Anthropic SDK & Claude Code

DeepInfra exposes an Anthropic-compatible Messages API. This means tools that target the Anthropic API — Claude Code, the Anthropic Python and TypeScript SDKs, and any framework with an Anthropic adapter — can point at DeepInfra and use open-source models.

Endpoint

https://api.deepinfra.com/anthropic

Two endpoints are available:

Endpoint	Description
`POST /anthropic/v1/messages`	Create a message (chat completion)
`POST /anthropic/v1/messages/count_tokens`	Count tokens for a message request

Authentication

Both standard Anthropic authentication methods are supported:

Header	Example
`Authorization`	`Bearer $DEEPINFRA_TOKEN`
`x-api-key`	`$DEEPINFRA_TOKEN`

You can also pass anthropic-version and anthropic-beta headers as needed.

Using the Anthropic SDK

Installation

pip install anthropic

npm install @anthropic-ai/sdk

Create a message

Point the client at the DeepInfra endpoint and pass a DeepInfra model name:

import anthropic

client = anthropic.Anthropic(
    base_url="https://api.deepinfra.com/anthropic",
    api_key="$DEEPINFRA_TOKEN",
)

message = client.messages.create(
    model="zai-org/GLM-5.2",
    max_tokens=1024,
    messages=[
        {"role": "user", "content": "Hello!"}
    ],
)

print(message.content[0].text)

import Anthropic from "@anthropic-ai/sdk";

const client = new Anthropic({
  baseURL: "https://api.deepinfra.com/anthropic",
  apiKey: "$DEEPINFRA_TOKEN",
});

const message = await client.messages.create({
  model: "zai-org/GLM-5.2",
  max_tokens: 1024,
  messages: [
    { role: "user", content: "Hello!" },
  ],
});

console.log(message.content[0].text);

curl "https://api.deepinfra.com/anthropic/v1/messages" \
  -H "Content-Type: application/json" \
  -H "x-api-key: $DEEPINFRA_TOKEN" \
  -H "anthropic-version: 2023-06-01" \
  -d '{
      "model": "zai-org/GLM-5.2",
      "max_tokens": 1024,
      "messages": [
        {
          "role": "user",
          "content": "Hello!"
        }
      ]
    }'

Using with Claude Code

Claude Code can use DeepInfra as its backend. Claude Code exposes four model slots — opus, sonnet, fable, and haiku — plus one extra custom entry in the /model picker. You map each slot to a DeepInfra model.

Recommended models

Claude Code’s slots are a capability/cost ladder — haiku < sonnet < opus < fable. fable sits above opus: it’s the slowest and most expensive slot, meant for the hardest long-horizon work. Map DeepInfra models onto it the same way, most capable first:

Slot	Model	Context	Why
`fable`	`moonshotai/Kimi-K3`	1M	Strongest open-weights agentic coder (Terminal-Bench 2.1 88.3, SWE Marathon 42.0, vendor-stated). Also the priciest of the set by a wide margin, and the slowest to finish — exactly the tradeoff the `fable` slot exists for.
`opus`	`deepseek-ai/DeepSeek-V4-Pro-0813`	1M	1.6T-param MoE built for advanced reasoning and long-running agent tasks. The official release, with much stronger agentic behaviour than the preview. Your escalation model when `sonnet` stalls.
`sonnet`	`deepseek-ai/DeepSeek-V4-Flash-0731`	1M	Daily driver. Near-flagship agentic quality (Terminal-Bench 2.1 82.7, vendor-stated) at a small fraction of the cost of the slots above it, with the full 1M context — the right economics for the model you run all day.
`haiku`	`inclusionAI/Ling-3.0-flash`	128K	Background tasks. ~400 tok/s and the cheapest of the set — tuned for token efficiency under tight latency and serving-cost budgets.
custom	`zai-org/GLM-5.2`	1M	Top third-party-measured open-weights model on Terminal-Bench 2.0 (81.0%), the closest benchmark to real agentic terminal work. Independently verified rather than vendor-stated — a good second opinion when a task matters and you want a different model family on it.

All five support prompt caching, which makes cache reads substantially cheaper than fresh input — worth enabling for long agent sessions. See each model’s page on deepinfra.com/models for current per-token pricing.

