Current as of March 2026. Opus 4.6 is the model you reach for when you need an agent to read an entire repository before touching anything. The 1M context window is the headline, but the reasoning quality is the real differentiator — it holds complex chains of logic together in a way that smaller models simply don’t.
Specs
| Provider | Anthropic |
| Input cost | $5.00 / M tokens |
| Output cost | $25 / M tokens |
| Context window | 1M tokens |
| Max output | 128K tokens |
| Parameters | N/A |
| Features | function_calling, vision, reasoning |
What it’s good at
Reasoning and Instruction Following
Multi-step logic is where it earns the price premium. It rarely hallucinates tool parameters or ignores a constraint buried deep in the system prompt. For long agentic runs, that reliability compounds.
Context Management
A 1M token window means you can feed it hundreds of files and it actually uses them. It doesn’t silently drop context the way some models do when the prompt gets large.
Where it falls short
High Latency and Cost
$5 input / $25 output is expensive, and you feel it. There’s a noticeable reasoning delay before the first token. For anything trivial, you’re wasting money and waiting longer than you need to.
Strict Safety Guardrails
It occasionally refuses benign requests — automated agent loops are particularly vulnerable to this. You might need to rephrase prompts that would sail through on Sonnet.
Best use cases with OpenClaw
- Complex Code Refactoring — 128K output plus genuine context retention makes it reliable for rewriting large modules without losing track of how they connect to the rest of the codebase.
- Long-Document Analysis — Feed it a 1,000-page document, ask specific technical questions. It doesn’t lose the thread.
Not ideal for
- Simple Chatbots — The intelligence is real but so is the price. You’re paying for capabilities you don’t need for basic Q&A.
- High-Throughput Real-time Apps — The latency is a wall. Users waiting on a UI response will 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
Export your key and you’re done. No special configuration for Anthropic models.
export ANTHROPIC_API_KEY="your-key-here"
That’s it. OpenClaw picks up Anthropic models automatically.
How it compares
- vs GPT-4o — GPT-4o is faster and cheaper at $5/$15 input/output, but Opus 4.6 is more consistent at following complex system prompts in multi-agent workflows. If your agent is doing something genuinely hard, the reliability gap matters.
- vs Gemini 1.5 Pro — Gemini has a 2M context window, which is impressive. But in practice Opus 4.6’s tool-calling is more reliable and its reasoning depth is harder to beat for structured tasks.
Bottom line
This is the model you use when Sonnet fails and the task actually matters. Keep it out of high-volume workflows — $25/M output adds up fast — but as a controller for complex multi-agent jobs, nothing else comes close right now.
TRY CLAUDE OPUS 4.6 ON HAIMAKER
For setup instructions, see our API key guide. For all available models, see the complete models guide.