AI Coding Live News

Alibaba Qwen upgraded Qwen3.8-Max to Qwen3.8-Max-0902: about 2.4T parameters, 1M context, and further post-training on Coding and Cowork for complex enterprise, research, and long-horizon workflows. QwenCloud API pricing is $2 input / $6 output per 1M tokens, with explicit/implicit cache hits at $0.17 / $0.25.

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 announced that the production Qwen3.8-Flash API is live on QwenCloud at $0.16 input and $0.47 output per 1M tokens. The multimodal MoE activates only 6B parameters per token and is positioned as cheaper and stronger than Qwen3.7-Plus on coding and office workloads.

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.