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 | GPT-5 Codexgpt-5-codex | GPT-6 Astra Progpt-6-astra-pro | GLM 5.3glm-5.3 |
|---|---|---|---|
| Pricing | |||
| Input | $0.625 / 1M | $10.00 / 1M | $1.40 / 1M |
| Output | $5.00 / 1M | $50.00 / 1M | $4.40 / 1M |
| Cache Write (5m) | $0.625 / 1M | $10.00 / 1M | $1.40 / 1M |
| Cache Write (1h) | $0.625 / 1M | $10.00 / 1M | $1.40 / 1M |
| Cache Read | $0.625 / 1M | $10.00 / 1M | $1.40 / 1M |
| Web Search | $0 / 1M | $0 / 1M | $0 / 1M |
| Context | |||
| Max context | 400K | 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 | OpenAI | OpenAI | Z.AI |
| Category | chat | chat | chat |
| Charge type | Pay As You Go | Pay As You Go | Pay As You Go |
| Released | — | — | — |
| Description | |||
| Summary | GPT-5 Codex is a coding-focused version of GPT-5 built for both interactive development and long autonomous engineering tasks. It can create projects, add features, debug, refactor, and review code, producing cleaner and more controllable outputs than GPT-5. It integrates with developer tools (CLI, IDEs, GitHub, cloud), supports adjustable reasoning effort, handles multimodal inputs, and uses tools for search and environment setup — making it purpose-built for agentic coding workflows. | 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. |