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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. Claude Sonnet 5.5AnthropicRemove
  2. Aura-2DeepgramRemove
  3. GPT-6 AstraOpenAIRemove
  4. Muse Spark 1.3MetaRemove

4 is the maximum. Remove one to add another.

claude-sonnet-5.5 vs aura-2 vs gpt-6-astra vs muse-spark-1.3
AttributeClaude Sonnet 5.5claude-sonnet-5.5Aura-2aura-2GPT-6 Astragpt-6-astraMuse Spark 1.3muse-spark-1.3
Pricing
Input$2.00 / 1M$0 / 1M$10.00 / 1M$1.25 / 1M
Output$10.00 / 1M$0 / 1M$50.00 / 1M$4.25 / 1M
Cache Write (5m)$2.50 / 1MNot applicable$10.00 / 1M$1.25 / 1M
Cache Write (1h)$4.00 / 1MNot applicable$10.00 / 1M$1.25 / 1M
Cache Read$0.20 / 1MNot applicable$10.00 / 1M$1.25 / 1M
Web Search$0 / 1M$0 / 1M$0 / 1M$0 / 1M
Context
Max context1MN/A1M1M
Max outputN/AN/AN/AN/A
Capabilities
VisionYesNoYesYes
Function CallingYesNoYesYes
JSON ModeYesNoYesYes
StreamingYesNoYesYes
Catalogue
ProviderAnthropicDeepgramOpenAIMeta
Categorychatvoicechatchat
Charge typePay As You GoPay As You GoPay As You GoPay As You Go
Released—2026-07-16——
Description
SummaryClaude 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.Deepgram Aura-2 text-to-speech for real-time voice applications. Billed per input character.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.