Claude Code is a console agent from Anthropic that can read project files, edit code, run commands and tests, search the repository, and work with git. The main inconvenience when using it is the cost: long sessions with a lot of context quickly eat up the subscription budget.
There is a solution. DeepSeek provides an endpoint compatible with the Anthropic API. It is enough to change the base address and token, and Claude Code will start working on DeepSeek models, without requiring any reinstallation or patches. In this post I will describe how to set this up on macOS, Linux and Windows.
Why is this needed
The reasons may be different:
– Price. DeepSeek models are noticeably cheaper than Claude, and for routine agent tasks – navigating a project, minor edits, running tests – top quality is not always needed.
– Availability. If you do not have a subscription or payment with an Anthropic card is not available for some reason, DeepSeek becomes a working option.
– Experiments. It’s interesting to compare how different models handle the same codebase in the same environment.
What you need
You need Claude Code installed and the DeepSeek API key. If Claude Code is not already installed, the order is as follows:
1. Install Node.js 18 or later. On Windows, you will additionally need Git for Windows.
2. Install Claude Code itself using the command below.
3. Check the installation.
npm install -g @anthropic-ai/claude-code
claude --version
If the version is displayed, then the installation was successful. The API key is created in your DeepSeek personal account on the keys page.
Configuration via environment variables
All integration comes down to environment variables. For macOS and Linux it looks like this:
export ANTHROPIC_BASE_URL=https://api.deepseek.com/anthropic
export ANTHROPIC_AUTH_TOKEN=ваш_ключ_deepseek
export ANTHROPIC_MODEL=deepseek-flash[1m]
export ANTHROPIC_DEFAULT_OPUS_MODEL=deepseek-flash[1m]
export ANTHROPIC_DEFAULT_SONNET_MODEL=deepseek-flash[1m]
export ANTHROPIC_DEFAULT_HAIKU_MODEL=deepseek-flash
export CLAUDE_CODE_SUBAGENT_MODEL=deepseek-flash
export CLAUDE_CODE_EFFORT_LEVEL=max
export CLAUDE_CODE_AUTO_COMPACT_WINDOW=786432
For Windows in PowerShell the syntax is different, but the variable names are the same:
$env:ANTHROPIC_BASE_URL="https://api.deepseek.com/anthropic"
$env:ANTHROPIC_AUTH_TOKEN="ваш_ключ_deepseek"
$env:ANTHROPIC_MODEL="deepseek-flash[1m]"
Let’s figure out what’s what here. The `ANTHROPIC_BASE_URL` variable redirects requests from Anthropic servers to DeepSeek. `ANTHROPIC_AUTH_TOKEN` substitutes your key. The remaining variables determine which model is used in which role. There are models with the `[1m]` suffix – this is an option with a context window of about a million tokens, which allows the agent to hold significantly more project files at a time. Accordingly, `CLAUDE_CODE_AUTO_COMPACT_WINDOW` is set to the size of this window so that automatic history compression does not work too early.
It is convenient not to export variables manually each time, but to register them in the Claude Code configuration. Then the settings will be picked up automatically upon startup.
{
"env": {
"ANTHROPIC_BASE_URL": "https://api.deepseek.com/anthropic",
"ANTHROPIC_AUTH_TOKEN": "ваш_ключ_deepseek",
"ANTHROPIC_MODEL": "deepseek-flash[1m]",
"ANTHROPIC_DEFAULT_HAIKU_MODEL": "deepseek-flash",
"CLAUDE_CODE_SUBAGENT_MODEL": "deepseek-flash"
}
}
After that, go to the project directory and run the agent:
cd ваш_проект
claude
Claude Code in VS Code
Claude Code is available not only in the terminal, but also as an extension for VS Code. It is installed in the normal way: open the extensions panel using Cmd+Shift+X, find “Claude Code” and click Install. The expansion is produced by Anthropic itself.
The extension contains its own copy of the CLI, and it reads the same ~/.claude/settings.json file as the terminal version. The settings from the previous section also apply here – but with one caveat, which most often causes confusion.
