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An Alibaba Group office building with the company's orange logo sign above the entrance and cars parked outside.
AI·

Alibaba's Qwen3.8-Max beats Fable 5 on Terminal-Bench, and the weights go public next week

Qwen3.8-Max is a 2.4-trillion-parameter MoE that tops Claude Fable 5 on Terminal-Bench 2.1 and trails it badly on SWE-bench Pro. It's the first open Max-tier Qwen.

The Hugging Face model page card for deepseek-ai/DeepSeek-V4-Flash-0731, showing the DeepSeek whale logo and the repository name
AI·

DeepSeek's new 304B agentic model now runs on a single 128GB workstation

Salvatore Sanfilippo repacked DeepSeek V4 Flash into a lossless MXFP4 GGUF that streams from SSD at over 20 tokens a second. The hardware bill, and where hosted still wins.

Two pelicans face each other with crossed beaks on dark water, mirrored in the surface, a nod to the informal 'pelican on a bicycle' LLM benchmark
AI·

Kimi K3 trades blows with Anthropic's Fable, and Moonshot is opening the weights

Kimi K3, GLM 5.2 and DeepSeek V4 put open-weight AI next to the frontier this month. What each model is good at, and why the benchmarks mislead.

Benchmark comparison card for GLM-5.2 showing it as the leading open weights model
AI·

GLM-5.2 was trained on Huawei chips, not Nvidia. The open weights beat GPT-5.5 on coding.

Zhipu AI's GLM-5.2 is a free-to-download model trained without Nvidia silicon. Here's what the benchmarks claim and why developers should care.

A MacBook Pro beside a Surface Book, both open on a white surface, USB-C ports in view
AI·

Running a coding agent fully on Apple Silicon, no cloud, is now an off-the-shelf stack

A popular Hacker News how-to walked through a fully local coding agent on Apple Silicon. Here's the realistic 2026 stack: runner, model, and harness.

Arcee AI Trinity branding from the Trinity-Large-Thinking blog post.
Open Source·

Arcee's Trinity-Large-Thinking is a 399B open MoE that costs 96% less than Opus

Arcee released Trinity-Large-Thinking on April 1: a 399B-param sparse MoE with 13B active, Apache 2.0 weights, $0.88 per million output tokens, and PinchBench just behind Opus 4.6.

DeepSeek social card from the V4 API documentation release post.
AI·

DeepSeek V4 lands: 1.6T-param open MoE, 1M-token context, and SWE-bench within 0.2 of Opus 4.6

DeepSeek shipped V4-Pro and V4-Flash under MIT on April 24. V4-Pro hits 80.6% on SWE-bench Verified. V4-Flash is $0.14 in / $0.28 out.

Header card from Simon Willison's 'Qwen3.6 beats Opus' post comparing pelican SVGs
AI·

Qwen 3.6-35B-A3B: the open MoE beating Opus 4.7 on Simon Willison's laptop

Alibaba's Qwen 3.6-35B-A3B is a 35B-param mixture-of-experts with only 3B active. Apache 2.0, runs on consumer GPUs, and it's already winning real tasks.