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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 Spark 1.3MetaRemove
  2. Perceptron Mk1.5PerceptronRemove
  3. S1Fish AudioRemove
  4. GPT-6 SolOpenAIRemove

4 is the maximum. Remove one to add another.

muse-spark-1.3 vs perceptron-mk1.5 vs s1 vs gpt-6-sol
AttributeMuse Spark 1.3muse-spark-1.3Perceptron Mk1.5perceptron-mk1.5S1s1GPT-6 Solgpt-6-sol
Pricing
Input$1.25 / 1M$0.15 / 1M$0 / 1M$2.00 / 1M
Output$4.25 / 1M$1.50 / 1M$0 / 1M$10.00 / 1M
Cache Write (5m)$1.25 / 1M$0.15 / 1MNot applicable$2.00 / 1M
Cache Write (1h)$1.25 / 1M$0.15 / 1MNot applicable$2.00 / 1M
Cache Read$1.25 / 1M$0.15 / 1MNot applicable$2.00 / 1M
Web Search$0 / 1M$0 / 1M$0 / 1M$0 / 1M
Context
Max context1M36.9KN/A1.1M
Max outputN/AN/AN/AN/A
Capabilities
VisionYesNoNoYes
Function CallingYesNoNoYes
JSON ModeYesNoNoYes
StreamingYesYesNoYes
Catalogue
ProviderMetaPerceptronFish AudioOpenAI
Categorychatchatvoicechat
Charge typePay As You GoPay As You GoPay As You GoPay As You Go
Released—2026-09-252026-07-29—
Description
SummaryMuse 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.Perceptron Mk1.5 chat model that accepts audio input. Audio and text input are billed per token.Fish Audio S1 text-to-speech. Billed per UTF-8 byte of input text.GPT-6 Sol is OpenAI's cost-efficient high-end model in the GPT-6 series, positioned between the flagship GPT-6 Astra and the fast GPT-6 Luna tier. It is designed for professional knowledge work, agentic coding, business workflow automation, and computer-use tasks, with particular strength in long-horizon software engineering across real-world codebases. GPT-6 Sol approaches Astra-level factual reliability at a significantly lower cost, while sharing its clear and concise communication style. This balance of capability, reliability, and efficiency makes it well suited for production agents, complex engineering workflows, and scalable professional applications