
Kimi K2.6
Moonshot AI's Kimi K2.6 is a 1T-parameter MoE with 32B active per token, 256K context, a 300-agent swarm running 4,000 coordinated steps, and the top SWE-Bench Pro score among open-weight models at 58.6%.
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Moonshot AI's Kimi K2.6 is a 1T-parameter MoE with 32B active per token, 256K context, a 300-agent swarm running 4,000 coordinated steps, and the top SWE-Bench Pro score among open-weight models at 58.6%.

Moonshot AI releases Kimi K2.6 under Modified MIT with open weights on HuggingFace, 300-agent swarm execution, and the highest SWE-Bench Pro score among open models.

Three new papers challenge assumptions in MoE routing design, prompt optimization workflows, and LLM reasoning chains - all published this week on arXiv.

Arcee Trinity-Large-Thinking is a 400B sparse MoE open-source reasoning model that ranks #2 on PinchBench at $0.85/M output tokens, 28x cheaper than Claude Opus 4.6.

Alibaba's 35B sparse MoE with 3B active parameters delivers 73.4% SWE-bench Verified, multimodal vision and video, 256K context, and DeltaNet hybrid architecture under Apache 2.0.

Alibaba's Qwen 3.6-35B-A3B activates only 3B of its 35B parameters per token, scores 73.4% on SWE-bench Verified, handles video and images, and ships under Apache 2.0.

Three papers this week challenge how we think about MoE expert routing, LLM context management, and the limits of activation steering.

Arcee AI ships Trinity-Large-Thinking, a 398B sparse MoE reasoning model under Apache 2.0 that hits 91.9% on PinchBench for $0.85 per million output tokens on OpenRouter.

Gemma 4 is Google DeepMind's most capable open model family: four variants from 2B to 31B, Apache 2.0 license, multimodal across text/image/video/audio, and the 31B Dense ranking #3 on Chatbot Arena against all open-weight models globally.

Google releases Gemma 4 with a 26B MoE, 31B Dense, and two edge variants under Apache 2.0 - claiming the highest intelligence-per-parameter of any open model.

NVIDIA Nemotron 3 Super is the strongest open-weight model for agentic coding as of March 2026, but its efficiency-first design means real trade-offs on general knowledge and chat quality.

Moonshot AI's Kimi K2.5 delivers best-in-class open-weight math and a genuinely novel multi-agent architecture, but a brutal hallucination rate and slow inference limit its real-world reliability.