Groq vs Perplexity: Token Pricing, Speed & Intelligence
Full comparison of Groq and Perplexity — live token pricing, latency, throughput, context window, strengths, weaknesses, and best use cases. Updated July 2026.
Groq
LPU-powered inference — the fastest tokens per second available
Groq runs custom Language Processing Units (LPUs) that deliver dramatically higher throughput than GPU-based inference — Llama 3.3 70B reaches 750+ tokens/second on Groq, versus 100–200 on typical GPU providers. Ideal for latency-sensitive applications, real-time chat, and high-volume batch workloads.
Perplexity
Sonar — search-augmented LLMs with real-time web grounding
Perplexity's Sonar models are designed for search-augmented generation, combining LLM reasoning with real-time web retrieval. Unlike standard LLMs, Sonar responses include citations and are grounded in current web content. Ideal for research assistants, news summarisation, and fact-checking applications.
Key metrics
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Live token pricing
Strengths & weaknesses
Groq
Perplexity
Key differentiators
Groq's custom LPU chips deliver 750+ tokens/sec on Llama 3.3 70B — 4–5× faster than any GPU-based provider.
Every Sonar response includes real-time web citations — the only LLM API purpose-built for grounded, verifiable answers.
Frequently asked questions
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. This is 4–5× faster than typical GPU-based providers, making it ideal for real-time applications.
How much does Groq cost?
Llama 3.3 70B costs $0.59/1M input and $0.79/1M output tokens. Llama 3.1 8B is just $0.05/$0.08 per 1M tokens — among the cheapest options for a capable open-weight model.
What is a Groq LPU?
A Language Processing Unit (LPU) is Groq's custom silicon designed specifically for sequential token generation. Unlike GPUs which are optimised for parallel matrix operations, LPUs excel at the autoregressive decoding step that dominates LLM inference latency.
Perplexity FAQs
What is Perplexity Sonar?
Sonar is Perplexity's family of search-augmented LLMs. Unlike standard LLMs, Sonar automatically searches the web and includes citations in every response. Sonar is available in standard and Pro (deep research) variants.
How much does Perplexity API cost?
Perplexity charges per 1M tokens plus a per-request fee for search operations. Check their pricing page for current rates as they vary by model and search depth.
When should I use Perplexity instead of GPT-4o?
Use Perplexity when your application needs real-time, verifiable information with citations — research tools, news summarisation, fact-checking, or any use case where accuracy on current events matters. For creative tasks, coding, or reasoning without web grounding, GPT-4o or Claude are better choices.
Provider resources
Groq — LPU-powered inference — the fastest tokens per second available
Groq runs custom Language Processing Units (LPUs) that deliver dramatically higher throughput than GPU-based inference — Llama 3.3 70B reaches 750+ tokens/second on Groq, versus 100–200 on typical GPU providers. Ideal for latency-sensitive applications, real-time chat, and high-volume batch workloads.
Groq's custom LPU chips deliver 750+ tokens/sec on Llama 3.3 70B — 4–5× faster than any GPU-based provider.
Perplexity — Sonar — search-augmented LLMs with real-time web grounding
Perplexity's Sonar models are designed for search-augmented generation, combining LLM reasoning with real-time web retrieval. Unlike standard LLMs, Sonar responses include citations and are grounded in current web content. Ideal for research assistants, news summarisation, and fact-checking applications.
Every Sonar response includes real-time web citations — the only LLM API purpose-built for grounded, verifiable answers.
Key strengths compared
Groq
- ▸750+ tokens/sec on Llama 3.3 70B — fastest GPU-class inference
- ▸Sub-100ms time-to-first-token for real-time applications
- ▸Very competitive pricing on open-weight models
Perplexity
- ▸Real-time web search with automatic citations
- ▸Sonar Pro for deep research with multi-step retrieval
- ▸Grounded responses reduce hallucination on factual queries
Provider category context
Groq is a inference api, founded in 2016. Perplexity is a inference api, founded in 2022. Both are inference api providers — the comparison is primarily about pricing, model selection, and feature differentiation within the same tier.
How to choose between them
Both Groq and Perplexity host open-weight models. The key differentiators are latency, throughput, and which specific model versions each provider offers. Check the speed metrics above — inference API providers often differ significantly on tokens-per-second for the same model. Pricing is typically competitive between them; availability of specific model versions (e.g., Llama 3.1 405B, DeepSeek V3) may be the deciding factor.