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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. GLM 5.3Z.AIRemove
  2. Gemini 3.7 FlashGoogleRemove
  3. Gemini 2.5 Pro DeepSearchGoogleRemove
glm-5.3 vs gemini-3.7-flash vs gemini-2.5-pro-deepsearch
AttributeGLM 5.3glm-5.3Gemini 3.7 Flashgemini-3.7-flashGemini 2.5 Pro DeepSearchgemini-2.5-pro-deepsearch
Pricing
Input$1.40 / 1M$0.375 / 1M$7.00 / 1M
Output$4.40 / 1M$1.88 / 1M$56.00 / 1M
Cache Write (5m)$1.40 / 1M$0.375 / 1M$7.00 / 1M
Cache Write (1h)$1.40 / 1M$0.375 / 1M$7.00 / 1M
Cache Read$1.40 / 1M$0.375 / 1M$7.00 / 1M
Web Search$0 / 1M$0 / 1M$0 / 1M
Context
Max context1M1M1.0M
Max outputN/AN/AN/A
Capabilities
VisionNoYesYes
Function CallingYesYesYes
JSON ModeYesYesYes
StreamingYesYesYes
Catalogue
ProviderZ.AIGoogleGoogle
Categorychatchatchat
Charge typePay As You GoPay As You GoPay As You Go
Released
Description
SummaryGLM-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 3.7 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.Gemini 2.5 Pro is Google's top reasoning model for coding, math, and scientific work. It uses built-in “thinking” to deliver more accurate, context-aware answers and ranks at the top of major benchmarks like LMArena, showing strong alignment and problem-solving ability.