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Model: "mai-thinking-1"
not much happened today
grok-4.6 grok-4.7 qwen3.8-max deepseek-v4-pro mai-thinking-1 solar-pro-4 xai alibaba deepseek microsoft upstage agentic-ai intelligence-index model-training open-weights long-context reasoning pricing reinforcement-learning tool-use pawelhuryn kimmonismus mustafasuleyman elonmusk yuchenjin finbarrtimbers
xAI's Grok 4.6 advances frontier pricing and performance, scoring 61 on the Intelligence Index and showing strong agentic results, with Grok 4.7 already in training. Alibaba's Qwen3.8-Max open weights release features a 2.4T parameter model with 95B active MoE, notable for day-0 serving and long-context capabilities but initially text-only. DeepSeek V4 Pro GA offers significant cost advantages, priced at $0.435/M input tokens, with mixed capability reviews. Microsoft's MAI-Thinking-1 debuts as a practical reasoning model focused on tool use, available in Foundry. Upstage's Solar Pro 4 improved its Intelligence Index ranking from 14 to 42.
Microsoft Build: MAI-Thinking-1 and MAI Family models, Surface RTX Spark Dev Box, and OpenClaw in Windows
mai-thinking-1 mai-code-1-flash holo-3.1 qwen-35b sonnet-4.6 claude-code codex microsoft openrouter fal baseten hcompany_ai teksedge nous-research teknim cognition windsurf perplexity-ai mixture-of-experts context-windows benchmarking reinforcement-learning prompt-optimization agentic-ai local-inference model-family-expansion model-reporting agent-native-devices software-development model-optimization hybrid-inference desktop-agents model-quantization mustafasuleyman eliebakouch hannahajishirzi asadovsky bj2rn lateinteraction lakshyaaagrawal theturingpost kimmonismus yusuf_i_mehdi pierceboggan lukehoban nielsrogge russelljkaplan
Microsoft introduced MAI-Thinking-1, a 35B parameter MoE model with 256K context, achieving 97% on AIME 2025 and outperforming Sonnet 4.6 in human preference tests. The broader 7-model MAI family spans reasoning, code, image, speech, and voice, with third-party availability on OpenRouter, fal, and Baseten. The detailed 109-page technical report revealed insights on scaling, MFU, RL/post-training, and data curation, highlighting no third-party distillation and advanced prompt optimization techniques. Microsoft emphasized agent-native devices and local inference with projects like Project Solara / Scout and the Surface RTX Spark Dev Box, alongside software innovations such as the Copilot desktop app and MAI-Code-1-Flash integration. Meanwhile, local-first computer-use agents like Holo 3.1 (Qwen-based, 0.8B to 35B parameters) support laptops and small workstations with optimized formats and strong benchmark results. Desktop shells for agents, including Hermes Desktop, Devin Desktop, and agent-neutral approaches compatible with Devin, Claude Code, and Codex, are proliferating, with hybrid local/cloud execution becoming the default architecture as seen in Perplexity Computer's hybrid agentic inference.
not much happened today
mai-thinking-1 mai-image-2.5 mai-code-1-flash gemma-4-12b microsoft google vllm-project ollama llama-cpp model-training reinforcement-learning model-architecture multimodality model-deployment model-efficiency fine-tuning on-device-ai eliebakouch nrehiew_ mustafasuleyman minjiyoon90 lateinteraction harold_matmul googlegemma googleaidevs mtschannen armandjoulin osanseviero
Microsoft released the detailed technical report for MAI-Thinking-1, a generalist reasoning model trained without third-party distillation, achieving 97% on AIME 2025 and outperforming Sonnet 4.6 in human preference tests. The report was praised for transparency, revealing no synthetic data use, a unique scaling ladder recipe, and detailed training data composition including 50% code and 17.5% STEM. Microsoft also introduced Frontier Tuning for workflow-specific model adaptation, claiming efficiency gains up to 10× and GPT-5.4-level quality in Excel tasks, alongside new models like MAI-Image-2.5 and MAI-Code-1-Flash. Meanwhile, Google launched Gemma 4 12B, an Apache 2.0 multimodal model with an innovative encoder-free architecture designed for on-device use with 16GB VRAM, collapsing vision and audio encoders into the LLM backbone, receiving positive community feedback and immediate tooling support.