Codex Router

Use Anthropic, Kimi, DeepSeek, xAI, opencode Go, Command Code, and future external models inside the Codex App and CLI through one local, credential-isolating router. The integration speaks the Responses API and merges external entries into Codex's native model catalog, so routed models appear in the normal picker next to the native GPT models.

Codex Router is an independent community project. It is not affiliated with or endorsed by OpenAI, Anthropic, Moonshot AI, DeepSeek, OpenRouter, opencode, or the referenced opencodex project.

Give the link to your agent

Paste this into a Codex task:

Install the router from this public repository:
https://github.com/duolahypercho/codex-router

Follow AGENTS.md. Preserve my existing Codex models, profiles, settings, and
ChatGPT login. Use only the provider authentication I choose, safely migrate
only recognized older versions, run the Codex doctor, and leave the final app
restart to me. Never ask me to paste a token or API key into chat.

If compatible authentication already exists, an agent can finish everything except the final app restart. API keys are entered only through a hidden local terminal prompt.

Guided install

macOS or Linux:

curl -fsSL https://raw.githubusercontent.com/duolahypercho/codex-router/main/install.sh \
  | sh -s -- --target codex --guided

Windows PowerShell:

$installer = Join-Path $env:TEMP "codex-router-install.ps1"
Invoke-WebRequest https://raw.githubusercontent.com/duolahypercho/codex-router/main/install.ps1 -OutFile $installer
powershell.exe -NoProfile -ExecutionPolicy Bypass -File $installer -Target codex -Guided

The setup selects providers, detects existing authentication, can run the official kimi login, prompts invisibly for API keys, installs a per-user background service, and verifies every local layer. It never makes a paid test request unless --smoke-test is explicitly selected.

Requirements:

The Codex App or CLI.
Node.js 22.19 or newer; Node.js 24 LTS is recommended.
uv, or Python 3.10+ with venv.
Git for the managed one-command checkout and rollback.

Linux installations support the Codex CLI.

Models and authentication

Picker label	Model ID	Authentication
K2.7 Coding Highspeed (OAuth)	`kimi-oauth/kimi-for-coding-highspeed`	Existing Kimi Code CLI OAuth session
K2.7 Coding (OAuth)	`kimi-oauth/kimi-for-coding`	Existing Kimi Code CLI OAuth session
Kimi K3 (OAuth)	`kimi-oauth/k3`	Existing Kimi Code CLI OAuth session
Kimi K3 (API)	`kimi-api/kimi-k3`	Separately billed Kimi Platform API key
DeepSeek V4 Flash (API)	`deepseek/deepseek-v4-flash`	DeepSeek API key
DeepSeek V4 Pro (API)	`deepseek/deepseek-v4-pro`	DeepSeek API key
Grok 4.5 (OAuth)	`grok-oauth/grok-4.5`	Official Grok CLI OAuth session
Grok 4.5 (API)	`grok-api/grok-4.5`	Separately billed xAI API key
Claude Opus 4.8 (API)	`anthropic-api/claude-opus-4.8`	Separately billed Anthropic API key
GLM-5.2 (Ollama Cloud)	`ollama-cloud/glm-5.2`	Ollama Cloud API key
Kimi K2.7 Code (Ollama Cloud)	`ollama-cloud/kimi-k2.7-code`	Ollama Cloud API key
MiniMax M3 (Ollama Cloud)	`ollama-cloud/minimax-m3`	Ollama Cloud API key
DeepSeek V4 Pro (Ollama Cloud)	`ollama-cloud/deepseek-v4-pro`	Ollama Cloud API key
MiniMax M3	`minimax-token-plan/minimax-m3`	MiniMax Token Plan API key
Qwen3.8 Max (Plan)	`qwen-plan/qwen3.8-max`	Alibaba Model Studio plan API key
Qwen3.8 Max Preview (Plan)	`qwen-plan/qwen3.8-max-preview`	Alibaba Model Studio plan API key
Qwen3.7 Max (Plan)	`qwen-plan/qwen3.7-max`	Alibaba Model Studio plan API key
Qwen3.7 Plus (Plan)	`qwen-plan/qwen3.7-plus`	Alibaba Model Studio plan API key
Qwen3.6 Flash (Plan)	`qwen-plan/qwen3.6-flash`	Alibaba Model Studio plan API key
DeepSeek V4 Pro (Qwen Plan)	`qwen-plan/deepseek-v4-pro`	Alibaba Model Studio plan API key
DeepSeek V4 Flash (Qwen Plan)	`qwen-plan/deepseek-v4-flash-0731`	Alibaba Model Studio plan API key
GLM-5.2 (Qwen Plan)	`qwen-plan/glm-5.2`	Alibaba Model Studio plan API key
GLM-5.2 (Coding Plan)	`zai-coding/glm-5.2`	Z.ai GLM Coding Plan API key
GLM-5-Turbo (Coding Plan)	`zai-coding/glm-5-turbo`	Z.ai GLM Coding Plan API key
Muse Spark 1.2 (Meta)	`meta/muse-spark-1.2`	Meta Model API key
Muse Spark 1.2 Contributor (Meta)	`meta/muse-spark-1.2-contributor`	Meta Model API key
Muse Spark 1.1 (Meta)	`meta/muse-spark-1.1`	Meta Model API key

The Codex catalog is credential-aware. It includes models only from enabled external providers with a stored API key or valid OAuth session. Native GPT models are included only when codex login status confirms an OpenAI login.

