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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-6.1 SolOpenAIRemove
  3. GPT-5.6 Sol ProOpenAIRemove
gemini-3.8-flash vs gpt-6.1-sol vs gpt-5.6-sol-pro
AttributeGemini 3.8 Flashgemini-3.8-flashGPT-6.1 Solgpt-6.1-solGPT-5.6 Sol Progpt-5.6-sol-pro
Pricing
Input$0.75 / 1M$2.00 / 1M$5.00 / 1M
Output$3.75 / 1M$10.00 / 1M$30.00 / 1M
Cache Write (5m)$0.75 / 1M$2.00 / 1M$5.00 / 1M
Cache Write (1h)$0.75 / 1M$2.00 / 1M$5.00 / 1M
Cache Read$0.75 / 1M$2.00 / 1M$5.00 / 1M
Web Search$0 / 1M$0 / 1M$0 / 1M
Context
Max context1M1M1M
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-6.1 Sol is an upgraded high-end model in OpenAI's GPT-6 series, positioned below the flagship GPT-6 Astra. It is optimized for agentic coding, computer use, document-heavy professional work, and multi-step business automation, delivering near-Astra-level capability at significantly lower cost. Compared with GPT-6 Sol, it offers improved factual reliability and stronger adherence to explicit constraints and user intent, making it well suited for complex, long-running agentic workflows where accurate and dependable execution is critical.GPT-5.6 Sol Pro uses the same underlying model as GPT-5.6 Sol, but runs with reasoning.mode set to pro for higher-quality responses on complex tasks. Optimized for deeper reasoning and more reliable execution, it is particularly well suited for advanced coding, long-horizon problem solving, and agentic workflows where accuracy and solution quality take priority over speed and cost.