Skip to content

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.

  1. GPT-4o Audio PreviewOpenAIRemove
  2. GPT-6 AstraOpenAIRemove
  3. Gemini 3.8 FlashGoogleRemove
gpt-4o-audio-preview vs gpt-6-astra vs gemini-3.8-flash
AttributeGPT-4o Audio Previewgpt-4o-audio-previewGPT-6 Astragpt-6-astraGemini 3.8 Flashgemini-3.8-flash
Pricing
Input$0.875 / 1M$10.00 / 1M$0.75 / 1M
Output$3.50 / 1M$50.00 / 1M$3.75 / 1M
Cache Write (5m)Not applicable$10.00 / 1M$0.75 / 1M
Cache Write (1h)Not applicable$10.00 / 1M$0.75 / 1M
Cache ReadNot applicable$10.00 / 1M$0.75 / 1M
Web Search$0 / 1M$0 / 1M$0 / 1M
Context
Max context128K1M1M
Max outputN/AN/AN/A
Capabilities
VisionNoYesYes
Function CallingNoYesYes
JSON ModeNoYesYes
StreamingYesYesYes
Catalogue
ProviderOpenAIOpenAIGoogle
Categoryvoicechatchat
Charge typePay As You GoPay As You GoPay As You Go
Released
Description
Summarygpt-4o-audio-preview adds support for audio inputs, allowing the model to understand nuances in audio recordings and enrich responses. It currently does not generate audio outputs, and audio input is billed per million audio tokens.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 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.