Skip to content

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. DeepSeek V4.1 FlashDeepSeekRemove
  3. Gemma 2 27BGoogleRemove
  4. Muse Spark 1.3MetaRemove

4 is the maximum. Remove one to add another.

glm-5.3 vs deepseek-v4.1-flash vs gemma-2-27b vs muse-spark-1.3
AttributeGLM 5.3glm-5.3DeepSeek V4.1 Flashdeepseek-v4.1-flashGemma 2 27Bgemma-2-27bMuse Spark 1.3muse-spark-1.3
Pricing
Input$1.40 / 1M$0.30 / 1M$0.81 / 1M$1.25 / 1M
Output$4.40 / 1M$1.20 / 1M$0.81 / 1M$4.25 / 1M
Cache Write (5m)$1.40 / 1M$0.30 / 1M$0.81 / 1M$1.25 / 1M
Cache Write (1h)$1.40 / 1M$0.30 / 1M$0.81 / 1M$1.25 / 1M
Cache Read$1.40 / 1M$0.30 / 1M$0.81 / 1M$1.25 / 1M
Web Search$0 / 1M$0 / 1M$0 / 1M$0 / 1M
Context
Max context1M1M8.2K1M
Max outputN/AN/AN/AN/A
Capabilities
VisionNoYesNoYes
Function CallingYesYesNoYes
JSON ModeYesYesNoYes
StreamingYesYesNoYes
Catalogue
ProviderZ.AIDeepSeekGoogleMeta
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.DeepSeek V4.1 Flash is a cost-efficient sparse Mixture-of-Experts (MoE) model in DeepSeek's V4.1 family, optimized for coding, reasoning, and agentic workflows. Despite its efficiency-focused positioning, DeepSeek reports that it surpasses the previous V4 Pro in performance, inference speed, and overall task completion time. The model is particularly strong at long-horizon, multi-step execution, making it well suited for coding agents, complex problem solving, and autonomous workflows that must reliably carry tasks through to completion.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.