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 5.3glm-5.3 | Gemini 3.1 Flash Image (Google Vertex AI)gemini-3.1-flash-image-vertex | DeepSeek V4.1 Flash 1017deepseek-v4.1-flash-1017 |
|---|---|---|---|
| Pricing | |||
| Input | $1.40 / 1M | — | $0.10 / 1M |
| Output | $4.40 / 1M | — | $0.50 / 1M |
| Cache Write (5m) | $1.40 / 1M | Not applicable | $0.10 / 1M |
| Cache Write (1h) | $1.40 / 1M | Not applicable | $0.10 / 1M |
| Cache Read | $1.40 / 1M | Not applicable | $0.10 / 1M |
| Web Search | $0 / 1M | — | $0 / 1M |
| Request | — | $0.067 / request | — |
| Billing | — | Pay Per Request | — |
| Context | |||
| Max context | 1M | N/A | 1M |
| Max output | N/A | N/A | N/A |
| Capabilities | |||
| Vision | No | Yes | Yes |
| Function Calling | Yes | No | Yes |
| JSON Mode | Yes | No | Yes |
| Streaming | Yes | No | Yes |
| Catalogue | |||
| Provider | Z.AI | DeepSeek | |
| Category | chat | image | chat |
| Charge type | Pay As You Go | Pay Per Request | Pay As You Go |
| Released | — | — | — |
| Description | |||
| Summary | 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. | Gemini 3.1 Flash Image served from Google Vertex AI. Billed per image at US$0.067. Call it through /v1/chat/completions with stream set to false; the image is returned in the message content as a base64 data URI. Maximum output resolution is 2K. A request for a larger size, such as 4K or 4096x4096, returns a 2K image with the same aspect ratio at the same price. When a size is reduced, the response carries an X-Image-Resolution-Capped header with the requested and delivered size, for example 4096x4096->2048x2048. | DeepSeek V4.1 Flash 1017 is the October 17, 2026 snapshot of DeepSeek’s cost-efficient Mixture-of-Experts (MoE) model, built on its innovative Causal Encoder-Decoder (CED) architecture. Featuring 552B total parameters with 8B activated for input and 16B for output, it combines efficient inference with native multimodal understanding through jointly trained text and visual representations. Optimized for coding, terminal and computer-use agents, and long-horizon workflows, the model introduces compressed KV caching that reduces cache memory usage to approximately one-quarter of the previous Flash generation. DeepSeek reports that it surpasses V4 Pro in performance, inference speed, and task completion time, making it well suited for cost-efficient autonomous agents, complex software engineering, and long-context analysis. |