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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 3.7 FlashGoogleRemove
  2. DeepSeek V3.2 (Thinking)DeepSeekRemove
  3. Muse Spark 1.3MetaRemove
gemini-3.7-flash vs deepseek-v3.2-thinking vs muse-spark-1.3
AttributeGemini 3.7 Flashgemini-3.7-flashDeepSeek V3.2 (Thinking)deepseek-v3.2-thinkingMuse Spark 1.3muse-spark-1.3
Pricing
Input$0.375 / 1M$0.19 / 1M$1.25 / 1M
Output$1.88 / 1M$0.275 / 1M$4.25 / 1M
Cache Write (5m)$0.375 / 1M$0.19 / 1M$1.25 / 1M
Cache Write (1h)$0.375 / 1M$0.19 / 1M$1.25 / 1M
Cache Read$0.375 / 1M$0.19 / 1M$1.25 / 1M
Web Search$0 / 1M$0 / 1M$0 / 1M
Context
Max context1M163.8K1M
Max outputN/AN/AN/A
Capabilities
VisionYesYesYes
Function CallingYesYesYes
JSON ModeYesYesYes
StreamingYesYesYes
Catalogue
ProviderGoogleDeepSeekMeta
Categorychatchatchat
Charge typePay As You GoPay As You GoPay As You Go
Released
Description
SummaryGemini 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.DeepSeek-V3.2 is an efficiency-focused large model that combines strong reasoning with reliable tool use. It introduces DeepSeek Sparse Attention to lower compute costs for long contexts while preserving quality, and uses large-scale reinforcement learning to reach GPT-5-class reasoning (including top IMO/IOI results). An agentic task-synthesis pipeline improves how it reasons with tools in interactive settings — and developers can toggle reasoning on or off as needed.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.