AssemblyAI vs Deep Infra: Token Pricing, Speed & Intelligence
Full comparison of AssemblyAI and Deep Infra — 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.
Deep Infra
Ultra-cheap open-weight inference — Llama 4, DeepSeek V4 Pro, Qwen3 at rock-bottom prices
Deep Infra is an inference-focused API provider that consistently offers the lowest per-token prices for open-weight models. The 2026 catalog includes Llama 4 Scout/Maverick, DeepSeek V4 Pro, Qwen3-235B, Kimi K3, and the full Llama 3.x family. Deep Infra is the go-to choice for cost-sensitive production inference where price is the primary constraint.
Key metric comparison
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Live token pricing
Strengths and weaknesses
AssemblyAI
Deep Infra
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.
The most price-competitive inference API for open-weight models — Llama 4, DeepSeek V4 Pro, and Qwen3 at prices 30–50% below comparable providers, with zero migration friction via OpenAI-compatible API.
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.
Deep Infra FAQs
What models does Deep Infra support in 2026?
Deep Infra hosts Llama 4 Scout and Maverick, DeepSeek V4 Pro, Qwen3-235B, Kimi K3, the full Llama 3.x family, Mistral, Mixtral, and many others. The catalog is updated rapidly as new open-weight models are released.
How cheap is Deep Infra compared to other providers?
Deep Infra is consistently among the more affordable providers for open-weight models. Check deepinfra.com/pricing for current rates on Llama 3.1 8B, Llama 3.3 70B, and other models.
Is Deep Infra OpenAI-compatible?
Yes. Deep Infra provides an OpenAI-compatible API, so you can use the OpenAI SDK by pointing it at the Deep Infra endpoint. This makes migration from OpenAI or other providers straightforward.
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.
Deep Infra — Ultra-cheap open-weight inference — Llama 4, DeepSeek V4 Pro, Qwen3 at rock-bottom prices
Deep Infra is an inference-focused API provider that consistently offers the lowest per-token prices for open-weight models. The 2026 catalog includes Llama 4 Scout/Maverick, DeepSeek V4 Pro, Qwen3-235B, Kimi K3, and the full Llama 3.x family. Deep Infra is the go-to choice for cost-sensitive production inference where price is the primary constraint.
The most price-competitive inference API for open-weight models — Llama 4, DeepSeek V4 Pro, and Qwen3 at prices 30–50% below comparable providers, with zero migration friction via OpenAI-compatible API.
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
Deep Infra
- Consistently lowest prices for open-weight models — often 30–50% below competitors
- 2026 catalog: Llama 4, DeepSeek V4 Pro, Qwen3-235B, Kimi K3
- Wide model catalog updated rapidly as new models release
- No proprietary models — open-weight only
- No enterprise SLAs or dedicated support
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
Deep Infra
- No proprietary models — open-weight only
- No enterprise SLAs or dedicated support
- Smaller ecosystem than Together AI or Fireworks
Provider category context
AssemblyAI is a frontier lab, founded in 2017. Deep Infra is a inference api, founded in 2023. AssemblyAI as a frontier lab trains and serves its own proprietary models. Deep Infra 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 Deep Infra if you need consistently lowest prices for open-weight models — often 30–50% below competitors. 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.