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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 AstraOpenAIRemove
  2. Whisper Large V3 TurboOpenAIRemove
  3. Muse Spark 1.3MetaRemove
gpt-6-astra vs whisper-large-v3-turbo vs muse-spark-1.3
AttributeGPT-6 Astragpt-6-astraWhisper Large V3 Turbowhisper-large-v3-turboMuse Spark 1.3muse-spark-1.3
Pricing
Input$10.00 / 1M$3.33 / 1M$1.25 / 1M
Output$50.00 / 1M$0 / 1M$4.25 / 1M
Cache Write (5m)$10.00 / 1MNot applicable$1.25 / 1M
Cache Write (1h)$10.00 / 1MNot applicable$1.25 / 1M
Cache Read$10.00 / 1MNot applicable$1.25 / 1M
Web Search$0 / 1M$0 / 1M$0 / 1M
Context
Max context1MN/A1M
Max outputN/AN/AN/A
Capabilities
VisionYesNoYes
Function CallingYesNoYes
JSON ModeYesNoYes
StreamingYesNoYes
Catalogue
ProviderOpenAIOpenAIMeta
Categorychatvoicechat
Charge typePay As You GoPay As You GoPay As You Go
Released
Description
SummaryGPT-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.Whisper Large V3 Turbo is an optimized version of OpenAI's Whisper Large V3 speech recognition model, designed for high-speed and cost-efficient transcription. It supports 99+ languages and accepts common audio formats including mp3, mp4, wav, webm, flac, and ogg. With a ~12% word error rate and real-time speed factors up to 216×, it delivers fast, scalable performance for latency-sensitive and high-throughput transcription workloads, making it ideal for real-time and large-scale speech processing applications.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.