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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. Veo 3GoogleRemove
  2. Gemini 3.8 FlashGoogleRemove
  3. GPT-6 Astra ProOpenAIRemove
  4. Qwen3.8 27BAlibabaRemove

4 is the maximum. Remove one to add another.

veo3 vs gemini-3.8-flash vs gpt-6-astra-pro vs qwen3.8-27b
AttributeVeo 3veo3Gemini 3.8 Flashgemini-3.8-flashGPT-6 Astra Progpt-6-astra-proQwen3.8 27Bqwen3.8-27b
Pricing
Request$1.26 / request
BillingPay Per Request
Cache Write (5m)Not applicable$0.75 / 1M$10.00 / 1M$0.45 / 1M
Cache Write (1h)Not applicable$0.75 / 1M$10.00 / 1M$0.45 / 1M
Cache ReadNot applicable$0.75 / 1M$10.00 / 1M$0.45 / 1M
Input$0.75 / 1M$10.00 / 1M$0.45 / 1M
Output$3.75 / 1M$50.00 / 1M$3.20 / 1M
Web Search$0 / 1M$0 / 1M$0 / 1M
Context
Max contextN/A1M1M262K
Max outputN/AN/AN/AN/A
Capabilities
VisionNoYesYesYes
Function CallingNoYesYesYes
JSON ModeNoYesYesYes
StreamingNoYesYesYes
Catalogue
ProviderGoogleGoogleOpenAIAlibaba
Categoryvideochatchatchat
Charge typePay Per RequestPay 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.GPT-6 Astra Pro uses the same underlying model as GPT-6 Astra, but runs with reasoning.mode set to pro for higher-quality responses on complex tasks. Optimized for deeper reasoning, greater accuracy, and more reliable multi-step execution, it is well suited for demanding coding, analysis, and agentic workflows where solution quality takes priority over speed and cost.Qwen3.8 27B is an open-weight dense vision-language model from Qwen, designed for coding, professional knowledge work, research, and multimodal interaction. It combines strong text and visual understanding with capabilities optimized for sustained, real-world agentic tasks. The model supports flexible thinking modes that can be enabled for deeper reasoning or disabled for faster execution, making it well suited for long-running agents, multimodal workflows, coding assistants, and cost-conscious self-hosted deployments.