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 Opus 5claude-opus-5 | GPT-6 Astra Progpt-6-astra-pro | GLM 5.3glm-5.3 |
|---|---|---|---|
| Pricing | |||
| Input | $5.00 / 1M | $10.00 / 1M | $1.40 / 1M |
| Output | $25.00 / 1M | $50.00 / 1M | $4.40 / 1M |
| Cache Write (5m) | $6.25 / 1M | $10.00 / 1M | $1.40 / 1M |
| Cache Write (1h) | $10.00 / 1M | $10.00 / 1M | $1.40 / 1M |
| Cache Read | $0.50 / 1M | $10.00 / 1M | $1.40 / 1M |
| Web Search | $0 / 1M | $0 / 1M | $0 / 1M |
| Context | |||
| Max context | 1M | 1M | 1M |
| Max output | N/A | N/A | N/A |
| Capabilities | |||
| Vision | Yes | Yes | No |
| Function Calling | Yes | Yes | Yes |
| JSON Mode | Yes | Yes | Yes |
| Streaming | Yes | Yes | Yes |
| Catalogue | |||
| Provider | Anthropic | OpenAI | Z.AI |
| Category | chat | chat | chat |
| Charge type | Pay As You Go | Pay As You Go | Pay As You Go |
| Released | — | — | — |
| Description | |||
| Summary | Claude Opus 5 is Anthropic's flagship model for advanced reasoning, coding, and long-horizon agentic workflows. It excels at end-to-end software engineering, code review, bug detection, visual analysis of charts and documents, complex office deliverables, and parallel subagent coordination. The model maintains reliable instruction following and tool use across extended tasks, while remaining effective at lower reasoning-effort settings for workloads that prioritize latency and token efficiency. | GPT-6 Astra Pro uses the same underlying model as GPT-6 Astra, but runs with reasoning.mode set to pro for higher-quality responses on complex tasks. Optimized for deeper reasoning, greater accuracy, and more reliable multi-step execution, it is well suited for demanding coding, analysis, and agentic workflows where solution quality takes priority over speed and cost. | GLM-5.3 is Z.ai's large-scale reasoning model designed for complex software engineering and long-horizon agentic workflows. It supports text input and output with a 1M-token context window, enabling sustained reasoning across large codebases and extended multi-step tasks. Building on GLM-5.2, it delivers stronger coding performance while improving the balance between capability and token efficiency, making it well suited for autonomous coding agents, large-scale engineering workflows, and complex task execution. |