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.
- Hy4 previewTencentRemove
- Perceptron Mk1.5PerceptronRemove
- mai-ds-r1:freeNot in the catalogueRemove
- Gemini 4 ArgonGoogleRemove
4 is the maximum. Remove one to add another.
| Attribute | Hy4 previewhy4-preview | Perceptron Mk1.5perceptron-mk1.5 | mai-ds-r1:freeNot in the catalogue | Gemini 4 Argongemini-4-argon |
|---|---|---|---|---|
| Pricing | ||||
| Input | $0.834 / 1M | $0.15 / 1M | — | $2.00 / 1M |
| Output | $2.50 / 1M | $1.50 / 1M | — | $10.00 / 1M |
| Cache Write (5m) | $0.834 / 1M | $0.15 / 1M | — | $2.00 / 1M |
| Cache Write (1h) | $0.834 / 1M | $0.15 / 1M | — | $2.00 / 1M |
| Cache Read | $0.834 / 1M | $0.15 / 1M | — | $2.00 / 1M |
| Web Search | $0 / 1M | $0 / 1M | — | $0 / 1M |
| Context | ||||
| Max context | 1M | 36.9K | — | 1M |
| Max output | N/A | N/A | — | N/A |
| Capabilities | ||||
| Vision | Yes | No | — | Yes |
| Function Calling | Yes | No | — | Yes |
| JSON Mode | Yes | No | — | Yes |
| Streaming | Yes | Yes | — | Yes |
| Catalogue | ||||
| Provider | Tencent | Perceptron | — | |
| Category | chat | chat | — | chat |
| Charge type | Pay As You Go | Pay As You Go | — | Pay As You Go |
| Released | — | 2026-09-25 | — | — |
| Description | ||||
| Summary | Tencent Hy4 Preview is a Mixture-of-Experts (MoE) model from Tencent, featuring 770B total parameters with 49B activated per token. It is designed for coding agents, complex tool-driven workflows, and professional productivity tasks that require strong planning and reliable execution. Optimized for context continuity and sustained multi-step work, Hy4 Preview is well suited for long-horizon coding, agentic automation, tool orchestration, and complex real-world workflows. | Perceptron Mk1.5 chat model that accepts audio input. Audio and text input are billed per token. | — | 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. |