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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. Llama 3 8BMetaRemove
  2. GPT-6 AstraOpenAIRemove
  3. Qwen3.8 27BAlibabaRemove
  4. GLM 5.3Z.AIRemove

4 is the maximum. Remove one to add another.

llama-3-8b vs gpt-6-astra vs qwen3.8-27b vs glm-5.3
AttributeLlama 3 8Bllama-3-8bGPT-6 Astragpt-6-astraQwen3.8 27Bqwen3.8-27bGLM 5.3glm-5.3
Pricing
Input$0.05 / 1M$10.00 / 1M$0.45 / 1M$1.40 / 1M
Output$0.08 / 1M$50.00 / 1M$3.20 / 1M$4.40 / 1M
Cache Write (5m)$0.05 / 1M$10.00 / 1M$0.45 / 1M$1.40 / 1M
Cache Write (1h)$0.05 / 1M$10.00 / 1M$0.45 / 1M$1.40 / 1M
Cache Read$0.05 / 1M$10.00 / 1M$0.45 / 1M$1.40 / 1M
Web Search$0 / 1M$0 / 1M$0 / 1M$0 / 1M
Context
Max context8.2K1M262K1M
Max outputN/AN/AN/AN/A
Capabilities
VisionNoYesYesNo
Function CallingNoYesYesYes
JSON ModeNoYesYesYes
StreamingNoYesYesYes
Catalogue
ProviderMetaOpenAIAlibabaZ.AI
Categorychatchatchatchat
Charge typePay As You GoPay As You GoPay As You GoPay 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.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.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.