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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. Gemma 4 31B (Free)GoogleRemove
  3. Gemini 3.8 FlashGoogleRemove
muse-spark-1.3 vs gemma-4-31b-it:free vs gemini-3.8-flash
AttributeMuse Spark 1.3muse-spark-1.3Gemma 4 31B (Free)gemma-4-31b-it:freeGemini 3.8 Flashgemini-3.8-flash
Pricing
Input$1.25 / 1M$0 / 1M$0.75 / 1M
Output$4.25 / 1M$0 / 1M$3.75 / 1M
Cache Write (5m)$1.25 / 1M$0.75 / 1M
Cache Write (1h)$1.25 / 1M$0.75 / 1M
Cache Read$1.25 / 1M$0 / 1M$0.75 / 1M
Web Search$0 / 1M$0 / 1M
Cache Write$0 / 1M
Context
Max context1M262.1K1M
Max outputN/AN/AN/A
Capabilities
VisionYesYesYes
Function CallingYesYesYes
JSON ModeYesYesYes
StreamingYesYesYes
Catalogue
ProviderMetaGoogleGoogle
Categorychatchatchat
Charge typePay As You GoFreePay 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.Gemma 4 31B Instruct is Google DeepMind's 30.7B dense multimodal model, supporting text and image inputs with text outputs. It features a 256K token context window, configurable thinking/reasoning modes, native function calling, and broad multilingual support across 140+ languages. The model delivers strong performance in coding, reasoning, and document understanding, making it well suited for developer workflows, multilingual applications, and structured knowledge tasks.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.