Claude Opus 4 vs Qwen3.8 Flash

Claude 4 Opus, released by Anthropic in May 2025, is the flagship model of the Claude 4 family, built for complex, long-horizon reasoning and advanced coding workflows. It is multimodal, supporting text (including voice), images, and tool use, and operates as a hybrid reasoning model—able to deliver quick answers in fast mode or switch to extended thinking for deeper, multi-step problem solving. With a ~200,000-token context window and a training cutoff around March 2025, it is optimized for handling large documents, long conversations, and sophisticated agentic tasks.

Positioned at the high end of Anthropic’s offerings, Opus 4 achieves state-of-the-art results on coding benchmarks like SWE-Bench (72.5%) and Terminal-Bench (43.2%). It is best suited for research, enterprise automation, and software development at scale. The model is classified at Anthropic’s ASL-3 safety level, denoting advanced oversight and safety features.

Qwen3.8-Flash is a multimodal mixture-of-experts model from the Qwen team at Alibaba, and the production counterpart of the open-weight Qwen3.8-Flash-Next preview that introduces the architecture intended for the Qwen4 family. The main model carries 125 billion parameters alongside a separate 51 billion parameter N-gram embedding table, while activating roughly 6 billion parameters per token. It accepts interleaved image and text input and returns text, handling 262,144 tokens of context natively with extension to 1,000,000 tokens using YaRN. The production configuration runs with the 1M context window by default and adds built-in tool support.

Four architectural changes separate it from earlier Qwen releases: hybrid attention that pairs Gated DeltaNet for history compression with Qwen Sparse Attention, which uses a lightweight indexer to select micro-blocks of context; a Gated Residual scheme; N-gram embeddings; and training with the Muon optimizer, refined around orthogonalization accuracy and the division of parameters between Muon and AdamW. Qwen reports training cost around one ninth that of Qwen3.7-Plus, with QSA attention kernels measured up to 7.6 times faster in prefill and 4.9 times faster in decode at 1M-token context. Reported scores include 58.7 on DeepSWE 1.1, 62.5 on SWE-bench Pro, 84.5 on AndroidWorld and 95.7 on MathVision.