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 | Gemini 3.8 Flashgemini-3.8-flash | GPT-6 Astra Progpt-6-astra-pro | GPT-4o Transcribegpt-4o-transcribe |
|---|---|---|---|
| Pricing | |||
| Input | $0.75 / 1M | $10.00 / 1M | $1.25 / 1M |
| Output | $3.75 / 1M | $50.00 / 1M | $0 / 1M |
| Cache Write (5m) | $0.75 / 1M | $10.00 / 1M | Not applicable |
| Cache Write (1h) | $0.75 / 1M | $10.00 / 1M | Not applicable |
| Cache Read | $0.75 / 1M | $10.00 / 1M | Not applicable |
| Web Search | $0 / 1M | $0 / 1M | $0 / 1M |
| Context | |||
| Max context | 1M | 1M | 128K |
| Max output | N/A | N/A | N/A |
| Capabilities | |||
| Vision | Yes | Yes | No |
| Function Calling | Yes | Yes | No |
| JSON Mode | Yes | Yes | Yes |
| Streaming | Yes | Yes | No |
| Catalogue | |||
| Provider | OpenAI | OpenAI | |
| Category | chat | chat | voice |
| Charge type | Pay As You Go | Pay As You Go | Pay As You Go |
| Released | — | — | — |
| Description | |||
| Summary | Gemini 3.8 Flash is Google's most intelligent Flash-class model, delivering significant improvements over Gemini 3.7 Flash across software engineering, agentic workflows, and complex multi-step reasoning. Designed to combine strong capability with Flash-tier efficiency, it is well suited for coding assistants, autonomous agents, and high-throughput production workflows that require responsive performance without sacrificing reasoning quality. | GPT-6 Astra Pro uses the same underlying model as GPT-6 Astra, but runs with reasoning.mode set to pro for higher-quality responses on complex tasks. Optimized for deeper reasoning, greater accuracy, and more reliable multi-step execution, it is well suited for demanding coding, analysis, and agentic workflows where solution quality takes priority over speed and cost. | GPT-4o Transcribe is OpenAI's high-quality speech-to-text model built on GPT-4o's audio capabilities. It delivers accurate transcription with strong language understanding, making it suitable for a wide range of audio processing tasks. Priced per token (input and output), it offers transparent, fine-grained billing, making it well suited for workflows that require scalable transcription, integration with LLM pipelines, and cost-aware processing. |