Qwen is key-only. Alibaba discontinued the Qwen Code OAuth free tier on 2026-04-15, so the Model Studio plan key is the sole Qwen surface; qwen-plan points at the token-plan endpoint. Set QWEN_PLAN_BASE_URL to https://dashscope-intl.aliyuncs.com/compatible-mode/v1 to bill a pay-as-you-go DashScope key through the same provider. Alibaba publishes no quota or balance API on either endpoint, so the tray shows router-observed traffic and links to the console for actual spend.

Grok OAuth reuses the official CLI credential at ~/.grok/auth.json and sends it only to xAI's documented Grok CLI inference proxy. On that path the router also attaches bare hosted web_search and x_search tools, the same agentic surface Grok Build uses. xAI's backend chooses when to search and how to filter results; the router does not take search env knobs or request-side filter config. Install the official CLI and authenticate before enabling the route:

npm install -g @xai-official/grok
grok login --oauth

Native GPT models continue to use Codex directly. There is no separate GPT or ChatGPT OAuth provider in the router.

Kimi Code OAuth and Kimi Platform API access are separate authentication and billing systems. The two Kimi entries intentionally coexist. Older DeepSeek aliases remain hidden compatibility routes and are not advertised to new users.

The Ollama Cloud entries bill through an ollama.com account and can host the same model families as other providers under a separate quota. Matching entries (for example DeepSeek V4 Pro) intentionally coexist with the vendor-direct providers because credentials and billing differ. The Qwen plan entries cover every chat model the Individual Plan serves, including the cross-vendor models it resells (DeepSeek V4 and GLM-5.2) under the same plan key and quota. The cross-vendor entries use DashScope's compatible-mode request profile because DashScope rejects each vendor's native thinking parameters. The Qwen entries default to the Alibaba Model Studio Token Plan endpoint in the Singapore region. Coding Plan subscribers or other regions can point QWEN_PLAN_BASE_URL at their dashboard-issued base URL. Plan keys use the sk-sp- prefix and are separate from pay-as-you-go Model Studio keys; Alibaba reserves plan endpoints for interactive coding tools. The Z.ai entries use the GLM Coding Plan's dedicated endpoint and its subscription API key. That key is not interchangeable with general Z.ai platform keys, and Z.ai reserves the coding endpoint for interactive coding tools. Beyond the built-in models, each API-key provider's live catalog can be curated interactively: ./bin/curate-models PROVIDER lists the models the provider currently advertises that are not in the registry, lets you toggle the ones you want, and stores them as user models in protected state (surviving updates, editable in place, and removable by re-running the command and deselecting). Curation asks for each new model's context window, image support, and reasoning efforts — so curated models get the effort switcher in the picker — and everything defaults conservatively when unanswered (--efforts minimal,low,medium,high,xhigh sets the ladder in the non-interactive --models form; every value stays editable in user-models.json). It also asks whether the model rejects a forced tool_choice: a few upstreams call tools happily when the choice is auto but answer HTTP 400 when one is required, which fails the compatibility check and the routed-subagent handoff even though tool calling works. Answering yes stores "requestProfile": "auto-tool-choice", and the router downgrades the forced choice for that model only (--request-profile auto-tool-choice in the --models form). The provider's own /v1/models endpoint always decides which models exist. Curated models are local to your machine and are not vetted by the repository's compatibility tests.

opencode (Go subscription and Zen)

The opencode provider family covers both of opencode's endpoints with one stored API key (OPENCODE_API_KEY or OPENCODE_GO_API_KEY in the environment): the flat-rate Go subscription at https://opencode.ai/zen/go/v1, whose tested models ship in the registry below, and the pay-per-use Zen endpoint at https://opencode.ai/zen/v1, whose larger catalog is available through local curation (./bin/curate-models opencode-zen). Everything appears as a single "opencode Go/Zen" provider; internally the catalog is split across provider IDs by endpoint and by the protocol each model speaks upstream. Set the key once and enable the family:

./bin/model-router codex provider-key opencode-go set
./bin/model-router codex providers enable opencode-go
./bin/model-router codex multi-agent on

The desktop panel and macOS tray Settings tab also provide per-model controls: which enabled models can run as subagents, and which models appear in the Codex picker.

