DeepSeek V4.1 Flash Economy Edition Is Live: Same Weights, 20% Off

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Key Highlights

• BytePlus official channel, full weights: requests go through BytePlus, loading the complete 552B model — no quantization, no distillation, no cut-down variant, matching the same-generation model served by the vendor’s own API
• One set of weights: the economy edition loads the same V4.1-Flash-1017 weights as the standard deepseek-v4.1-flash, and the vendor states output quality is identical — only generation speed differs
• 20% cheaper at every hour: officially priced at 80% of the standard edition in the same time window; on APIYI that means $0.24 input / $0.96 output per 1M tokens versus $0.30 / $1.20
• Paying with latency: the vendor estimates roughly 2 seconds to first token and 15 ms per output token (~67 tokens/s), with the standard edition marked faster
• Specs are not cut down: a 1,024K context window, 384K max output, a 128K reasoning budget, plus vision, function calling, MCP, and prefix completion
• One peak-price tier only: the vendor bills by peak and off-peak windows, while APIYI bills the peak rate ($0.24 / $0.96) at all hours; further savings come from recharge bonuses
• Chat endpoint only: available in the Default and SVIP groups through /v1/chat/completions; no Anthropic format, so Claude Code is out

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Background

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In Depth

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Slower, not dumber

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Specs: 1M context and 384K output, nothing cut

• max_tokens includes the reasoning chain across the V4.1 Flash family. When reasoning is truncated, answer quality drops noticeably, so set the value explicitly for batch work instead of letting it default to 128K
• parallel_tool_calls is accepted but ignored. The vendor notes that V4.1 Flash models, including the economy edition, may still return several tool calls at once
• reasoning_effort accepts none / low / high / max and defaults to high. Passing none turns reasoning off, and that is the single most effective cost lever for batch jobs

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Billing: one peak-price tier, with discounts via recharge bonuses

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Where it sits in the V4 family

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Practical Use

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Recommended scenarios

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Code sample

import os
from openai import OpenAI

client = OpenAI(
    api_key=os.environ["APIYI_API_KEY"],
    base_url="https://api.apiyi.com/v1",
)

resp = client.chat.completions.create(
    model="deepseek-v4-1-flash-economy-261017",
    messages=[
        {"role": "system", "content": "You clean data. Output JSON only."},
        {"role": "user", "content": "Extract {issue, resolution, sentiment} from this ticket: ..."},
    ],
    reasoning_effort="none",   # turn reasoning off to save output tokens
    max_tokens=1024,
)
print(resp.choices[0].message.content)

resp = client.chat.completions.create(
    model="deepseek-v4-1-flash-economy-261017",
    messages=[{"role": "user", "content": "List the three main risks in this earnings report: ..."}],
    reasoning_effort="high",
    max_tokens=8192,
)

from concurrent.futures import ThreadPoolExecutor

def run_one(text: str) -> str:
    r = client.chat.completions.create(
        model="deepseek-v4-1-flash-economy-261017",
        messages=[{"role": "user", "content": text}],
        reasoning_effort="none",
        max_tokens=512,
    )
    return r.choices[0].message.content

with ThreadPoolExecutor(max_workers=16) as pool:
    results = list(pool.map(run_one, batch_texts))

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Best practices

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Pricing and Availability

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Pricing

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Stacking recharge promotions

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Summary

• Batch pipelines that run day and night: economy edition with reasoning off and a stable prefix keeps the unit price near the floor of token pricing
• Repetitive lookups with a near-perfect cache hit rate: cache reads are priced the same on both editions, so the gap comes from uncached input — run the numbers against your own hit rate
• Driving the unit price as low as possible: stack recharge bonuses on the peak price
• Interactive coding, real-time chat, Claude Code: use the deepseek-v4.1-flash standard edition rather than retrofitting compatibility onto the economy tier