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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. Gemini 3.7 FlashGoogleRemove
  2. GPT-6 AstraOpenAIRemove
  3. GPT-4o TranscribeOpenAIRemove
gemini-3.7-flash vs gpt-6-astra vs gpt-4o-transcribe
AttributeGemini 3.7 Flashgemini-3.7-flashGPT-6 Astragpt-6-astraGPT-4o Transcribegpt-4o-transcribe
Pricing
Input$0.375 / 1M$10.00 / 1M$1.25 / 1M
Output$1.88 / 1M$50.00 / 1M$0 / 1M
Cache Write (5m)$0.375 / 1M$10.00 / 1MNot applicable
Cache Write (1h)$0.375 / 1M$10.00 / 1MNot applicable
Cache Read$0.375 / 1M$10.00 / 1MNot applicable
Web Search$0 / 1M$0 / 1M$0 / 1M
Context
Max context1M1M128K
Max outputN/AN/AN/A
Capabilities
VisionYesYesNo
Function CallingYesYesNo
JSON ModeYesYesYes
StreamingYesYesNo
Catalogue
ProviderGoogleOpenAIOpenAI
Categorychatchatvoice
Charge typePay As You GoPay As You GoPay As You Go
Released
Description
SummaryGemini 3.7 Flash is Google's fast multimodal model designed for agentic workflows, coding, and complex multi-step reasoning. It combines responsive inference with reliable problem-solving capabilities, making it well suited for interactive and production-scale applications. Optimized for speed and dependable multi-step execution, Gemini 3.7 Flash is a strong choice for coding assistants, autonomous agents, and high-throughput workflows that require both low latency and capable reasoning.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.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.