Use the dated slugs — DeepSeek-V4-Pro-0813 and DeepSeek-V4-Flash-0731. The undated deepseek-ai/DeepSeek-V4-Pro and deepseek-ai/DeepSeek-V4-Flash are the earlier preview checkpoints, which the dated releases supersede.

Option A: `.claude/settings.json` (recommended)

Instead of exporting environment variables, put the configuration in a Claude Code settings file. Use ~/.claude/settings.json to apply it to all projects, or <repo>/.claude/settings.local.json for a single project (that file is gitignored by default, so your key stays out of version control). Get an API key at deepinfra.com/dash/api_keys.

.claude/settings.json

{
  "env": {
    "ANTHROPIC_BASE_URL": "https://api.deepinfra.com/anthropic",
    "ANTHROPIC_AUTH_TOKEN": "<YOUR_DEEPINFRA_API_KEY>",

    "ANTHROPIC_DEFAULT_FABLE_MODEL": "moonshotai/Kimi-K3",
    "ANTHROPIC_DEFAULT_FABLE_MODEL_NAME": "Kimi K3",
    "ANTHROPIC_DEFAULT_OPUS_MODEL": "deepseek-ai/DeepSeek-V4-Pro-0813",
    "ANTHROPIC_DEFAULT_OPUS_MODEL_NAME": "DeepSeek V4 Pro 0813",
    "ANTHROPIC_DEFAULT_SONNET_MODEL": "deepseek-ai/DeepSeek-V4-Flash-0731",
    "ANTHROPIC_DEFAULT_SONNET_MODEL_NAME": "DeepSeek V4 Flash 0731",
    "ANTHROPIC_DEFAULT_HAIKU_MODEL": "inclusionAI/Ling-3.0-flash",
    "ANTHROPIC_DEFAULT_HAIKU_MODEL_NAME": "Ling 3.0 Flash",

    "ANTHROPIC_CUSTOM_MODEL_OPTION": "zai-org/GLM-5.2",
    "ANTHROPIC_CUSTOM_MODEL_OPTION_NAME": "GLM-5.2"
  }
}

Reload Claude Code (or restart your VS Code window) and all five models show up in the /model picker. To go back to Anthropic’s own models, delete the env block and reload. Notes on this file:

ANTHROPIC_BASE_URL has no /v1 suffix — Claude Code appends /v1/messages itself.
ANTHROPIC_AUTH_TOKEN is sent as Authorization: Bearer …. ANTHROPIC_API_KEY (sent as x-api-key) works too.
Each *_MODEL_NAME sets the label shown in the picker; it is cosmetic.
ANTHROPIC_CUSTOM_MODEL_OPTION adds one extra picker entry. Any other DeepInfra model still works via /model <name> as free text.
VS Code extension: if the picker doesn’t pick up the config after a window reload, also add the same variables under claudeCode.environmentVariables in your VS Code settings.

Option B: shell function with environment variables

If you’d rather keep your default Claude Code setup untouched and switch per-invocation, add a dedicated shell function to your ~/.bashrc or ~/.zshrc:

deepinfra() {
  export ANTHROPIC_BASE_URL=https://api.deepinfra.com/anthropic
  export ANTHROPIC_AUTH_TOKEN=$DEEPINFRA_TOKEN
  export ANTHROPIC_MODEL=deepseek-ai/DeepSeek-V4-Flash-0731
  export ANTHROPIC_DEFAULT_FABLE_MODEL=moonshotai/Kimi-K3
  export ANTHROPIC_DEFAULT_OPUS_MODEL=deepseek-ai/DeepSeek-V4-Pro-0813
  export ANTHROPIC_DEFAULT_SONNET_MODEL=deepseek-ai/DeepSeek-V4-Flash-0731
  export ANTHROPIC_DEFAULT_HAIKU_MODEL=inclusionAI/Ling-3.0-flash
  export CLAUDE_CODE_SUBAGENT_MODEL=inclusionAI/Ling-3.0-flash
  export CLAUDE_CODE_MAX_OUTPUT_TOKENS=16384
  claude "$@"
}

Then run deepinfra instead of claude to launch Claude Code via DeepInfra. Your regular claude command stays unchanged.

