Groq vs Meta: Token Pricing, Speed & Intelligence
Full comparison of Groq and Meta — 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.
Meta
Llama 4 & Muse Spark — the world's most widely deployed open-weight models
Meta AI is the creator of the Llama model family, the most widely used open-weight LLMs in the world. Llama models are available via Meta's own API and through dozens of third-party inference providers. The Llama 4 series includes Behemoth (2T params), Scout, and Maverick, with 1M-token context windows. Meta also offers Muse Spark, a proprietary multimodal model. Because Llama weights are open, teams can self-host on GPU cloud for dramatically lower per-token costs at scale.
Key metrics
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Live token pricing
Strengths & weaknesses
Groq
Meta
Key differentiators
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.
Meta FAQs
What is the Llama 4 context window?
Llama 4 Scout and Maverick support 1,000,000-token (1M) context windows. Llama 4 Behemoth also targets 1M context. This makes Llama 4 competitive with Gemini 1.5 Pro for long-document and multi-document tasks.
How much does the Meta Llama API cost?
Llama 3.2 1B is $0.02/1M tokens in/out. Llama 3.2 3B is $0.03/$0.05. Llama 3.1 8B is $0.02/$0.05. Llama 3.2 90B Vision is $1.20/$1.20. Muse Spark 1.1 is $1.25/$4.25. Llama 4 Behemoth pricing is not yet publicly listed.
Can I self-host Llama models?
Yes — all Llama 3.x and Llama 4 Scout/Maverick weights are publicly available under the Llama Community License. You can run them on any GPU cloud provider. A single H100 at ~$2.50/hr can serve Llama 3.1 8B at very high throughput, making self-hosting cost-effective above ~10M tokens/day.
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.
Meta — Llama 4 & Muse Spark — the world's most widely deployed open-weight models
Meta AI is the creator of the Llama model family, the most widely used open-weight LLMs in the world. Llama models are available via Meta's own API and through dozens of third-party inference providers. The Llama 4 series includes Behemoth (2T params), Scout, and Maverick, with 1M-token context windows. Meta also offers Muse Spark, a proprietary multimodal model. Because Llama weights are open, teams can self-host on GPU cloud for dramatically lower per-token costs at scale.
The only frontier-class model family available as open weights — enabling self-hosted inference on GPU cloud at a fraction of API pricing for high-volume 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
Meta
- ▸Open-weight models — self-host on any GPU cloud for lowest per-token cost at scale
- ▸Llama 4 Behemoth: 2T parameter frontier model with 1M context window
- ▸Widest third-party hosting ecosystem — available on AWS, Azure, GCP, Together AI, Groq, and 20+ others
Provider category context
Groq is a inference api, founded in 2016. Meta is a open source host, founded in 2023. The category difference means these providers serve partially overlapping use cases — compare the model lists and pricing tables above to find the best fit for your specific workload.
How to choose between them
Choose Groq if you need 750+ tokens/sec on llama 3.3 70b — fastest gpu-class inference. Choose Meta if you need open-weight models — self-host on any gpu cloud for lowest per-token cost at scale. 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.