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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. S1Fish AudioRemove
  2. GPT-6 Astra ProOpenAIRemove
  3. Claude Sonnet 5.5AnthropicRemove
  4. Muse Spark 1.3MetaRemove

4 is the maximum. Remove one to add another.

s1 vs gpt-6-astra-pro vs claude-sonnet-5.5 vs muse-spark-1.3
AttributeS1s1GPT-6 Astra Progpt-6-astra-proClaude Sonnet 5.5claude-sonnet-5.5Muse Spark 1.3muse-spark-1.3
Pricing
Input$0 / 1M$10.00 / 1M$2.00 / 1M$1.25 / 1M
Output$0 / 1M$50.00 / 1M$10.00 / 1M$4.25 / 1M
Cache Write (5m)Not applicable$10.00 / 1M$2.50 / 1M$1.25 / 1M
Cache Write (1h)Not applicable$10.00 / 1M$4.00 / 1M$1.25 / 1M
Cache ReadNot applicable$10.00 / 1M$0.20 / 1M$1.25 / 1M
Web Search$0 / 1M$0 / 1M$0 / 1M$0 / 1M
Context
Max contextN/A1M1M1M
Max outputN/AN/AN/AN/A
Capabilities
VisionNoYesYesYes
Function CallingNoYesYesYes
JSON ModeNoYesYesYes
StreamingNoYesYesYes
Catalogue
ProviderFish AudioOpenAIAnthropicMeta
Categoryvoicechatchatchat
Charge typePay As You GoPay As You GoPay As You GoPay As You Go
Released2026-07-29———
Description
SummaryFish Audio S1 text-to-speech. Billed per UTF-8 byte of input text.GPT-6 Astra Pro uses the same underlying model as GPT-6 Astra, but runs with reasoning.mode set to pro for higher-quality responses on complex tasks. Optimized for deeper reasoning, greater accuracy, and more reliable multi-step execution, it is well suited for demanding coding, analysis, and agentic workflows where solution quality takes priority over speed and cost.Claude Sonnet 5.5 is Anthropic's Sonnet-class model for well-scoped everyday work, serving as a direct upgrade to Sonnet 5. It excels at feature development, bug fixing, and creating polished documents, presentations, and spreadsheets, while offering clearer writing and communication than its predecessor.Muse 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.