Alibaba Cloud added deepseek-v4.1-flash-1017 to Token Plan Personal Edition, and the update takes over from deepseek-v4.1-flash in the plan's model catalogue. DeepSeek rebuilt it with self-feedback-based reinforcement learning: agent tasks finish faster, and the model writes less on the way to the answer, on the same 1M-token context. Subscribers running deepseek-v4.1-flash switch one configuration line, and the plan starts at $6 a month.
DeepSeek's release notes describe the training change and its effect on agent work: tasks complete in less time, and outputs come back shorter on the way to the answer. DeepSeek frames the combination as a performance-and-cost improvement. The release also leans into wish-driven programming, where a one-line wish becomes a working project, with game development and 3D modeling as the showcase tasks.
The specs carry over from the model it replaces. Context runs to 1M tokens, maximum output to 393,216, and input takes text and images while output stays text. The context cache is implicit on this model: stable prefixes qualify without opt-in, and the published rate card bills a cached input token at a tenth of a fresh one.
Token Plan Personal Edition is a subscription for individual developers. A flat monthly fee buys a Credits pool, with Lite starting at $6 a month and 11,500 Credits that reset each subscription month, and model calls draw down that pool instead of a pay-as-you-go bill. A limited-time night discount keeps the DeepSeek models on its list, deepseek-v4.1-flash-1017 included, at half the Credits between 22:00 and 08:00 (UTC+8). One API Key covers the whole catalogue, so a workflow that needs a generated image, a video clip, or a voice track spends from the same pool.
The plan covers interactive use inside coding and agent tools. Automation scripts, application backends, and batch jobs belong on a pay-as-you-go Key.
New to the plan? The earlier article walks through subscription, the sk-sp- Key, and the Base URLs end to end.
deepseek-v4.1-flash on Token Plan?If that describes your setup, the change is one line. Point your tool at deepseek-v4.1-flash-1017. The Key and the Base URL stay as they are, and the chain works. New subscribers start on the Token Plan subscription page and can follow the quick start for per-tool steps.
A config that tuned thinking on the old model needs a second change. The API reference assigns reasoning_effort to the other DeepSeek models; deepseek-v4.1-flash-1017 takes enable_thinking instead. The parameter sits outside the OpenAI spec, so plain HTTP takes it at the top of the JSON body, and the Python SDK carries it through extra_body:
from openai import OpenAI
import os
client = OpenAI(
api_key=os.getenv("TOKEN_PLAN_API_KEY"), # sk-sp-...
base_url="https://token-plan.ap-southeast-1.maas.aliyuncs.com/compatible-mode/v1",
)
completion = client.chat.completions.create(
model="deepseek-v4.1-flash-1017",
messages=[{"role": "user", "content": "Refactor this parser to be streaming-friendly."}],
extra_body={"enable_thinking": False}, # routine edits
# extra_body={"enable_thinking": True}, # hard architectural work
)
print(completion.choices[0].message.content)The setting is a Credits decision as much as a quality one. The chain of thought bills as output tokens, with room for a 384K-token chain ahead of the answer, so thinking left on for a variable rename pays for reasoning nobody reads. Defaults run temperature 1.0, top_p 1.0, and max_tokens 393,216; set a ceiling that fits the answer you expect.
Function calling, web search, and the Responses API work on this model, and structured output speaks JSON Object but not JSON Schema. Prefix completion, Batch calls, and fine-tuning do not work. The DeepSeek API reference carries the full parameter list.
Point your tool at deepseek-v4.1-flash-1017, set enable_thinking to match the task, and keep the rest of the setup as it is.
Pricing, promotions, and model availability change. Confirm the current details in the Alibaba Cloud Model Studio console.
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