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GoModel vs LiteLLM

Both put one API in front of many AI providers. The difference is what that layer costs on every request: GoModel is a single Go binary on the hot path, LiteLLM a Python proxy with a much larger provider catalog. Here is how they compare, measured and honest.

Get started with GoModel Read the full benchmark

Comparison

GoModel vs LiteLLM Comparison

A gateway sits on every request, so its runtime footprint charges rent forever. Numbers below are from the reproducible 29 August 2026benchmark run - GoModel 0.1.83 against LiteLLM 1.98.0, same mock backend, same AWS box. Read the full benchmark → Every run is recorded in thereproducible benchmark repository, so you can rerun it with one command.

14×

throughput

3,610 vs 250 req/s sustained

18×

lower latency overhead

2.35 vs 42.4 ms p50

51×

less memory

43 MB vs 2.1 GB peak under load

54×

faster cold start

0.58 vs 31.3 s to first request

Category	GoModel	LiteLLM
Measured on the hot path
Latency overhead (p50)	2.35 ms	42.44 ms
Latency tail (p99)	8.80 ms	61.93 ms
Sustained throughput	3,610 req/s	250 req/s
Peak RAM under load	42.7 MB	2,173 MB
Efficiency (req/s per CPU %)	36.5	2.0
Cold start to first request	0.58 s	31.25 s
Docker image (compressed)	14.4 MB	353.9 MB
Beyond the benchmark
Runtime & deployment	One Go binary	Python + per-vCPU workers
Provider coverage	31 providers	100+ providers
Config	Env vars + optional YAML	YAML config file
Audit logs	Included, MIT	Enterprise license
SSO & access control	OIDC SSO in GoModel Pro, no seat cap	Enterprise license, SSO capped at 5 users on free
Dependency surface	Small, std-lib-heavy Go tree	Large Python dependency graph
License	MIT	MIT core + enterprise license

Benchmark: 20,000 requests per workload at concurrency 10, five randomized trials, six API workloads, on an AWS c7i.large (2 vCPU, 4 GiB). LiteLLM ran its recommended two workers; retries and GoModel's circuit breaker were disabled for fairness. LiteLLM's broader provider catalog is a real advantage - the benchmark measures the request path, not product maturity.

Which gateway fits your setup?

Different teams optimize for different things. A fair split, based on what each project actually ships today.

Choose GoModel when

Gateway overhead is real money or real latency: high request volume, latency-sensitive products, or fast local models where 40 ms per call dwarfs inference.
You want audit logs, budgets, rate limits, and the admin dashboard under a plain MIT license - no enterprise tier.
Operations should stay small: one binary or a 14 MB image that runs the same on a laptop, an edge box, or Kubernetes.
You serve both OpenAI SDK and Anthropic SDK clients from one endpoint.

LiteLLM is the better fit when

You need long-tail providers beyond GoModel's 31 - LiteLLM's catalog of 100+ is a genuine advantage.
Your platform tooling is Python-first and already builds on the LiteLLM SDK rather than the proxy.
You depend today on features GoModel has on the roadmap, like OAuth 2.0 or SAML. (OIDC SSO itself ships inGoModel Pro - with no seat cap.)

Try it against your own workload

Benchmarks are an argument; your traffic is the proof. GoModel speaks the OpenAI and Anthropic APIs natively, so pointing an existing app at it is a base-URL change.

1 · Run GoModel

docker run --rm -p 8080:8080 \
  -e LOG_FORMAT=text \
  -e OPENAI_API_KEY="your-openai-key" \
  enterpilot/gomodel

2 · Point your SDK at it

from openai import OpenAI

client = OpenAI(
    base_url="http://localhost:8080/v1",
    api_key="your-gomodel-key",
)

Full setup, providers, and configuration live in the documentation. Questions? Ask on Discord or book a 30-minute call.