Fireworks AI vs Groq: Token Pricing, Speed & Intelligence
Full comparison of Fireworks AI and Groq — live token pricing, latency, throughput, context window, strengths, weaknesses, and best use cases. Updated July 2026.
Fireworks AI
Production-grade open-source inference with fast cold starts
Fireworks AI provides optimised inference for open-weight models with a focus on production reliability and low latency. They host Llama, DeepSeek R1, and other popular open-source models with competitive per-token pricing and a serverless deployment model that minimises cold-start times.
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.
Key metrics
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Live token pricing
Strengths & weaknesses
Fireworks AI
Groq
Key differentiators
Production-grade reliability with 320+ tokens/sec throughput and DeepSeek R1 reasoning at $3/1M input — a strong balance of speed and capability.
Groq's custom LPU chips deliver 750+ tokens/sec on Llama 3.3 70B — 4–5× faster than any GPU-based provider.
Frequently asked questions
Fireworks AI FAQs
What models does Fireworks AI offer?
Fireworks AI hosts Llama 3.3 70B, DeepSeek R1, Mixtral, and other popular open-weight models. They focus on production-ready models with optimised inference rather than the broadest possible catalog.
How much does Fireworks AI cost?
Llama 3.3 70B costs $0.90/1M tokens (input and output). DeepSeek R1 is $3.00/1M input and $8.00/1M output. Pricing is competitive with other inference API providers.
How does Fireworks AI compare to Together AI?
Fireworks AI offers faster throughput (320 vs 190 tokens/sec on Llama 3.3 70B) and stronger production reliability. Together AI has a larger model catalog and fine-tuning support. Choose Fireworks for production speed, Together for model variety.
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.
Provider resources
Fireworks AI — Production-grade open-source inference with fast cold starts
Fireworks AI provides optimised inference for open-weight models with a focus on production reliability and low latency. They host Llama, DeepSeek R1, and other popular open-source models with competitive per-token pricing and a serverless deployment model that minimises cold-start times.
Production-grade reliability with 320+ tokens/sec throughput and DeepSeek R1 reasoning at $3/1M input — a strong balance of speed and capability.
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.
Key strengths compared
Fireworks AI
- ▸320+ tokens/sec on Llama 3.3 70B — fast GPU inference
- ▸DeepSeek R1 hosting with strong reasoning capability
- ▸Production-grade reliability with SLAs
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
Provider category context
Fireworks AI is a inference api, founded in 2022. Groq is a inference api, founded in 2016. 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 Fireworks AI and Groq 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.