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 | Claude Fable 5.2claude-fable-5.2 | Gemini 4 Argongemini-4-argon | Jina Embeddings V5 Text Smalljina-embeddings-v5-text-small |
|---|---|---|---|
| Pricing | |||
| Input | $8.00 / 1M | $2.00 / 1M | $0.05 / 1M |
| Output | $40.00 / 1M | $10.00 / 1M | — Not priced per output token |
| Cache Write (5m) | $10.00 / 1M | $2.00 / 1M | $0.05 / 1M |
| Cache Write (1h) | $16.00 / 1M | $2.00 / 1M | $0.05 / 1M |
| Cache Read | $0.20 / 1M | $2.00 / 1M | $0.05 / 1M |
| Web Search | $0 / 1M | $0 / 1M | — |
| Context | |||
| Max context | 1M | 1M | 8.2K |
| Max output | N/A | N/A | N/A |
| Capabilities | |||
| Vision | Yes | Yes | No |
| Function Calling | Yes | Yes | No |
| JSON Mode | Yes | Yes | No |
| Streaming | Yes | Yes | No |
| Catalogue | |||
| Provider | Anthropic | Jina AI | |
| Category | chat | chat | embedding |
| Charge type | Pay As You Go | Pay As You Go | Pay As You Go |
| Released | — | — | — |
| Description | |||
| Summary | Claude Fable 5.2 is Anthropic's most capable model for advanced technical work and open-ended problem solving, delivering significant improvements in coding, reasoning, and vision over Fable 5.1. It excels at exceptionally complex tasks, including large-scale software migrations, electronic system design, and advanced scientific and mathematical research. With up to 30% faster output generation, clearer communication, and always-on adaptive extended thinking, Fable 5.2 supports configurable reasoning effort from low to max (default high), with thinking preserved across turns. Designed for frontier engineering, scientific discovery, and ambitious long-horizon agentic workflows, it complements Opus 5.5 as a specialized option for the most demanding technical challenges. | 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. | — |