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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 3.7 FlashGoogleRemove
  2. Gemini 3.8 FlashGoogleRemove
  3. GPT-5.1-Codex-MaxOpenAIRemove
gemini-3.7-flash vs gemini-3.8-flash vs gpt-5.1-codex-max
AttributeGemini 3.7 Flashgemini-3.7-flashGemini 3.8 Flashgemini-3.8-flashGPT-5.1-Codex-Maxgpt-5.1-codex-max
Pricing
Input$0.375 / 1M$0.75 / 1M$0.375 / 1M
Output$1.88 / 1M$3.75 / 1M$3.00 / 1M
Cache Write (5m)$0.375 / 1M$0.75 / 1M$0.375 / 1M
Cache Write (1h)$0.375 / 1M$0.75 / 1M$0.375 / 1M
Cache Read$0.375 / 1M$0.75 / 1M$0.375 / 1M
Web Search$0 / 1M$0 / 1M$0 / 1M
Context
Max context1M1M400K
Max outputN/AN/AN/A
Capabilities
VisionYesYesYes
Function CallingYesYesYes
JSON ModeYesYesYes
StreamingYesYesYes
Catalogue
ProviderGoogleGoogleOpenAI
Categorychatchatchat
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.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-5.1 Codex Max is OpenAI's advanced agentic coding model, built for long-running, high-context development work. Using an upgraded 5.1 reasoning stack and training on real engineering workflows, it delivers faster performance, stronger reasoning, and better token efficiency across the full software lifecycle.