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 | GPT-5.3-Codexgpt-5.3-codex |
|---|---|---|
| Pricing | ||
| Input | $2.00 / 1M | $1.75 / 1M |
| Output | $10.00 / 1M | $14.00 / 1M |
| Cache Write (5m) | $2.00 / 1M | $1.75 / 1M |
| Cache Write (1h) | $2.00 / 1M | $1.75 / 1M |
| Cache Read | $2.00 / 1M | $1.75 / 1M |
| Web Search | $0 / 1M | $0 / 1M |
| Context | ||
| Max context | 1M | 400K |
| Max output | N/A | N/A |
| Capabilities | ||
| Vision | Yes | Yes |
| Function Calling | Yes | Yes |
| JSON Mode | Yes | Yes |
| Streaming | Yes | Yes |
| Catalogue | ||
| Provider | OpenAI | |
| Category | chat | chat |
| 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. | GPT-Codex-5.3 is OpenAI's most advanced agentic coding model, designed for software engineering workflows that extend beyond single prompts into long-running, tool-driven execution. It combines the frontier coding performance of earlier Codex models with stronger reasoning and professional knowledge capabilities, enabling reliable handling of complex refactors, multi-step debugging, research-driven development, and autonomous task execution. Optimized for developer productivity, GPT-Codex-5.3 supports interactive collaboration during execution, allowing users to steer tasks in real time without losing context. With improved agentic reliability, faster inference, and stronger performance on long-horizon engineering tasks, it is well suited for coding agents, IDE and CLI workflows, and end-to-end software development pipelines where persistence, tool use, and execution continuity are critical. |