LongCat-2.5-PreviewvsMuse Wonder
Both language models, compared at each maker’s own API list prices. Every figure links to its source.
Compare at your usage
Every model priced for the same request8,000 tokens in, 2,000 out
| LongCat-2.5-Preview | Muse Wonder on Meta AI API | |
|---|---|---|
| Rate applied | Not offered: no current API price | Base rate |
| Inputper 1M tokens | — | $4.00 |
| Cache readper 1M tokens | — | $0.20 |
| Outputper 1M tokens | — | $20.00 |
| Cost per request | — | $0.072 |
| Per 1,000 requests | — | $72.00 |
- Muse Wonder Meta Model API pricing and rate limits · as of Nov 1, 2026
Every model is priced for the same request at its maker’s own list prices (each with its source and date), so bold marks the cheapest. A model that can’t serve the request is named, not ranked; a condition its seller doesn’t price says which rate was used.
Key differences
These differences use verified vendor specifications or each maker’s official API on the same region, currency, meter and billing conditions. Different conditions remain side by side in the full table; they do not establish which model is cheaper. Declared features describe support, not measured quality or a recommendation. Missing evidence does not mean a feature is unsupported.
Full comparison
| LongCat-2.5-Preview Meituan | Muse Wonder Meta | |
|---|---|---|
| Overview | ||
| Released | Sep 25, 2026 | Oct 12, 2026 |
| Specs | ||
| Context | 1M | 1.05M |
| Max output | 128K | 131.07K |
| Input modalities | Text, Image | Text, Image, Audio, Video, PDF |
| Output modalities | Text | Text |
| Reasoning | ||
| Tool use | ||
| Structured output | Not stated | |
| Pricing · per 1M tokens | ||
| Input price | — | $4.00 |
| Output price | — | $20.00 |
| Cache read price | — | $0.20 |
| Blended price (3 input : 1 output) | — | $8.00 |
| Cited scores | ||
| Epoch Capabilities Index | — | 162 |
| LMArena Text | — | 1,507 max effort |
| LMArena Text · Coding | — | 1,545 max effort |
| LMArena Text · Chinese | — | 1,533 max effort |
| Epoch AI · FrontierMath Tiers 1–3 | — | 82.1% max effort |
Headline prices may come from different channels, regions or tiers. Check each source before choosing a model; missing facts stay blank.
Model details and sources
- LongCat-2.5-Preview → Preview
- Muse Wonder → Available
Cited scores
- Epoch AI, 'Capabilities & benchmarking'. Published online at epoch.ai. Retrieved from 'https://epoch.ai/benchmarks'. Licensed CC BY 4.0. · Epoch AI Benchmarking Hub data · CC BY 4.0 · as of Oct 28, 2026
- Epoch AI, 'Capabilities & benchmarking'. Published online at epoch.ai. Retrieved from 'https://epoch.ai/benchmarks'. Licensed CC BY 4.0. · Epoch AI Benchmarking Hub data · CC BY 4.0 · as of Nov 5, 2026
- Epoch AI, 'Capabilities & benchmarking'. Published online at epoch.ai. Retrieved from 'https://epoch.ai/benchmarks'. Licensed CC BY 4.0. · Epoch AI Benchmarking Hub data · CC BY 4.0 · as of Nov 4, 2026
- EQ-Bench by Samuel J. Paech (eqbench.com; EQ-bench/EQ-bench-site on GitHub), MIT License. · EQ-Bench: 4 · MIT · as of Oct 24, 2026
- EQ-Bench by Samuel J. Paech (eqbench.com; EQ-bench/EQ-bench-site on GitHub), MIT License. · EQ-Bench: Creative Writing v3 · MIT · as of Oct 28, 2026
- LiveBench (White et al.), release 2026-06-25, livebench.ai, licensed CC BY-SA 4.0. This board, adapted from it, is shared under CC BY-SA 4.0. · LiveBench release 2026-06-25 · CC BY-SA 4.0 · as of Jun 25, 2026
- LMArena (Arena), leaderboard-dataset on Hugging Face, licensed CC BY 4.0 · LMArena leaderboard dataset (Hugging Face) · CC BY 4.0 · as of Oct 28, 2026