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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. Gemma 4 31BGoogleRemove
  2. DeepSeek V4.1 FlashDeepSeekRemove
  3. Gemini 3.7 FlashGoogleRemove
gemma-4-31b-it vs deepseek-v4.1-flash vs gemini-3.7-flash
AttributeGemma 4 31Bgemma-4-31b-itDeepSeek V4.1 Flashdeepseek-v4.1-flashGemini 3.7 Flashgemini-3.7-flash
Pricing
Input$0.14 / 1M$0.30 / 1M$0.375 / 1M
Output$0.40 / 1M$1.20 / 1M$1.88 / 1M
Cache Write (5m)$0.14 / 1M$0.30 / 1M$0.375 / 1M
Cache Write (1h)$0.14 / 1M$0.30 / 1M$0.375 / 1M
Cache Read$0.14 / 1M$0.30 / 1M$0.375 / 1M
Web Search$0 / 1M$0 / 1M$0 / 1M
Context
Max context262.1K1M1M
Max outputN/AN/AN/A
Capabilities
VisionYesYesYes
Function CallingYesYesYes
JSON ModeYesYesYes
StreamingYesYesYes
Catalogue
ProviderGoogleDeepSeekGoogle
Categorychatchatchat
Charge typePay As You GoPay As You GoPay As You Go
Released
Description
SummaryGemma 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.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.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.