Alibaba Model Studio route for DeepSeek V4 Flash. List prices on this page are busy-hour rates (Beijing 08:00–22:00); off-peak (22:00–08:00) is half. Cache-hit is 3× the official DeepSeek cache rate; thinking mode uses the same token rates.
Key strengths
- Bailian-hosted V4 Flash
- Busy hours 08:00–22:00 Beijing time
- Higher cache-hit rate than official
- Fast general and coding tasks
Use cases
- High-volume chat on Alibaba
- Budget coding assistants
- Lightweight agents
- Off-peak batch jobs
Alibaba's adbmysql/deepseek-v4-flash is a frontier text generation model in the Qwen family. It excels at complex reasoning, agentic workflows, code generation, and long-form writing tasks, with native support for streaming, tool calling, JSON mode, and multi-turn conversations.
The model handles long-context inputs gracefully and is particularly effective for software engineering, multi-step research, and end-to-end project execution. Its tokenizer and pricing are optimized for high-throughput production workloads, with a competitive cost profile relative to other models in its tier.
adbmysql/deepseek-v4-flash is fully OpenAI-compatible — drop in your existing OpenAI Python or Node SDK and switch `baseURL` to `https://api.tokenlx.ai`. TokenLX transparently routes your requests to the optimal provider endpoint while preserving streaming, function-calling, and structured-output semantics.
Performance
Compare different providers across TokenLX · All locations.
Effective Pricing
Actual cost per million tokens across providers over the past 7 days. Prices above are busy-hour rates (Beijing 08:00–22:00). Off-peak (22:00–08:00) is half.
Recent activity
Total usage per day on TokenLX (last 30 days).
Sample code & API
TokenLX normalizes requests and responses across providers. Use any OpenAI SDK or our native SDK.
from openai import OpenAI
client = OpenAI(
base_url="https://api.tokenlx.ai/v1",
api_key="sk-aihub-...",
)
# Non-streaming
response = client.chat.completions.create(
model="adb-deepseek-v4-flash",
messages=[
{"role": "system", "content": "You are a helpful assistant."},
{"role": "user", "content": "Hello!"},
],
)
print(response.choices[0].message.content)
# Streaming
stream = client.chat.completions.create(
model="adb-deepseek-v4-flash",
messages=[{"role": "user", "content": "Tell me a story"}],
stream=True,
)
for chunk in stream:
print(chunk.choices[0].delta.content or "", end="", flush=True)Replace sk-aihubrouter-… with your key from the dashboard.
API Endpoints
Sends a request for a model response for the given chat conversation. Supports both streaming and non-streaming modes.
Creates a streaming or non-streaming response using the OpenAI Responses API format.
Creates a message using the Anthropic Messages API format. Supports text, images, tools, and extended thinking.