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 | o4 Mini Deep Researcho4-mini-deep-research | Claude Fable 5.1claude-fable-5.1 | DeepSeek V4.1 Flashdeepseek-v4.1-flash |
|---|---|---|---|
| Pricing | |||
| Input | $2.00 / 1M | $10.00 / 1M | $0.30 / 1M |
| Output | $8.00 / 1M | $50.00 / 1M | $1.20 / 1M |
| Cache Write (5m) | $2.00 / 1M | $12.50 / 1M | $0.30 / 1M |
| Cache Write (1h) | $2.00 / 1M | $20.00 / 1M | $0.30 / 1M |
| Cache Read | $2.00 / 1M | $1.00 / 1M | $0.30 / 1M |
| Web Search | $0 / 1M | $0 / 1M | $0 / 1M |
| Context | |||
| Max context | 200K | 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 | OpenAI | Anthropic | DeepSeek |
| Category | chat | chat | chat |
| Charge type | Pay As You Go | Pay As You Go | Pay As You Go |
| Released | — | — | — |
| Description | |||
| Summary | o4-mini-deep-research is a faster, lower-cost version of OpenAI's deep-research model, designed for complex, multi-step investigations. It automatically relies on web_search for information gathering, which always adds extra usage cost. | Claude Fable 5.1 is an upgraded version of Fable 5, delivering broad improvements with particularly strong gains in agentic coding, long-running workflows, and professional knowledge work. It excels at large code refactors, front-end and visual code generation, financial analysis, and complex analytical tasks. Compared with Fable 5, it also produces more concise plans and summaries while maintaining strong performance across extended tasks, making it a natural upgrade for existing Fable workflows and a strong option alongside Opus 5 for reasoning-intensive applications. | DeepSeek V4.1 Flash is a cost-efficient sparse Mixture-of-Experts (MoE) model in DeepSeek's V4.1 family, optimized for coding, reasoning, and agentic workflows. Despite its efficiency-focused positioning, DeepSeek reports that it surpasses the previous V4 Pro in performance, inference speed, and overall task completion time. The model is particularly strong at long-horizon, multi-step execution, making it well suited for coding agents, complex problem solving, and autonomous workflows that must reliably carry tasks through to completion. |