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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. Llama 3.2 1b InstructMetaRemove
  3. Gemini 3.8 FlashGoogleRemove
gemini-3.7-flash vs llama-3.2-1b-instruct vs gemini-3.8-flash
AttributeGemini 3.7 Flashgemini-3.7-flashLlama 3.2 1b Instructllama-3.2-1b-instructGemini 3.8 Flashgemini-3.8-flash
Pricing
Input$0.375 / 1M$0.0025 / 1M$0.75 / 1M
Output$1.88 / 1M$0.005 / 1M$3.75 / 1M
Cache Write (5m)$0.375 / 1M$0.0025 / 1M$0.75 / 1M
Cache Write (1h)$0.375 / 1M$0.0025 / 1M$0.75 / 1M
Cache Read$0.375 / 1M$0.0025 / 1M$0.75 / 1M
Web Search$0 / 1M$0 / 1M$0 / 1M
Context
Max context1MN/A1M
Max outputN/AN/AN/A
Capabilities
VisionYesNoYes
Function CallingYesYesYes
JSON ModeYesYesYes
StreamingYesYesYes
Catalogue
ProviderGoogleMetaGoogle
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.Llama 3.2 1B is a lightweight 1-billion-parameter model built for efficient NLP tasks like summarization, conversation, and multilingual analysis. It runs well in low-resource environments, supports eight core languages (and can be fine-tuned for more), making it a good fit for developers who need capable, multilingual AI without heavy compute costs.Gemini 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.