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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 3.8 FlashGoogleRemove
  2. Gemini 3.7 FlashGoogleRemove
  3. Gemma 4 31BGoogleRemove
gemini-3.8-flash vs gemini-3.7-flash vs gemma-4-31b-it
AttributeGemini 3.8 Flashgemini-3.8-flashGemini 3.7 Flashgemini-3.7-flashGemma 4 31Bgemma-4-31b-it
Pricing
Input$0.75 / 1M$0.375 / 1M$0.14 / 1M
Output$3.75 / 1M$1.88 / 1M$0.40 / 1M
Cache Write (5m)$0.75 / 1M$0.375 / 1M$0.14 / 1M
Cache Write (1h)$0.75 / 1M$0.375 / 1M$0.14 / 1M
Cache Read$0.75 / 1M$0.375 / 1M$0.14 / 1M
Web Search$0 / 1M$0 / 1M$0 / 1M
Context
Max context1M1M262.1K
Max outputN/AN/AN/A
Capabilities
VisionYesYesYes
Function CallingYesYesYes
JSON ModeYesYesYes
StreamingYesYesYes
Catalogue
ProviderGoogleGoogleGoogle
Categorychatchatchat
Charge typePay As You GoPay As You GoPay As You Go
Released
Description
SummaryGemini 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.Gemma 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.