textbooks for them, with these 3 types of data. They have to read, and they have to practice.