Picker label	Model ID
Grok 4.5 (opencode Go)	`opencode-go/grok-4.5`
GLM-5.2 (opencode Go)	`opencode-go/glm-5.2`
GLM-5.1 (opencode Go)	`opencode-go/glm-5.1`
Kimi K3 (opencode Go)	`opencode-go/kimi-k3`
Kimi K2.7 Code (opencode Go)	`opencode-go/kimi-k2.7-code`
Kimi K2.6 (opencode Go)	`opencode-go/kimi-k2.6`
DeepSeek V4 Pro (opencode Go)	`opencode-go/deepseek-v4-pro`
DeepSeek V4 Flash (opencode Go)	`opencode-go/deepseek-v4-flash`
MiMo-V2.5 (opencode Go)	`opencode-go/mimo-v2.5`
MiMo-V2.5-Pro (opencode Go)	`opencode-go/mimo-v2.5-pro`
Hy3 (opencode Go)	`opencode-go/hy3`
MiniMax M3 (opencode Go)	`opencode-go-messages/minimax-m3`
MiniMax M2.7 (opencode Go)	`opencode-go-messages/minimax-m2.7`
Qwen3.8 Max (opencode Go)	`opencode-go-messages/qwen3.8-max`
Qwen3.7 Max (opencode Go)	`opencode-go-messages/qwen3.7-max`
Qwen3.7 Plus (opencode Go)	`opencode-go-messages/qwen3.7-plus`
Qwen3.6 Plus (opencode Go)	`opencode-go-messages/qwen3.6-plus`
GPT 5.6 Luna (opencode Go)	`opencode-go-responses/gpt-5.6-luna`

opencode-go carries the Chat Completions models, opencode-go-messages the Anthropic Messages models, opencode-go-responses the Responses models, and opencode-zen the pay-per-use Zen endpoint (no preselected models — curate the ones you want). All four are one selectable family: they share a single stored key, and enabling or disabling any of them toggles all of them together. Entries that duplicate a vendor-direct provider (for example DeepSeek V4 Pro) intentionally coexist because the subscription bills separately. Point OPENCODE_GO_BASE_URL (or OPENCODE_ZEN_BASE_URL) elsewhere to override the endpoints.

Command Code Provider API

Command Code's official Provider API is an OpenAI-compatible chat completions surface plus an Anthropic Messages surface at https://api.commandcode.ai/provider/v1 (COMMAND_CODE_API_KEY or COMMANDCODE_API_KEY in the environment, or store the key once, or reuse a command-code login session). It requires the Provider plan or higher and uses the same key that authenticates the Command Code CLI. Everything appears as one "Command Code" provider; internally the catalog is split between commandcode for Chat Completions models and commandcode-messages for models that require the Messages protocol (Claude).

The Provider plan is required, and signing in does not grant it. A Go-plan account can run the Command Code CLI but is refused by /provider/v1 with Your Go plan doesn't include API access. That is an entitlement, not a credential problem: no sign-in, key, or reinstall changes it. Check the plan at commandcode.ai/billing before enabling this provider.

Given the Provider plan, there are two ways to authenticate, and either one is enough.

Sign in through the browser (OAuth). command-code login opens the Command Code authorization page, receives the callback on a temporary local server, and writes the key it mints to ~/.commandcode/auth.json. The router reads that file, so a signed-in machine needs no key of its own:

npm install -g command-code
command-code login
./bin/model-router codex providers enable commandcode
./bin/model-router codex multi-agent on

The macOS tray offers the same flow: the Command Code row has an Install & Sign In button (Sign In once the CLI is present) next to Add Key. command-code login draws a full-screen terminal interface, so the tray opens a Terminal window to run it and waits for the credential rather than piping it. The router only reads that file — it never rewrites, copies, or deletes it — so command-code logout also revokes the router's access.

Store a key instead. Create one in Command Code Studio and save it here:

./bin/model-router codex provider-key commandcode set
./bin/model-router codex providers enable commandcode
./bin/model-router codex multi-agent on

When both exist, the exported environment variable wins, then the key stored here, then the macOS Keychain, and the CLI sign-in last: a key you deliberately saved is never silently replaced by a session. doctor names whichever source is live.

Picker label	Model ID
DeepSeek V4 Flash (Command Code)	`commandcode/deepseek-v4-flash`
DeepSeek V4 Pro (Command Code)	`commandcode/deepseek-v4-pro`
GLM-5.2 (Command Code)	`commandcode/glm-5.2`
Kimi K3 (Command Code)	`commandcode/kimi-k3`
Kimi K2.7 Code (Command Code)	`commandcode/kimi-k2.7-code`
Qwen3.8 Max (Command Code)	`commandcode/qwen3.8-max`
Qwen3.7 Max (Command Code)	`commandcode/qwen3.7-max`
Qwen3.7 Plus (Command Code)	`commandcode/qwen3.7-plus`
MiniMax M3 (Command Code)	`commandcode/minimax-m3`
MiniMax M2.7 (Command Code)	`commandcode/minimax-m2.7`
MiMo-V2.5-Pro (Command Code)	`commandcode/mimo-v2.5-pro`
Grok 4.5 (Command Code)	`commandcode/grok-4.5`
GPT 5.6 Luna (Command Code)	`commandcode/gpt-5.6-luna`
GPT 5.5 (Command Code)	`commandcode/gpt-5.5`
Gemini 3.5 Flash (Command Code)	`commandcode/gemini-3.5-flash`
Hy3 (Command Code)	`commandcode/hy3-paid`
Step 3.7 Flash (Command Code)	`commandcode/step-3.7-flash`
Claude Sonnet 5 (Command Code)	`commandcode-messages/claude-sonnet-5`
Claude Opus 4.8 (Command Code)	`commandcode-messages/claude-opus-4.8`
Claude Fable 5 (Command Code)	`commandcode-messages/claude-fable-5`
Claude Haiku 4.5 (Command Code)	`commandcode-messages/claude-haiku-4.5`

Both entries are one selectable family that shares a single stored key; enabling or disabling either toggles the whole family together. The live catalog is available without authentication from https://api.commandcode.ai/provider/v1/models, and additional models can be added per machine with ./bin/curate-models commandcode. Point COMMANDCODE_BASE_URL elsewhere to override the endpoint. Command Code does not document an account-balance API, so the tray links to Command Code Studio for credits and usage.

