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 | GPT-5 Nanogpt-5-nano-2025-08-07 | GLM 5.3glm-5.3 |
|---|---|---|---|
| Pricing | |||
| Input | $0.075 / 1M | $0.0175 / 1M | $1.40 / 1M |
| Output | $0.25 / 1M | $0.14 / 1M | $4.40 / 1M |
| Cache Write (5m) | $0.075 / 1M | $0.0175 / 1M | $1.40 / 1M |
| Cache Write (1h) | $0.075 / 1M | $0.0175 / 1M | $1.40 / 1M |
| Cache Read | $0.075 / 1M | $0.0175 / 1M | $1.40 / 1M |
| Web Search | $0 / 1M | $0 / 1M | $0 / 1M |
| Context | |||
| Max context | 1M | 400K | 1M |
| Max output | N/A | N/A | N/A |
| Capabilities | |||
| Vision | Yes | No | No |
| Function Calling | Yes | Yes | Yes |
| JSON Mode | Yes | Yes | Yes |
| Streaming | Yes | Yes | Yes |
| Catalogue | |||
| Provider | Z.AI | OpenAI | Z.AI |
| Category | chat | chat | chat |
| Charge type | Pay As You Go | Pay As You Go | Pay As You Go |
| Released | — | — | — |
| 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. | GPT-5-Nano is the smallest and fastest GPT-5 variant, built for ultra-low latency and cost-sensitive use cases like developer tools and real-time interactions. While it offers shallower reasoning than larger GPT-5 models, it preserves core instruction-following and safety features and succeeds GPT-4.1-nano as a lightweight option. | 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. |