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 | Whisper 1whisper-1 | Gemini 3.1 Flash Lite Image (Google Vertex AI)gemini-3.1-flash-lite-image-vertex | Muse Wondermuse-wonder |
|---|---|---|---|
| Pricing | |||
| Input | $75.00 / 1M | — | $4.00 / 1M |
| Output | $75.00 / 1M | — | $20.00 / 1M |
| Cache Write (5m) | Not applicable | Not applicable | $4.00 / 1M |
| Cache Write (1h) | Not applicable | Not applicable | $4.00 / 1M |
| Cache Read | Not applicable | Not applicable | $4.00 / 1M |
| Web Search | $0 / 1M | — | $0 / 1M |
| Request | — | $0.0336 / request | — |
| Billing | — | Pay Per Request | — |
| Context | |||
| Max context | N/A | N/A | 1M |
| Max output | N/A | N/A | N/A |
| Capabilities | |||
| Vision | No | Yes | Yes |
| Function Calling | No | No | Yes |
| JSON Mode | Yes | No | Yes |
| Streaming | No | No | Yes |
| Catalogue | |||
| Provider | OpenAI | Meta | |
| Category | voice | image | chat |
| Charge type | Pay As You Go | Pay Per Request | Pay As You Go |
| Released | — | — | — |
| Description | |||
| Summary | Whisper (whisper-1) is OpenAI's open-source automatic speech recognition (ASR) model, designed for audio transcription and translation. It supports 50+ languages and processes audio files up to 25 MB, accepting formats such as mp3, mp4, wav, and webm. Optimized for reliable speech-to-text conversion across diverse audio inputs, Whisper is priced per minute of audio, billed to the nearest second, making it well suited for transcription, localization, and voice-driven applications. | Gemini 3.1 Flash Lite Image served from Google Vertex AI. Billed per image at US$0.0336. Call it through /v1/chat/completions with stream set to false; the image is returned in the message content as a base64 data URI. Maximum output resolution is 1K. A request for a larger size returns a 1K image with the same aspect ratio at the same price. When a size is reduced, the response carries an X-Image-Resolution-Capped header with the requested and delivered size. | 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. |