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 | GLM 5.3 Flashglm-5.3-flash | Perceptron Mk1.5perceptron-mk1.5 | Muse Wondermuse-wonder |
|---|---|---|---|
| Pricing | |||
| Input | $0.075 / 1M | $0.15 / 1M | $4.00 / 1M |
| Output | $0.25 / 1M | $1.50 / 1M | $20.00 / 1M |
| Cache Write (5m) | $0.075 / 1M | $0.15 / 1M | $4.00 / 1M |
| Cache Write (1h) | $0.075 / 1M | $0.15 / 1M | $4.00 / 1M |
| Cache Read | $0.075 / 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 | Z.AI | 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 | GLM-5.3-Flash is Z.AI's efficient native multimodal model, designed for coding and long-horizon agentic workflows. It combines strong multimodal capabilities with an architecture optimized for responsive, cost-efficient task execution. Built on a hybrid sparse and linear attention architecture, GLM-5.3-Flash maintains accurate long-context behavior while reducing computational overhead, making it well suited for coding agents, extended multi-step tasks, and scalable production workloads. | 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. |