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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. Muse Spark OpenMetaRemove
  3. Qwen3.8 27BAlibabaRemove
  4. Gemini 4 ArgonGoogleRemove

4 is the maximum. Remove one to add another.

perceptron-mk1.5 vs muse-spark-open vs qwen3.8-27b vs gemini-4-argon
AttributePerceptron Mk1.5perceptron-mk1.5Muse Spark Openmuse-spark-openQwen3.8 27Bqwen3.8-27bGemini 4 Argongemini-4-argon
Pricing
Input$0.15 / 1M$1.25 / 1M$0.45 / 1M$2.00 / 1M
Output$1.50 / 1M$4.25 / 1M$3.20 / 1M$10.00 / 1M
Cache Write (5m)$0.15 / 1M$1.25 / 1M$0.45 / 1M$2.00 / 1M
Cache Write (1h)$0.15 / 1M$1.25 / 1M$0.45 / 1M$2.00 / 1M
Cache Read$0.15 / 1M$1.25 / 1M$0.45 / 1M$2.00 / 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
ProviderPerceptronMetaAlibabaGoogle
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.Muse Spark Open is an open-weight, coding-optimized model designed for agentic software engineering workflows. It delivers strong first-attempt accuracy, reliable tool calling, and deep codebase understanding, enabling effective development across real-world repositories. Optimized for autonomous coding agents, complex code navigation, and tool-driven development, Muse Spark Open is well suited for software engineering automation, debugging, code generation, and multi-step coding workflows where accuracy and reliable execution are essential.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.Gemini 4 Argon is Google's flagship reasoning model, built for complex software engineering, deep mathematical and scientific problem solving, and autonomous multi-step agents. Google trained it to keep context across long, multi-step tasks and to turn long documents mixing text, tables, charts and images into a single grounded output. It leads the Vals Index across finance, legal, coding and tax work, and supports long decode of up to 1M output tokens in a single response.