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 | GPT-6 Lunagpt-6-luna | Perceptron Mk1.5perceptron-mk1.5 | Grok 4.20 Multi-Agentgrok-4.20-multi-agent |
|---|---|---|---|
| Pricing | |||
| Input | $0.10 / 1M | $0.15 / 1M | $2.00 / 1M |
| Output | $0.50 / 1M | $1.50 / 1M | $6.00 / 1M |
| Cache Write (5m) | $0.10 / 1M | $0.15 / 1M | $2.00 / 1M |
| Cache Write (1h) | $0.10 / 1M | $0.15 / 1M | $2.00 / 1M |
| Cache Read | $0.10 / 1M | $0.15 / 1M | $2.00 / 1M |
| Web Search | $0 / 1M | $0 / 1M | $0 / 1M |
| Context | |||
| Max context | 1.1M | 36.9K | 2M |
| 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 | OpenAI | Perceptron | xAI |
| Category | chat | chat | chat |
| Charge type | Pay As You Go | Pay As You Go | Pay As You Go |
| Released | — | 2026-09-25 | — |
| Description | |||
| Summary | GPT-6 Luna is the fast, cost-efficient model in OpenAI's GPT-6 series, optimized for high-volume and latency-sensitive workloads such as chat, classification, and lightweight agentic tasks. It combines low-cost, responsive inference with the GPT-6 family’s improvements in factual reliability and clear, concise communication. At higher reasoning effort, GPT-6 Luna can also handle complex software engineering and computer-use workflows that previously required a Sol-tier model, making it a versatile choice for scalable production applications that need to balance speed, cost, and capability. | Perceptron Mk1.5 chat model that accepts audio input. Audio and text input are billed per token. | Grok 4.20 Multi-Agent is a specialized variant of xAI's Grok 4.20 designed for collaborative, agent-based workflows. It enables multiple agents to operate in parallel, coordinating tool use and synthesizing information to handle complex, multi-step tasks. Optimized for deep research and large-scale problem solving, the model supports configurable reasoning effort: 4 agents for low/medium settings and up to 16 agents for high/xhigh settings, enabling scalable parallel reasoning and execution. |