Latency

Latency in AI coding is the time delay between sending a request to an AI model and receiving the first useful response. It directly impacts developer experience: low latency makes AI feel like a natural extension of your thinking, while high latency breaks your flow and makes AI assistance feel like waiting for a slow colleague. Different AI coding interactions have different latency requirements and tolerances.

Code completion is the most latency-sensitive AI interaction. Inline suggestions must appear within 100-300 milliseconds to feel responsive while typing. If suggestions arrive after you have already typed the next few characters, they feel stale and disruptive. This constraint drives the use of small, fast models for completion, even if they are less capable than frontier models. Chat and generation interactions tolerate higher latency (1-5 seconds for first token) because the user expects to wait for a response to their question.

Several factors affect latency in AI coding tools. Model size is primary: smaller models like Claude Haiku respond in under 500ms while larger models like Claude Opus may take 2-5 seconds for the first token. Prompt length affects processing time: a 50,000-token context takes longer to process than a 2,000-token context. Network conditions introduce variable delay: cloud-hosted models add network round-trip time while local models eliminate it. Server load causes variable latency: peak usage times may show higher latency.

Streaming mitigates perceived latency by showing the first token as soon as it is generated rather than waiting for the complete response. A response that takes 10 seconds to fully generate but starts showing tokens after 500ms feels dramatically more responsive than waiting the full 10 seconds. This is why virtually all AI coding tools use streaming by default.

Supermaven claims the lowest latency among AI completion tools, optimizing their model serving infrastructure for sub-100ms suggestions. GitHub Copilot optimizes for completion latency using specialized small models, typically showing suggestions within 200-300ms. Cursor's Tab completion uses fast models for responsive inline suggestions while its Chat and Composer use more capable but slower models.

Claude Code's latency depends on the Anthropic API and the model selected: Haiku is fastest, Sonnet is balanced, and Opus is slowest but most capable. Tabnine offers local model options that eliminate network latency entirely. For developers prioritizing speed, tools with local inference (Tabnine, Supermaven, local models through Ollama with Continue) offer the lowest possible latency at the cost of model capability.

What is Latency?

The time delay between sending a request to an AI model and receiving the first response, affecting the responsiveness of coding tools.

Why is Latency important in AI coding?

Latency in AI coding is the time delay between sending a request to an AI model and receiving the first useful response. It directly impacts developer experience: low latency makes AI feel like a natural extension of your thinking, while high latency breaks your flow and makes AI assistance feel like waiting for a slow colleague. Different AI coding interactions have different latency requirements and tolerances. Code completion is the most latency-sensitive AI interaction. Inline suggestions must appear within 100-300 milliseconds to feel responsive while typing. If suggestions arrive after you have already typed the next few characters, they feel stale and disruptive. This constraint drives the use of small, fast models for completion, even if they are less capable than frontier models. Chat and generation interactions tolerate higher latency (1-5 seconds for first token) because the user expects to wait for a response to their question. Several factors affect latency in AI coding tools. Model size is primary: smaller models like Claude Haiku respond in under 500ms while larger models like Claude Opus may take 2-5 seconds for the first token. Prompt length affects processing time: a 50,000-token context takes longer to process than a 2,000-token context. Network conditions introduce variable delay: cloud-hosted models add network round-trip time while local models eliminate it. Server load causes variable latency: peak usage times may show higher latency. Streaming mitigates perceived latency by showing the first token as soon as it is generated rather than waiting for the complete response. A response that takes 10 seconds to fully generate but starts showing tokens after 500ms feels dramatically more responsive than waiting the full 10 seconds. This is why virtually all AI coding tools use streaming by default.

How do I use Latency effectively?

Use the fastest available model for inline completions where sub-300ms latency is critical, and reserve capable models for Chat and Composer interactions If completion latency bothers you, try Supermaven or Tabnine's local mode which eliminates network round-trip time entirely Keep prompts concise for interactive use: shorter prompts get faster responses. Move static context to CLAUDE.md or .cursorrules rather than repeating it in every prompt