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DeepSeek V3.1 Fast
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DeepSeek V3.1 Fast

DeepSeek-V3.1-Fast
DeepSeek
The model provider is the Sophon platform. DeepSeek V3.1 Fast is the high-TPS speed version of DeepSeek V3.1. Hybrid thinking mode: By modifying the chat template, a single model can simultaneously support both thinking and non-thinking modes. Smarter tool usage: Through post-training optimization, the model’s performance in tool utilization and agent tasks has improved significantly.

Pricing

  • Input Tokens: $1.096 /M tokens
  • Output Tokens: $3.288 /M tokens

Input Modalities

  • Text

Output Modalities

  • Text

Capabilities

  • Tools
  • Tool calling
  • Structured outputs

Providers

Sophnet DeepSeek-V3.1-Fast
Pricing$1.096$3.288
Context163K
Max output163K
Latency0.2S
Throughput150.0TPS
Uptime
0.00% uptime 2 days ago
0.00% uptime yesterday
0.00% uptime today

Performance for DeepSeek-V3.1-Fast

Uptime is the percentage of requests that succeeded over the past 72 hours. AIHubMix continuously monitors every provider and automatically retries with the next-best provider when one returns an error or responds too slowly; Latency is total round-trip time (lower is better); Throughput is how fast the model writes (tokens per second, higher is better).

Uptime
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Latency
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Throughput
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Try this model

Python
import os
from openai import OpenAI

client = OpenAI(
    api_key=os.environ["AIHUBMIX_API_KEY"],
    base_url="https://shkq.org/v1",
)

response = client.chat.completions.create(
    model="DeepSeek-V3.1-Fast",
    messages=[
      {
        "role": "user",
        "content": "Hello, how are you?"
      }
    ],
    max_tokens=1024,
    stream=False,
)

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

Frequently asked questions

What is DeepSeek V3.1 Fast?

The model provider is the Sophon platform. DeepSeek V3.1 Fast is the high-TPS speed version of DeepSeek V3.1. Hybrid thinking mode: By modifying the chat template, a single model can simultaneously support both thinking and non-thinking modes. Smarter tool usage: Through post-training optimization, the model’s performance in tool utilization and agent tasks has improved significantly.

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