Token Speed Calculator

Our token generation speed estimates are based on benchmark testing, published performance metrics, and real-world observations across various deployment scenarios. We regularly update these figures as models evolve and new information becomes available. The speeds represent average performance under typical conditions.

Output tokens generate slower because the model must create new content rather than just process existing text. During generation, the model performs an autoregressive process, where each new token depends on all previously generated tokens. Input processing can be done in parallel, while output generation is inherently sequential, leading to the speed difference.

TTFT (Time To First Token) represents the delay between submitting a prompt and receiving the first generated token. It's a critical metric for user experience, as users perceive responsiveness based on this initial latency. TTFT is typically higher than the subsequent per-token generation time because it includes model loading, prompt processing, and initial computation setup. Lower TTFT values create a more responsive feel, even if the overall generation isn't faster.

To optimize token generation speed: 1) Use smaller models when possible, as they generate tokens faster, 2) Run inference on GPU rather than CPU, 3) Consider batching requests if processing multiple inputs, 4) Reduce the context length if you don't need the full window, 5) Use models optimized for inference like distilled versions, 6) Adjust generation parameters like temperature and top_p for an optimal balance between quality and speed.

The same model can have different speeds across providers due to: 1) Hardware differences - providers use various GPU/TPU configurations, 2) Software optimizations - each provider implements custom inference optimizations, 3) Server load balancing - response times vary with current demand, 4) Network infrastructure - data center locations and network quality affect latency, 5) Implementation details - even with the same architecture, specific model versions may differ slightly.