Claude Sonnet 5.5 vs Muse Wonder

Claude Sonnet 5.5 is a proprietary multimodal language model from Anthropic and the second release in the Claude 5.5 family, following Claude Opus 5.5. It accepts interleaved text and image input and returns text, operating with a 1M token context window and a maximum output of 128K tokens per request. The model uses adaptive thinking by default, allocating variable reasoning effort per request rather than exposing a manual extended thinking toggle, and its training data cutoff is June 2026. Anthropic positions it as a faster, lower cost complement to Opus 5.5 for well scoped everyday tasks, bug fixing, and producing documents, slides, and spreadsheets.

On visual and agentic evaluations reported at launch, Sonnet 5.5 scores 61.6% on Chartography, a chart recognition test, compared with 15.6% for Claude Sonnet 5, and 80.1% on OSWorld 2.1, a computer use benchmark measuring screenshot driven control of a desktop environment, compared with 57.0% for Sonnet 5. It reports 70.6% on Terminal-Bench 4.0 for agentic coding. Anthropic describes it as the first Sonnet model able to complete Pokemon Red from screenshots alone, and it generates output more than 30% faster than Sonnet 5 while using fewer tokens for equivalent work.

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.