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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. Perceptron Mk1.5PerceptronRemove
  2. Gemini 3.1 Flash Image (Google Vertex AI)GoogleEnterprise & partner accessRemove
  3. Muse WonderMetaRemove
  4. GLM 5.3Z.AIRemove

4 is the maximum. Remove one to add another.

perceptron-mk1.5 vs gemini-3.1-flash-image-vertex vs muse-wonder vs glm-5.3
AttributePerceptron Mk1.5perceptron-mk1.5Gemini 3.1 Flash Image (Google Vertex AI)gemini-3.1-flash-image-vertexMuse Wondermuse-wonderGLM 5.3glm-5.3
Pricing
Input$0.15 / 1M—$4.00 / 1M$1.40 / 1M
Output$1.50 / 1M—$20.00 / 1M$4.40 / 1M
Cache Write (5m)$0.15 / 1MNot applicable$4.00 / 1M$1.40 / 1M
Cache Write (1h)$0.15 / 1MNot applicable$4.00 / 1M$1.40 / 1M
Cache Read$0.15 / 1MNot applicable$4.00 / 1M$1.40 / 1M
Web Search$0 / 1M—$0 / 1M$0 / 1M
Request—$0.067 / request——
Billing—Pay Per Request——
Context
Max context36.9KN/A1M1M
Max outputN/AN/AN/AN/A
Capabilities
VisionNoYesYesNo
Function CallingNoNoYesYes
JSON ModeNoNoYesYes
StreamingYesNoYesYes
Catalogue
ProviderPerceptronGoogleMetaZ.AI
Categorychatimagechatchat
Charge typePay As You GoPay Per RequestPay As You GoPay As You Go
Released2026-09-25———
Description
SummaryPerceptron Mk1.5 chat model that accepts audio input. Audio and text input are billed per token.Gemini 3.1 Flash Image served from Google Vertex AI. Billed per image at US$0.067. Call it through /v1/chat/completions with stream set to false; the image is returned in the message content as a base64 data URI. Maximum output resolution is 2K. A request for a larger size, such as 4K or 4096x4096, returns a 2K image with the same aspect ratio at the same price. When a size is reduced, the response carries an X-Image-Resolution-Capped header with the requested and delivered size, for example 4096x4096->2048x2048.Muse 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.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.