# Cohere Command R — Long-Context Tool-Use Model for Agents

> Command R+ is Cohere's flagship LLM. 128K context, native tool use, RAG-tuned, multilingual. Cheaper than Claude Sonnet, comparable on tool-use benchmarks.

## Install

Copy the content below into your project:

## Quick Use

1. Sign up at dashboard.cohere.com → copy API key
2. `pip install cohere` (or `npm install cohere-ai`)
3. `co.chat(model='command-r-plus-08-2024', message='...')` — add `tools=` and `documents=` for tool use / RAG

---

## Intro

Command R+ is Cohere's flagship enterprise LLM — 128K context, native tool use, multilingual (10+ languages with strong fluency), and specifically tuned for RAG and agent workloads. Pricing slots between GPT-4o-mini and Claude Sonnet, with comparable benchmark performance on tool use and multi-step reasoning. Best for: enterprise agents, multilingual customer-facing AI, RAG pipelines where citations matter. Works with: Cohere API, AWS Bedrock, Azure, Oracle Cloud. Setup time: 2 minutes.

---

### Hello, Command R+

```python
import cohere

co = cohere.Client(os.environ["COHERE_API_KEY"])

response = co.chat(
    model="command-r-plus-08-2024",
    message="Compare LFP and NMC battery chemistries.",
    temperature=0.3,
)

print(response.text)
```

### Native tool use

```python
tools = [{
    "name": "get_weather",
    "description": "Get current weather",
    "parameter_definitions": {
        "city": {"description": "City name", "type": "str", "required": True},
    },
}]

response = co.chat(
    model="command-r-plus-08-2024",
    message="What's the weather in Tokyo and Berlin? Compare.",
    tools=tools,
)

# Loop until no more tool calls
while response.tool_calls:
    tool_results = []
    for tc in response.tool_calls:
        result = call_my_tool(tc.name, tc.parameters)
        tool_results.append({"call": tc, "outputs": [{"result": result}]})

    response = co.chat(
        model="command-r-plus-08-2024",
        message="",
        tools=tools,
        tool_results=tool_results,
    )

print(response.text)
```

### Built-in RAG mode

```python
documents = [
    {"title": "Doc 1", "snippet": "PyTorch is..."},
    {"title": "Doc 2", "snippet": "TensorFlow is..."},
]

response = co.chat(
    model="command-r-plus-08-2024",
    message="Compare PyTorch and TensorFlow",
    documents=documents,
)

print(response.text)
# Output cites docs by ID — see response.citations
for c in response.citations:
    print(f"{c.text} → {c.document_ids}")
```

### Pricing snapshot (vs alternatives)

| Model | Input $/1M tok | Output $/1M tok |
|---|---|---|
| Claude 3.5 Sonnet | $3 | $15 |
| Command R+ | $2.50 | $10 |
| GPT-4o | $2.50 | $10 |
| Command R | $0.50 | $1.50 |
| GPT-4o-mini | $0.15 | $0.60 |

---

### FAQ

**Q: Is Command R free?**
A: Cohere offers free trial credits on signup. After that, pay-per-token via cohere.com or via AWS Bedrock / Azure with their billing. Free tier suitable for prototyping; production needs a paid plan.

**Q: How does Command R+ compare to Claude Sonnet?**
A: On English benchmarks Sonnet leads slightly. Command R+ is competitive on tool-use and multilingual tasks at lower price. For enterprise / multilingual / RAG-heavy use cases Command R+ is often the better $/quality.

**Q: Does Command R support function calling like OpenAI?**
A: Yes — native tool use is first-class. Schema is similar but uses `parameter_definitions` instead of `parameters`. Cohere's SDK normalizes; with raw API account for the format diff.

