Claude Opus 5.5 vs Muse Wonder

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.

Muse Wonder is a proprietary multimodal reasoning model from Meta, released on October 12, 2026 as the first step past the Muse Spark line on Meta's scaling ladder. It launched together with Muse Wonder Full, a larger configuration that is available only through Muse Code subscription plans, while Muse Wonder itself is served through the Meta Model API under the identifier muse-wonder-1.0. The model accepts text, images, video, audio, and PDF documents as input and returns text, with a context window of 1,048,576 tokens and graded reasoning effort settings. Meta trained both models at its Prometheus data center on larger base models than Muse Spark 1.3, and attributes their gains on unfamiliar tasks to post-training that transfers more effectively from a stronger base.

Meta positions Muse Wonder for long-running personal and professional agents: it plans multi-step work, delegates to sub-agents, calls tools in parallel, recovers from errors, and operates browsers and desktop or mobile applications. Two training objectives shape this behaviour. Active Memory carries goals, constraints, and earlier decisions across conversations and retrieves the details relevant to the current step, and Active Focus keeps the model trying alternative approaches when a task gets difficult while asking for clarification before important actions. Visual inputs such as screenshots, charts, and video feed the same reasoning loop, and the model emits bounding box coordinates for grounding queries. Meta's model card lists visual and spatial reasoning, especially 3D tasks, as a known limitation and recommends validating performance for applications that depend on precise spatial understanding.