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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 SolOpenAIRemove
  3. Muse Spark 1.3MetaRemove
gemini-3.8-flash vs gpt-6-sol vs muse-spark-1.3
AttributeGemini 3.8 Flashgemini-3.8-flashGPT-6 Solgpt-6-solMuse Spark 1.3muse-spark-1.3
Pricing
Input$0.75 / 1M$2.00 / 1M$1.25 / 1M
Output$3.75 / 1M$10.00 / 1M$4.25 / 1M
Cache Write (5m)$0.75 / 1M$2.00 / 1M$1.25 / 1M
Cache Write (1h)$0.75 / 1M$2.00 / 1M$1.25 / 1M
Cache Read$0.75 / 1M$2.00 / 1M$1.25 / 1M
Web Search$0 / 1M$0 / 1M$0 / 1M
Context
Max context1M1.1M1M
Max outputN/AN/AN/A
Capabilities
VisionYesYesYes
Function CallingYesYesYes
JSON ModeYesYesYes
StreamingYesYesYes
Catalogue
ProviderGoogleOpenAIMeta
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 Sol is OpenAI's cost-efficient high-end model in the GPT-6 series, positioned between the flagship GPT-6 Astra and the fast GPT-6 Luna tier. It is designed for professional knowledge work, agentic coding, business workflow automation, and computer-use tasks, with particular strength in long-horizon software engineering across real-world codebases. GPT-6 Sol approaches Astra-level factual reliability at a significantly lower cost, while sharing its clear and concise communication style. This balance of capability, reliability, and efficiency makes it well suited for production agents, complex engineering workflows, and scalable professional applicationsMuse Spark 1.3 is Meta's multimodal reasoning model designed for long-running agentic, multi-agent, and coding workflows. It maintains context and information across extended tasks, enabling reliable execution in complex, multi-step environments. The model is optimized to resolve conflicting information, seek clarification or confirmation when necessary, and execute concisely, making it well suited for autonomous agents, collaborative multi-agent systems, and long-horizon software engineering workflows.