Model override environment variables

The same variable names work in both options — as JSON keys under env, or as shell exports:

Environment variable	Description	Example
`ANTHROPIC_MODEL`	The primary model Claude Code uses for all tasks	`deepseek-ai/DeepSeek-V4-Flash-0731`
`ANTHROPIC_DEFAULT_FABLE_MODEL`	Model used for the `fable` alias (deepest reasoning, slowest, priciest)	`moonshotai/Kimi-K3`
`ANTHROPIC_DEFAULT_OPUS_MODEL`	Model used for the `opus` alias (hard tasks, escalation)	`deepseek-ai/DeepSeek-V4-Pro-0813`
`ANTHROPIC_DEFAULT_SONNET_MODEL`	Model used for the `sonnet` alias (daily coding)	`deepseek-ai/DeepSeek-V4-Flash-0731`
`ANTHROPIC_DEFAULT_HAIKU_MODEL`	Model used for the `haiku` alias and background tasks (tab completions, commit messages)	`inclusionAI/Ling-3.0-flash`
`CLAUDE_CODE_SUBAGENT_MODEL`	Model used for subagents (parallel background tasks)	`inclusionAI/Ling-3.0-flash`
`ANTHROPIC_CUSTOM_MODEL_OPTION`	Adds one extra entry to the `/model` picker	`zai-org/GLM-5.2`

Each *_MODEL variable above has an optional *_MODEL_NAME companion (e.g. ANTHROPIC_DEFAULT_OPUS_MODEL_NAME) that sets the display label in the picker.

ANTHROPIC_DEFAULT_HAIKU_MODEL is used for lightweight background tasks like tab completions and commit messages. Pick a fast, cheap model here to keep costs low. The older ANTHROPIC_SMALL_FAST_MODEL variable is deprecated — use ANTHROPIC_DEFAULT_HAIKU_MODEL instead.

Streaming

Streaming works the same as the Anthropic API — use stream=True (Python) or stream: true (JS/cURL):

with client.messages.stream(
    model="zai-org/GLM-5.2",
    max_tokens=1024,
    messages=[{"role": "user", "content": "Write a short poem about open source."}],
) as stream:
    for text in stream.text_stream:
        print(text, end="", flush=True)

Token counting

Count the tokens in a message request before sending it:

curl "https://api.deepinfra.com/anthropic/v1/messages/count_tokens" \
  -H "Content-Type: application/json" \
  -H "x-api-key: $DEEPINFRA_TOKEN" \
  -d '{
      "model": "zai-org/GLM-5.2",
      "messages": [
        {
          "role": "user",
          "content": "Hello, how are you?"
        }
      ]
    }'

Notes

You are running open-source models via the Anthropic protocol, not Anthropic’s Claude models.
Model names use DeepInfra identifiers (e.g. zai-org/GLM-5.2), not Anthropic model names.
Not all Anthropic-specific features may be supported. Standard message creation, streaming, and token counting work as expected.

Chat Completions

Use the OpenAI-compatible API instead.

Authentication

API keys and scoped JWTs.

Getting Started

Chat Completions

More APIs

Batch API

Deploy Private Models

GPU Instances

Sandboxes

Hosted Agents

Integrations

API Features

Account & Security

Tutorials

Anthropic SDK & Claude Code

Endpoint

Authentication

Using the Anthropic SDK

Installation

Create a message

Using with Claude Code

Recommended models

Option A: `.claude/settings.json` (recommended)

Option B: shell function with environment variables

Model override environment variables

Streaming

Token counting

Notes

Chat Completions

Authentication

​Endpoint

​Authentication

​Using the Anthropic SDK

​Installation

​Create a message

​Using with Claude Code

​Recommended models

​Option A: .claude/settings.json (recommended)

​Option B: shell function with environment variables

​Model override environment variables

​Streaming

​Token counting

​Notes

Chat Completions

Authentication

Endpoint

Authentication

Using the Anthropic SDK

Installation

Create a message

Using with Claude Code

Recommended models

Option A: `.claude/settings.json` (recommended)

Option B: shell function with environment variables

Model override environment variables

Streaming

Token counting

Notes