Progress in model reasoning: chain of thought, reasoning models, and breakthroughs and controversies around math and logic benchmarks.
Anthropic released Claude Opus 5.5, and about an hour later OpenAI launched GPT-6 Sol and GPT-6 Luna.
Why it matters: The author hands-on tested models from all three providers and compiled a full price table, letting readers compare the new round of price cuts and real-world performance across tiers.
OpenAI has released GPT-6 Astra, positioned as its most powerful business-focused model, with advanced reasoning and computer use capabilities, plus stronger writing and design judgment.
Why it matters: The official launch of a business-oriented model highlights reasoning, computer operation, and writing/design judgment, letting readers assess where it fits in their workflows.
OpenAI shared an AI-generated solution approach to the Navier–Stokes Millennium Prize Problem, including a solution write-up and a Lean formalized proof. The material comes from an official OpenAI announcement; the feed does not include the full text, so specifics of the proof and methods should be checked via the original link.
Why it matters: OpenAI has publicly released an AI-generated approach to the Navier–Stokes Millennium Prize Problem with a Lean formalized proof, letting readers track AI's actual output in mathematical proof work.
IBM released the Granite 4.2 reasoning model family, comprising 3B, 8B and 30B dense decoder models built on the Granite 4.1 base (pretrained from scratch on about 15T tokens, with a five-stage strategy extending context to 512K), post-trained with SFT and multi-stage GRPO reinforcement learning, all open-sourced under the Apache 2.0 license.
Why it matters: The official disclosure covers the full training pipeline from pretraining and SFT to multi-stage GRPO and agentic RL, transferable to similar model-building workflows.
Thinking Machines has released the open-source multimodal model Inkling on Hugging Face, with about 1T total parameters (975B, a MoE with 41B active), natively accepting image, text, and audio inputs, supporting 1M context, trained on 45 trillion tokens, and shipping BF16 and NVFP4 weights.
Why it matters: The official blog provides architecture details, VRAM requirements for each tier, and deployment configurations, letting readers evaluate multimodal deployment costs and model selection.
Hugging Face published part three of its Profiling in PyTorch series, using the profiler on an NVIDIA A100 to compare traces of six attention implementations: naive attention, in-place masking, and SDPA math/efficient/flash/cuDNN.
Why it matters: The original benchmarks kernel-level differences across six attention implementations with the profiler, teaching readers to read from traces how in-place saves a copy and what each SDPA backend really costs.
Mistral released the Apache-2.0 open-source model Leanstral 1.5, with 119B total parameters and 6B activated parameters, trained via mid-training, supervised fine-tuning and CISPO reinforcement learning.
Why it matters: The official post discloses the full training pipeline, benchmark figures and real code verification cases, letting readers gauge how practical formal verification models are for mathematical proofs and open-source bug finding.
Google DeepMind released DiffusionGemma, an open-source experimental model that generates whole blocks of text via a text diffusion approach, achieving up to 4x inference speedups on dedicated GPUs (over 1,000 tokens/second on an NVIDIA H100 and over 700 tokens/second on an RTX 5090).
Why it matters: The official release specifies the hardware behind the 4x speedup and output quality trade-offs, letting readers judge whether it suits local low-concurrency workloads.
Mistral AI released Mistral Small 4, the first small model to unify Magistral reasoning, Pixtral multimodal and Devstral agentic coding capabilities into a single model, open sourced under the Apache 2.0 license.
Why it matters: The official release provides full architecture parameters, benchmark comparisons and deployment requirements, letting readers assess whether it can replace a multi-model stack.
Mistral has released its new Mistral 3 model series, including the MoE-architecture Mistral Large 3 (41B active, 675B total parameters) and Ministral 3 models in 3B, 8B, and 14B sizes, all open sourced under Apache 2.0.
Why it matters: The official announcement fully lists model specs, open source licenses, deployment options, and available platforms, so readers can assess feasibility of running them in their own environments.
Mistral AI released its first reasoning model, Magistral, in two versions: the open-source Magistral Small (24B, Apache 2.0) and the enterprise Magistral Medium.
Why it matters: With both open-source and commercial versions, official AIME2024 scores, open licensing and deployment channels, readers can use this to evaluate model selection.