---

## Source & Thanks

> Built by [Cohere](https://github.com/cohere-ai). Commercial product with free trial.
>
> [docs.cohere.com](https://docs.cohere.com/docs/command-r-plus) — Command R+ documentation

---

<!-- ZH -->

## 快速使用

1. 在 dashboard.cohere.com 注册，复制 API key
2. `pip install cohere`（或 `npm install cohere-ai`）
3. `co.chat(model='command-r-plus-08-2024', message='...')`，加 `tools=` 和 `documents=` 启用工具 / RAG

---

## 简介

Command R+ 是 Cohere 旗舰企业 LLM —— 128K 上下文、原生工具使用、多语言（10+ 语言强流畅度）、专为 RAG 和 agent 工作负载调优。价格在 GPT-4o-mini 和 Claude Sonnet 之间，工具使用和多步推理 benchmark 上相当。适合企业 agent、多语言客户面 AI、需要引用的 RAG 流水线。兼容 Cohere API、AWS Bedrock、Azure、Oracle Cloud。装机时间 2 分钟。

---

### Hello, Command R+

```python
import cohere

co = cohere.Client(os.environ["COHERE_API_KEY"])

response = co.chat(
    model="command-r-plus-08-2024",
    message="Compare LFP and NMC battery chemistries.",
    temperature=0.3,
)

print(response.text)
```

### 原生工具使用

```python
tools = [{
    "name": "get_weather",
    "description": "Get current weather",
    "parameter_definitions": {
        "city": {"description": "City name", "type": "str", "required": True},
    },
}]

response = co.chat(
    model="command-r-plus-08-2024",
    message="What's the weather in Tokyo and Berlin? Compare.",
    tools=tools,
)

# 循环直到没有更多工具调用
while response.tool_calls:
    tool_results = []
    for tc in response.tool_calls:
        result = call_my_tool(tc.name, tc.parameters)
        tool_results.append({"call": tc, "outputs": [{"result": result}]})

    response = co.chat(
        model="command-r-plus-08-2024",
        message="",
        tools=tools,
        tool_results=tool_results,
    )

print(response.text)
```

### 内置 RAG 模式

```python
documents = [
    {"title": "Doc 1", "snippet": "PyTorch is..."},
    {"title": "Doc 2", "snippet": "TensorFlow is..."},
]

response = co.chat(
    model="command-r-plus-08-2024",
    message="Compare PyTorch and TensorFlow",
    documents=documents,
)

print(response.text)
# 输出按 doc ID 引用 —— 看 response.citations
for c in response.citations:
    print(f"{c.text} → {c.document_ids}")
```

### 价格快照（vs 其他）

| 模型 | 输入 $/百万 token | 输出 $/百万 token |
|---|---|---|
| Claude 3.5 Sonnet | $3 | $15 |
| Command R+ | $2.50 | $10 |
| GPT-4o | $2.50 | $10 |
| Command R | $0.50 | $1.50 |
| GPT-4o-mini | $0.15 | $0.60 |

---

### FAQ

**Q: Command R 免费吗？**
A: Cohere 注册送试用 credit。之后通过 cohere.com 按 token 付费，或通过 AWS Bedrock / Azure 用它们的计费。免费档够原型，生产要付费档。

**Q: Command R+ 跟 Claude Sonnet 比怎样？**
A: 英文 benchmark Sonnet 略领先。Command R+ 在工具使用和多语言任务上有竞争力，价格更低。企业 / 多语言 / RAG 重的用例下 Command R+ 通常是更好的性价比。

**Q: Command R 像 OpenAI 那样支持 function calling 吗？**
A: 支持 —— 原生工具使用是一等公民。Schema 类似，但用 `parameter_definitions` 而非 `parameters`。Cohere SDK 帮你规整；裸 API 注意格式差异。

---

## 来源与感谢

> Built by [Cohere](https://github.com/cohere-ai). Commercial product with free trial.
>
> [docs.cohere.com](https://docs.cohere.com/docs/command-r-plus) — Command R+ documentation


---
Source: https://tokrepo.com/en/workflows/cohere-command-r-long-context-tool-use-model-for-agents
Author: Cohere