Current as of March 2026. DeepSeek V3 is GPT-4o-level performance for $0.14/M input. That’s not a typo. If you’re running high-volume agents and watching your token spend, this is worth a serious look.
Specs
| Provider | DeepSeek |
| Input cost | $0.14 / M tokens |
| Output cost | $0.28 / M tokens |
| Context window | 66K tokens |
| Max output | 8K tokens |
| Parameters | N/A |
| Features | Standard chat |
What it’s good at
Price
$0.14/M input and $0.28/M output puts it roughly 20x cheaper than GPT-4o. For batch workloads — sentiment analysis, data extraction, classification at scale — nothing else comes close at this price.
Coding and technical tasks
Genuinely strong on Python and system-level code. I’ve seen it outperform Claude 3.5 Haiku on logic-heavy debugging. It’s not just cheap; it’s actually capable.
Where it falls short
Context window
66K is thin by modern standards. Once you have a system prompt and a few retrieved document chunks in there, you’re already constrained. Don’t plan a RAG pipeline around this model without thinking through your chunking strategy first.
Latency from the West
DeepSeek’s servers are in China. If you’re in North America or Europe, expect higher round-trip times and the occasional connection reset. It’s workable for async batch jobs, annoying for anything interactive.
Best use cases with OpenClaw
- High-volume batch jobs — When you’re processing millions of small tasks, the cost difference between V3 and GPT-4o-mini is the difference between viable and expensive.
- Agentic tool use — JSON schema adherence is solid and it follows system instructions reliably. Works well as the backbone model for tool-calling agents.
Not ideal for
- RAG-heavy workflows — The context window fills up faster than you’d expect once you add retrieval chunks. Models with 128K+ windows handle this much more comfortably.
- Interactive UIs — Latency spikes make it frustrating for end users. If someone’s watching the typing indicator, pick something faster.
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
Configure OpenClaw to use the OpenAI-compatible provider pointing at api.deepseek.com. Set the model ID explicitly to deepseek-chat — there’s no free tier, so make sure your API key has credits before testing.
{
"models": {
"mode": "merge",
"providers": {
"deepseek": {
"baseUrl": "https://api.deepseek.com/v1",
"apiKey": "YOUR-DEEPSEEK-API-KEY",
"api": "openai-completions",
"models": [
{
"id": "deepseek-chat",
"name": "DeepSeek V3",
"cost": {
"input": 0.14,
"output": 0.28
},
"contextWindow": 65536,
"maxTokens": 8192
}
]
}
}
}
}
How it compares
- vs GPT-4o-mini — V3 is noticeably stronger on complex reasoning and math. GPT-4o-mini wins on latency and has a 128K context window, which matters more than people expect.
- vs Claude 3.5 Haiku — Haiku handles nuanced instructions and creative constraints better. V3 is cheaper and more reliable for pure coding and technical work.
Bottom line
Best ROI in its class if you can live with the 66K context cap and some latency variance. For async, high-volume, technical workloads, it’s hard to beat at this price.
For setup instructions, see our API key guide. For all available models, see the complete models guide.