When Meta tested Muse Wonder’s memory, its researchers changed facts partway through long conversations and asked models to recover answers. The test was published with Monday’s release of a new family of AI models designed to handle tasks across repeated conversations.
The company introduced Muse Wonder and Muse Wonder Full on October 12 for tasks that can require repeated conversations and many steps, including coding and handling documents. It calls their ability to retrieve relevant information across conversations Active Memory. Developers can add Wonder, currently offered in preview, to their own software or use it in Muse Code, Meta’s coding assistant. Wonder Full is available through selected Muse Code subscriptions.
On MemoryBench, Meta’s internal test, Wonder scored 85.6 and Wonder Full 85.5, compared with 81.5 for Anthropic’s Claude Opus 5.5 and 81.4 for OpenAI’s GPT-6 Astra. Those scores summarize judgments by another AI model using weighted grading rules. Meta’s description gives no question count or margin of uncertainty.
Each test conversation contains 64,000 to 100,000 tokens, the pieces of text a model processes. Researchers mix facts with unrelated conversation, introduce contradictions and revise details. Answering correctly requires tracking which information still applies. These are controlled conversations assembled for evaluation; the report does not describe a trial following customers over months of everyday use.
Research on AI memory has been examining that problem for several years. In LongMemEval, first published in October 2024, Di Wu and colleagues created 500 questions to test five abilities: extracting information, combining facts from separate sessions, understanding time, updating knowledge and declining to answer when evidence is missing.
Wu’s team used AI to draft conversations, then had people screen and edit them. Facts could emerge indirectly: a question about car insurance could reveal a recent car purchase. Researchers tracked where each fact appeared, giving them a record against which to check an assistant’s answer. Changing how conversations were divided and searched improved later answers. This older study did not test Wonder.
How Muse Code keeps project notes
Meta’s Muse Code documentation makes some of those choices visible. Its coding assistant keeps lasting project knowledge in ordinary text files. Meta suggests recording deployment procedures, unusual software behavior and “the reason a workaround exists.” Such notes give the assistant information a new session would otherwise lack.
At the start of a session, Muse Code loads an index and paths to as many as 48 other memory files. It reads individual files as needed. A background process can also supply a relevant note before the assistant responds. This arrangement lets developers preserve a short record of an earlier decision without feeding every past conversation into every new request.
Developers choose how widely to share those notes. Personal project memory is stored on their machine outside the shared code repository. Project memory travels with that repository to colleagues who copy it. A third option keeps personal notes across projects. These controls govern Muse Code; Meta’s consumer Muse service has its own application design.
Businesses building their own assistants also choose who retains conversation history. Meta’s Responses interface lets an application send earlier material with each request or ask Meta’s servers to store responses and reconnect them on later turns. The application supplies saved text when the model answers; adding a note does not require retraining the model.
Meta’s training-data policy distinguishes how customers pay. Wonder’s pay-as-you-go service uses Standard mode, which excludes customer content from model training. Muse Code subscriptions default to Contributor mode, which permits Meta to train future models on submitted prompts, code, files and responses. Subscribers can change that setting.
Meta bills developers for text they send to Wonder and text it returns. Its rates are $4 per million input tokens for Wonder and $20 per million output tokens. Reused input that qualifies for its prompt cache costs 20 cents per million tokens. The cache lets the service reuse processing of an earlier portion of a request, reducing the charge when an application sends that material again.
For people using Muse Code directly, Wonder Full is included in plans costing $50 or $200 a month, with different usage allowances. Meta says Wonder will reach its consumer Muse service soon, beginning with subscribers. Its October 12 announcement did not give a date for that rollout.
