Having spent the last several weeks conducting a formal performance analysis of Claude Code for a large-scale code migration project, I’ve observed significant latency variance that appears strongly correlated with input file size. While the official documentation provides general performance guidelines, I have yet to see a community-driven, granular breakdown of request/response times across a spectrum of realistic file sizes. This gap in empirical data makes capacity planning and workflow optimization challenging.
My preliminary, non-exhaustive tests suggest a non-linear relationship between prompt size (in tokens, proxied by file size) and end-to-end latency. The observed behavior points to potential bottlenecks in the context window processing phase, rather than a simple linear scaling of generation time. I propose a benchmarking framework to quantify this.
**Proposed Benchmark Parameters:**
* **Model:** `claude-3-5-sonnet-20241022` (latest Code-specialized model as of this writing).
* **Metric:** End-to-end latency, measured from the moment the API call is dispatched to the moment the final code block is received. This includes network round-trip, model processing, and streaming time.
* **Task:** A consistent, non-trivial code generation/refactoring instruction applied to different file sizes. Example: "Add comprehensive error handling to this module, using the provided logging library."
* **File Size Tiers:** We should test across a logarithmic scale:
* Small: 5-10 KB (e.g., a single utility class)
* Medium: 50-100 KB (e.g., a moderate-sized module with several functions)
* Large: 250-500 KB (approaching the practical upper bound for a single file review)
* **Control Variables:** API region (e.g., `us-east-1`), consistent temperature (`0.2`), and identical max tokens to avoid truncation.
A sample test harness for a Python implementation might look like this:
```python
import anthropic
import time
import json
client = anthropic.Anthropic(api_key="YOUR_KEY")
MODEL = "claude-3-5-sonnet-20241022"
def benchmark_file(file_path: str, instruction: str) -> dict:
with open(file_path, 'r') as f:
code_content = f.read()
start_time = time.perf_counter()
message = client.messages.create(
model=MODEL,
max_tokens=4096,
temperature=0.2,
system="You are a senior software engineer.",
messages=[{
"role": "user",
"content": f"{instruction}nn```n{code_content}n```"
}]
)
end_time = time.perf_counter()
latency = end_time - start_time
return {
"file_size_kb": len(code_content) / 1024,
"latency_seconds": latency,
"input_tokens": message.usage.input_tokens,
"output_tokens": message.usage.output_tokens
}
```
I have begun collecting data using a similar setup, but a single dataset is insufficient to establish robust trends. I am particularly interested in the community's observations on the following:
* At what approximate file size does latency begin to increase disproportionately? Is there a noticeable "knee in the curve"?
* How does streaming output affect perceived vs. actual latency for large files? Does the time-to-first-token scale linearly?
* Are there any observable differences in latency profiles between simple code analysis tasks (e.g., "explain this function") versus complex code generation tasks on the same file?
* Has anyone compared the latency characteristics of the `claude-3-5-sonnet` model specifically for code tasks against its predecessor (`claude-3-opus`) or competitors like GPT-4's code-specialized variants?
Sharing structured results—even if anecdotal—would help us all build a more efficient workflow. For my part, I will compile my findings from over 200 sample runs across different file types (Python, JavaScript, Go) and post a follow-up with aggregated charts and statistical summaries.