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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 2.5 Flash Preview (thinking)GoogleRemove
  2. GPT-6 AstraOpenAIRemove
  3. Gemini 3.8 FlashGoogleRemove
gemini-2.5-flash-preview:thinking vs gpt-6-astra vs gemini-3.8-flash
AttributeGemini 2.5 Flash Preview (thinking)gemini-2.5-flash-preview:thinkingGPT-6 Astragpt-6-astraGemini 3.8 Flashgemini-3.8-flash
Pricing
Input$0.075 / 1M$10.00 / 1M$0.75 / 1M
Output$1.75 / 1M$50.00 / 1M$3.75 / 1M
Cache Write (5m)$0.075 / 1M$10.00 / 1M$0.75 / 1M
Cache Write (1h)$0.075 / 1M$10.00 / 1M$0.75 / 1M
Cache Read$0.075 / 1M$10.00 / 1M$0.75 / 1M
Web Search$0 / 1M$0 / 1M$0 / 1M
Context
Max context1.0M1M1M
Max outputN/AN/AN/A
Capabilities
VisionYesYesYes
Function CallingYesYesYes
JSON ModeYesYesYes
StreamingYesYesYes
Catalogue
ProviderGoogleOpenAIGoogle
Categorychatchatchat
Charge typePay As You GoPay As You GoPay As You Go
Released
Description
SummaryGemini 2.5 Flash Preview (May 2025) is Google's high-performance general model built for advanced reasoning, coding, math, and science. It includes built-in “thinking” features to deliver more accurate, context-aware answers.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.