Claude Opus 5.5 vs Qwen3.8 Flash

Claude Opus 5.5 is a proprietary multimodal reasoning model from Anthropic and the first entry in the Claude 5.5 family. It accepts interleaved text and image input and returns text, with a one million token context window and up to 128,000 output tokens per response. Adaptive thinking is always enabled on this model and cannot be disabled; thinking depth is instead governed by an effort parameter with five levels, where medium is the default, a change from the high default used by Claude Opus 5 and earlier Opus models. Anthropic reports a knowledge cutoff of June 2026.

On the visual side, Anthropic characterizes Opus 5.5 as its strongest Opus release for vision and computer use, describing improved reading of dense documents, charts, screenshots, and diagrams for document extraction and visual analysis tasks. Published results include 89.0% on Chartography with tools and 81.8% on OSWorld 2.0 under partial credit scoring, alongside 48.7% under strict scoring reported in the system card. The accompanying system card states that Opus 5.5 scored higher than Opus 5 on every evaluation in its capability summary, with the largest gains concentrated in agentic coding, visual reasoning, computer use, and long-horizon knowledge work. The model ships with safety classifiers covering biology and cybersecurity that can route blocked requests to earlier Claude models.

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.