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.
- Perceptron Mk1.5PerceptronRemove
- mai-ds-r1:freeNot in the catalogueRemove
- GPT-6 AstraOpenAIRemove
- Gemini 4 ArgonGoogleRemove
4 is the maximum. Remove one to add another.
| Attribute | Perceptron Mk1.5perceptron-mk1.5 | mai-ds-r1:freeNot in the catalogue | GPT-6 Astragpt-6-astra | Gemini 4 Argongemini-4-argon |
|---|---|---|---|---|
| Pricing | ||||
| Input | $0.15 / 1M | — | $10.00 / 1M | $2.00 / 1M |
| Output | $1.50 / 1M | — | $50.00 / 1M | $10.00 / 1M |
| Cache Write (5m) | $0.15 / 1M | — | $10.00 / 1M | $2.00 / 1M |
| Cache Write (1h) | $0.15 / 1M | — | $10.00 / 1M | $2.00 / 1M |
| Cache Read | $0.15 / 1M | — | $10.00 / 1M | $2.00 / 1M |
| Web Search | $0 / 1M | — | $0 / 1M | $0 / 1M |
| Context | ||||
| Max context | 36.9K | — | 1M | 1M |
| Max output | N/A | — | N/A | N/A |
| Capabilities | ||||
| Vision | No | — | Yes | Yes |
| Function Calling | No | — | Yes | Yes |
| JSON Mode | No | — | Yes | Yes |
| Streaming | Yes | — | Yes | Yes |
| Catalogue | ||||
| Provider | Perceptron | — | OpenAI | |
| Category | chat | — | chat | chat |
| Charge type | Pay As You Go | — | Pay As You Go | Pay As You Go |
| Released | 2026-09-25 | — | — | — |
| Description | ||||
| Summary | Perceptron Mk1.5 chat model that accepts audio input. Audio and text input are billed per token. | — | GPT-6 Astra is OpenAI's flagship model for demanding end-to-end professional work, designed for advanced analysis, software engineering, deep research, scientific tasks, and document creation. It is particularly strong in long-horizon agentic workflows, including tasks that require sustained reasoning, tool orchestration, and computer and browser use, making it well suited for complex autonomous workflows and production-grade knowledge work. | 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. |