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 | DeepSeek V4 Flashdeepseek-v4-flash | GPT-6 Astra Progpt-6-astra-pro | GLM 5.3 Flashglm-5.3-flash |
|---|---|---|---|
| Pricing | |||
| Input | $0.14 / 1M | $10.00 / 1M | $0.075 / 1M |
| Output | $0.28 / 1M | $50.00 / 1M | $0.25 / 1M |
| Cache Write (5m) | $0.14 / 1M | $10.00 / 1M | $0.075 / 1M |
| Cache Write (1h) | $0.14 / 1M | $10.00 / 1M | $0.075 / 1M |
| Cache Read | $0.14 / 1M | $10.00 / 1M | $0.075 / 1M |
| Web Search | $0 / 1M | $0 / 1M | $0 / 1M |
| Context | |||
| Max context | 1.0M | 1M | 1M |
| Max output | N/A | N/A | N/A |
| Capabilities | |||
| Vision | Yes | Yes | Yes |
| Function Calling | Yes | Yes | Yes |
| JSON Mode | Yes | Yes | Yes |
| Streaming | Yes | Yes | Yes |
| Catalogue | |||
| Provider | DeepSeek | OpenAI | Z.AI |
| Category | chat | chat | chat |
| Charge type | Pay As You Go | Pay As You Go | Pay As You Go |
| Released | — | — | — |
| Description | |||
| Summary | DeepSeek V4 Flash is an efficiency-optimized Mixture-of-Experts (MoE) model with 284B total parameters and 13B activated per token, designed for fast inference and high-throughput workloads. It supports a 1M-token context window, enabling large-scale reasoning and long-context processing. Built with hybrid attention for efficiency, the model maintains strong performance in reasoning and coding while offering configurable reasoning modes. It is well suited for coding assistants, chat systems, and agent workflows where responsiveness and cost efficiency are critical. | 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-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. |