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 | Gemini 4 Argongemini-4-argon | Mistral Embed 2312mistral-embed-2312 |
|---|---|---|
| Pricing | ||
| Input | $2.00 / 1M | $0.125 / 1M |
| Output | $10.00 / 1M | — Not priced per output token |
| Cache Write (5m) | $2.00 / 1M | $0.125 / 1M |
| Cache Write (1h) | $2.00 / 1M | $0.125 / 1M |
| Cache Read | $2.00 / 1M | $0.125 / 1M |
| Web Search | $0 / 1M | — |
| Context | ||
| Max context | 1M | 8.2K |
| Max output | N/A | N/A |
| Capabilities | ||
| Vision | Yes | No |
| Function Calling | Yes | No |
| JSON Mode | Yes | No |
| Streaming | Yes | No |
| Catalogue | ||
| Provider | Mistral AI | |
| Category | chat | embedding |
| Charge type | Pay As You Go | Pay As You Go |
| Released | — | — |
| Description | ||
| Summary | Gemini 4 Argon is Google's flagship reasoning model, built for complex software engineering, deep mathematical and scientific problem solving, and autonomous multi-step agents. Google trained it to keep context across long, multi-step tasks and to turn long documents mixing text, tables, charts and images into a single grounded output. It leads the Vals Index across finance, legal, coding and tax work, and supports long decode of up to 1M output tokens in a single response. | 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. |