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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. DeepSeek V4.1 FlashDeepSeekRemove
  2. Gemini 3.7 FlashGoogleRemove
  3. GPT-5 ProOpenAIRemove
deepseek-v4.1-flash vs gemini-3.7-flash vs gpt-5-pro
AttributeDeepSeek V4.1 Flashdeepseek-v4.1-flashGemini 3.7 Flashgemini-3.7-flashGPT-5 Progpt-5-pro
Pricing
Input$0.30 / 1M$0.375 / 1M$7.50 / 1M
Output$1.20 / 1M$1.88 / 1M$60.00 / 1M
Cache Write (5m)$0.30 / 1M$0.375 / 1M$7.50 / 1M
Cache Write (1h)$0.30 / 1M$0.375 / 1M$7.50 / 1M
Cache Read$0.30 / 1M$0.375 / 1M$7.50 / 1M
Web Search$0 / 1M$0 / 1M$0 / 1M
Context
Max context1M1M400K
Max outputN/AN/AN/A
Capabilities
VisionYesYesYes
Function CallingYesYesYes
JSON ModeYesYesYes
StreamingYesYesYes
Catalogue
ProviderDeepSeekGoogleOpenAI
Categorychatchatchat
Charge typePay As You GoPay As You GoPay As You Go
Released
Description
SummaryDeepSeek 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.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.GPT-5 Pro is OpenAI's top model, optimized for complex, high-stakes tasks that require careful step-by-step reasoning and precise instruction following. It delivers stronger code quality, clearer writing, and better factual reliability, with support for test-time routing and intent cues like “think hard about this.” It also reduces hallucinations and sycophancy while improving performance across coding, writing, and health-related workloads.