Benchmark snapshot
This page is a short reference for one public benchmark run comparing GoModel and LiteLLM on OpenAI-compatible traffic. The full article contains the complete write-up, all charts, and the original discussion: GoModel vs LiteLLM Benchmark: Speed, Throughput, and Resource Usage.This benchmark is a point-in-time snapshot published on March 5, 2026. Treat
it as data, not dogma. Gateway performance depends on workload, provider mix,
deployment setup, and tuning.
Visual snapshot
Chart source and full context: Original benchmark post.At a glance
In this benchmark run, GoModel came out ahead on the main operational signals most teams care about:- Added latency
- Throughput under concurrency
- CPU overhead
- Memory overhead
Test shape
The comparison used a simple like-for-like setup:- OpenAI-compatible
/v1/chat/completions - The same prompt and request shape on both sides
- Concurrency levels of
1,4, and8 - A focus on clean runs with
0%errors - Metrics including req/s, latency percentiles, CPU usage, and RSS memory
Reference table
Key readouts
Some useful reads from that March 5, 2026 run:- Lower p95 latency at every tested concurrency level.
- Higher throughput across the benchmark matrix.
45-46 MBRSS, while LiteLLM stayed near320-321 MB.- Less CPU in these runs.
52.75 req/s versus
LiteLLM at 35.81 req/s.
Reproduce it yourself
All the tooling used in the published benchmark is available in this repository.Prerequisites
- Go 1.26.3+
- Python 3.10+ with
matplotlibandnumpy jq,curl- A Groq API key (or any OpenAI-compatible provider — adjust the script)
litellm[proxy](pip install "litellm[proxy]")
Scripts
The benchmark suite lives indocs/about/benchmark-tools/:
Quick start
benchmark-results/ containing
JSON result files, gateway logs, and a REPORT.md with the results table.
Tuning
You can override defaults via environment variables:compare.sh for the full list of knobs.