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 | Claude Fable 5.1claude-fable-5.1 | GLM 5.3 Flashglm-5.3-flash | Veo 3 Proveo3-pro |
|---|---|---|---|
| Pricing | |||
| Input | $10.00 / 1M | $0.075 / 1M | — |
| Output | $50.00 / 1M | $0.25 / 1M | — |
| Cache Write (5m) | $12.50 / 1M | $0.075 / 1M | Not applicable |
| Cache Write (1h) | $20.00 / 1M | $0.075 / 1M | Not applicable |
| Cache Read | $1.00 / 1M | $0.075 / 1M | Not applicable |
| Web Search | $0 / 1M | $0 / 1M | — |
| Request | — | — | $5.60 / request |
| Billing | — | — | Pay Per Request |
| Context | |||
| Max context | 1M | 1M | N/A |
| Max output | N/A | N/A | N/A |
| Capabilities | |||
| Vision | Yes | Yes | No |
| Function Calling | Yes | Yes | No |
| JSON Mode | Yes | Yes | No |
| Streaming | Yes | Yes | No |
| Catalogue | |||
| Provider | Anthropic | Z.AI | |
| Category | chat | chat | video |
| Charge type | Pay As You Go | Pay As You Go | Pay Per Request |
| Released | — | — | — |
| Description | |||
| Summary | Claude Fable 5.1 is an upgraded version of Fable 5, delivering broad improvements with particularly strong gains in agentic coding, long-running workflows, and professional knowledge work. It excels at large code refactors, front-end and visual code generation, financial analysis, and complex analytical tasks. Compared with Fable 5, it also produces more concise plans and summaries while maintaining strong performance across extended tasks, making it a natural upgrade for existing Fable workflows and a strong option alongside Opus 5 for reasoning-intensive applications. | 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. | — |