benchmarks
Latency versus accuracy in inline completion engines
Learn how code completion engines balance response time and suggestion quality, and why both latency and accuracy metrics matter for developer productivity.
Code completion engines face a fundamental tension: the models that produce the most accurate suggestions often take longer to respond, while the fastest engines sometimes sacrifice correctness for speed. This tradeoff matters because developers interrupt their flow when completions lag, yet shipping broken code is worse than waiting an extra hundred milliseconds.
Inline completion latency—the delay between your last keystroke and when the suggestion appears—typically ranges from 50 milliseconds to over 500 milliseconds depending on the model, context size, and infrastructure. Accuracy, measured by how often developers accept a suggestion without editing it, varies from under 20% to above 40% in production. Neither metric tells the full story alone; a completion that arrives instantly but misleads you costs more time than one that takes 300 milliseconds and gets it right.
Why latency matters more than you think
When a completion engine takes longer than 200 milliseconds to respond, most developers have already typed their next characters. The suggestion then appears mid-word, forcing you to stop, read the greyed-out text, decide whether to accept or reject, and resume typing. That cognitive interrupt costs 2-5 seconds of lost flow, compounded across dozens of completions per hour.
Sub-100 millisecond latency feels instantaneous: the suggestion appears as you finish the current token, blending into your typing rhythm. Between 100 and 200 milliseconds introduces noticeable lag but remains tolerable. Beyond 300 milliseconds, completions become disruptive enough that many developers disable the feature entirely.
Three factors drive latency:
- Model size and architecture: Smaller models (under 1 billion parameters) respond in 50-150 milliseconds on typical hardware; larger models (7B+ parameters) can exceed 500 milliseconds without GPU acceleration.
- Context window: Analyzing 10,000 tokens of surrounding code takes longer than processing 2,000 tokens, though modern attention mechanisms have narrowed the gap.
- Infrastructure: Cloud-based engines add network round-trip time (20-100 milliseconds depending on region); local inference eliminates network latency but demands CPU or GPU headroom.
Measuring accuracy in practice
Acceptance rate—the percentage of suggestions a developer accepts without modification—is the industry's proxy for accuracy, but it conflates several distinct outcomes:
| Outcome | Counts as acceptance? | What it means |
|---------|----------------------|---------------|
| Suggestion is correct and complete | Yes | True positive |
| Suggestion is correct but incomplete, developer accepts and continues typing | Yes | Partial success |
| Suggestion is close enough, developer accepts then edits | No | False positive |
| No suggestion shown | N/A | Model declined to guess |
| Suggestion is wrong, developer rejects | No | True negative |
A 35% acceptance rate might mean the engine produces excellent multi-line completions that developers use as-is, or it might mean the engine guesses frequently and gets lucky often enough. Measuring edit distance after acceptance reveals more: if 80% of accepted suggestions require no further changes, the model is genuinely accurate; if only 40% are used verbatim, the engine is interrupting flow without delivering value.
The best benchmarks track time saved per session rather than raw acceptance rates. An engine that suggests five perfect completions in an hour saves more time than one that offers fifty mediocre guesses requiring edits.
The latency-accuracy frontier
No engine maximizes both dimensions simultaneously. Instead, production systems choose a point on the curve:
Fast, narrow models (50-100ms latency, 20-30% acceptance) guess the next few tokens based on immediate context. They minimize interruption but rarely complete entire functions. Best for developers who want unobtrusive nudges.
Balanced models (150-250ms latency, 30-40% acceptance) analyze several thousand tokens of context and attempt multi-line suggestions. They balance responsiveness with usefulness. Most popular commercial engines target this range.
Deep context models (300-600ms latency, 35-50% acceptance) ingest tens of thousands of tokens across multiple files, producing architectural-aware suggestions. The higher latency disrupts flow unless the completion truly saves 10+ seconds of typing.
Speculative hybrid approaches run a fast model immediately while a slower, more accurate model works in parallel. If the slower model finishes before you accept or reject the fast suggestion, it replaces the initial guess. This strategy delivers sub-100ms perceived latency with higher accuracy, at the cost of doubled compute.
Benchmarking methodology that matters
Synthetic benchmarks that measure latency on an isolated server or score suggestions against held-out code miss how engines perform under real-world conditions. Useful benchmarks must include:
1. Realistic context: Measure latency and accuracy within large, multi-file codebases (10,000+ lines) with dependencies, not toy examples.
2. Network conditions: Cloud engines should report p50, p95 and p99 latency across geographic regions and varying network quality.
3. Hardware variability: Local models perform differently on M-series MacBooks, Intel laptops and Linux workstations; benchmarks should specify the reference machine.
4. Language and framework diversity: Performance varies dramatically between Python, TypeScript, Rust and Go, and between frameworks (React vs Svelte, FastAPI vs Flask).
5. Time-to-first-token vs full-completion latency: Streaming suggestions improve perceived responsiveness; measure both initial display and complete suggestion arrival.
Without these controls, a "benchmark" becomes marketing rather than useful data.
Practical recommendations for teams
When evaluating completion engines, prioritize the metric that matches your workflow:
- For rapid prototyping or scripting work, choose the lowest-latency option that doesn't actively mislead. A 30% acceptance rate with 80ms latency beats 38% accuracy at 400ms.
- For production code in statically typed languages, favor accuracy over speed. An extra 150 milliseconds is worthwhile if the completion respects your type system and avoids deprecated APIs.
- For large refactoring tasks, disable inline completion entirely and use agentic coding approaches that propose pull requests asynchronously rather than interrupting every keystroke.
Monitor your team's actual acceptance rates and completion latency in production. If acceptance drops below 25%, developers perceive the engine as noise. If p95 latency exceeds 300 milliseconds, you're likely losing more time to interruptions than you gain from suggestions.
Goatfied's BUILD environment runs coding agents that work asynchronously on full tasks, sidestepping the latency-accuracy tradeoff for inline completion by moving AI assistance to a separate workflow where multi-second inference time is acceptable in exchange for thorough, tested changes.
Frequently asked questions
What is an acceptable code completion latency?
Below 150 milliseconds feels instantaneous to most developers. Between 150-250 milliseconds is noticeable but tolerable. Above 300 milliseconds, completions disrupt flow more than they help. Aim for p95 latency under 200 milliseconds in production.
How do you measure code completion accuracy?
Track the acceptance rate (percentage of suggestions used without rejection), edit distance after acceptance (what fraction require modification), and time saved per session. A useful completion saves more time than it costs in cognitive interruption. Acceptance rate alone can be misleading if developers accept poor suggestions out of habit.
Should I use a local or cloud-based completion engine?
Local engines eliminate network latency (saving 20-100ms per suggestion) and work offline, but require spare CPU/GPU capacity. Cloud engines access larger models and update instantly but add round-trip time. For latency-sensitive workflows, local wins; for accuracy-critical work, cloud models may justify the added delay.
Do larger models always produce better completions?
Larger models generally produce more contextually aware suggestions, but they also increase latency unless you have dedicated GPU acceleration. A 7B-parameter model on CPU might take 500ms to respond, while a well-tuned 1B-parameter model completes in 100ms. For inline completion, the smaller model often delivers better developer experience despite lower raw accuracy.
Can I benchmark code completion engines myself?
Yes, but you need realistic conditions: run the engine in your actual codebase, measure latency at p50/p95/p99 (not just average), track acceptance rates over days (not hours), and test across languages and file sizes your team uses. Synthetic benchmarks on toy repositories rarely predict real-world performance. For a broader look at coding AI performance, see our Goatfied vs Cursor vs GitHub Copilot benchmark.