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Compare models

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. Qwen3.8 27BAlibabaRemove
  3. Muse Spark 1.3MetaRemove
  4. MiniMax M1MiniMaxRemove

4 is the maximum. Remove one to add another.

glm-5.3 vs qwen3.8-27b vs muse-spark-1.3 vs minimax-m1
AttributeGLM 5.3glm-5.3Qwen3.8 27Bqwen3.8-27bMuse Spark 1.3muse-spark-1.3MiniMax M1minimax-m1
Pricing
Input$1.40 / 1M$0.45 / 1M$1.25 / 1M$0.30 / 1M
Output$4.40 / 1M$3.20 / 1M$4.25 / 1M$1.65 / 1M
Cache Write (5m)$1.40 / 1M$0.45 / 1M$1.25 / 1M$0.30 / 1M
Cache Write (1h)$1.40 / 1M$0.45 / 1M$1.25 / 1M$0.30 / 1M
Cache Read$1.40 / 1M$0.45 / 1M$1.25 / 1M$0.30 / 1M
Web Search$0 / 1M$0 / 1M$0 / 1M$0 / 1M
Context
Max context1M262K1M1M
Max outputN/AN/AN/AN/A
Capabilities
VisionNoYesYesNo
Function CallingYesYesYesNo
JSON ModeYesYesYesNo
StreamingYesYesYesNo
Catalogue
ProviderZ.AIAlibabaMetaMiniMax
Categorychatchatchatchat
Charge typePay As You GoPay 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.Qwen3.8 27B is an open-weight dense vision-language model from Qwen, designed for coding, professional knowledge work, research, and multimodal interaction. It combines strong text and visual understanding with capabilities optimized for sustained, real-world agentic tasks. The model supports flexible thinking modes that can be enabled for deeper reasoning or disabled for faster execution, making it well suited for long-running agents, multimodal workflows, coding assistants, and cost-conscious self-hosted deployments.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.