Compare models
Put up to 4 models beside each other — token prices, context windows, capabilities and provider, from the same catalogue the model pages read.
| Attribute | GPT Image 2.5 Flaregpt-image-2.5-flare | Claude Fable 5.2claude-fable-5.2 | Muse Wondermuse-wonder |
|---|---|---|---|
| Pricing | |||
| Input | $5.00 / 1M | $8.00 / 1M | $4.00 / 1M |
| Output | $30.00 / 1M | $40.00 / 1M | $20.00 / 1M |
| Cache Write (5m) | Not applicable | $10.00 / 1M | $4.00 / 1M |
| Cache Write (1h) | Not applicable | $16.00 / 1M | $4.00 / 1M |
| Cache Read | Not applicable | $0.20 / 1M | $4.00 / 1M |
| Web Search | — | $0 / 1M | $0 / 1M |
| Context | |||
| Max context | N/A | 1M | 1M |
| Max output | N/A | N/A | N/A |
| Capabilities | |||
| Vision | Yes | Yes | Yes |
| Function Calling | No | Yes | Yes |
| JSON Mode | No | Yes | Yes |
| Streaming | No | Yes | Yes |
| Catalogue | |||
| Provider | OpenAI | Anthropic | Meta |
| Category | image | chat | chat |
| Charge type | Pay As You Go | Pay As You Go | Pay As You Go |
| Released | — | — | — |
| Description | |||
| Summary | OpenAI GPT Image 2.5 Flare. Billed by tokens at OpenAI's price: text input US$5, image input US$8 and image output US$30 per 1M tokens. | Claude Fable 5.2 is Anthropic's most capable model for advanced technical work and open-ended problem solving, delivering significant improvements in coding, reasoning, and vision over Fable 5.1. It excels at exceptionally complex tasks, including large-scale software migrations, electronic system design, and advanced scientific and mathematical research. With up to 30% faster output generation, clearer communication, and always-on adaptive extended thinking, Fable 5.2 supports configurable reasoning effort from low to max (default high), with thinking preserved across turns. Designed for frontier engineering, scientific discovery, and ambitious long-horizon agentic workflows, it complements Opus 5.5 as a specialized option for the most demanding technical challenges. | Muse Wonder is Meta’s frontier multimodal reasoning model, designed for complex personal and professional agentic workflows. It supports text, image, video, audio, and PDF inputs with text output and features a 1M-token context window for sustained reasoning across long-running tasks. Optimized for autonomous execution and multi-agent orchestration, Muse Wonder can plan, delegate to subagents, coordinate parallel tool use, recover from errors, and operate browsers and applications across desktop and mobile environments. With cross-conversation context retrieval and its Active Focus training approach, it is well suited for complex software engineering, multi-file refactoring, computer use, and end-to-end workflows requiring persistence, adaptability, and minimal supervision. |