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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. SonarPerplexityRemove
  3. GLM 5.3 FlashZ.AIRemove
  4. DeepSeek V4.1 FlashDeepSeekRemove

4 is the maximum. Remove one to add another.

gpt-6-astra-pro vs sonar vs glm-5.3-flash vs deepseek-v4.1-flash
AttributeGPT-6 Astra Progpt-6-astra-proSonarsonarGLM 5.3 Flashglm-5.3-flashDeepSeek V4.1 Flashdeepseek-v4.1-flash
Pricing
Input$10.00 / 1M$1.00 / 1M$0.075 / 1M$0.30 / 1M
Output$50.00 / 1M$1.00 / 1M$0.25 / 1M$1.20 / 1M
Cache Write (5m)$10.00 / 1M$1.00 / 1M$0.075 / 1M$0.30 / 1M
Cache Write (1h)$10.00 / 1M$1.00 / 1M$0.075 / 1M$0.30 / 1M
Cache Read$10.00 / 1M$1.00 / 1M$0.075 / 1M$0.30 / 1M
Web Search$0 / 1M$0 / 1M$0 / 1M$0 / 1M
Context
Max context1M127.1K1M1M
Max outputN/AN/AN/AN/A
Capabilities
VisionYesNoYesYes
Function CallingYesNoYesYes
JSON ModeYesNoYesYes
StreamingYesNoYesYes
Catalogue
ProviderOpenAIPerplexityZ.AIDeepSeek
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-Flash is Z.AI's efficient native multimodal model, designed for coding and long-horizon agentic workflows. It combines strong multimodal capabilities with an architecture optimized for responsive, cost-efficient task execution. Built on a hybrid sparse and linear attention architecture, GLM-5.3-Flash maintains accurate long-context behavior while reducing computational overhead, making it well suited for coding agents, extended multi-step tasks, and scalable production workloads.DeepSeek V4.1 Flash is a cost-efficient sparse Mixture-of-Experts (MoE) model in DeepSeek's V4.1 family, optimized for coding, reasoning, and agentic workflows. Despite its efficiency-focused positioning, DeepSeek reports that it surpasses the previous V4 Pro in performance, inference speed, and overall task completion time. The model is particularly strong at long-horizon, multi-step execution, making it well suited for coding agents, complex problem solving, and autonomous workflows that must reliably carry tasks through to completion.