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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 31B (Free)GoogleRemove
  2. GLM 5.3Z.AIRemove
  3. Muse Spark 1.3MetaRemove
gemma-4-31b-it:free vs glm-5.3 vs muse-spark-1.3
AttributeGemma 4 31B (Free)gemma-4-31b-it:freeGLM 5.3glm-5.3Muse Spark 1.3muse-spark-1.3
Pricing
Input$0 / 1M$1.40 / 1M$1.25 / 1M
Output$0 / 1M$4.40 / 1M$4.25 / 1M
Cache Write$0 / 1M
Cache Read$0 / 1M$1.40 / 1M$1.25 / 1M
Cache Write (5m)$1.40 / 1M$1.25 / 1M
Cache Write (1h)$1.40 / 1M$1.25 / 1M
Web Search$0 / 1M$0 / 1M
Context
Max context262.1K1M1M
Max outputN/AN/AN/A
Capabilities
VisionYesNoYes
Function CallingYesYesYes
JSON ModeYesYesYes
StreamingYesYesYes
Catalogue
ProviderGoogleZ.AIMeta
Categorychatchatchat
Charge typeFreePay As You GoPay As You Go
Released
Description
SummaryGemma 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.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.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.