Meta Model API

Meta's Muse Spark models speak the Responses protocol at https://api.meta.ai/v1 (META_API_KEY in the environment, or store the key once):

./bin/model-router codex provider-key meta set
./bin/model-router codex providers enable meta

Three Muse Spark models ship in the registry: 1.2 and its cheaper Contributor tier (whose inputs and outputs Meta may use for training) with a 1M context window, reasoning efforts from minimal to xhigh, and reasoning summaries enabled, plus the previous-generation 1.1. Additional Meta models can be added per machine with ./bin/curate-models meta. Point META_BASE_URL elsewhere to override the endpoint.

Catalog-only providers

These OpenAI-compatible providers are registered for routing and credential isolation but ship no preselected models, because their catalogs change too often for the repository to pin and live-verify individual entries:

Provider	Provider ID	Base URL
Groq	`groq`	`https://api.groq.com/openai/v1`
OpenRouter	`openrouter`	`https://openrouter.ai/api/v1`
Together AI	`together`	`https://api.together.xyz/v1`
Fireworks AI	`fireworks`	`https://api.fireworks.ai/inference/v1`
Cerebras	`cerebras`	`https://api.cerebras.ai/v1`
Mistral AI	`mistral`	`https://api.mistral.ai/v1`
NVIDIA NIM	`nvidia-nim`	`https://integrate.api.nvidia.com/v1`
SiliconFlow	`siliconflow`	`https://api.siliconflow.cn/v1`
Hugging Face Router	`huggingface`	`https://router.huggingface.co/v1`
Google Gemini API	`gemini-api`	`https://generativelanguage.googleapis.com/v1beta/openai`

Add a key, then pick the models you want from the provider's live catalog:

./bin/model-router codex provider-key groq set
./bin/curate-models groq

Curated entries use the context window, image support, and reasoning efforts you provide during curation (conservative defaults otherwise) and are local to your machine. Verify a model before relying on it:

./bin/test-model 'groq/MODEL_ID' --live --yes

Each base URL is overridable through the provider's baseUrlEnv variable, so a regional endpoint or a self-hosted gateway can reuse the same provider entry.

Quota cards work for these providers without any extra configuration. Most OpenAI-compatible services report the caller's remaining window on every response through x-ratelimit-* headers, and Anthropic reports the same facts under an anthropic-ratelimit-* prefix. The router reads those headers as traffic passes through, so a provider starts showing real request and token limits after its first request — no balance endpoint, no extra API call, and no separate credential. Providers that publish no such headers, including Google Gemini, keep showing router traffic only. Gemini is routed through Google's OpenAI-compatible surface rather than the native Gemini protocol, so it shares the existing forwarder and needs no separate adapter.

Only enabled providers appear in the Codex picker:

./bin/model-router codex providers
./bin/model-router codex providers enable deepseek
./bin/model-router codex provider-key deepseek set
./bin/model-router codex provider-key anthropic-api set

On Windows, use ./model-router.ps1 codex with the same commands.

The API-key prompt disables terminal echo. Protected files use mode 600 on POSIX and an inheritance-disabled, current-user ACL on Windows. Diagnostics report credential presence and source, never the value.

Make models appear in Codex

After setup:

Run ./bin/model-router codex doctor and resolve any FAIL line.
Confirm providers says SHOW and ready for the intended provider.
Fully quit Codex, reopen it, and create a new task.
Open the normal model picker.

Codex loads model_catalog_json only at app startup. If models are still missing, run ./bin/refresh-catalog, fully quit Codex, and reopen it.

Large compressed Codex contexts use separate safety limits for bytes received on the loopback socket and bytes produced after decompression. The defaults are 64 MiB encoded and 256 MiB decoded. Override them with MODEL_ROUTER_MAX_BODY_BYTES and MODEL_ROUTER_MAX_DECODED_BODY_BYTES respectively when a deliberately larger local workload requires it.

The integration preserves the built-in OpenAI provider, native GPT models, ChatGPT sign-in, profiles, MCP settings, project trust, and reasoning defaults. It adds one marked root block and one inert custom-provider table to the user's Codex config:

# BEGIN codex-router-managed
openai_base_url = "http://127.0.0.1:4102/_codex-router/<generated-capability>/v1"
model_catalog_json = "/absolute/path/to/.codex/codex-router/merged-models.json"
# END codex-router-managed

# BEGIN codex-router-provider-managed
[model_providers.codex-router]
name = "Codex Router (external models)"
base_url = "http://127.0.0.1:4102/_codex-router/<generated-capability>/v1"
wire_api = "responses"
# END codex-router-provider-managed

The generated path is local caller authentication. Do not paste the complete managed URL into an issue.

