AssemblyAI vs OctoAI: Token Pricing, Speed & Intelligence
Full comparison of AssemblyAI and OctoAI — live token pricing, latency, throughput, context window, strengths, weaknesses, and best use cases. Rates sourced from public APIs — verify before use.
AssemblyAI
Universal-2 speech-to-text with speaker diarisation and audio intelligence
AssemblyAI provides a speech-to-text and audio intelligence API. Its Universal-2 model offers high-accuracy transcription with built-in speaker diarisation, sentiment analysis, topic detection, and PII redaction. The Nano model targets cost-sensitive batch workloads. Pricing is per audio hour.
OctoAI
Acquired by NVIDIA — OctoAI inference is now available via NVIDIA NIM
OctoAI was acquired by NVIDIA in 2024 and its inference platform has been integrated into NVIDIA NIM (Inference Microservices). Existing OctoAI customers have been migrated to NVIDIA NIM, which provides the same optimised open-weight model inference on NVIDIA GPU infrastructure with TensorRT-LLM acceleration. New users should use NVIDIA NIM directly for access to Llama 4, Mistral, Qwen3, and other open-weight models.
Key metric comparison
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Live token pricing
Strengths and weaknesses
AssemblyAI
OctoAI
Key differentiators
Universal-2 bundles transcription with speaker diarisation, sentiment, topic detection, and PII redaction — plus LeMUR for LLM-powered audio Q&A — in a single API call.
OctoAI's inference technology lives on inside NVIDIA NIM — the same hardware-aware optimisation, now with TensorRT-LLM acceleration and NVIDIA's global GPU infrastructure behind it.
Frequently asked questions
AssemblyAI FAQs
How is AssemblyAI priced?
AssemblyAI charges per audio hour. Universal-2 is $0.37/hour and Nano is $0.10/hour. Additional audio intelligence features (diarisation, sentiment, PII redaction) are priced separately. See assemblyai.com/pricing for current rates.
What is Universal-2?
Universal-2 is AssemblyAI's flagship transcription model with built-in speaker diarisation, sentiment analysis, topic detection, and PII redaction. It is designed for meeting intelligence and call analytics use cases.
What is LeMUR?
LeMUR (Leveraging Large Language Models to Understand Recorded Audio) is an AssemblyAI feature that lets you run LLM-powered Q&A, summarisation, and action-item extraction directly over transcribed audio without building a separate pipeline.
OctoAI FAQs
What happened to OctoAI?
OctoAI was acquired by NVIDIA in 2024. Its inference platform has been integrated into NVIDIA NIM (Inference Microservices). Existing customers were migrated to NIM, which provides the same optimised open-weight model inference on NVIDIA GPU infrastructure.
Can I still use the OctoAI API?
The original OctoAI API endpoints have been deprecated. Existing OctoAI customers should migrate to NVIDIA NIM, which provides equivalent functionality with TensorRT-LLM optimisation and access to the full NVIDIA model catalog.
What is NVIDIA NIM?
NVIDIA NIM (Inference Microservices) are containerized AI inference packages optimized for NVIDIA GPUs using TensorRT-LLM. They can be accessed via the NVIDIA API catalog at build.nvidia.com or self-hosted on any NVIDIA GPU infrastructure.
Provider resources
AssemblyAI — Universal-2 speech-to-text with speaker diarisation and audio intelligence
AssemblyAI provides a speech-to-text and audio intelligence API. Its Universal-2 model offers high-accuracy transcription with built-in speaker diarisation, sentiment analysis, topic detection, and PII redaction. The Nano model targets cost-sensitive batch workloads. Pricing is per audio hour.
Universal-2 bundles transcription with speaker diarisation, sentiment, topic detection, and PII redaction — plus LeMUR for LLM-powered audio Q&A — in a single API call.
OctoAI — Acquired by NVIDIA — OctoAI inference is now available via NVIDIA NIM
OctoAI was acquired by NVIDIA in 2024 and its inference platform has been integrated into NVIDIA NIM (Inference Microservices). Existing OctoAI customers have been migrated to NVIDIA NIM, which provides the same optimised open-weight model inference on NVIDIA GPU infrastructure with TensorRT-LLM acceleration. New users should use NVIDIA NIM directly for access to Llama 4, Mistral, Qwen3, and other open-weight models.
OctoAI's inference technology lives on inside NVIDIA NIM — the same hardware-aware optimisation, now with TensorRT-LLM acceleration and NVIDIA's global GPU infrastructure behind it.
Key strengths compared
AssemblyAI
- Universal-2 includes speaker diarisation, sentiment, topics, and PII redaction out of the box
- Audio intelligence features reduce need for post-processing pipelines
- Nano tier at $0.10/hour for cost-sensitive batch transcription
- Speech and audio only — no general-purpose LLM or image models
- Per-hour pricing model differs from token-based LLM APIs
OctoAI
- Now backed by NVIDIA's full GPU infrastructure and TensorRT-LLM stack
- Seamless migration path for existing OctoAI customers to NVIDIA NIM
- Access to NVIDIA's full open-weight model catalog via NIM
- OctoAI as a standalone product no longer exists — new users should use NVIDIA NIM directly
- Migration required for any OctoAI-specific API integrations
Key weaknesses compared
AssemblyAI
- Speech and audio only — no general-purpose LLM or image models
- Per-hour pricing model differs from token-based LLM APIs
- Advanced audio intelligence features add cost on top of base transcription
OctoAI
- OctoAI as a standalone product no longer exists — new users should use NVIDIA NIM directly
- Migration required for any OctoAI-specific API integrations
- Self-hosted path requires NVIDIA GPU infrastructure investment
Provider category context
AssemblyAI is a frontier lab, founded in 2017. OctoAI is a inference api, founded in 2022. AssemblyAI as a frontier lab trains and serves its own proprietary models. OctoAI as an inference API provider hosts open-weight models — typically offering lower prices for equivalent capability tiers but without access to proprietary frontier models.
How to choose between them
Choose AssemblyAI if you need Meeting intelligence. Choose OctoAI if you need now backed by nvidia's full gpu infrastructure and tensorrt-llm stack. For high-volume production workloads, run a cost comparison using the token pricing table above with your actual prompt/completion token ratio — the cheapest provider depends heavily on your input-to-output token ratio.