Deepgram vs Groq: Token Pricing, Speed & Intelligence
Full comparison of Deepgram and Groq — live token pricing, latency, throughput, context window, strengths, weaknesses, and best use cases. Rates sourced from public APIs — verify before use.
Deepgram
Nova-3 speech-to-text API — real-time and batch transcription at low latency
Deepgram is a speech-to-text API provider specialising in low-latency audio transcription. Its Nova-3 model delivers real-time streaming transcription with high accuracy across accents and domains. Nova-3 Medical is tuned for clinical vocabulary. Deepgram also hosts Whisper Large for teams that need an open-weights baseline. Pricing is per audio hour rather than per token.
Groq
LPU + LPX inference — high-throughput tokens per second, $350M Series A
Groq runs custom Language Processing Units (LPUs) and the newer LPX chips that work alongside NVIDIA next-generation GPUs for high-throughput inference. Groq raised a $350M Series A in 2026 and is building hundreds of megawatts of capacity. Check listed rates and provider documentation for current model availability and throughput figures.
Key metric comparison
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Live token pricing
Strengths and weaknesses
Deepgram
Groq
Key differentiators
Purpose-built speech-to-text API with Nova-3 at ~300 ms latency and a medical-domain variant — priced per audio hour rather than per token.
Groq's custom LPU/LPX chips deliver high-throughput inference significantly faster than conventional GPU systems, backed by $350M in Series A funding and hundreds of megawatts of planned capacity.
Frequently asked questions
Deepgram FAQs
How is Deepgram priced?
Deepgram charges per audio hour rather than per token. Nova-3 is $0.218/hour, Nova-3 Medical is $0.435/hour, and Whisper Large is $0.218/hour. See deepgram.com/pricing for current rates.
What is Deepgram Nova-3?
Nova-3 is Deepgram's latest speech recognition model, offering high accuracy with approximately 300 ms P50 latency for real-time streaming. It supports 30+ languages and is optimised for conversational and call-centre audio.
Does Deepgram support real-time transcription?
Yes. Deepgram's streaming API delivers transcription results with ~300 ms latency, making it suitable for live captions, voice agents, and real-time call analytics.
Groq FAQs
How fast is Groq inference?
Groq delivers 750+ tokens/second on Llama 3.3 70B and 1,200+ tokens/second on Llama 3.1 8B using custom LPU/LPX chips, making it well-suited for real-time and low-latency applications.
What is Groq's LPX chip?
The LPX is Groq's next-generation inference chip, introduced alongside the existing LPU lineup. It expands Groq's capacity and enables even higher throughput for large-scale deployments.
How much does Groq cost?
Groq uses per-token pricing across its model catalog. Check console.groq.com/docs/openai for current rates on Llama 3.3 70B, Llama 3.1 8B, and other models.
Provider resources
Deepgram — Nova-3 speech-to-text API — real-time and batch transcription at low latency
Deepgram is a speech-to-text API provider specialising in low-latency audio transcription. Its Nova-3 model delivers real-time streaming transcription with high accuracy across accents and domains. Nova-3 Medical is tuned for clinical vocabulary. Deepgram also hosts Whisper Large for teams that need an open-weights baseline. Pricing is per audio hour rather than per token.
Purpose-built speech-to-text API with Nova-3 at ~300 ms latency and a medical-domain variant — priced per audio hour rather than per token.
Groq — LPU + LPX inference — high-throughput tokens per second, $350M Series A
Groq runs custom Language Processing Units (LPUs) and the newer LPX chips that work alongside NVIDIA next-generation GPUs for high-throughput inference. Groq raised a $350M Series A in 2026 and is building hundreds of megawatts of capacity. Check listed rates and provider documentation for current model availability and throughput figures.
Groq's custom LPU/LPX chips deliver high-throughput inference significantly faster than conventional GPU systems, backed by $350M in Series A funding and hundreds of megawatts of planned capacity.
Key strengths compared
Deepgram
- Nova-3 delivers industry-leading accuracy with ~300 ms P50 latency for real-time streaming
- Nova-3 Medical tuned for clinical and healthcare vocabulary
- Per-audio-hour pricing is predictable for high-volume transcription workloads
- Speech-to-text only — no LLM chat, image, or embedding models
- No per-token pricing; cost structure differs from general-purpose LLM APIs
Groq
- 750+ tokens/sec on Llama 3.3 70B — high-throughput GPU-class inference (Groq-published)
- Sub-100ms time-to-first-token for real-time applications
- LPX chips alongside LPUs for expanded capacity
- Limited model selection vs. Together AI or Fireworks
- No vision model support on most models
Key weaknesses compared
Deepgram
- Speech-to-text only — no LLM chat, image, or embedding models
- No per-token pricing; cost structure differs from general-purpose LLM APIs
- Smaller ecosystem than general-purpose providers
Groq
- Limited model selection vs. Together AI or Fireworks
- No vision model support on most models
- No fine-tuning capability
Provider category context
Deepgram is a frontier lab, founded in 2015. Groq is a inference api, founded in 2016. Deepgram as a frontier lab trains and serves its own proprietary models. Groq 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 Deepgram if you need Real-time transcription. Choose Groq if you need 750+ tokens/sec on llama 3.3 70b — high-throughput gpu-class inference (groq-published). 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.