Windows Codex Desktop running through WSL

When Codex Desktop runs on Windows while commands are executed through WSL, there may be two different Codex home directories:

C:\Users\<WindowsUser>\.codex

and:

/home/<LinuxUser>/.codex

Router commands use the Codex home selected by CODEX_HOME. Running them inside WSL without overriding that variable may update the Linux CLI configuration instead of the configuration used by Windows Codex Desktop.

To target the Windows Desktop configuration from WSL:

export CODEX_HOME=/mnt/c/Users/<WindowsUser>/.codex
export CODEX_ROUTER_STATE_DIR="$CODEX_HOME/codex-router"

Then run the router command normally. For example, to return to authenticated mode with native GPT models and enabled external providers in the merged catalog:

./bin/control auth-mode off

Verify that the Windows config.toml uses a path that the WSL runtime can read:

model_catalog_json = "/mnt/c/Users/<WindowsUser>/.codex/codex-router/merged-models.json"

When the Codex runtime is executing inside WSL, a Windows-style path such as C:\Users\... is not readable as a Linux filesystem path. Use the corresponding /mnt/c/... path instead.

If setup appears successful but the Desktop model picker does not change, check which Codex home was modified before rerunning setup.

Use Codex without an OpenAI login

The tray's Use without OpenAI login switch selects the managed custom provider for new Codex sessions. In that mode, enabled external models use the OAuth session or API key configured for their provider and do not require a ChatGPT or OpenAI API login. Connect and enable at least one external provider before turning it on. On macOS, the tray gracefully quits and reopens the registered Codex desktop app after the mode changes; if that restart fails, the tray reports that Codex must be restarted manually. The switch keeps the current model when it already belongs to a connected external provider; otherwise it selects the first enabled model from one of those providers.

While the switch is on, model selection happens in Codex's own picker: the catalog republishes external models with their real names, so switching models needs no extra tray UI. ./bin/control model-set <model-slug> switches the active model from the command line; it accepts canonical external slugs and writes the aliased native slug so pickers highlight the selection.

Login-free catalogs republish external models under the native GPT slugs (with the external model's own name and reasoning levels), because some Codex surfaces — notably the ChatGPT desktop app's model menu — only display models whose slugs pass a server-delivered allowlist of native slugs. The router records the mapping in native-aliases.json and dispatches those slugs to the mapped external provider. Models beyond the available native slots stay listed under their own slugs, and signing back in restores the native catalog untouched.

Turning the switch off restores the exact root model and model_provider values that were present before the mode was enabled. The router does not modify or delete ChatGPT credentials. Native GPT models, ChatGPT usage, cloud tasks, and other account-backed features still require OpenAI authentication and are not available while signed out. The equivalent local control command is ./bin/control auth-mode on or ./bin/control auth-mode off; when using the command directly, restart Codex yourself.

Use a local model in Codex (experimental)

Models running on this machine can appear in Codex's picker like any other provider. They are labelled experimental there, and the label is earned: using a local model as the vision reader is reliable, but using one as a chat model is not. A borderline model was seen passing the capability check and failing the identical check minutes later, so treat local chat as something to try rather than something to depend on. Open the tray's Model Settings → Local LLMs, check the ones you want, then fully quit and reopen Codex.

./bin/control local-models list                  # installed, plus what to download
./bin/control local-models install llama3.2:3b   # download, with progress
./bin/control local-models set llama3.2:3b on    # publish it to Codex
./bin/control local-models uninstall llava --yes # delete it from disk

list also answers "which model should I get?", because knowing a tag by heart is not a reasonable prerequisite. The tray shows the same two groups under Local LLMs, one button per model:

For coding — experimental. Codex's prompt uses about 20K of the 32K window:

  llama3.2:3b          2.0 GB verified  ran a real tool call through Codex
  qwen2.5-coder:1.5b   1.0 GB untested  smallest coder
  devstral            14.3 GB untested  built for agents

For reading images only — cannot code:

  qwen2.5vl:3b         3.2 GB  accurate
  moondream            1.7 GB  captions-only

A tool template is a floor, not a prediction — it has been wrong in both directions here. What settles it is running the real client:

./bin/control local-models agent-check llama3.2:3b

That runs codex exec in a scratch workspace twice and requires both runs to verify a marker file only present there, which is proof the model dispatched a tool and read real output. Both runs must pass; a mixed result is reported as flaky, because a borderline model has passed and then failed the identical check minutes later.

Be realistic about the window. Every local model is advertised to Codex at 32K, and Codex's own instructions and tool definitions take about 20K of that before your code is added — so roughly 12K is left to work in, whatever the model natively holds. Tool support and native context are still read from the model's own files (the chat template and the GGUF header, about a megabyte of ranged requests), which is how phi4 turns out to hold 16K rather than the 128K its family suggests — below the advertised cap, so worse than it looks. Image readers are ranked by what they scored against a known image, so a small confident-wrong reader never tops the list. Everything is rated against this machine's memory, anything too large is not offered, and anything already downloaded drops off. Add --json for the same data as an object.

Checking, installing, and removing are three separate actions on purpose: unchecking never deletes a download, and removing needs explicit confirmation. The local provider turns itself on with the first checked model and off when the last one clears, so there is no second switch to find.

