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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. Muse Spark 1.3MetaRemove
  3. Whisper Large V3OpenAIRemove
gpt-6-astra vs muse-spark-1.3 vs whisper-large-v3
AttributeGPT-6 Astragpt-6-astraMuse Spark 1.3muse-spark-1.3Whisper Large V3whisper-large-v3
Pricing
Input$10.00 / 1M$1.25 / 1M$9.25 / 1M
Output$50.00 / 1M$4.25 / 1M$0 / 1M
Cache Write (5m)$10.00 / 1M$1.25 / 1MNot applicable
Cache Write (1h)$10.00 / 1M$1.25 / 1MNot applicable
Cache Read$10.00 / 1M$1.25 / 1MNot applicable
Web Search$0 / 1M$0 / 1M$0 / 1M
Context
Max context1M1MN/A
Max outputN/AN/AN/A
Capabilities
VisionYesYesNo
Function CallingYesYesNo
JSON ModeYesYesYes
StreamingYesYesNo
Catalogue
ProviderOpenAIMetaOpenAI
Categorychatchatvoice
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.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.Whisper Large V3 is OpenAI's advanced open-source automatic speech recognition (ASR) model, supporting both audio transcription and translation across 99+ languages. It accepts common audio formats including mp3, mp4, wav, webm, flac, and ogg, and delivers strong performance in noisy, real-world conditions. With 1.55B parameters and a low 10.3% word error rate, it provides accurate, multilingual transcription with support for word- and segment-level timestamps, making it well suited for high-quality, noise-robust speech processing applications.