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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. Muse WonderMetaRemove
  2. Perceptron Mk1.5PerceptronRemove
  3. DeepSeek V4.1 FlashDeepSeekRemove
  4. GLM 5.3Z.AIRemove

4 is the maximum. Remove one to add another.

muse-wonder vs perceptron-mk1.5 vs deepseek-v4.1-flash vs glm-5.3
AttributeMuse Wondermuse-wonderPerceptron Mk1.5perceptron-mk1.5DeepSeek V4.1 Flashdeepseek-v4.1-flashGLM 5.3glm-5.3
Pricing
Input$4.00 / 1M$0.15 / 1M$0.30 / 1M$1.40 / 1M
Output$20.00 / 1M$1.50 / 1M$1.20 / 1M$4.40 / 1M
Cache Write (5m)$4.00 / 1M$0.15 / 1M$0.30 / 1M$1.40 / 1M
Cache Write (1h)$4.00 / 1M$0.15 / 1M$0.30 / 1M$1.40 / 1M
Cache Read$4.00 / 1M$0.15 / 1M$0.30 / 1M$1.40 / 1M
Web Search$0 / 1M$0 / 1M$0 / 1M$0 / 1M
Context
Max context1M36.9K1M1M
Max outputN/AN/AN/AN/A
Capabilities
VisionYesNoYesNo
Function CallingYesNoYesYes
JSON ModeYesNoYesYes
StreamingYesYesYesYes
Catalogue
ProviderMetaPerceptronDeepSeekZ.AI
Categorychatchatchatchat
Charge typePay As You GoPay As You GoPay As You GoPay As You Go
Released—2026-09-25——
Description
SummaryMuse Wonder is Meta’s frontier multimodal reasoning model, designed for complex personal and professional agentic workflows. It supports text, image, video, audio, and PDF inputs with text output and features a 1M-token context window for sustained reasoning across long-running tasks. Optimized for autonomous execution and multi-agent orchestration, Muse Wonder can plan, delegate to subagents, coordinate parallel tool use, recover from errors, and operate browsers and applications across desktop and mobile environments. With cross-conversation context retrieval and its Active Focus training approach, it is well suited for complex software engineering, multi-file refactoring, computer use, and end-to-end workflows requiring persistence, adaptability, and minimal supervision.Perceptron Mk1.5 chat model that accepts audio input. Audio and text input are billed per token.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.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.