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.
- GLM 5.3 FlashZ.AIRemove
- mai-ds-r1:freeNot in the catalogueRemove
- Transcribe 1 ProFish AudioRemove
- Muse WonderMetaRemove
4 is the maximum. Remove one to add another.
| Attribute | GLM 5.3 Flashglm-5.3-flash | mai-ds-r1:freeNot in the catalogue | Transcribe 1 Protranscribe-1-pro | Muse Wondermuse-wonder |
|---|---|---|---|---|
| Pricing | ||||
| Input | $0.075 / 1M | — | — Not priced per input token | $4.00 / 1M |
| Output | $0.25 / 1M | — | — Not priced per output token | $20.00 / 1M |
| Cache Write (5m) | $0.075 / 1M | — | Not applicable | $4.00 / 1M |
| Cache Write (1h) | $0.075 / 1M | — | Not applicable | $4.00 / 1M |
| Cache Read | $0.075 / 1M | — | Not applicable | $4.00 / 1M |
| Web Search | $0 / 1M | — | $0 / 1M | $0 / 1M |
| Context | ||||
| Max context | 1M | — | N/A | 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 | — | No | Yes |
| Catalogue | ||||
| Provider | Z.AI | — | Fish Audio | Meta |
| Category | chat | — | voice | chat |
| Charge type | Pay As You Go | — | Pay As You Go | Pay As You Go |
| Released | — | — | 2026-09-24 | — |
| 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. | — | Fish Audio Transcribe 1 Pro speech-to-text with speaker labels; transcripts include speaker tags such as <|speaker:0|>. Billed per second of audio. | 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. |