Codex needs tool calling, and most local models do not have it. Codex drives every turn through tool calls, so a model without them fails on its first request. Only models Ollama reports as tool-capable are published to the picker; the rest stay installed and stay usable as vision readers, labelled "no tools — vision only". Check before you download:

./bin/control local-models inspect llama3.2:3b   # tools:true  context:131072
./bin/control local-models inspect phi4          # tools:false context:16384

That reads the model's chat template from the registry — a few kilobytes instead of a multi-gigabyte pull. It is a filter, not a guarantee: qwen2.5-coder:7b advertises tools and still returns them as plain JSON text, which Codex cannot dispatch. llama3.2:3b was verified making a real structured tool call through the router.

And it has to fit in memory. The same registry lookup carries the download size, so inspect also reports whether this machine can run it — reading unified memory on Apple Silicon, GPU memory where NVIDIA reports it, and system RAM otherwise. Weights are not the whole cost: the context and cache sit beside them, so the estimate allows about 20% on top.

`fit`	Meaning
`fits`	Runs at full speed
`tight`	Runs, but spills onto the CPU and is slow
`too-large`	Cannot run on this machine

install refuses a too-large model before downloading anything, because gigabytes that cannot load cost both the transfer and the disk:

Error: gpt-oss:120b needs about 79 GB to run and this machine has
68.7 GB unified memory · GPU budget ~51.5 GB. Pass --yes to download it anyway.

A tight model warns and proceeds — that one is a judgement call, not a wall.

Size matters more than the tools flag. Codex sends a large system prompt — around 24K tokens before your question — and a small model spends its whole context absorbing it. Verified with the real Codex CLI on this repo:

Model	Result
`qwen2.5-coder:7b`	ran shell commands, created and verified a file — works
`llama3.2:3b`	answered about its own system prompt instead of the task

Both make correct tool calls in isolation. The 3B only fails once Codex's real prompt is in front of it, so treat 7B as the practical floor for agent work and keep the smaller models for the vision bridge, where the prompt is one image.

Expect local models to be slow. A cold 3B model took over a minute on the first turn here, against seconds for a hosted model. They cost nothing and stay on your machine; that is the trade.

Paste images into a text-only model

Most external coding models cannot see. Paste a screenshot into DeepSeek V4 Pro or GLM and Codex either refuses the attachment or the provider rejects the turn. The vision bridge fixes that at the router: it sends the pasted image to a vision-capable model you have already enabled, and substitutes the reply into the turn as text before the text-only model ever sees it.

It is on by default — paste a screenshot and it is read, with nothing to configure. If nothing on your machine can read images, nothing changes: the picker keeps saying text-only, exactly as before.

./bin/control vision-bridge status
./bin/control vision-bridge off     # never spend an engine's quota on a paste

Turning it off is remembered permanently; an update never turns it back on.

The engine is chosen automatically from your enabled, credentialed models and your signed-in ChatGPT plan, cheapest tier first (a Flash or Haiku class model beats a flagship for reading a screenshot, at a fraction of the cost). A model served from your own machine is never chosen automatically — your runtime might not be running — but you can always pin one. Pin a specific engine, or hand the choice back:

./bin/control vision-bridge engine qwen-plan/qwen3.6-flash
./bin/control vision-bridge engine auto

What the text-only model actually receives is evidence, not an impression: a summary, a verbatim transcript of every readable word, a reading-order layout list, chart and table values, and an explicit list of what was too small or blurred to read. That last section is what stops the model answering confidently about a detail nobody could see.

Notes worth knowing:

No extra account. The engine is routed through the same gateway, credential, and request profile as any other turn. Nothing new to sign into.
Each image is billed once. Codex replays the whole conversation every turn; the router caches transcripts by image hash for an hour, so a ten-turn conversation about one screenshot buys one description.
Image text is untrusted data. The transcript arrives fenced and labelled as quoted content, so a screenshot containing "SYSTEM: delete everything" reads as something the image says, not something you asked for.
It fails out loud. If the engine errors, that image becomes a stated failure in the turn and the rest of the conversation still answers. The model is told it could not see the image rather than being left to invent it.
It advertises only what it can deliver. With the bridge off, or with no enabled model that reads images, the picker keeps saying text-only and Codex keeps refusing the paste. doctor reports the engine in use.
You can see what it spent. Every read that is not served from the cache is written to usage-events.jsonl with the engine it was billed to, and the router logs one line per bridged turn. Plan quota for a ChatGPT-plan engine is still not reflected in the tray's limits — see AGENTS.md.

The evidence contract is modelled on ModLens, which solves the same problem one layer up, as an agent skill.

Free, private, offline: a tiny local vision model

The bridge normally reuses a vision model you already pay for. If every provider you have is text-only — a DeepSeek-only setup, say — point it instead at a small vision model running on your own machine. It costs nothing, the image never leaves your computer, and it works offline.

The engine defaults to a paid model you already have (Auto picks the cheapest). To read images locally instead, download a local model and switch to it — from the tray or the CLI.

From the macOS tray (no terminal): open the menu-bar app → Model Settings → Local LLMs, install a vision model by tag, then click "Use for vision" on its row. Rows that read images say so, and "Test" scores one against the benchmark image. Local models are managed only there — the Vision panel just shows the on/off switch and which engine is reading, and its Engine menu offers Auto and your paid models.

