Current as of March 2026. DeepSeek R1 is a 685B parameter reasoning model that punches at o1-preview quality for about 1/20th the price. The MIT license is a genuine plus if you’re building something you might want to self-host later.
Specs
| Provider | DeepSeek |
| Input cost | $0.55 / M tokens |
| Output cost | $2.19 / M tokens |
| Context window | 65K tokens |
| Max output | 8K tokens |
| Parameters | 685B |
| Features | function_calling, reasoning |
What it’s good at
Price vs. reasoning quality
$0.55/M input and $2.19/M output for a model that can actually reason. OpenAI o1-preview costs roughly 20x more for comparable logical depth. For OpenClaw agents running hundreds of reasoning cycles, that difference compounds fast.
Chain-of-thought tasks
Complex debugging, math-heavy workflows, multi-step logic — this is where R1 earns its keep. It thinks before it responds, which matters when correctness beats raw speed.
Where it falls short
Small context window
65K tokens is tight. Once you factor in a system prompt and a few tool call exchanges, you’re already burning through it. Large-scale RAG is painful; you’ll need aggressive chunking.
Output cap
8K max output is the other wall. Fine for most tool responses, but if you need the model to generate a full refactored file or a lengthy technical spec, you’ll hit it.
Best use cases with OpenClaw
- Reasoning-heavy agents — Native reasoning and function calling let it plan multi-step tasks more reliably than standard chat models at this price point.
- Code debugging loops — Strong on intricate logic bugs where you want a model that actually traces through what went wrong.
Not ideal for
- Large codebase analysis — The 65K window can’t hold much code. You’ll end up splitting context more than you’d like.
- Interactive UI — Reasoning models spend time generating internal thought chains before the first token appears. Not great for anything where users are watching a cursor blink.
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
Point your OpenClaw provider configuration to api.deepseek.com using the OpenAI-compatible schema. If you want to see the model’s internal reasoning in your logs, you’ll need to handle reasoning tokens separately — they don’t appear in the standard response output.
{
"models": {
"mode": "merge",
"providers": {
"deepseek": {
"baseUrl": "https://api.deepseek.com/v1",
"apiKey": "YOUR-DEEPSEEK-API-KEY",
"api": "openai-completions",
"models": [
{
"id": "deepseek-r1",
"name": "DeepSeek R1",
"cost": {
"input": 0.55,
"output": 2.19
},
"contextWindow": 65336,
"maxTokens": 8192
}
]
}
}
}
}
How it compares
- vs OpenAI o1-preview — R1 is in the same reasoning tier but ~20x cheaper. o1 wins on context window (128K vs 65K) and has better API reliability, which matters for production agents.
- vs Claude 3.5 Sonnet — Sonnet is faster and gives you 200K context. R1 tends to edge it out on pure math and logical proofs, but Sonnet is more consistent on instruction-following.
Bottom line
If you need serious reasoning on a budget and your inputs fit in 65K tokens, R1 is the obvious pick. Just know what you’re trading away on context and output size.
For setup instructions, see our API key guide. For all available models, see the complete models guide.