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Groq vs Hyperbolic: Token Pricing, Speed & Intelligence

Full comparison of Groq and Hyperbolic — 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.

SpeedReal-time chatVoice AICost-efficiencyBatch processing
Open-weight hostHosts open weights

Hyperbolic

Open-source inference marketplace — Llama, DeepSeek R1, and more

Hyperbolic provides a marketplace for open-source model inference, hosting Llama 3.3, DeepSeek R1, and other popular models at competitive prices. Their platform emphasises accessibility and affordability, making frontier open-weight models available to developers and researchers at low cost.

Cost-efficiencyResearchOpen-sourceExperimentationBudget workloads
Open-weight hostHosts open weights

Key metrics

Groq
Hyperbolic
Cheapest input ($/1M)

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Cheapest output ($/1M)

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Peak throughput

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Best latency (TTFT)

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Intelligence score

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Context window

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Live token pricing

Strengths & weaknesses

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
OpenAI-compatible API
Free tier available
Limited model selection vs. Together AI or Fireworks
No vision model support on most models
No fine-tuning capability

Hyperbolic

Among the lowest prices for open-weight model inference
DeepSeek R1 and Llama 3.3 available at competitive rates
Marketplace model — broad model selection
OpenAI-compatible API
Good for research and experimentation
Less established reliability than larger providers
Throughput lower than Groq/Cerebras for speed-critical apps
No fine-tuning support

Key differentiators

Groq

Groq's custom LPU chips deliver 750+ tokens/sec on Llama 3.3 70B — 4–5× faster than any GPU-based provider.

Hyperbolic

One of the most affordable inference marketplaces for open-weight models — ideal for researchers and cost-sensitive workloads.

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.

Hyperbolic FAQs

What models does Hyperbolic offer?

Hyperbolic hosts Llama 3.3 70B, DeepSeek R1, and other popular open-weight models. Their marketplace approach means the catalog evolves frequently.

How does Hyperbolic pricing compare to competitors?

Hyperbolic is among the most affordable options for open-weight model inference, often undercutting Together AI and Fireworks AI on price. This makes it attractive for high-volume or cost-sensitive workloads.

Is Hyperbolic reliable for production use?

Hyperbolic is newer and less established than providers like Together AI or Fireworks AI. It's well-suited for research, prototyping, and cost-sensitive workloads, but for mission-critical production use, a more established provider may be preferable.

Provider resources

Groq

Provider pageWebsitePricingDocs

Hyperbolic

Provider pageWebsitePricingDocs

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.

Hyperbolic — Open-source inference marketplace — Llama, DeepSeek R1, and more

Hyperbolic provides a marketplace for open-source model inference, hosting Llama 3.3, DeepSeek R1, and other popular models at competitive prices. Their platform emphasises accessibility and affordability, making frontier open-weight models available to developers and researchers at low cost.

One of the most affordable inference marketplaces for open-weight models — ideal for researchers and cost-sensitive workloads.

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

Hyperbolic

  • ▸Among the lowest prices for open-weight model inference
  • ▸DeepSeek R1 and Llama 3.3 available at competitive rates
  • ▸Marketplace model — broad model selection

Provider category context

Groq is a inference api, founded in 2016. Hyperbolic is a inference api, founded in 2023. 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 Hyperbolic 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.