AI Coding Live News

ggml-org released llama.cpp v0.6.0 on Oct 5 (v0.5.0 was Sep 23). It brings GLM-5.3-Flash (GLM5-Next, a 320B text+vision hybrid MoE) into a stable tag, adds MTP speculative decoding for Qwen4Exp (~1.5x decode on DGX Spark), introduces the llama_batch_ext / llama_process() API for mixed token and embedding batches, adds few-row MMA mat-mul kernels on Metal (up to ~3x for speculative and batched decoding), and bumps ggml to v0.26.0. Session file formats are bumped too.

Alibaba Qwen released open-weight Qwen3.8-Flash, a multimodal Mixture-of-Experts model billed as an early preview of the Qwen4 architecture. The production-scale checkpoint is 125B parameters plus 51B N-gram embeddings, with only 6B activated per token. The new stack combines Gated DeltaNet plus Qwen Sparse Attention, Gated Residual branches, N-gram embeddings that can live in host memory, and a refined Muon optimizer. Qwen said it trained at about one-ninth the cost of Qwen3.7-Plus while beating it across the board, especially on coding and office work: 58.7 on DeepSWE 1.1, 62.5 on SWE-bench Pro, 73.9 on CoWorkBench, 84.5 on AndroidWorld, and 95.7 on MathVision with CI. Native context is 262K tokens, extendable to 1M with YaRN; at 1M context the QSA kernel is up to 7.6x faster in prefill and 4.9x faster in decode. QwenCloud lists roughly $0.15 input / $0.47 output per million tokens, and day-0 runtimes arrived from NVIDIA NeMo, SGLang, Unsloth, OpenRouter, and OpenCode Go.

Alibaba Qwen released Qwen3.8-Flash, an open-weights 125B MoE model (6B active) previewing the Qwen4 architecture with 1/9 training cost, 262K native context, and ultra-cheap API pricing.

Alibaba Qwen confirmed that the 125B multimodal MoE Qwen3.8-Flash-Next can now run locally on about 75GB of RAM via Unsloth GGUFs, with N-gram embeddings offloaded to CPU or unified memory for near-VRAM speed. It is the day-0 local path for the Qwen4 architecture preview weights.

Alibaba Qwen open-sourced Qwen3.8-Flash, a multimodal MoE with 125B parameters plus 51B n-gram embeddings and only 6B active per token. It scores 58.7 on DeepSWE 1.1 and 62.5 on SWE-bench Pro, was trained at 1/9 the cost of Qwen3.7-Plus, and ships Qwen3.8-Flash-Next as an early look at the Qwen4 architecture.

Alibaba Qwen open-sourced Qwen3.8-Flash, a 125B-parameter multimodal MoE activating only 6B parameters per token. Delivering 62.5 on SWE-bench Pro and 58.7 on DeepSWE 1.1 at 1/9 the training cost of Qwen3.7-Plus, it previews the Qwen4 architecture with 262K native context (1M with YaRN) and low API pricing ($0.16 / $0.47 per 1M tokens).

Alibaba released Qwen3.8-Flash, a multimodal MoE previewing the Qwen4 architecture. Featuring 125B total params with 6B active per token, it scores 62.5 on SWE-bench Pro and 58.7 on DeepSWE 1.1. Trained at 1/9 the cost of Qwen3.7-Plus, it lists on QwenCloud at ~$0.16 in / $0.47 out per 1M tokens.