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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. Claude Sonnet 5.5AnthropicRemove
  3. GPT-6 AstraOpenAIRemove
  4. Muse Spark 1.3MetaRemove

4 is the maximum. Remove one to add another.

s1 vs claude-sonnet-5.5 vs gpt-6-astra vs muse-spark-1.3
AttributeS1s1Claude Sonnet 5.5claude-sonnet-5.5GPT-6 Astragpt-6-astraMuse Spark 1.3muse-spark-1.3
Pricing
Input$0 / 1M$2.00 / 1M$10.00 / 1M$1.25 / 1M
Output$0 / 1M$10.00 / 1M$50.00 / 1M$4.25 / 1M
Cache Write (5m)Not applicable$2.50 / 1M$10.00 / 1M$1.25 / 1M
Cache Write (1h)Not applicable$4.00 / 1M$10.00 / 1M$1.25 / 1M
Cache ReadNot applicable$0.20 / 1M$10.00 / 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 AudioAnthropicOpenAIMeta
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.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.GPT-6 Astra is OpenAI's flagship model for demanding end-to-end professional work, designed for advanced analysis, software engineering, deep research, scientific tasks, and document creation. It is particularly strong in long-horizon agentic workflows, including tasks that require sustained reasoning, tool orchestration, and computer and browser use, making it well suited for complex autonomous workflows and production-grade knowledge work.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.