Input verification occurs before launch
Before starting, the extension checks the credentials from its own `claudeCode.environmentVariables` setting, and not from settings.json. The values from settings.json reach the running process – that is, the API address and selected models are picked up correctly – but they do not pass the extension’s own login check. If you see the login screen even though everything is already working in the terminal, this is the reason.
This can be cured with a few lines in the VS Code settings: duplicate the variables in `claudeCode.environmentVariables` and disable the login request.
{
"claudeCode.environmentVariables": [
{ "name": "ANTHROPIC_BASE_URL", "value": "https://api.deepseek.com/anthropic" },
{ "name": "ANTHROPIC_AUTH_TOKEN", "value": "ваш_ключ_deepseek" },
{ "name": "ANTHROPIC_MODEL", "value": "deepseek-flash[1m]" }
],
"claudeCode.disableLoginPrompt": true
}
macOS nuance
If you launch VS Code from the Dock or Finder, it does not inherit environment variables from ~/.zshrc. This is standard macOS behavior and is specifically discussed in the Claude Code documentation. That is, `export ANTHROPIC_BASE_URL=…` in your shell for the extension simply will not work – this variable will not be in its environment.
There are two conclusions from this. Firstly, the settings in `claudeCode.environmentVariables` are more reliable than shell variables. Secondly, if you still prefer a shell, launch the editor from the terminal, where the variables are already exported:
code .
What won’t work
On a third-party provider, some extension features are not available because they require a claude.ai account:
– there will be no plan usage bar, voice input and Web tab for cloud sessions;
– logout commands are not shown in the menu;
– `/usage` will show the consumption and number of tokens of the current session instead of plan limits;
– Remote Control does not work if the base address is not Anthropic.
This is expected behavior and not a sign that the setup failed.
Separately, I would like to add that JetBrains has its own plugin, but it is designed differently: it does not contain a built-in CLI, but launches one already installed in the integrated terminal. There are no settings for environment variables, so you should launch the IDE from the terminal with already exported variables.
How DeepSeek understands Claude model names
Claude Code internally refers to models by names like claude-opus, claude-sonnet and claude-haiku. DeepSeek intercepts these names and replaces them with its own:
– everything that starts with claude-opus goes to deepseek-v4-pro;
– everything that starts with claude-sonnet or claude-haiku goes to deepseek-flash;
– the unknown model name is also reduced to deepseek-flash.
This means that even without explicitly specifying the models, the integration will work – but it is better to control explicitly which models and at what tariff will serve the request. That is why in the settings above all roles are written manually.
What to pay attention to
Compatibility is incomplete, and you should know this in advance. DeepSeek simply ignores some of the capabilities of the Anthropic API:
– Caching prompts. The `cache_control` field is not supported. Claude Code actively uses the cache so as not to overpay for sending the same context repeatedly. This will not happen here, so long sessions are more expensive than you would expect from the price per token. Periodically start a new dialogue instead of endlessly continuing the old one.
– Thinking Budget. The `thinking` parameter is supported, but the `budget_tokens` inside it are ignored.
– Miscellaneous. The `top_k`, `service_tier`, `container` fields and the connection of MCP servers from the API are also ignored.
– MCP. The built-in MCP server mechanism on the DeepSeek side does not work, although the agent’s usual tools – reading files, editing, running commands, searching – function normally.
Separately, it is worth mentioning proxies. There are projects in the community like `ds-cc-proxy`, which are placed between Claude Code and DeepSeek and take on the task of smoothing out minor incompatibilities, as well as separating the main session and subagents into different models. If the standard setup behaves unstable, such an intermediate layer can help.
Claude Code Router
The method described above puts one model on all tasks. But the agent does very different things: he understands the structure of the project, corrects little things, and sometimes solves a truly complex problem. It is logical that different models should be used for different types of work. This is exactly what the claude-code-router (CCR) can do – a local gateway that stands between Claude Code and model providers.
