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

4 is the maximum. Remove one to add another.

gpt-6-sol-pro vs s1 vs claude-sonnet-5.5 vs muse-spark-1.3
AttributeGPT-6 Sol Progpt-6-sol-proS1s1Claude Sonnet 5.5claude-sonnet-5.5Muse Spark 1.3muse-spark-1.3
Pricing
Input$2.00 / 1M$0 / 1M$2.00 / 1M$1.25 / 1M
Output$10.00 / 1M$0 / 1M$10.00 / 1M$4.25 / 1M
Cache Write (5m)$2.00 / 1MNot applicable$2.50 / 1M$1.25 / 1M
Cache Write (1h)$2.00 / 1MNot applicable$4.00 / 1M$1.25 / 1M
Cache Read$2.00 / 1MNot applicable$0.20 / 1M$1.25 / 1M
Web Search$0 / 1M$0 / 1M$0 / 1M$0 / 1M
Context
Max context1.1MN/A1M1M
Max outputN/AN/AN/AN/A
Capabilities
VisionYesNoYesYes
Function CallingYesNoYesYes
JSON ModeYesNoYesYes
StreamingYesNoYesYes
Catalogue
ProviderOpenAIFish AudioAnthropicMeta
Categorychatvoicechatchat
Charge typePay As You GoPay As You GoPay As You GoPay As You Go
Released—2026-07-29——
Description
SummaryGPT-6 Sol Pro uses the same underlying model as GPT-6 Sol, but runs with reasoning.mode set to pro for higher-quality responses on complex and demanding tasks. Optimized for deeper reasoning, greater accuracy, and more reliable multi-step execution, it is particularly well suited for agentic coding, long-horizon software engineering, professional analysis, and complex automated workflows where solution quality takes priority over latency and cost.Fish 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.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.