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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. Gemma 4 31B (Free)GoogleRemove
  3. Gemini 3.8 FlashGoogleRemove
glm-5.3 vs gemma-4-31b-it:free vs gemini-3.8-flash
AttributeGLM 5.3glm-5.3Gemma 4 31B (Free)gemma-4-31b-it:freeGemini 3.8 Flashgemini-3.8-flash
Pricing
Input$1.40 / 1M$0 / 1M$0.75 / 1M
Output$4.40 / 1M$0 / 1M$3.75 / 1M
Cache Write (5m)$1.40 / 1M$0.75 / 1M
Cache Write (1h)$1.40 / 1M$0.75 / 1M
Cache Read$1.40 / 1M$0 / 1M$0.75 / 1M
Web Search$0 / 1M$0 / 1M
Cache Write$0 / 1M
Context
Max context1M262.1K1M
Max outputN/AN/AN/A
Capabilities
VisionNoYesYes
Function CallingYesYesYes
JSON ModeYesYesYes
StreamingYesYesYes
Catalogue
ProviderZ.AIGoogleGoogle
Categorychatchatchat
Charge typePay As You GoFreePay 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.Gemma 4 31B Instruct is Google DeepMind's 30.7B dense multimodal model, supporting text and image inputs with text outputs. It features a 256K token context window, configurable thinking/reasoning modes, native function calling, and broad multilingual support across 140+ languages. The model delivers strong performance in coding, reasoning, and document understanding, making it well suited for developer workflows, multilingual applications, and structured knowledge tasks.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.