What does it give
– Routing by rules. You can set which model serves the main session, which one serves background tasks, which one serves the scheduling mode. It makes sense to use an expensive model only for complex reasoning, and to give away the routine to a cheap one.
– Backup options. If the provider returns an error, the request goes to the next model in the chain, rather than dropping the session.
– Observability. The interface shows the request log, delays, token consumption and cost – something that you can only guess about with a direct connection.
– One address for several agents. Providers, keys and rules live in one place, and clients connect to one local address.
First about versions – this is important
It’s worth warning here because it will save you time. CCR has been heavily redesigned over its history, and almost all the articles you find in a search describe the outdated version.
In older versions, the configuration was in the file ~/.claude-code-router/config.json with the Providers and Router blocks, and everything was launched with the `ccr code` command. Now it doesn’t work that way anymore. The current version (3.x) stores settings in the SQLite database and is managed via a web interface. The old config.json is read exactly once as a source for migration if there is no database yet – after that, edits to it do not affect anything.
That is, if you found instructions on the Internet with editing config.json and the `ccr code` command and nothing worked for you, you are editing a file that no one reads anymore. It’s not your fault.
Installation
You will need Node.js 22 or later.
npm install -g @musistudio/claude-code-router
ccr ui
The `ccr ui` command brings up the service in the background and opens the management interface in the browser. It is useful to know the other commands: `ccr start` starts a service, `ccr stop` stops it, `ccr serve` works in the foreground and is convenient when you need to see logs.
By default, the management interface lives on port 3458, and the gateway itself for models lives on port 3456.
Settings
Everything is done in the web interface, without manually editing files:
1. In the Providers section, add the DeepSeek provider and specify its key. Please note: the full address up to the chat/completions point is needed here, not just the domain.
2. In the Models section, describe what this model is – the description helps routing.
3. In the Agent Config section, set the default model.
4. In the Routing section, configure the rules: which model responds to which requests.
5. On the API Keys page, create a CCR client key – this is what Claude Code will use, not the DeepSeek key.
After this, all that remains is to send Claude Code to the gateway: specify the gateway address shown in the interface as the base address, and the CCR client key as the token.
A nice thing: routing rules can be written not only with fields, but also with a JavaScript script, if the logic is more complex than comparing one field.
Is it worth using it
Connecting DeepSeek to Claude Code is primarily a way to reduce costs without giving up a convenient agent environment. You get the same interface, the same tools, and the same workflow, but on a different model. The setup takes a few minutes and is completely reversible: just remove the environment variables to return to Anthropic models.
The limitations are also clear: there is no prompt cache, incomplete compatibility with respect to API fields, and the quality of the model may differ for complex architectural tasks. For routine work in the repository – navigation, refactoring, running tests – this is more than enough.
Example: Oni-Extended
A living example of such a combination is my project Oni-Extended, a fork of the port of the game Oni (Bungie, 2001) for Apple Silicon. The game sources are written in C and have been around for more than twenty years; there have never been any tests in them, and the file sizes are measured in tens of thousands of lines. However, new features have been added to the project via Claude Code from DeepSeek: fast saving and loading via F5/F9, a key hold block, and a -nodamage launch flag that disables damage.
This is a good illustration of what was said above. All tasks relate to routine work in a large repository – the agent examines other people’s code, finds connections between modules and tests hypotheses, rather than designing an architecture from scratch. The cost of such sessions on DeepSeek turns out to be noticeably lower than on Anthropic models, despite the fact that the result is verified by the project’s own infrastructure: assembly, level run stand and offline tests.
Links
https://platform.deepseek.com/api_keys
https://api-docs.deepseek.com
https://docs.anthropic.com/en/docs/claude-code
https://github.com/anthropics/claude-code
https://github.com/musistudio/claude-code-router
https://ccrdesk.top/en/guides/cli/
https://github.com/zefir1990/Oni-Extended
Sources
https://api-docs.deepseek.com/guides/anthropic_api
https://api-docs.deepseek.com/quick_start/agent_integrations/claude_code
https://ccrdesk.top/en/routing/
https://code.claude.com/docs/en/vs-code
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