---
title: "Grok 2 Vision for OpenClaw: Pricing, Setup, and What It's Good At"
description: "Everything you need to know about using Grok 2 Vision with OpenClaw. Costs $2.00/M input, $10/M output. 33K 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-xai-hero.jpg
keywords: "grok 2 vision openclaw, grok 2 vision pricing, grok 2 vision review, xai openclaw, grok 2 vision api"
category: model-guide
modelData:
  provider: "xAI"
  provider_slug: "xai"
  full_id: "xai/grok-2-vision"
  input_cost_per_1m: 2
  output_cost_per_1m: 10
  max_input_tokens: 32768
  max_output_tokens: 32768
  features: ["function_calling", "vision", "web_search"]
  parameter_count: null
  architecture: null
  license: null
faq:
  - question: "What is the exact context limit?"
    answer: "The model supports a maximum of 32,768 tokens for both input and output combined."
  - question: "How much does it cost to use?"
    answer: "Input tokens are priced at $2.00 per million and output tokens at $10.00 per million."
  - question: "Does it support web search?"
    answer: "Yes, it has a built-in web_search feature that can be toggled via the API."
---

Current as of March 2026. Grok 2 Vision adds image understanding to the Grok 2 base at the same $2/$10 price point. The catch is the context window drops to 33K — which is genuinely tight for a vision model, since image tokens eat into that budget fast.

## Specs

| | |
|---|---|
| **Provider** | xAI |
| **Input cost** | $2.00 / M tokens |
| **Output cost** | $10 / M tokens |
| **Context window** | 33K tokens |
| **Max output** | 33K tokens |
| **Parameters** | N/A |
| **Features** | function_calling, vision, web_search |

## What it's good at

#### Pricing

$2/M input for a vision model is hard to beat. GPT-4o charges $5/M for the same capability. If you're processing a lot of images and tight on budget, this is one of the cheapest options available.

#### Inference speed

Responses come back quickly for vision tasks, which matters for UI automation agents where you need fast screen-read cycles.

#### OpenAI compatibility

The API follows the OpenAI spec closely enough that it works as a drop-in replacement in most agent frameworks. The config change is minimal.

## Where it falls short

#### Context window

33K tokens is the real problem here. A single high-resolution image can consume a significant chunk of that, leaving little room for conversation history or long system prompts. This is the reason to pick a different model more often than anything else.

#### Function calling reliability

It misses arguments or ignores JSON schema constraints more often than Claude 3.5 Sonnet. If your tool-calling schema is complex, budget for retry handling.

## Best use cases with OpenClaw

- **High-volume OCR** — The $2/$10 price makes bulk document and image processing economical. If you're processing thousands of receipts, invoices, or screenshots, the cost math works out.
- **Simple visual agents** — Short "see and click" tasks where the conversation history stays short and the visual cues are unambiguous. Don't try to build a long agentic loop on 33K tokens.

## Not ideal for

- **Long-form document reasoning** — The 33K limit prevents loading multiple large images or maintaining a lengthy conversation history simultaneously.
- **Complex multi-step reasoning** — Hallucination rate increases on multi-step tasks compared to Sonnet. The base reasoning capability just isn't as strong.

## 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 the OpenAI provider base URL to api.x.ai/v1 and ensure your API key is funded via the xAI console. You must manually set the model ID to xai/grok-2-vision in your environment variables.

```json
{
  "models": {
    "mode": "merge",
    "providers": {
      "xai": {
        "baseUrl": "https://api.x.ai/v1",
        "apiKey": "YOUR-XAI-API-KEY",
        "api": "openai-completions",
        "models": [
          {
            "id": "grok-2-vision",
            "name": "Grok 2 Vision",
            "cost": {
              "input": 2,
              "output": 10
            },
            "contextWindow": 32768,
            "maxTokens": 32768
          }
        ]
      }
    }
  }
}
```

## How it compares

- **vs GPT-4o** — GPT-4o is more reliable for complex tool use but costs 2.5x more for input tokens.
- **vs Claude 3.5 Sonnet** — Sonnet has a much larger 200K context window and superior coding logic for a higher price.
- **vs Gemini 1.5 Flash** — Flash is cheaper and offers a 1M context window, though Grok 2 Vision often handles OCR with better accuracy.

## Bottom line

Grok 2 Vision earns its place for high-volume, budget-conscious image processing. The 33K context limit is a real constraint though — if your use case needs any conversational depth or multiple images per request, it'll bite you.

<a href="https://app.haimaker.ai/sign-up?utm_source=model_guide&utm_medium=cta&utm_campaign=grok-2-vision-openclaw" class="cta-button">TRY GROK 2 VISION 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).*