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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. GLM 5.3Z.AIRemove
  3. Qwen3.8 27BAlibabaRemove
  4. Qwen3 14BAlibabaRemove

4 is the maximum. Remove one to add another.

gpt-6-astra-pro vs glm-5.3 vs qwen3.8-27b vs qwen3-14b
AttributeGPT-6 Astra Progpt-6-astra-proGLM 5.3glm-5.3Qwen3.8 27Bqwen3.8-27bQwen3 14Bqwen3-14b
Pricing
Input$10.00 / 1M$1.40 / 1M$0.45 / 1M$0.08 / 1M
Output$50.00 / 1M$4.40 / 1M$3.20 / 1M$0.24 / 1M
Cache Write (5m)$10.00 / 1M$1.40 / 1M$0.45 / 1M$0.08 / 1M
Cache Write (1h)$10.00 / 1M$1.40 / 1M$0.45 / 1M$0.08 / 1M
Cache Read$10.00 / 1M$1.40 / 1M$0.45 / 1M$0.08 / 1M
Web Search$0 / 1M$0 / 1M$0 / 1M$0 / 1M
Context
Max context1M1M262K41.0K
Max outputN/AN/AN/AN/A
Capabilities
VisionYesNoYesNo
Function CallingYesYesYesNo
JSON ModeYesYesYesNo
StreamingYesYesYesNo
Catalogue
ProviderOpenAIZ.AIAlibabaAlibaba
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.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.