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Put up to 4 models beside each other — token prices, context windows, capabilities and provider, from the same catalogue the model pages read.

  1. Qwen3.8 2.4T A95BAlibabaRemove
  2. GLM 5.3Z.AIRemove
  3. Gemini 3.8 FlashGoogleRemove
qwen3.8-2.4t-a95b vs glm-5.3 vs gemini-3.8-flash
AttributeQwen3.8 2.4T A95Bqwen3.8-2.4t-a95bGLM 5.3glm-5.3Gemini 3.8 Flashgemini-3.8-flash
Pricing
Input$1.80 / 1M$1.40 / 1M$0.75 / 1M
Output$5.40 / 1M$4.40 / 1M$3.75 / 1M
Cache Write (5m)$1.80 / 1M$1.40 / 1M$0.75 / 1M
Cache Write (1h)$1.80 / 1M$1.40 / 1M$0.75 / 1M
Cache Read$1.80 / 1M$1.40 / 1M$0.75 / 1M
Web Search$0 / 1M$0 / 1M$0 / 1M
Context
Max context262K1M1M
Max outputN/AN/AN/A
Capabilities
VisionYesNoYes
Function CallingYesYesYes
JSON ModeYesYesYes
StreamingYesYesYes
Catalogue
ProviderAlibabaZ.AIGoogle
Categorychatchatchat
Charge typePay As You GoPay As You GoPay As You Go
Released
Description
SummaryQwen3.8 2.4T A95B is Qwen's open-weight sparse Mixture-of-Experts (MoE) model and the open-weight counterpart to Qwen3.8 Max. It features 2.4T total parameters with 95B activated per token, combining frontier-scale capacity with efficient sparse inference. Designed for coding, research, complex reasoning, and agentic workflows, the model is well suited for demanding long-horizon tasks and advanced autonomous systems while providing the flexibility and customization benefits of open weights.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.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.