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Compare models

Put up to 4 models beside each other — token prices, context windows, capabilities and provider, from the same catalogue the model pages read.

  1. MiniMax M3MiniMaxRemove
  2. Gemini 3.8 FlashGoogleRemove
  3. Gemini 3.7 FlashGoogleRemove
  4. Muse Spark 1.3MetaRemove

4 is the maximum. Remove one to add another.

minimax-m3 vs gemini-3.8-flash vs gemini-3.7-flash vs muse-spark-1.3
AttributeMiniMax M3minimax-m3Gemini 3.8 Flashgemini-3.8-flashGemini 3.7 Flashgemini-3.7-flashMuse Spark 1.3muse-spark-1.3
Pricing
Input$0.30 / 1M$0.75 / 1M$0.375 / 1M$1.25 / 1M
Output$1.20 / 1M$3.75 / 1M$1.88 / 1M$4.25 / 1M
Cache Write (5m)$0.30 / 1M$0.75 / 1M$0.375 / 1M$1.25 / 1M
Cache Write (1h)$0.30 / 1M$0.75 / 1M$0.375 / 1M$1.25 / 1M
Cache Read$0.30 / 1M$0.75 / 1M$0.375 / 1M$1.25 / 1M
Web Search$0 / 1M$0 / 1M$0 / 1M$0 / 1M
Context
Max context1M1M1M1M
Max outputN/AN/AN/AN/A
Capabilities
VisionYesYesYesYes
Function CallingYesYesYesYes
JSON ModeYesYesYesYes
StreamingYesYesYesYes
Catalogue
ProviderMiniMaxGoogleGoogleMeta
Categorychatchatchatchat
Charge typePay As You GoPay As You GoPay As You GoPay As You Go
Released
Description
SummaryMiniMax-M3 is a multimodal foundation model from MiniMax, supporting text, image, and video inputs with text output and a 1M-token context window. It is designed for long-horizon agentic workflows, coding, and tool-driven task execution, enabling sustained reasoning across complex tasks. Built on MiniMax Sparse Attention (MSA), the model dramatically improves long-context efficiency by replacing full attention with KV-block selection, reducing compute costs at 1M-token contexts while maintaining strong performance. Trained as a native multimodal model and optimized for multi-turn, production-style collaboration, MiniMax-M3 excels at extended, multi-step workflows rather than single-turn interactions.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.Gemini 3.7 Flash is Google's fast multimodal model designed for agentic workflows, coding, and complex multi-step reasoning. It combines responsive inference with reliable problem-solving capabilities, making it well suited for interactive and production-scale applications. Optimized for speed and dependable multi-step execution, Gemini 3.7 Flash is a strong choice for coding assistants, autonomous agents, and high-throughput workflows that require both low latency and capable reasoning.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.