Speed is the defining difference between Groq and OpenAI's API in 2026. Both provide access to powerful language models; the fundamental divergence is that Groq uses custom LPU hardware optimized exclusively for inference speed, while OpenAI uses NVIDIA H100 GPU clusters that balance training and inference workloads. The result: Groq generates tokens 5–12× faster on comparable model sizes. This comparison gives you the actual numbers so you can make an informed choice for your application.
This comparison pits Groq's open-source models (Llama, Mixtral) against OpenAI's proprietary models (GPT-4o, GPT-4o mini). OpenAI's models are generally more capable at complex reasoning tasks, so the tradeoff isn't purely speed — it's speed vs. model quality. We address this fully below.
Speed Benchmarks: Tokens Per Second
Full Comparison Table
| Metric | Groq (Llama 3.1 8B) | Groq (Llama 3.3 70B) | OpenAI GPT-4o | OpenAI GPT-4o mini |
|---|---|---|---|---|
| Output speed | ~750 T/s | ~270 T/s | ~63 T/s | ~110 T/s |
| Time to first token | <100ms | <200ms | 300–600ms | 200–400ms |
| Input cost / 1M tokens | $0.05 | $0.59 | $2.50 | $0.15 |
| Output cost / 1M tokens | $0.08 | $0.79 | $10.00 | $0.60 |
| Model quality (reasoning) | Good | Very good | Excellent | Good |
| Proprietary models | No (OSS only) | No | Yes (GPT-4o) | Yes |
| Fine-tuning | No | No | Yes | Yes |
| Vision / multimodal | Llama 3.2 Vision | Limited | Full (GPT-4o) | Yes |
| Free tier | Yes (generous) | Yes | Limited trial | Limited trial |
Latency Deep Dive: Time to First Token
Time to First Token (TTFT) — the delay between sending a request and receiving the first generated token — is often more important than total generation speed for user-facing applications. A chatbot that starts responding in 80ms feels instant; one that pauses for 600ms before starting feels sluggish regardless of how fast it generates after that.
- Groq Llama 3.1 8B: TTFT of 50–120ms (median ~80ms)
- Groq Llama 3.3 70B: TTFT of 120–250ms (median ~180ms)
- OpenAI GPT-4o: TTFT of 300–700ms (median ~450ms)
- OpenAI GPT-4o mini: TTFT of 200–500ms (median ~320ms)
When to Choose Groq vs OpenAI
Speed is a core product feature (voice AI, real-time chat, live coding); you need high-volume cheap inference; open-source models (Llama 70B) are capable enough for your task; you want a generous free tier for development; cost is a primary constraint.
You need GPT-4o's frontier reasoning capabilities; you require fine-tuning on your data; multimodal (image+text) is central to your use case; you need deep OpenAI ecosystem integrations (Assistants API, Code Interpreter); or compliance/enterprise features require OpenAI's enterprise agreements.
The Hybrid Approach
Many sophisticated teams use both. Groq powers the real-time, high-frequency interactions (chat responses, code completions, live suggestions) where speed determines UX quality. OpenAI's GPT-4o handles the occasional deep analysis, complex reasoning, and document understanding tasks where its frontier model capability outweighs the latency cost. This tiered approach optimizes both user experience and operating cost simultaneously.