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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. Claude Sonnet 4.6AnthropicRemove
  3. Muse Spark 1.3MetaRemove
gemini-3.8-flash vs claude-sonnet-4-6 vs muse-spark-1.3
AttributeGemini 3.8 Flashgemini-3.8-flashClaude Sonnet 4.6claude-sonnet-4-6Muse Spark 1.3muse-spark-1.3
Pricing
Input$0.75 / 1M$2.40 / 1M$1.25 / 1M
Output$3.75 / 1M$12.00 / 1M$4.25 / 1M
Cache Write (5m)$0.75 / 1M$3.00 / 1M$1.25 / 1M
Cache Write (1h)$0.75 / 1M$4.80 / 1M$1.25 / 1M
Cache Read$0.75 / 1M$0.24 / 1M$1.25 / 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
ProviderGoogleAnthropicMeta
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.Sonnet 4.6 is Anthropic's most capable Sonnet-class model, delivering frontier-level performance across coding, agent workflows, and professional knowledge tasks. It excels at iterative development, complex codebase navigation, and end-to-end project execution, supported by strong contextual understanding and persistent task handling. Beyond engineering tasks, Sonnet 4.6 produces polished documents and analyses while demonstrating reliable computer-use capabilities for web QA, workflow automation, and structured productivity workflows, making it well suited for both development and professional applications.Muse 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.