Chainfeeds Summary:
The report systematically reviews the key advancements of large models in terms of inference capabilities, architectural roadmap, open-source ecosystem, and engineering implementation.
Article source:
https://x.com/rasbt/status/2006015301717028989
Article Author:
Sebastian Raschka
Opinion:
Sebastian Raschka: Here are some of the most noteworthy "surprises" of 2025 that I believe will happen: Several inference models have already achieved gold-level performance in major math competitions (including an unnamed model from OpenAI, Gemini Deep Think, and DeepSeekMath-V2 from open-source weights). I'm not surprised this was going to happen, but I am surprised it happened in 2025, not 2026 as I had originally expected. Llama 4 (or the Llama family as a whole) has almost fallen out of favor in the open-source weights community, while Qwen has surpassed Llama in popularity (measured by downloads and the number of derivative models, data from Nathan Lambert's ATOM project). Mistral AI has adopted the DeepSeek V3 architecture in its latest flagship model, Mistral 3, which will be released in December 2025. In addition to Qwen3 and DeepSeek R1/V3.2, there is a significant increase in open-source state-of-the-art competitors, including Kimi, GLM, MiniMax, and Yi. Cheaper and more efficient hybrid architectures have become a core priority for leading labs (such as Qwen3-Next, Kimi Linear, and Nemotron 3), no longer just being explored by peripheral labs. OpenAI released an open-source weight model (gpt-oss), which I wrote about separately earlier this year. MCP (joined the Linux Foundation) has rapidly become the de facto standard for tool and data access in agent-based LLM systems. I originally thought this ecosystem would remain fragmented until at least 2026. A few predictions for 2026: We are likely to see a consumer-facing, industry-wide diffusion of models for low-cost, reliable, and low-latency inference, with Gemini Diffusion potentially leading the way. The open-source weight community will gradually adopt more agent-based LLMs with native tool invocation capabilities. RLVR will expand from mathematics and programming to more fields, such as chemistry and biology. Traditional RAG will gradually cease to be the default solution for document retrieval. Developers will rely more on stronger long-context capabilities, especially as stronger small models mature. Significant performance and benchmark improvements will come from toolchain optimizations and inference-time scaling, rather than from the training process or the model itself. Model advancements will appear more like a victory for systems engineering than a breakthrough in a single architectural point. [Original text in English]
Content source 
