Latency-sensitive products live or die on response time, but the fastest provider is not always the right one. This tool puts Groq (ultra-fast open-model inference) and OpenAI (flexible, frontier-grade) side by side on both time-to-response and monthly cost for your specific workload, so you can pick the tradeoff that fits.
Why this comparison is non-obvious
The natural assumption is that faster always wins. But inference speed and model quality are currently decoupled: Groq’s hardware achieves high token throughput by running specific open models (Llama, Mixtral, Gemma), while OpenAI hosts proprietary models that often outperform those on complex tasks. Choosing between them is therefore a two-axis decision — latency AND quality — not just a cost calculation.
A second non-obvious factor is that latency has two components. Time to first token (TTFT) is how long before the stream starts and controls when a user sees any response. Tokens per second (TPS) is how fast the rest arrives and controls total response time for longer outputs. Groq’s hardware advantage shows most strongly in TPS.
How it works
Latency is dominated by how fast a provider emits tokens:
response_time = time_to_first_token + (completion_tokens / tokens_per_second)
monthly_cost = requests_per_month × cost_per_requestGroq’s custom LPU hardware generates tokens at very high throughput, so for the same completion length its response time is considerably shorter than typical hosted GPU inference. The cost columns multiply your request volume by each provider’s per-token price for a clean monthly comparison.
Worked example
For a voice assistant generating short responses — say 100 completion tokens per request at 10 requests per second:
- Groq at high TPS: response time is roughly TTFT (short) + 100 tokens / high TPS → very fast total latency
- OpenAI at moderate TPS: same formula but longer time per token
At scale, the monthly cost difference compounds rapidly with request volume. The tool shows both so you can see whether the speed premium also comes with a cost premium or saving for your specific numbers.
Tips for choosing
- Real-time UX (voice, autocomplete, agents): Groq’s throughput usually wins if an open model meets your quality bar.
- Complex reasoning, code generation, or tasks needing GPT-4 class capability: OpenAI or another frontier provider, accepting the higher latency.
- Hybrid routing: send simple, latency-critical calls to Groq and reserve OpenAI for the hard prompts — this is a common pattern in production agent architectures and often gives the best cost-and-speed balance.
- Check model availability: Groq’s model selection changes; verify that a specific Llama or Mixtral version you need is currently served before building against it.
All prices in the tool are editable presets — confirm current rates on each provider’s pricing page before committing.