Current as of March 2026. DeepSeek V3.2 sits above V3.1 on capability with a slightly different cost structure — input is $0.28/M but output drops to $0.40/M, which is actually cheaper on the output side. The 164K output window is the same.
Specs
| Provider | DeepSeek |
| Input cost | $0.28 / M tokens |
| Output cost | $0.40 / M tokens |
| Context window | 164K tokens |
| Max output | 164K tokens |
| Parameters | N/A |
| Features | function_calling, reasoning |
What it’s good at
Output-heavy workloads
At $0.40/M output, V3.2 is cheaper per output token than GPT-4o-mini ($0.60/M). If your agents generate long responses — full files, reports, structured data — that flipped cost advantage adds up.
164K output window
Same as V3.1: you can generate entire modules in one shot. Combined with the native reasoning features, this makes it useful for complex refactoring tasks where you need the model to both understand the problem and produce a lot of code.
Function calling
Handles tool calls and logical chains reliably. Good for structured OpenClaw agent workflows where the model needs to plan and execute a sequence of steps.
Where it falls short
API reliability
This is the real issue with DeepSeek: 503 errors and slow response times are common during peak hours. Build retry logic into your OpenClaw setup before you depend on this in production. Set your request timeout to at least 60 seconds.
Slow time-to-first-token
It’s noticeably slower than Flash-class models to start streaming. Fine for background tasks, frustrating for anything interactive.
Content filters
Same story as other DeepSeek models — strict filters on geopolitical and cultural topics. Most developer workloads won’t hit them, but they exist.
Best use cases with OpenClaw
- Large-scale code generation — 164K output and solid reasoning means you can generate a full module or refactor a large class without truncation.
- Output-intensive research agents — If your agents produce a lot of tokens per cycle, the low output cost makes long runs financially manageable.
Not ideal for
- Interactive chat — Time-to-first-token is too high for anything with a human on the other end.
- Primary production model — Provider downtime is frequent enough that you need a fallback. Don’t route critical traffic here without one.
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
Use the OpenAI-compatible provider in OpenClaw pointing to api.deepseek.com. Bump the default request timeout to at least 60 seconds — the default will cause spurious timeouts during slow processing windows.
{
"models": {
"mode": "merge",
"providers": {
"deepseek": {
"baseUrl": "https://api.deepseek.com/v1",
"apiKey": "YOUR-DEEPSEEK-API-KEY",
"api": "openai-completions",
"models": [
{
"id": "deepseek-v3.2",
"name": "DeepSeek V3.2",
"cost": {
"input": 0.28,
"output": 0.4
},
"contextWindow": 163840,
"maxTokens": 163840
}
]
}
}
}
}
How it compares
- vs GPT-4o-mini — GPT-4o-mini is cheaper on input ($0.15 vs $0.28) but costs more on output ($0.60 vs $0.40) and caps output at 16K. V3.2 wins for output-heavy workloads.
- vs Gemini 1.5 Flash — Gemini is faster and more reliable. V3.2 is sharper on complex coding and reasoning tasks.
Bottom line
V3.2 makes the most sense when your bottleneck is output volume and cost. If API reliability matters more than price, look at Gemini or GPT-4o-mini first.
For setup instructions, see our API key guide. For all available models, see the complete models guide.