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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 5.5AnthropicRemove
  3. Muse WonderMetaRemove
gemini-3.8-flash vs claude-sonnet-5.5 vs muse-wonder
AttributeGemini 3.8 Flashgemini-3.8-flashClaude Sonnet 5.5claude-sonnet-5.5Muse Wondermuse-wonder
Pricing
Input$0.75 / 1M$2.00 / 1M$4.00 / 1M
Output$3.75 / 1M$10.00 / 1M$20.00 / 1M
Cache Write (5m)$0.75 / 1M$2.50 / 1M$4.00 / 1M
Cache Write (1h)$0.75 / 1M$4.00 / 1M$4.00 / 1M
Cache Read$0.75 / 1M$0.10 / 1M$4.00 / 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
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.Claude Sonnet 5.5 is Anthropic's Sonnet-class model for well-scoped everyday work, serving as a direct upgrade to Sonnet 5. It excels at feature development, bug fixing, and creating polished documents, presentations, and spreadsheets, while offering clearer writing and communication than its predecessor.Muse Wonder is Meta’s frontier multimodal reasoning model, designed for complex personal and professional agentic workflows. It supports text, image, video, audio, and PDF inputs with text output and features a 1M-token context window for sustained reasoning across long-running tasks. Optimized for autonomous execution and multi-agent orchestration, Muse Wonder can plan, delegate to subagents, coordinate parallel tool use, recover from errors, and operate browsers and applications across desktop and mobile environments. With cross-conversation context retrieval and its Active Focus training approach, it is well suited for complex software engineering, multi-file refactoring, computer use, and end-to-end workflows requiring persistence, adaptability, and minimal supervision.