Skip to main content

​
Endpoint

POST https://www.samuraiapi.in/v1/chat/completions

​
Request Parameters

ParameterTypeRequiredDefaultDescription
modelstring✅—Model ID (e.g. gpt-4o, claude-3-5-sonnet-20241022)
messagesarray✅—Conversation history with role + content
temperaturenumber—1Creativity: 0 = deterministic, 2 = very creative
max_tokensinteger—model defaultMax tokens to generate
streamboolean—falseStream partial tokens via SSE
top_pnumber—1Nucleus sampling threshold
frequency_penaltynumber—0Reduce repetition. Range: -2.0 to 2.0
presence_penaltynumber—0Encourage new topics. Range: -2.0 to 2.0
stopstring/array——Up to 4 stop sequences
ninteger—1Number of completions to return
userstring——Your end-user ID for monitoring

​
Code Examples

from openai import OpenAI

client = OpenAI(
    api_key="sk-samurai-YOUR_KEY",
    base_url="https://www.samuraiapi.in/v1"
)

response = client.chat.completions.create(
    model="gpt-4o",
    messages=[
        {"role": "system", "content": "You are a helpful assistant."},
        {"role": "user", "content": "Explain quantum entanglement in simple terms."}
    ],
    temperature=0.7,
    max_tokens=300
)

print(response.choices[0].message.content)
print(f"Used {response.usage.total_tokens} tokens")

​
Response Format

{
  "id": "chatcmpl-abc123xyz",
  "object": "chat.completion",
  "created": 1715000000,
  "model": "gpt-4o",
  "choices": [
    {
      "index": 0,
      "message": {
        "role": "assistant",
        "content": "Quantum entanglement is like having two magic coins..."
      },
      "finish_reason": "stop"
    }
  ],
  "usage": {
    "prompt_tokens": 28,
    "completion_tokens": 145,
    "total_tokens": 173
  }
}

​
Multi-turn Conversations

Maintain context by including the full conversation history: 
messages = [{"role": "system", "content": "You are a helpful assistant."}]

# Turn 1
messages.append({"role": "user", "content": "What is the capital of Japan?"})
r = client.chat.completions.create(model="gpt-4o", messages=messages)
reply = r.choices[0].message.content
messages.append({"role": "assistant", "content": reply})

# Turn 2 — model remembers the context
messages.append({"role": "user", "content": "What is its population?"})
r = client.chat.completions.create(model="gpt-4o", messages=messages)
print(r.choices[0].message.content)
# => "Tokyo has a population of approximately 13.9 million in the city proper..."

​
Try It Live

Interactive Playground

Test the chat API directly in your browser with your API key.
ModelBest ForInput $/1MOutput $/1M
gpt-4oGeneral purpose, vision$1.25$5.00
gpt-4o-miniFast, cheap, great quality$0.075$0.30
gpt-4.1Long context (1M tokens)$1.00$4.00
claude-3-5-sonnet-20241022Coding, reasoning$1.50$7.50
claude-3-5-haiku-20241022Fast Anthropic model$0.40$2.00
gemini-2.5-flash-preview-05-20Fastest Google model$0.075$0.30
deepseek-chatUltra cheap, smart$0.007$0.014
llama-3.3-70b-instructBest open-source$0.05$0.16

​
Endpoint

POST https://api.samuraiapi.in/v1/chat/completions

​
Request Body

ParameterTypeRequiredDescription
modelstring✅Model ID (e.g. gpt-4o, claude-3-5-sonnet-20241022)
messagesarray✅Array of message objects with role and content
temperaturenumber—Sampling temperature 0–2. Default: 1
max_tokensinteger—Maximum tokens to generate
streamboolean—Enable streaming. Default: false
top_pnumber—Nucleus sampling. Default: 1
frequency_penaltynumber—Penalize frequent tokens (-2 to 2)
presence_penaltynumber—Penalize new topics (-2 to 2)
stopstring/array—Stop sequences
ninteger—Number of completions to generate
userstring—Unique user identifier for abuse monitoring

​
Message Roles

RoleDescription
systemSets the assistant’s behavior and persona
userMessages from the human user
assistantPrevious assistant responses (for multi-turn)

​
Code Examples

from openai import OpenAI

client = OpenAI(
    api_key="sk-samurai-YOUR_KEY",
    base_url="https://api.samuraiapi.in/v1"
)

response = client.chat.completions.create(
    model="gpt-4o",
    messages=[
        {"role": "system", "content": "You are a helpful assistant."},
        {"role": "user", "content": "Explain quantum computing in simple terms."}
    ],
    temperature=0.7,
    max_tokens=500
)

print(response.choices[0].message.content)

​
Example Response

{
  "id": "chatcmpl-abc123",
  "object": "chat.completion",
  "created": 1710000000,
  "model": "gpt-4o",
  "choices": [
    {
      "index": 0,
      "message": {
        "role": "assistant",
        "content": "Quantum computing uses quantum bits (qubits)..."
      },
      "finish_reason": "stop"
    }
  ],
  "usage": {
    "prompt_tokens": 32,
    "completion_tokens": 150,
    "total_tokens": 182
  }
}

​
Multi-turn Conversations

Pass previous messages to maintain context: 
messages = [
    {"role": "system", "content": "You are a helpful assistant."}
]

# First turn
messages.append({"role": "user", "content": "What is the capital of France?"})
response = client.chat.completions.create(model="gpt-4o", messages=messages)
assistant_reply = response.choices[0].message.content
messages.append({"role": "assistant", "content": assistant_reply})

# Second turn
messages.append({"role": "user", "content": "What is its population?"})
response = client.chat.completions.create(model="gpt-4o", messages=messages)
print(response.choices[0].message.content)
ModelProviderContextInput $/1MOutput $/1M
gpt-4oOpenAI128K$1.25$5.00
gpt-4o-miniOpenAI128K$0.075$0.30
claude-3-5-sonnet-20241022Anthropic200K$1.50$7.50
gemini-2.0-flashGoogle1M$0.05$0.20
deepseek-chatDeepSeek64K$0.007$0.014
llama-3.3-70b-instructMeta131K$0.05$0.16