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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. GPT-6 Astra ProOpenAIRemove
  2. Gemini 3.8 FlashGoogleRemove
  3. GLM 5.3Z.AIRemove
  4. Qwen3.8 27BAlibabaRemove

4 is the maximum. Remove one to add another.

gpt-6-astra-pro vs gemini-3.8-flash vs glm-5.3 vs qwen3.8-27b
AttributeGPT-6 Astra Progpt-6-astra-proGemini 3.8 Flashgemini-3.8-flashGLM 5.3glm-5.3Qwen3.8 27Bqwen3.8-27b
Pricing
Input$10.00 / 1M$0.75 / 1M$1.40 / 1M$0.45 / 1M
Output$50.00 / 1M$3.75 / 1M$4.40 / 1M$3.20 / 1M
Cache Write (5m)$10.00 / 1M$0.75 / 1M$1.40 / 1M$0.45 / 1M
Cache Write (1h)$10.00 / 1M$0.75 / 1M$1.40 / 1M$0.45 / 1M
Cache Read$10.00 / 1M$0.75 / 1M$1.40 / 1M$0.45 / 1M
Web Search$0 / 1M$0 / 1M$0 / 1M$0 / 1M
Context
Max context1M1M1M262K
Max outputN/AN/AN/AN/A
Capabilities
VisionYesYesNoYes
Function CallingYesYesYesYes
JSON ModeYesYesYesYes
StreamingYesYesYesYes
Catalogue
ProviderOpenAIGoogleZ.AIAlibaba
Categorychatchatchatchat
Charge typePay As You GoPay As You GoPay As You GoPay As You Go
Released
Description
SummaryGPT-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.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.GLM-5.3 is Z.ai's large-scale reasoning model designed for complex software engineering and long-horizon agentic workflows. It supports text input and output with a 1M-token context window, enabling sustained reasoning across large codebases and extended multi-step tasks. Building on GLM-5.2, it delivers stronger coding performance while improving the balance between capability and token efficiency, making it well suited for autonomous coding agents, large-scale engineering workflows, and complex task execution.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.