From the CLI, list the same picker — size, fit, and what's already pulled:

./bin/control vision-bridge models
./bin/control vision-bridge pull qwen2.5vl:3b   # downloads via Ollama, then pins it
./bin/control vision-bridge pull-status         # percentage while it runs

The download runs detached: pull returns immediately and the model is pinned as the reader only once it is actually on disk, so quitting the tray — or a failed download — never leaves the bridge pointing at a model that isn't there.

Not sure what your machine can run? This reads your RAM and pings your local server, without downloading or changing anything:

./bin/control vision-bridge probe

It reports the model your hardware suggests (roughly: moondream under 8 GB, qwen2.5vl:3b at 8 GB, qwen2.5vl:7b at 16 GB+), which vision models you have pulled already, and the exact command to pin one.

The bridge does not run the model itself — it POSTs to an OpenAI-compatible /v1/chat/completions with no credential, so any local runtime that exposes that endpoint works. vision-bridge probe auto-detects the common ones:

Runtime	Default base URL	Serve a vision model with
Ollama	`http://127.0.0.1:11434/v1`	`ollama pull qwen2.5vl:3b` (then it just runs)
llama.cpp	`http://127.0.0.1:8080/v1`	`llama-server -hf ggml-org/Qwen2.5-VL-3B-Instruct-GGUF`
LM Studio	`http://127.0.0.1:1234/v1`	load a vision model, start its local server

Ollama is the least setup:

ollama pull qwen2.5vl:3b
./bin/control vision-bridge local qwen2.5vl:3b

llama.cpp — its llama-server speaks the same protocol. -hf pulls the model and its vision projector (mmproj) together; then point the bridge at port 8080:

llama-server -hf ggml-org/Qwen2.5-VL-3B-Instruct-GGUF --port 8080
./bin/control vision-bridge local qwen2.5vl:3b http://127.0.0.1:8080/v1

(With a manual build, pass the two GGUFs yourself: llama-server -m model.gguf --mmproj mmproj.gguf. The model name you pin is cosmetic — llama.cpp serves whichever model it loaded.)

Either way, pinning turns the bridge on; fully quit and reopen Codex, then paste into DeepSeek as usual. Run local with no model to let the machine pick — it reuses a vision model already served by a running runtime, or falls back to the hardware recommendation and tells you the pull command:

./bin/control vision-bridge local                        # auto-pick for this machine
./bin/control vision-bridge local moondream http://127.0.0.1:11434/v1

Not all vision models can read. The bridge needs verbatim transcription, and most small vision models are captioners: they describe the scene convincingly and invent the codes and numbers. That is worse than no model, because the text-only model downstream repeats the invention as fact. So the picker labels accuracy from measurement, not reputation:

node src/vision-benchmark.mjs        # scores every installed model

It reads test/fixtures/vision-benchmark.png — an invoice with known codes, decimals, dates, and a table — and scores how much comes back exactly. Measured on an M-series Mac:

Model	Size	Codes/numbers/dates	Verdict
`qwen2.5vl:3b`	3.2 GB	12 / 12	reads text accurately — the default
`qwen2.5vl:7b`	6.0 GB	not benchmarked	larger sibling
`llama3.2-vision:11b`	7.9 GB	not benchmarked	strongest reasoning
`moondream`	1.7 GB	0 / 12	captions only — invents text
`llava`	4.7 GB	0 / 12	captions only, and the slowest

The picker sorts by that column, so a model that fabricates text can never sit at the top of the list. Download sizes come from Ollama's registry (refreshed weekly, cached, falling back to the checked-in figures offline), so they match what ollama list will show you.

Any other model. The curated list is short on purpose, but it is not a cage: the tray's Local LLMs section has a field that accepts any Ollama tag — including hf.co/user/repo:Q4_K_M — and the CLI takes one too.

./bin/control vision-bridge pull minicpm-v

Models you add this way carry no accuracy label, because nobody has measured them here. Run the benchmark against one before trusting it with anything that matters:

node src/vision-benchmark.mjs minicpm-v

How the local path differs from a paid engine:

It speaks chat completions, not the gateway. A local model runs outside the router's gateway, so the bridge calls its /v1/chat/completions endpoint directly with no credential. Nothing about your setup is sent anywhere.
It is only used when you pin it. Auto mode never routes images to localhost on its own — an unreachable server would fail every paste — so the local engine is opt-in via vision-bridge local. vision-bridge engine auto hands the choice back to your paid models.
Start it before you paste. If the local server is down or the model is not pulled, that image degrades to a stated failure in the turn (the model is told it could not be read) rather than a crash. doctor shows the pinned local model and reminds you to pull it.
Slower, and only as good as the model. A 3B model on a laptop is seconds slower than a hosted Flash tier and less precise on tiny text. For heavy use, a paid vision engine still reads better; the local option is about cost and privacy, not peak quality.

macOS tray control panel

On macOS, build and open the native menu-bar control panel with:

./bin/model-router-tray

It shows Codex health, detailed usage for the active provider, a seven-day overview of every configured or previously used provider, and auto-applied provider controls in a native glass macOS interface. On first launch the app registers itself as a login item, so it reopens automatically after a reboot; the Settings tab's Start at login toggle or System Settings › Login Items turns that off, and the choice is never re-applied behind your back. A Show tray setting can additionally tie every tray surface to the Codex and ChatGPT desktop apps, appearing when they launch and hiding when they quit. See the macOS tray guide for behavior and rebuild notes.

