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.
- Gemini 3.8 FlashGoogleRemove
- Perceptron Mk1.5PerceptronRemove
- mai-ds-r1:freeNot in the catalogueRemove
- Muse WonderMetaRemove
4 is the maximum. Remove one to add another.
| Attribute | Gemini 3.8 Flashgemini-3.8-flash | Perceptron Mk1.5perceptron-mk1.5 | mai-ds-r1:freeNot in the catalogue | Muse Wondermuse-wonder |
|---|---|---|---|---|
| Pricing | ||||
| Input | $0.75 / 1M | $0.15 / 1M | — | $4.00 / 1M |
| Output | $3.75 / 1M | $1.50 / 1M | — | $20.00 / 1M |
| Cache Write (5m) | $0.75 / 1M | $0.15 / 1M | — | $4.00 / 1M |
| Cache Write (1h) | $0.75 / 1M | $0.15 / 1M | — | $4.00 / 1M |
| Cache Read | $0.75 / 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 3.8 Flash is Google's most intelligent Flash-class model, delivering significant improvements over Gemini 3.7 Flash across software engineering, agentic workflows, and complex multi-step reasoning. Designed to combine strong capability with Flash-tier efficiency, it is well suited for coding assistants, autonomous agents, and high-throughput production workflows that require responsive performance without sacrificing reasoning quality. | 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. |