deepseek官网目前不稳定，开个贴收集免费使用deepseek-r1的渠道
直接chat和api的都行，但是要免费，最好写上限制
15 个赞
azure现在免费但是卡，grop 70b的，挺快。二者现在都是限速，限速多少不清楚
2 个赞
我还以为你收集了。。
3 个赞
Reasoning
Reasoning models excel at complex problem-solving tasks that require step-by-step analysis, logical deduction, and structured thinking and solution validation. With Groq inference speed, these types of models can deliver instant reasoning capabilities critical for real-time applications.
Why Speed Matters for Reasoning
Reasoning models are capable of complex decision making with explicit reasoning chains that are part of the token output and used for decision-making, which make low-latency and fast inference essential. Complex problems often require multiple chains of reasoning tokens where each step build on previous results. Low latency compounds benefits across reasoning chains and shaves off minutes of reasoning to a response in seconds.
Supported Model
| Model ID | Model |
|---|---|
deepseek-r1-distill-llama-70b |
DeepSeek R1 (Distil-Llama 70B) |
Reasoning Format
Groq API supports explicit reasoning formats through the reasoning_format parameter, giving you fine-grained control over how the model’s reasoning process is presented. This is particularly valuable for valid JSON outputs, debugging, and understanding the model’s decision-making process.
Note: The format defaults to raw or parsed when JSON mode or tool use are enabled as those modes do not support raw. If reasoning is explicitly set to raw with JSON mode or tool use enabled, we will return a 400 error.
Options for Reasoning Format
reasoning_format Options |
Description |
|---|---|
parsed |
Separates reasoning into a dedicated field while keeping the response concise. |
raw |
Includes reasoning within think tags in the content. |
hidden |
Returns only the final answer for maximum efficiency. |
Quick Start
PythonJavaScriptcurl
1from groq import Groq
2
3client = Groq()
4completion = client.chat.completions.create(
5 model="deepseek-r1-distill-llama-70b",
6 messages=[
7 {
8 "role": "user",
9 "content": "How many r's are in the word strawberry?"
10 }
11 ],
12 temperature=0.6,
13 max_completion_tokens=1024,
14 top_p=0.95,
15 stream=True,
16 reasoning_format="raw"
17)
18
19for chunk in completion:
20 print(chunk.choices[0].delta.content or "", end="")
Quick Start with Tool use
curl https://api.groq.com//openai/v1/chat/completions -s \
-H "authorization: bearer $GROQ_API_KEY" \
-d '{
"model": "deepseek-r1-distill-llama-70b",
"messages": [
{
"role": "user",
"content": "What is the weather like in Paris today?"
}
],
"tools": [
{
"type": "function",
"function": {
"name": "get_weather",
"description": "Get current temperature for a given location.",
"parameters": {
"type": "object",
"properties": {
"location": {
"type": "string",
"description": "City and country e.g. Bogotá, Colombia"
}
},
"required": [
"location"
],
"additionalProperties": false
},
"strict": true
}
}
]}'
Recommended Configuration Parameters
| Parameter | Default | Range | Description |
|---|---|---|---|
messages |
- | - | Array of message objects. Important: Avoid system prompts - include all instructions in the user message! |
temperature |
0.6 | 0.0 - 2.0 | Controls randomness in responses. Lower values make responses more deterministic. Recommended range: 0.5-0.7 to prevent repetitions or incoherent outputs |
max_completion_tokens |
1024 | - | Maximum length of model’s response. Default may be too low for complex reasoning - consider increasing for detailed step-by-step solutions |
top_p |
0.95 | 0.0 - 1.0 | Controls diversity of token selection |
stream |
false | boolean | Enables response streaming. Recommended for interactive reasoning tasks |
stop |
null | string/array | Custom stop sequences |
seed |
null | integer | Set for reproducible results. Important for benchmarking - run multiple tests with different seeds |
json_mode |
- | boolean | Set to enable JSON mode for structured output. |
reasoning_format |
raw |
"parsed", "raw", "hidden" |
Controls how model reasoning is presented in the response. Must be set to either parsed or hidden when using tool calling or JSON mode. |
Optimizing Performance
Temperature and Token Management
The model performs best with temperature settings between 0.5-0.7, with lower values (closer to 0.5) producing more consistent mathematical proofs and higher values allowing for more creative problem-solving approaches. Monitor and adjust your token usage based on the complexity of your reasoning tasks - while the default max_completion_tokens is 1024, complex proofs may require higher limits.
Prompt Engineering
To ensure accurate, step-by-step reasoning while maintaining high performance:
- DeepSeek-R1 works best when all instructions are included directly in user messages rather than system prompts.
- Structure your prompts to request explicit validation steps and intermediate calculations.
- Avoid few-shot prompting and go for zero-shot prompting only.
3 个赞
这是发动群众的力量收集
我还以为你收集了，我进来看看，你空手套白狼是吧
2 个赞
没有才要收集啊
2 个赞
硅基能用了但是输出很少很容易截断 
2 个赞
空手入白刃（bushi
1 个赞
我也是
2 个赞
1.硅基流动（接码相当于免费吧（狗头））
2.kluster （之前注册送100
站里有分享的账号，输出速度一般）
3.chutes（目前可以直接用官key免费，输出速度可观）
4.getmerlin（站里有神墨大佬逆的api）
5.lambda（可在线使用chat）
6.noagi（免费在线用多种大模型，注册无验证）
目前我个人知道的就这些了
39 个赞
硅基 r1 只能用付费余额
1 个赞
现在可以用赠金了
1 个赞
现在可以用赠送的了
1 个赞
kluster已跪，登陆发现余额全没了 
现在才知道，感谢二位佬分享
开人机验证之前注册的没有清空 
只是输出太短了 相当于没有
1 个赞
NVIDIA的NIM也上了，注册送额度，应该也算吧。
话说标题是不是用“征集”要更好一点…
1 个赞
Nvidia的NIM和fireworksai都可以