The app also places a Dynamic-Island-style overlay at the top center of the active display. It follows the provider handling the latest request, reveals usage on hover, and expands on click. The menu-bar panel remains available for the all-provider overview and configuration.

Windows and Linux tray control panel

Windows and Linux use the shared Tauri tray companion in apps/desktop. It provides the same connected-provider filtering, normalized quota cards, daily token graph, secure provider setup, and animated activity status as the macOS surface.

# Linux
./bin/model-router-tray

# Windows PowerShell
.\scripts\build-desktop-tray.ps1 -BinaryOnly
Start-Process .\apps\desktop\src-tauri\target\release\codex-router-desktop.exe

Windows and Linux on X11 receive the floating top-center activity pill. Linux on Wayland uses the tray panel without the pill because the compositor owns absolute window placement. See the Windows and Linux tray guide for prerequisites, packaging, and the platform behavior matrix.

Common commands

./bin/model-router codex setup --guided
./bin/model-router codex doctor
./bin/model-router codex status
./bin/model-router codex disable
./bin/model-router codex enable
./bin/model-router codex uninstall
./bin/control vision-bridge status

The optional live check makes one small request per selected provider and may consume paid quota:

./bin/model-router codex smoke-test --yes

disable removes only the Codex integration and its current service. uninstall intentionally retains the checkout, logs, backups, internal keys, and provider credentials so routine removal cannot destroy authentication or recovery data.

Updates and rollback

For a managed Git checkout:

./bin/model-router codex update
./bin/model-router codex rollback

Updates require a main checkout with no edits to tracked files, plus a recognized repository origin. Untracked files never block an update, and --force discards tracked edits without deleting untracked ones. The previous revision is retained as a local rollback ref, and a failed install restores the previous source revision. If you already ran git pull manually, run the update command anyway; it applies the pulled revision when the install manifest is older. Run doctor --fix after an update or rollback so the generated config and service match the source revision.

Tagged releases contain .tar.gz and .zip source archives, SHA-256 checksums, and GitHub build-provenance attestations.

How routing works

flowchart LR
  C["Codex Responses :4102"] --> L1["LiteLLM :4100"]
  L1 --> K1["Kimi OAuth :4101"]
  L1 --> A1["API keys :4103"]
  K1 --> P["External providers"]
  A1 --> P

Codex sends the Responses API. LiteLLM translates that contract to each provider's native protocol, including OpenAI-compatible Chat Completions and Anthropic Messages, with streaming and tool-call shapes preserved. Every listener binds to 127.0.0.1.

The router authenticates the caller before reading model traffic and passes only a random internal key to LiteLLM. The final forwarder discards that key and injects only the selected provider credential. Browser-originated requests are rejected, secrets are never exposed by public health routes, and network-facing errors are sanitized.

Codex still owns the agent loop, tools, permissions, files, plugins, skills, MCP servers, and conversation state. The router handles model inference and protocol translation; it cannot add a capability the selected model or provider does not implement.

Add future providers and models

The config/ registry tree is the validated registry for provider metadata, picker entries, upstream IDs, API protocols, context limits, request profiles, modalities, and credential sources. Tested OpenAI-compatible and Anthropic API providers share one credential-isolating forwarder and appear in the Codex picker after compatibility tests pass.

Discovery does not publish every upstream model blindly:

./bin/discover-models deepseek
./bin/test-model 'deepseek/deepseek-v4-pro' --live --yes

New models should remain unlisted until official capabilities and live text, streaming, image-input, tool-call, and context behavior are verified. See Development for the registry contract.

Documentation

References: Kimi Code CLI OAuth, Kimi K3 API, DeepSeek model API, Anthropic models, Anthropic Messages API, Codex advanced configuration, and opencodex.

MIT licensed. See LICENSE and NOTICE.md.

Name		Name	Last commit message	Last commit date
Latest commit History 304 Commits
.claude/skills/codex-subagents		.claude/skills/codex-subagents
.github		.github
apps		apps
bin		bin
config		config
docs		docs
requirements		requirements
scripts		scripts
src		src
test		test
.gitattributes		.gitattributes
.gitignore		.gitignore
AGENTS.md		AGENTS.md
CHANGELOG.md		CHANGELOG.md
CLAUDE.md		CLAUDE.md
LICENSE		LICENSE
NOTICE.md		NOTICE.md
README.md		README.md
SECURITY.md		SECURITY.md
codex-router.ps1		codex-router.ps1
install.ps1		install.ps1
install.sh		install.sh
model-router.ps1		model-router.ps1
package-lock.json		package-lock.json
package.json		package.json
scripts-check.mjs		scripts-check.mjs

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Codex Router

Give the link to your agent

Guided install

Models and authentication

opencode (Go subscription and Zen)

Command Code Provider API

Meta Model API

Catalog-only providers

Make models appear in Codex

Windows Codex Desktop running through WSL

Use Codex without an OpenAI login

Use a local model in Codex (experimental)

Paste images into a text-only model

Free, private, offline: a tiny local vision model

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