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 | Gemini 3.5 Transcribegemini-3.5-transcribe | GLM 5.3 Flashglm-5.3-flash |
|---|---|---|---|
| Pricing | |||
| Input | $2.00 / 1M | — Not priced per input token | $0.075 / 1M |
| Output | $10.00 / 1M | — Not priced per output token | $0.25 / 1M |
| Cache Write (5m) | $2.00 / 1M | Not applicable | $0.075 / 1M |
| Cache Write (1h) | $2.00 / 1M | Not applicable | $0.075 / 1M |
| Cache Read | $2.00 / 1M | Not applicable | $0.075 / 1M |
| Web Search | $0 / 1M | $0 / 1M | $0 / 1M |
| Context | |||
| Max context | 1M | 98.3K | 1M |
| Max output | N/A | N/A | N/A |
| Capabilities | |||
| Vision | Yes | Yes | Yes |
| Function Calling | Yes | Yes | Yes |
| JSON Mode | Yes | No | Yes |
| Streaming | Yes | No | Yes |
| Catalogue | |||
| Provider | Z.AI | ||
| Category | chat | voice | chat |
| Charge type | Pay As You Go | Pay As You Go | Pay As You Go |
| Released | — | 2026-09-25 | — |
| 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. | Google Gemini 3.5 Transcribe speech-to-text. Billed per input and output token. | GLM-5.3-Flash is Z.AI's efficient native multimodal model, designed for coding and long-horizon agentic workflows. It combines strong multimodal capabilities with an architecture optimized for responsive, cost-efficient task execution. Built on a hybrid sparse and linear attention architecture, GLM-5.3-Flash maintains accurate long-context behavior while reducing computational overhead, making it well suited for coding agents, extended multi-step tasks, and scalable production workloads. |