Muse Wonder vs GPT-6 Astra
At 6,000 chat requests per month (1,500 in / 400 out), Muse Wonder costs $84 and GPT-6 Astra costs $210 — roughly 2.5× the spend.
| Spec | Muse Wonder | GPT-6 Astra |
|---|---|---|
| Vendor | Meta | OpenAI |
| Input / 1M tokens | $4 | $10 |
| Output / 1M tokens | $20 | $50 |
| Cached input / 1M | $0.200 | $1 |
| Context window | 1M tokens | 1.1M tokens |
| Max output | — | 128K tokens |
| Input modalities | text | text, image |
| Released | — | — |
| Cost per 1M cached reads | $0.200 | $1 |
Published list prices, last verified Oct 15, 2026. Source links are on each model's page.
Cost at three workloads
| Workload | Muse Wonder | GPT-6 Astra | Difference |
|---|---|---|---|
| Customer-support reply (1,500 in / 400 out) | $0.014 | $0.035 | Muse Wonder cheaper by $0.021 |
| Document summary (20,000 in / 1,000 out) | $0.100 | $0.250 | Muse Wonder cheaper by $0.150 |
| Code review pass (8,000 in / 1,500 out) | $0.062 | $0.155 | Muse Wonder cheaper by $0.093 |
Which one fits your workload
- Long context: GPT-6 Astra accepts more — 1.1M versus 1M tokens. If your retrieved context regularly exceeds 1M, only one of these will hold it.
- Long answers: Muse Wonder has the lower output price ($20 vs $50 per million), which matters most when answers are long.
- High-volume, short answers: Muse Wonder is cheaper on input ($4 vs $10 per million).
- Caching: at least one of these vendors publishes a discounted cached-input rate; if you resend the same system prompt and context, model the cache-hit rate or you will overestimate cost.
This comparison uses published price and specification data only. It does not rank output quality — run your own evaluation on your own prompts before switching a production default.