All tags
Model: "sonnet-4.6"
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.
MiniMax 2.7: GLM-5 at 1/3 cost SOTA Open Model
minimax-m2.7 sonnet-4.6 glm-5 mimo-v2-pro mamba-3 qwen-3.5 kimi-k2.5 gpt-5.4-mini minimax xiaomi artificial-analysis ollama trae yupp openrouter vercel zo opencode kilocode cartesia self-evolving-agents reasoning cost-efficiency token-efficiency hybrid-architecture harness-engineering agent-harnesses skills memory-optimization architecture feedback-loops api inference execution-environment
MiniMax M2.7 is the headline model release, described as a "self-evolving agent" with strong performance metrics including 56.22% on SWE-Pro, 57.0% on Terminal Bench 2, and parity with Sonnet 4.6. It features recursive self-improvement in skills, memory, and architecture. Artificial Analysis places M2.7 on the cost/performance frontier with an Intelligence Index score of 50, matching GLM-5 (Reasoning) but at a fraction of the cost. Distribution is available via platforms like Ollama cloud and OpenRouter. Xiaomi’s MiMo-V2-Pro is noted as a serious Chinese API-only reasoning model with a score of 49 on the Intelligence Index and favorable token efficiency. Cartesia’s Mamba-3 is highlighted as an SSM optimized for inference-heavy use, with early reactions focusing on hybrid transformer architectures like Qwen3.5 and Kimi Linear. The report emphasizes a shift from prompting to harness engineering, where the execution environment and agent harnesses, including skills and MCP, are becoming key differentiators in AI system design. This includes discussions on tools, repo legibility, constraints, and feedback loops, with mentions of DSPy and GPT-5.4 mini as important components in this evolving landscape.
not much happened today
gemini-3.1-pro gpt-5.2 opus-4.6 sonnet-4.6 claude-opus-4.6 google-deepmind anthropic context-arena artificial-analysis epoch-ai scaling01 retrieval benchmarking evaluation-methodology token-limits cost-efficiency instruction-following software-reasoning model-reliability dillonuzar artificialanlys yuchenj_uw theo minimax_ai epochairesearch paul_cal scaling01 metr_evals idavidrein xlr8harder htihle arena
Gemini 3.1 Pro demonstrates strong retrieval capabilities and cost efficiency compared to GPT-5.2 and Opus 4.6, though users report tooling and UI issues. The SWE-bench Verified evaluation methodology is under scrutiny for consistency, with updates bringing results closer to developer claims. Benchmarking debates arise over what frontier models truly measure, especially with ARC-AGI puzzles. Claude Opus 4.6 shows a noisy but notable 14.5-hour time horizon on software tasks, with token limits causing practical failures. Sonnet 4.6 improves significantly in code and instruction-following benchmarks, but user backlash grows due to product regressions.