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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. GPT-4o Mini TranscribeOpenAIRemove
  3. DeepSeek V4.1 FlashDeepSeekRemove
gemini-3.7-flash vs gpt-4o-mini-transcribe vs deepseek-v4.1-flash
AttributeGemini 3.7 Flashgemini-3.7-flashGPT-4o Mini Transcribegpt-4o-mini-transcribeDeepSeek V4.1 Flashdeepseek-v4.1-flash
Pricing
Input$0.375 / 1M$0.625 / 1M$0.30 / 1M
Output$1.88 / 1M$0.625 / 1M$1.20 / 1M
Cache Write (5m)$0.375 / 1MNot applicable$0.30 / 1M
Cache Write (1h)$0.375 / 1MNot applicable$0.30 / 1M
Cache Read$0.375 / 1MNot applicable$0.30 / 1M
Web Search$0 / 1M$0 / 1M$0 / 1M
Context
Max context1M128K1M
Max outputN/AN/AN/A
Capabilities
VisionYesNoYes
Function CallingYesNoYes
JSON ModeYesYesYes
StreamingYesNoYes
Catalogue
ProviderGoogleOpenAIDeepSeek
Categorychatvoicechat
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.GPT-4o Mini Transcribe is a smaller, cost-efficient speech-to-text model built on GPT-4o Mini's audio capabilities. It is designed for high-volume transcription workloads, delivering reliable performance with lower cost and latency. Priced per token (input and output), it provides transparent, fine-grained billing, making it well suited for scalable transcription pipelines, real-time applications, and cost-sensitive deployments.DeepSeek V4.1 Flash is a cost-efficient sparse Mixture-of-Experts (MoE) model in DeepSeek's V4.1 family, optimized for coding, reasoning, and agentic workflows. Despite its efficiency-focused positioning, DeepSeek reports that it surpasses the previous V4 Pro in performance, inference speed, and overall task completion time. The model is particularly strong at long-horizon, multi-step execution, making it well suited for coding agents, complex problem solving, and autonomous workflows that must reliably carry tasks through to completion.