Current as of March 2026. Sonnet 4.6 is where I land most of the time for OpenClaw agents. The 64K output limit is the main reason — you can generate an entire module in one shot instead of babysitting truncated responses. When GPT-4o starts hallucinating tool schemas on complex logic, this is the fallback.
Specs
| Provider | Anthropic |
| Input cost | $3.00 / M tokens |
| Output cost | $15 / M tokens |
| Context window | 200K tokens |
| Max output | 64K tokens |
| Parameters | N/A |
| Features | function_calling, vision, reasoning |
What it’s good at
Tool Calling Precision
It follows JSON schemas more reliably than almost anything else at this price point. In my experience, it’s the biggest practical advantage — fewer broken agent loops, less prompt engineering to work around hallucinated arguments.
64K Output Buffer
You can generate an entire code module or long-form doc in one pass. With most models you hit the output ceiling mid-function and have to stitch things together manually.
Contextual Reasoning
The 200K context window stays coherent even when the relevant code is buried deep in a large prompt. It doesn’t degrade the way some models do when you fill up the buffer.
Where it falls short
Output Cost Premium
$15 per million output tokens adds up fast. If your agent produces verbose reasoning traces or iterates many times per task, watch your spend. The 5:1 output-to-input cost ratio is real.
Inference Latency
It’s noticeably slower than Flash-class models. Fine for batch or background tasks, annoying for anything interactive.
Best use cases with OpenClaw
- Autonomous Coding — 64K output plus solid reasoning means you can write and refactor complex files without the model losing track of what it was doing.
- Visual Data Extraction — Vision works well here. Parsing a dense UI screenshot or technical diagram into structured JSON is a legitimate use case, not a gimmick.
Not ideal for
- Simple Text Summarization — Haiku or GPT-4o-mini handle this for a fraction of the cost. There’s no reason to burn $15/M output on summarizing a Slack thread.
- Real-time Chatbots — The latency is a dealbreaker for anything requiring snappy responses. Users notice.
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 picks the best model for each task so you don’t have to.
OpenClaw setup
Set your ANTHROPIC_API_KEY and that’s genuinely it. No extra config, no wrapper.
export ANTHROPIC_API_KEY="your-key-here"
That’s it. OpenClaw picks up Anthropic models automatically.
How it compares
- vs GPT-4o — Sonnet 4.6 is stricter about following system instructions and less likely to refuse during complex coding tasks. GPT-4o is faster and sometimes cheaper depending on your output volume.
- vs Gemini 1.5 Pro — Gemini has a larger context window, but Sonnet 4.6 wins on tool-calling reliability in my testing. The extra context headroom rarely matters unless you’re genuinely loading hundreds of files.
Bottom line
If you’re building a serious agent in OpenClaw and you need it to actually work, Sonnet 4.6 is the right default. Just keep an eye on output costs — they’re the one thing that’ll bite you at scale.
TRY CLAUDE SONNET 4.6 ON HAIMAKER
For setup instructions, see our API key guide. For all available models, see the complete models guide.