---
title: "Qwen3 Coder Plus for OpenClaw: Pricing, Setup, and What It's Good At"
description: "Everything you need to know about using Qwen3 Coder Plus with OpenClaw. Costs $1.00/M input, $5.00/M output. 998K context window. Here's when to use it and when to pick something else."
date: 2026-03-12
updatedDate: 2026-03-12
image: /images/models/provider-qwen-hero.jpg
keywords: "qwen3 coder plus openclaw, qwen3 coder plus pricing, qwen3 coder plus review, qwen (alibaba) openclaw, qwen3 coder plus api"
category: model-guide
modelData:
  provider: "Qwen (Alibaba)"
  provider_slug: "qwen"
  full_id: "qwen/qwen3-coder-plus"
  input_cost_per_1m: 1
  output_cost_per_1m: 5
  max_input_tokens: 997952
  max_output_tokens: 65536
  features: ["function_calling", "reasoning"]
  parameter_count: null
  architecture: null
  license: null
faq:
  - question: "What is the exact pricing for Qwen3 Coder Plus?"
    answer: "Input tokens cost $1 per million and output tokens cost $5 per million."
  - question: "How many tokens can it actually output in one go?"
    answer: "The model supports a maximum output of 66,000 tokens, which is enough for several large source files."
  - question: "Does it support function calling?"
    answer: "Yes, it has native support for function calling and reasoning features for complex agentic tasks."
---

Current as of March 2026. Qwen3 Coder Plus takes the context window to 998K — nearly a million tokens — with reasoning added. The output ceiling drops to 66K compared to base Qwen3 Coder's 262K, but you're getting a much larger input window in exchange. At $1/$5 per million, it's priced between budget and frontier.

## Specs

| | |
|---|---|
| **Provider** | Qwen (Alibaba) |
| **Input cost** | $1.00 / M tokens |
| **Output cost** | $5.00 / M tokens |
| **Context window** | 998K tokens |
| **Max output** | 66K tokens |
| **Parameters** | N/A |
| **Features** | function_calling, reasoning |

## What it's good at

#### 998K Input Window

Drop in a full monorepo and let the model reason about it holistically. No RAG, no chunking, no missed cross-file references from retrieval misses.

#### 66K Output

Enough for a substantial feature implementation or comprehensive documentation run. Not unlimited, but far beyond the 8K caps on many competitors.

#### Reasoning

The reasoning layer catches architectural issues and circular dependencies that straight code generation misses. More reliable for hard refactoring problems.

## Where it falls short

#### Proprietary

Same licensing constraint as Qwen3 Coder. Not open-source.

#### Latency

Reasoning adds time. For large context inputs, you'll wait noticeably before the first token appears.

## Best use cases with OpenClaw

- **Full-Repository Refactoring** — The 998K window lets the model see the whole project at once. Changes in one module get validated against the rest without retrieval gaps.
- **Technical Documentation** — Feed in hundreds of source files and generate coherent docs. 66K output handles even large codebases.

## Not ideal for

- **Autocomplete or Fast Suggestions** — Reasoning latency rules this out for anything real-time.
- **Budget-tight Prototyping** — At $5/M output, iterative agent loops with frequent regenerations add up quickly.

## Run it through Haimaker

Skip juggling API keys. One Haimaker key gives you access to every model on the platform. Tell OpenClaw:

```
Add Haimaker as a custom provider to my OpenClaw config. Use these details:

- Provider name: haimaker
- Base URL: https://api.haimaker.ai/v1
- API key: [PASTE YOUR HAIMAKER API KEY HERE]
- API type: openai-completions

Add the auto-router model:
- haimaker/auto (reasoning: false, context: 128000, max tokens: 32000)

Create an alias "auto" for easy switching. Apply the config when done.
```

Or skip model selection entirely — Haimaker's [auto-router](/blog/openclaw-auto-router-setup/) picks the best model for each task so you don't have to.

## OpenClaw setup

Point OpenClaw to api.haimaker.ai/v1 or your local Ollama instance. Ensure your timeout settings are high enough to accommodate the reasoning phase and large 66K output generation.

```json
{
  "models": {
    "mode": "merge",
    "providers": {
      "qwen": {
        "baseUrl": "https://api.haimaker.ai/v1",
        "apiKey": "YOUR-QWEN-API-KEY",
        "api": "openai-completions",
        "models": [
          {
            "id": "qwen3-coder-plus",
            "name": "Qwen3 Coder Plus",
            "cost": {
              "input": 1,
              "output": 5
            },
            "contextWindow": 997952,
            "maxTokens": 65536
          }
        ]
      }
    }
  }
}
```

## How it compares

- **vs Qwen3 Coder** — Base Qwen3 Coder gives 262K context with symmetric input/output limits. Plus gives 998K input but caps output at 66K. Choose based on whether you need bigger input or bigger output.
- **vs GPT-4o** — Qwen3 Coder Plus costs $1/M input vs GPT-4o's $2.50/M, with a much larger context window. GPT-4o has better reasoning on hard problems.

## Bottom line

If you're tired of RAG retrieval misses and need to put nearly a million tokens of code in front of a model that can reason about it, this is the practical choice at this price point.

<a href="https://app.haimaker.ai/sign-up?utm_source=model_guide&utm_medium=cta&utm_campaign=qwen3-coder-plus-openclaw" class="cta-button">TRY QWEN3 CODER PLUS ON HAIMAKER</a>

---

*For setup instructions, see our [API key guide](/blog/openclaw-api-key-setup). For all available models, see the [complete models guide](/blog/best-models-for-openclaw).*