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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 3.8 FlashGoogleRemove
  2. GPT-5.1 Codex (Mini)OpenAIRemove
  3. GPT-6 Astra ProOpenAIRemove
gemini-3.8-flash vs gpt-5.1-codex-mini vs gpt-6-astra-pro
AttributeGemini 3.8 Flashgemini-3.8-flashGPT-5.1 Codex (Mini)gpt-5.1-codex-miniGPT-6 Astra Progpt-6-astra-pro
Pricing
Input$0.75 / 1M$0.125 / 1M$10.00 / 1M
Output$3.75 / 1M$1.00 / 1M$50.00 / 1M
Cache Write (5m)$0.75 / 1M$0.125 / 1M$10.00 / 1M
Cache Write (1h)$0.75 / 1M$0.125 / 1M$10.00 / 1M
Cache Read$0.75 / 1M$0.125 / 1M$10.00 / 1M
Web Search$0 / 1M$0 / 1M$0 / 1M
Context
Max context1M400K1M
Max outputN/AN/AN/A
Capabilities
VisionYesYesYes
Function CallingYesYesYes
JSON ModeYesYesYes
StreamingYesYesYes
Catalogue
ProviderGoogleOpenAIOpenAI
Categorychatchatchat
Charge typePay As You GoPay As You GoPay As You Go
Released
Description
SummaryGemini 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-5.1 Codex is a coding-focused version of GPT-5.1 designed for both interactive development and long autonomous engineering tasks. It can build projects, add features, debug, refactor, and review code with higher steerability and cleaner outputs than GPT-5.1. It integrates with developer tools (CLI, IDEs, GitHub, cloud), supports adjustable reasoning effort, handles images/screenshots for UI work, and uses tools for search and environment setup — making it purpose-built for agentic coding workflows.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.