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.
- mai-ds-r1:freeNot in the catalogueRemove
- Gemini 4 ArgonGoogleRemove
- Perceptron Mk1.5PerceptronRemove
- Muse WonderMetaRemove
4 is the maximum. Remove one to add another.
| Attribute | mai-ds-r1:freeNot in the catalogue | Gemini 4 Argongemini-4-argon | Perceptron Mk1.5perceptron-mk1.5 | Muse Wondermuse-wonder |
|---|---|---|---|---|
| Pricing | ||||
| Input | — | $2.00 / 1M | $0.15 / 1M | $4.00 / 1M |
| Output | — | $10.00 / 1M | $1.50 / 1M | $20.00 / 1M |
| Cache Write (5m) | — | $2.00 / 1M | $0.15 / 1M | $4.00 / 1M |
| Cache Write (1h) | — | $2.00 / 1M | $0.15 / 1M | $4.00 / 1M |
| Cache Read | — | $2.00 / 1M | $0.15 / 1M | $4.00 / 1M |
| Web Search | — | $0 / 1M | $0 / 1M | $0 / 1M |
| Context | ||||
| Max context | — | 1M | 36.9K | 1M |
| Max output | — | N/A | N/A | N/A |
| Capabilities | ||||
| Vision | — | Yes | No | Yes |
| Function Calling | — | Yes | No | Yes |
| JSON Mode | — | Yes | No | Yes |
| Streaming | — | Yes | Yes | Yes |
| Catalogue | ||||
| Provider | — | Perceptron | Meta | |
| Category | — | chat | chat | chat |
| Charge type | — | Pay As You Go | Pay As You Go | Pay As You Go |
| Released | — | — | 2026-09-25 | — |
| Description | ||||
| Summary | — | Gemini 4 Argon is Google's flagship reasoning model, built for complex software engineering, deep mathematical and scientific problem solving, and autonomous multi-step agents. Google trained it to keep context across long, multi-step tasks and to turn long documents mixing text, tables, charts and images into a single grounded output. It leads the Vals Index across finance, legal, coding and tax work, and supports long decode of up to 1M output tokens in a single response. | Perceptron Mk1.5 chat model that accepts audio input. Audio and text input are billed per token. | 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. |