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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 3.8 FlashGoogleRemove
  3. Qwen3.8 27BAlibabaRemove
  4. Muse WonderMetaRemove

4 is the maximum. Remove one to add another.

perceptron-mk1.5 vs gemini-3.8-flash vs qwen3.8-27b vs muse-wonder
AttributePerceptron Mk1.5perceptron-mk1.5Gemini 3.8 Flashgemini-3.8-flashQwen3.8 27Bqwen3.8-27bMuse Wondermuse-wonder
Pricing
Input$0.15 / 1M$0.75 / 1M$0.45 / 1M$4.00 / 1M
Output$1.50 / 1M$3.75 / 1M$3.20 / 1M$20.00 / 1M
Cache Write (5m)$0.15 / 1M$0.75 / 1M$0.45 / 1M$4.00 / 1M
Cache Write (1h)$0.15 / 1M$0.75 / 1M$0.45 / 1M$4.00 / 1M
Cache Read$0.15 / 1M$0.75 / 1M$0.45 / 1M$4.00 / 1M
Web Search$0 / 1M$0 / 1M$0 / 1M$0 / 1M
Context
Max context36.9K1M262K1M
Max outputN/AN/AN/AN/A
Capabilities
VisionNoYesYesYes
Function CallingNoYesYesYes
JSON ModeNoYesYesYes
StreamingYesYesYesYes
Catalogue
ProviderPerceptronGoogleAlibabaMeta
Categorychatchatchatchat
Charge typePay As You GoPay As You GoPay 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 3.8 Flash is Google's most intelligent Flash-class model, delivering significant improvements over Gemini 3.7 Flash across software engineering, agentic workflows, and complex multi-step reasoning. Designed to combine strong capability with Flash-tier efficiency, it is well suited for coding assistants, autonomous agents, and high-throughput production workflows that require responsive performance without sacrificing reasoning quality.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 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.