Skip to content

Compare models

Put up to 4 models beside each other — token prices, context windows, capabilities and provider, from the same catalogue the model pages read.

  1. GPT-6 AstraOpenAIRemove
  2. Gemma 4 31B (Free)GoogleRemove
  3. Gemini 3.8 FlashGoogleRemove
gpt-6-astra vs gemma-4-31b-it:free vs gemini-3.8-flash
AttributeGPT-6 Astragpt-6-astraGemma 4 31B (Free)gemma-4-31b-it:freeGemini 3.8 Flashgemini-3.8-flash
Pricing
Input$10.00 / 1M$0 / 1M$0.75 / 1M
Output$50.00 / 1M$0 / 1M$3.75 / 1M
Cache Write (5m)$10.00 / 1M$0.75 / 1M
Cache Write (1h)$10.00 / 1M$0.75 / 1M
Cache Read$10.00 / 1M$0 / 1M$0.75 / 1M
Web Search$0 / 1M$0 / 1M
Cache Write$0 / 1M
Context
Max context1M262.1K1M
Max outputN/AN/AN/A
Capabilities
VisionYesYesYes
Function CallingYesYesYes
JSON ModeYesYesYes
StreamingYesYesYes
Catalogue
ProviderOpenAIGoogleGoogle
Categorychatchatchat
Charge typePay As You GoFreePay As You Go
Released
Description
SummaryGPT-6 Astra is OpenAI's flagship model for demanding end-to-end professional work, designed for advanced analysis, software engineering, deep research, scientific tasks, and document creation. It is particularly strong in long-horizon agentic workflows, including tasks that require sustained reasoning, tool orchestration, and computer and browser use, making it well suited for complex autonomous workflows and production-grade knowledge work.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.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.