I don't see any significant advantage over mature routers like Bifrost.
Are there even any benchmarks?
Are there even any benchmarks?
Benchmarking AI gateways properly is harder than it looks. Feature sets differ meaningfully - exact vs semantic caching, cluster mode, guardrails, audit logging - and each carries its own latency cost. What actually matters for most users is end-to-end latency including provider overhead (200–2000ms), and in that frame Bifrost, LiteLLM, and GoModel are all perfectly fine.
I ran some comparisons but I'm not happy with the methodology, and I'd rather not spread misleading information. Once I have time to do it properly I'll write it up and share a link here. Honestly, I'd also love to see benchmarks done by someone other than the AI gateway builders. :)
Where GoModel actually differs today:
- image size: 16.96 MB vs Bifrost's 69.84 MB. It matters for sidecar, edge, and cold-start scenarios.
- per-tenant keys, guardrails, and audit logs are all in the OSS repo - not gated.
- AI interaction visualization that makes debugging individual request/response flows much easier.Other than that almost useless it’s faster when this will be io bound and not cpu bound.
What I noticed: the website shows a diagram of the litellm SDK communicating with the gateway proxy of GoModel, poor design of benchmarks, the scope of the project in readme vs. depth.
I don't have professional experience in GoLang, so will not comment on quality of code.
There are some genuinely good things about this project and the effort here, but with solid position of Bifrost sitting at a version above 1.0.0 and so many other initiatives in this space, it's a tough market.
You can use it like this:
from litellm import completion
print(completion(
model="openai/gpt-4.1-nano",
api_base="http://localhost:8080/v1",
api_key="your-gomodel-key",
messages=[{"role": "user", "content": "hi"}],
).choices[0].message.content)