Everyone's comparing benchmark scores. Here's what the model card and independent measurements say you should plan for before wiring it into production — five gotchas. Meta launched two models: Muse Wonder and Muse Wonder Full. Only Wonder ...
Everyone's comparing benchmark scores. Here's what the model card and independent measurements say you should plan for before wiring it into production — five gotchas.
1. The best model isn't on the API
Meta launched two models: Muse Wonder and Muse Wonder Full. Only Wonder is on the API (muse-wonder-1.0).
Wonder Full — the one that actually beats competitors on benchmarks — is subscription-only through paid Muse Code plans. If you're architecting around the benchmark numbers you saw in headlines, check which model they belong to. Most of the impressive ones are Full.
2. Meta's cheap era is officially over
Wonder costs $4/1M input, $20/1M output — identical to Claude Opus 5.5, cent for cent. Cached input is $0.20/1M on both.
If you picked Meta historically for price, recalibrate. The decision is now purely about capability fit, not cost advantage.
3. Design for cached input or bleed money
At $0.20/1M, cached input is 100x cheaper than fresh input. With a 1M-token context window, the winning architecture is obvious: keep your system prompts, docs, and conversation history in cache.
Anyone porting an Opus pipeline over can keep the same caching strategy — the pricing structure mirrors Anthropic's deliberately.
4. Time to first token is brutal
Artificial Analysis measured Wonder's time-to-first-token at 19.27 seconds vs a 5.52-second median for comparable models. Throughput after that is fine (~83 tokens/sec), but if you're building anything interactive — chat, agents with user-facing steps — that first-token latency will define your UX.
This is a batch/async workload model wearing an API costume. Plan accordingly: streaming UX, optimistic UI, or background jobs.
5. Web search costs extra — and adds up
API web search is billed at $2.50 per 1,000 queries, on top of tokens. If your agent searches heavily (research agents, shopping assistants), model that cost separately. At high search volume it can exceed the token bill.
Bonus: know the knowledge gap
On general-knowledge evals, Wonder scores notably below Opus 5.5 and GPT-6 Astra — but with a low hallucination rate. Translation: it says "I don't know" instead of inventing answers. For RAG pipelines where the context carries the facts, that's actually a feature. For open-domain Q&A without retrieval, it's a liability.
TL;DR: Same price as Opus 5.5, best version not on API, ~19s first-token latency, cache aggressively, budget web search separately.
Sources: Meta model card, TechCrunch, Artificial Analysis, VentureBeat. Prices as of Oct 13, 2026.