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.
4 is the maximum. Remove one to add another.
| Attribute | Perceptron Mk1.5perceptron-mk1.5 | GPT-6 Astragpt-6-astra | Aura-2aura-2 | Muse Spark 1.3muse-spark-1.3 |
|---|---|---|---|---|
| Pricing | ||||
| Input | $0.15 / 1M | $10.00 / 1M | $0 / 1M | $1.25 / 1M |
| Output | $1.50 / 1M | $50.00 / 1M | $0 / 1M | $4.25 / 1M |
| Cache Write (5m) | $0.15 / 1M | $10.00 / 1M | Not applicable | $1.25 / 1M |
| Cache Write (1h) | $0.15 / 1M | $10.00 / 1M | Not applicable | $1.25 / 1M |
| Cache Read | $0.15 / 1M | $10.00 / 1M | Not applicable | $1.25 / 1M |
| Web Search | $0 / 1M | $0 / 1M | $0 / 1M | $0 / 1M |
| Context | ||||
| Max context | 36.9K | 1M | N/A | 1M |
| Max output | N/A | N/A | N/A | N/A |
| Capabilities | ||||
| Vision | No | Yes | No | Yes |
| Function Calling | No | Yes | No | Yes |
| JSON Mode | No | Yes | No | Yes |
| Streaming | Yes | Yes | No | Yes |
| Catalogue | ||||
| Provider | Perceptron | OpenAI | Deepgram | Meta |
| Category | chat | chat | voice | chat |
| Charge type | Pay As You Go | Pay As You Go | Pay As You Go | Pay As You Go |
| Released | 2026-09-25 | — | 2026-07-16 | — |
| 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. | Deepgram Aura-2 text-to-speech for real-time voice applications. Billed per input character. | Muse Spark 1.3 is Meta's multimodal reasoning model designed for long-running agentic, multi-agent, and coding workflows. It maintains context and information across extended tasks, enabling reliable execution in complex, multi-step environments. The model is optimized to resolve conflicting information, seek clarification or confirmation when necessary, and execute concisely, making it well suited for autonomous agents, collaborative multi-agent systems, and long-horizon software engineering workflows. |