Celeris AI vs Groq: Token Pricing, Speed & Intelligence
Full comparison of Celeris AI and Groq — live token pricing, latency, throughput, context window, strengths, weaknesses, and best use cases. Updated July 2026.
Celeris AI
High-throughput frontier reasoning with Celeris-1
Celeris AI is a frontier AI lab focused on high-throughput reasoning models. Celeris-1 is their flagship model, combining strong benchmark performance on coding, math, and agentic tasks with competitive inference speed. The model supports a 256K-token context window, prompt caching, and function calling.
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
Celeris AI
Groq
Key differentiators
Celeris-1 targets the gap between o3-class reasoning quality and GPT-4o-class speed, offering frontier-tier intelligence scores at throughput rates competitive with non-reasoning models.
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
Celeris AI FAQs
What is Celeris AI?
Celeris AI is a frontier AI lab that develops high-throughput reasoning models. Their flagship Celeris-1 model targets the intersection of strong reasoning capability and fast inference.
How does Celeris-1 compare to o3 and Claude Opus?
Celeris-1 sits in the same intelligence score range as o3 and Claude Opus 5, with competitive throughput. It is priced similarly to Claude Opus 5 at $3/1M input and $15/1M output.
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
Celeris AI — High-throughput frontier reasoning with Celeris-1
Celeris AI is a frontier AI lab focused on high-throughput reasoning models. Celeris-1 is their flagship model, combining strong benchmark performance on coding, math, and agentic tasks with competitive inference speed. The model supports a 256K-token context window, prompt caching, and function calling.
Celeris-1 targets the gap between o3-class reasoning quality and GPT-4o-class speed, offering frontier-tier intelligence scores at throughput rates competitive with non-reasoning models.
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
Celeris AI
- ▸Strong reasoning and coding benchmarks
- ▸High throughput at frontier tier
- ▸Competitive prompt caching pricing
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
Celeris AI is a frontier lab, founded in 2025. Groq is a inference api, founded in 2016. Celeris AI 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 Celeris AI if you need strong reasoning and coding benchmarks. Choose Groq if you need 750+ tokens/sec on llama 3.3 70b — fastest gpu-class inference. 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.