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Put up to 4 models beside each other — token prices, context windows, capabilities and provider, from the same catalogue the model pages read.
| Attribute | Mistral Embed 2312mistral-embed-2312 | GLM 5.3glm-5.3 |
|---|---|---|
| Pricing | ||
| Input | $0.125 / 1M | $1.40 / 1M |
| Output | $0 / 1M | $4.40 / 1M |
| Cache Write (5m) | $0.125 / 1M | $1.40 / 1M |
| Cache Write (1h) | $0.125 / 1M | $1.40 / 1M |
| Cache Read | $0.125 / 1M | $1.40 / 1M |
| Web Search | — | $0 / 1M |
| Context | ||
| Max context | 8.2K | 1M |
| Max output | N/A | N/A |
| Capabilities | ||
| Vision | No | No |
| Function Calling | No | Yes |
| JSON Mode | No | Yes |
| Streaming | No | Yes |
| Catalogue | ||
| Provider | Mistral AI | Z.AI |
| Category | embedding | chat |
| Charge type | Pay As You Go | Pay As You Go |
| Released | — | — |
| Description | ||
| Summary | Mistral Embed is Mistral AI's text embedding model, built for semantic search and RAG workflows. It generates 1024-dimensional vectors that capture meaningful relationships between pieces of text. | 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. |