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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. Muse Spark 1.3MetaRemove
  2. GPT-5 CodexOpenAIRemove
  3. Gemini 3.8 FlashGoogleRemove
muse-spark-1.3 vs gpt-5-codex vs gemini-3.8-flash
AttributeMuse Spark 1.3muse-spark-1.3GPT-5 Codexgpt-5-codexGemini 3.8 Flashgemini-3.8-flash
Pricing
Input$1.25 / 1M$0.625 / 1M$0.75 / 1M
Output$4.25 / 1M$5.00 / 1M$3.75 / 1M
Cache Write (5m)$1.25 / 1M$0.625 / 1M$0.75 / 1M
Cache Write (1h)$1.25 / 1M$0.625 / 1M$0.75 / 1M
Cache Read$1.25 / 1M$0.625 / 1M$0.75 / 1M
Web Search$0 / 1M$0 / 1M$0 / 1M
Context
Max context1M400K1M
Max outputN/AN/AN/A
Capabilities
VisionYesYesYes
Function CallingYesYesYes
JSON ModeYesYesYes
StreamingYesYesYes
Catalogue
ProviderMetaOpenAIGoogle
Categorychatchatchat
Charge typePay As You GoPay As You GoPay As You Go
Released
Description
SummaryMuse 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.GPT-5 Codex is a coding-focused version of GPT-5 built for both interactive development and long autonomous engineering tasks. It can create projects, add features, debug, refactor, and review code, producing cleaner and more controllable outputs than GPT-5. It integrates with developer tools (CLI, IDEs, GitHub, cloud), supports adjustable reasoning effort, handles multimodal inputs, and uses tools for search and environment setup — making it purpose-built for agentic coding workflows.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.