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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. Gemini 3.7 FlashGoogleRemove
  2. Qwen3.8 2.4T A95BAlibabaRemove
  3. GLM 5.3 FlashZ.AIRemove
gemini-3.7-flash vs qwen3.8-2.4t-a95b vs glm-5.3-flash
AttributeGemini 3.7 Flashgemini-3.7-flashQwen3.8 2.4T A95Bqwen3.8-2.4t-a95bGLM 5.3 Flashglm-5.3-flash
Pricing
Input$0.375 / 1M$1.80 / 1M$0.075 / 1M
Output$1.88 / 1M$5.40 / 1M$0.25 / 1M
Cache Write (5m)$0.375 / 1M$1.80 / 1M$0.075 / 1M
Cache Write (1h)$0.375 / 1M$1.80 / 1M$0.075 / 1M
Cache Read$0.375 / 1M$1.80 / 1M$0.075 / 1M
Web Search$0 / 1M$0 / 1M$0 / 1M
Context
Max context1M262K1M
Max outputN/AN/AN/A
Capabilities
VisionYesYesYes
Function CallingYesYesYes
JSON ModeYesYesYes
StreamingYesYesYes
Catalogue
ProviderGoogleAlibabaZ.AI
Categorychatchatchat
Charge typePay As You GoPay As You GoPay As You Go
Released
Description
SummaryGemini 3.7 Flash is Google's fast multimodal model designed for agentic workflows, coding, and complex multi-step reasoning. It combines responsive inference with reliable problem-solving capabilities, making it well suited for interactive and production-scale applications. Optimized for speed and dependable multi-step execution, Gemini 3.7 Flash is a strong choice for coding assistants, autonomous agents, and high-throughput workflows that require both low latency and capable reasoning.Qwen3.8 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-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.