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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 26B A4B (Free)GoogleRemove
  3. Muse Spark 1.3MetaRemove
glm-5.3 vs gemma-4-26b-a4b-it:free vs muse-spark-1.3
AttributeGLM 5.3glm-5.3Gemma 4 26B A4B (Free)gemma-4-26b-a4b-it:freeMuse Spark 1.3muse-spark-1.3
Pricing
Input$1.40 / 1M$0 / 1M$1.25 / 1M
Output$4.40 / 1M$0 / 1M$4.25 / 1M
Cache Write (5m)$1.40 / 1M$1.25 / 1M
Cache Write (1h)$1.40 / 1M$1.25 / 1M
Cache Read$1.40 / 1M$0 / 1M$1.25 / 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.AIGoogleMeta
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 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.Muse Spark 1.3 is Meta's multimodal reasoning model designed for long-running agentic, multi-agent, and coding workflows. It maintains context and information across extended tasks, enabling reliable execution in complex, multi-step environments. The model is optimized to resolve conflicting information, seek clarification or confirmation when necessary, and execute concisely, making it well suited for autonomous agents, collaborative multi-agent systems, and long-horizon software engineering workflows.