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.
- GPT-6 Luna ProOpenAIRemove
- Gemini 3.5 TranscribeGoogleRemove
- S1Fish AudioRemove
- Muse Spark 1.3MetaRemove
4 is the maximum. Remove one to add another.
| Attribute | GPT-6 Luna Progpt-6-luna-pro | Gemini 3.5 Transcribegemini-3.5-transcribe | S1s1 | Muse Spark 1.3muse-spark-1.3 |
|---|---|---|---|---|
| Pricing | ||||
| Input | $0.10 / 1M | $0 / 1M | $0 / 1M | $1.25 / 1M |
| Output | $0.50 / 1M | $0 / 1M | $0 / 1M | $4.25 / 1M |
| Cache Write (5m) | $0.10 / 1M | Not applicable | Not applicable | $1.25 / 1M |
| Cache Write (1h) | $0.10 / 1M | Not applicable | Not applicable | $1.25 / 1M |
| Cache Read | $0.10 / 1M | Not applicable | Not applicable | $1.25 / 1M |
| Web Search | $0 / 1M | $0 / 1M | $0 / 1M | $0 / 1M |
| Context | ||||
| Max context | 1.1M | 98.3K | N/A | 1M |
| Max output | N/A | N/A | N/A | N/A |
| Capabilities | ||||
| Vision | Yes | Yes | No | Yes |
| Function Calling | Yes | Yes | No | Yes |
| JSON Mode | Yes | No | No | Yes |
| Streaming | Yes | No | No | Yes |
| Catalogue | ||||
| Provider | OpenAI | Fish Audio | Meta | |
| Category | chat | voice | voice | chat |
| Charge type | Pay As You Go | Pay As You Go | Pay As You Go | Pay As You Go |
| Released | — | 2026-09-25 | 2026-07-29 | — |
| Description | ||||
| Summary | GPT-6 Luna Pro uses the same underlying model as GPT-6 Luna, but runs with reasoning.mode set to pro for higher-quality responses on complex tasks. It combines Luna's speed and cost efficiency with deeper reasoning and more reliable multi-step execution, making it well suited for advanced coding, computer use, and agentic workflows where higher solution quality is needed without moving to a larger GPT-6 tier. | Google Gemini 3.5 Transcribe speech-to-text. Billed per input and output token. | Fish Audio S1 text-to-speech. Billed per UTF-8 byte of input text. | 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. |