XDA-Developers recently covered a small trick that lets you run the ChatGPT desktop app against a local model instead of OpenAI’s servers. The tool behind it, opencodex, rewrites Codex CLI’s config.toml and sits between the desktop client and whatever backend you point it at, forwarding requests to Ollama, LM Studio, or a cloud provider of your choosing. It works, and it is free. It is also a single-maintainer GitHub project with no release cadence to speak of, which is exactly the kind of thing that sends people looking for opencodex alternatives before they build a workflow around it. A handful of other proxies and gateways solve the same problem: keep the ChatGPT desktop interface, or a similar coding client, while swapping out the model underneath. Here is how seven of them compare.
Quick comparison table
| Tool | Best for | Self-hosted | Starting price | Standout feature |
|---|---|---|---|---|
| LiteLLM | Widest provider coverage | Yes | Free (open source) | Single proxy for 100+ model providers |
| OpenRouter | Zero setup routing | No | Free tier, pay per token | One API key, dozens of hosted models |
| Ollama | Running models locally | Yes | Free | Built-in OpenAI-compatible server |
| LM Studio | Desktop model management | Yes | Free | GUI model picker with a local server toggle |
| anycodex | Direct opencodex swap | Yes | Free (open source) | More provider adapters than upstream |
| CodexHub | Desktop app with telemetry | Yes | Free (open source) | Rust and Tauri client with usage dashboards |
| LocalAI | Full OpenAI drop-in | Yes | Free (open source) | Text, image, and audio endpoints in one binary |
Why users look at opencodex alternatives
opencodex does one job well: it patches Codex’s configuration so the ChatGPT desktop app talks to a different endpoint. That narrow scope is also its limit. The project has one maintainer, which means bug fixes and compatibility patches depend on one person’s schedule. When OpenAI changes the desktop client’s internals, a single-maintainer fork can lag for weeks before someone notices and ships a patch.
Protocol coverage is the second friction point. opencodex speaks the OpenAI-compatible format, which covers Ollama and LM Studio cleanly but leaves Anthropic’s native API and other non-OpenAI schemas out of reach without extra translation. Anyone running a mixed setup, a local Llama model for routine tasks and a hosted Claude or Gemini model for harder ones, ends up needing a proxy that understands more than one dialect.
Model picker UX matters more than it sounds. Editing a TOML file to switch models works for a one-person setup but breaks down the moment you want to test three models in an afternoon. Telemetry is a fair question too: some of these tools log nothing, others ship anonymous usage stats, and a few keep a local dashboard of tokens and latency. Licence terms differ as well. Most of this category sits under MIT, which permits closed-source forks and commercial use with no obligation to share changes; a couple of adjacent tools use GPL variants that require derivative works to stay open. Worth checking before you build a product on top of any of them.
The alternatives
LiteLLM
LiteLLM is a Python proxy that speaks the OpenAI chat completions format on one side and translates to more than 100 model providers on the other, including every major cloud vendor and most local runtimes. Point the ChatGPT desktop app or any OpenAI-compatible client at a LiteLLM server and it routes each request to whichever backend a routing rule assigns. It ships as both a Python library and a standalone proxy server with a config file, plus a hosted dashboard for spend tracking and rate limits.
Where it falls short: The full feature set, load balancing, fallback chains, budget alerts, lives behind the proxy server’s YAML config, which has a steeper learning curve than opencodex’s single TOML edit. Running it also means keeping a Python process alive in the background.
Pricing: Free and open source under MIT. A hosted enterprise tier with SSO and audit logs is available separately for teams, priced on request.
Migrating from opencodex: Swap opencodex’s config.toml endpoint for LiteLLM’s proxy URL (typically http://localhost:4000), then define your backends, Ollama, LM Studio, or a cloud key, in LiteLLM’s config.yaml. Provider setup takes about fifteen minutes for a single-model rig, longer if you want routing rules across several models.
Bottom line: Pick LiteLLM if you want one proxy that can grow into a multi-provider, multi-user setup without switching tools again.
OpenRouter
OpenRouter is a hosted routing service rather than something you self-host. Point any OpenAI-compatible client at OpenRouter’s API and one key gives access to dozens of models, from open-weight releases to the major closed models, billed per token with no subscription required. It is the fastest way to test a model lineup without running any of them yourself.
Where it falls short: It is not local. Every request leaves your machine, which defeats the privacy motivation that draws most people to opencodex in the first place. It only helps if your goal is model variety, not on-device inference.
Pricing: Free credits on signup, then pay-per-token pricing that mirrors or slightly marks up each underlying provider’s rate. No flat monthly fee.
Migrating from opencodex: Replace the local endpoint in your client config with OpenRouter’s API URL and key. No local runtime to install, but also no offline mode; expect this to take about five minutes.
Download: Website
Bottom line: Pick OpenRouter when you want to compare models quickly and don’t need the requests to stay on your own hardware.
Ollama
Ollama is the runtime opencodex actually proxies to for most local setups, and it can skip the middleman entirely. Recent versions expose an OpenAI-compatible endpoint at /v1 alongside its native API, so a client that speaks OpenAI’s chat format can talk to Ollama directly without any proxy in between. It handles model downloads, quantization, and GPU offloading on its own.
Where it falls short: Ollama does not rewrite the ChatGPT desktop app’s configuration for you the way opencodex does; you still need to point the client at Ollama’s endpoint yourself, and multi-provider routing (mixing local and cloud models) is out of scope.
Pricing: Free and open source.
Migrating from opencodex: If opencodex is already forwarding to Ollama, drop the proxy layer and point the desktop app straight at http://localhost:11434/v1. This removes a moving part rather than adding one, and takes a couple of minutes.
