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 Opus 5.5claude-opus-5.5 | Gemini 4 Argongemini-4-argon |
|---|---|---|
| Pricing | ||
| Input | $4.00 / 1M | $2.00 / 1M |
| Output | $20.00 / 1M | $10.00 / 1M |
| Cache Write (5m) | $5.00 / 1M | $2.00 / 1M |
| Cache Write (1h) | $8.00 / 1M | $2.00 / 1M |
| Cache Read | $0.20 / 1M | $2.00 / 1M |
| Web Search | $0 / 1M | $0 / 1M |
| Context | ||
| Max context | 1M | 1M |
| Max output | N/A | N/A |
| Capabilities | ||
| Vision | Yes | Yes |
| Function Calling | Yes | Yes |
| JSON Mode | Yes | Yes |
| Streaming | Yes | Yes |
| Catalogue | ||
| Provider | Anthropic | |
| Category | chat | chat |
| Charge type | Pay As You Go | Pay As You Go |
| Released | — | — |
| Description | ||
| Summary | Claude Opus 5.5 is Anthropic's flagship model for advanced reasoning, coding, and long-horizon agentic workflows, succeeding Opus 5. It excels at multi-step changes across large codebases, code review and bug detection, financial and scientific analysis, and understanding dense charts, diagrams, and screenshots, with stronger grounding when reporting figures and citing sources. Compared with Opus 5, it completes comparable tasks with fewer steps and lower token usage while providing clearer, more concise progress reporting. With adaptive thinking and configurable effort levels, Opus 5.5 can balance reasoning depth, latency, and cost, making it well suited for both demanding autonomous workflows and latency-sensitive professional tasks. | 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. |