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Model: "minimax-m2.5"
not much happened today
claude-4.6 claude-opus-4.6 claude-sonnet-4.6 qwen-3.5 qwen3.5-397b-a17b glm-5 gemini-3.1-pro minimax-m2.5 anthropic alibaba scaling01 arena artificial-analysis benchmarking token-efficiency ai-agent-autonomy reinforcement-learning asynchronous-learning model-performance open-weights reasoning software-engineering agentic-engineering eshear theo omarsar0 grad62304977 scaling01
Anthropic released Claude Opus/Sonnet 4.6, showing a significant intelligence index jump but with increased token usage and cost. Anthropic also shared insights on AI agent autonomy, highlighting human-in-the-loop prevalence and software engineering tool calls. Alibaba launched Qwen 3.5 with discussions on reasoning efficiency and token bloat, plus open-sourced Qwen3.5-397B-A17B FP8 weights. The GLM-5 technical report introduced asynchronous agent reinforcement learning and compute-efficient techniques. Rumors about Gemini 3.1 Pro suggest longer reasoning capabilities, while MiniMax M2.5 appeared on community leaderboards. The community debates benchmark reliability and model performance nuances.
MiniMax-M2.5: SOTA coding, search, toolcalls, $1/hour
minimax-m2.5 glm-5 minimax-ai togethercompute huggingface intel wandb reinforcement-learning agent-based-models model-quantization benchmarking model-efficiency multi-turn-dialogue infrastructure-optimization cost-efficiency on-device-ai
MiniMax-M2.5 is now open source, featuring an "agent-native" reinforcement learning framework called Forge trained across 200k+ RL environments for coding, tool use, and workflows. It boasts strong benchmark scores like 80.2% SWE-Bench Verified and emphasizes cost-efficiency with claims like "$1 per hour at 100 tps" and good on-device performance. The Forge RL system uses multi-level prefix caching and high rollout compute share (~60%) to generate millions of trajectories daily. Independent reviews note improved stability and multi-turn viability but high token usage. The ecosystem rapidly adopted MiniMax-M2.5 with quantized releases including 2-bit GGUF and INT4 formats. Meanwhile, Together markets GLM-5 as a leading open-source model for long-horizon agents with 77.8% SWE-Bench Verified and MoE efficiency using DeepSeek Sparse Attention.