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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. Gemma 4 26B A4BGoogleRemove
  2. GLM 5.3Z.AIRemove
  3. Gemini 3.8 FlashGoogleRemove
gemma-4-26b-a4b-it vs glm-5.3 vs gemini-3.8-flash
AttributeGemma 4 26B A4Bgemma-4-26b-a4b-itGLM 5.3glm-5.3Gemini 3.8 Flashgemini-3.8-flash
Pricing
Input$0.13 / 1M$1.40 / 1M$0.75 / 1M
Output$0.40 / 1M$4.40 / 1M$3.75 / 1M
Cache Write (5m)$0.13 / 1M$1.40 / 1M$0.75 / 1M
Cache Write (1h)$0.13 / 1M$1.40 / 1M$0.75 / 1M
Cache Read$0.13 / 1M$1.40 / 1M$0.75 / 1M
Web Search$0 / 1M$0 / 1M$0 / 1M
Context
Max context262.1K1M1M
Max outputN/AN/AN/A
Capabilities
VisionYesNoYes
Function CallingYesYesYes
JSON ModeYesYesYes
StreamingYesYesYes
Catalogue
ProviderGoogleZ.AIGoogle
Categorychatchatchat
Charge typePay As You GoPay As You GoPay As You Go
Released
Description
SummaryGemma 4 26B A4B IT is an instruction-tuned Mixture-of-Experts (MoE) model from Google DeepMind, featuring 25.2B total parameters with only 3.8B activated per token—delivering near 31B-class quality at a fraction of the compute cost. It supports multimodal inputs including text, images, and video (up to 60s at 1fps). The model includes a 256K token context window, native function calling, configurable thinking/reasoning modes, and structured output support. Released under the Apache 2.0 license, it is well suited for efficient, production-ready multimodal and agentic applications.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.