Bottom line: Pick Ollama alone if your only backend is local models and you don’t need a router in front of it.
LM Studio
LM Studio is a desktop application for downloading, managing, and running local models through a graphical interface, and it includes a one-click toggle to expose those models over a local OpenAI-compatible server. The model picker lives in the same window where you load and test models, which solves the config-file friction that opencodex leaves unaddressed.
Where it falls short: LM Studio is closed-source, unlike most of the tools on this list, and its resource footprint is heavier than a bare command-line runtime since it ships a full GUI.
Pricing: Free for personal and most commercial use, per the app’s licence terms.
Migrating from opencodex: Enable the local server from LM Studio’s Developer tab, load a model, and point the ChatGPT desktop app at the server address shown in that tab (usually http://localhost:1234/v1). No config file editing required.
Download: Website
Bottom line: Pick LM Studio if you want a visual model picker instead of hand-editing configuration files.
anycodex
anycodex is a direct fork of opencodex built specifically to add provider adapters the original project doesn’t cover, including a few Anthropic-compatible and self-hosted backends. It keeps the same core approach, rewriting Codex’s client-side config, while extending the list of endpoints it can forward to.
Where it falls short: As a fork of a smaller project, it carries the same single-maintainer risk as opencodex itself, just with a different name attached. Community size and issue response time are both limited.
Pricing: Free and open source.
Migrating from opencodex: Configuration is nearly identical since anycodex kept the same file structure and CLI flags. Swapping in is close to a drop-in replacement, usually under ten minutes including testing the new provider adapters.
Download: GitHub
Bottom line: Pick anycodex if opencodex almost works for you and the missing piece is one extra provider it doesn’t yet support.
CodexHub
CodexHub is a Rust and Tauri desktop client that wraps the same proxy concept in a native app window, adding a usage dashboard that tracks tokens, latency, and per-model spend over time. The native binary starts faster than a Python-based proxy and uses less memory at idle.
Where it falls short: The telemetry dashboard, useful for tracking usage, means CodexHub keeps local logs of every request by default. Anyone who wants a proxy that logs nothing should check the settings before assuming it is silent.
Pricing: Free and open source.
Migrating from opencodex: Import your existing provider list through CodexHub’s setup wizard, or add each backend (Ollama, LM Studio, cloud key) manually in its settings panel. Expect about ten minutes for a typical single-provider migration.
Download: GitHub
Bottom line: Pick CodexHub if you want a native app window and usage tracking rather than a background process with no interface.
LocalAI
LocalAI is a broad, self-hosted drop-in replacement for the OpenAI API that covers chat completions, image generation, and audio transcription in a single binary. It supports a wide range of model backends beyond just chat models, which makes it useful for anyone building a workflow that needs more than text generation from the same local server.
Where it falls short: The setup is more involved than a single-purpose proxy since LocalAI configures separate model backends for each modality, and first-run resource requirements are higher if you enable image or audio endpoints alongside chat.
Pricing: Free and open source.
Migrating from opencodex: Point the ChatGPT desktop app at LocalAI’s /v1 endpoint after defining your chat model in its YAML config. Text-only migration takes about fifteen minutes; adding image or audio endpoints takes longer.
Bottom line: Pick LocalAI if you want one self-hosted server that eventually needs to do more than route chat requests.
How to choose
Pick LiteLLM if you’re routing between several providers, local and cloud, and want proxy features like fallback chains and spend tracking built in. Pick OpenRouter if privacy is not the goal and you’d rather test a wide model lineup without running anything locally. Pick Ollama or LM Studio alone if your setup is purely local and a router in front of it would just be an extra hop; LM Studio wins if you want a GUI, Ollama if you want a lighter background process.
Pick anycodex if opencodex is nearly right and you just need a provider it doesn’t support yet, since the migration is closest to a drop-in swap. Pick CodexHub if a native app window with usage tracking matters more than running invisibly in the background. Pick LocalAI if chat is only the first modality you plan to route locally, and you expect to add image or audio generation later.
Stay on opencodex if your setup is a single local model behind the ChatGPT desktop app and you don’t need multi-provider routing, a GUI, or usage tracking. It is a small, focused tool, and for a one-model, one-machine setup, the alternatives above add complexity you may not need yet.
FAQ
Is LiteLLM better than opencodex?
LiteLLM covers far more providers and adds routing features opencodex doesn’t attempt, but it requires running a proxy server rather than a lightweight config rewrite. For a single local model, opencodex is simpler; for multi-provider setups, LiteLLM does more.
Can I use the ChatGPT desktop app with Ollama without opencodex?
Yes. Recent versions of Ollama expose an OpenAI-compatible endpoint directly, so you can point a compatible client at it without any proxy layer in between.
What is the cheapest opencodex alternative?
Every self-hosted option here, LiteLLM, Ollama, LM Studio, anycodex, CodexHub, and LocalAI, is free and open source (or free to use, in LM Studio’s case). OpenRouter is the only one with usage-based billing, since it runs the models on its own servers.
Does switching to a proxy like LiteLLM keep my data local?
Only if you route to a local backend like Ollama or LM Studio. LiteLLM and LocalAI are proxies, not runtimes; where your data goes depends on which backend you configure behind them. OpenRouter always sends requests to hosted models.
Is there a GUI alternative to opencodex’s config file editing?
LM Studio and CodexHub both offer graphical interfaces for managing backends and models, avoiding manual config file edits.
What do people use instead of opencodex for local LLM proxying?
LiteLLM and LocalAI are the most common self-hosted picks for multi-provider routing, while Ollama and LM Studio cover single-runtime setups without a proxy at all.
