> ## Documentation Index
> Fetch the complete documentation index at: https://docs.apiyi.com/llms.txt
> Use this file to discover all available pages before exploring further.

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

> DeepSeek V4.1 Flash Economy Edition (deepseek-v4-1-flash-economy-261017) is now on APIYI with the same weights and identical output quality as the standard model, at $0.24 / $0.96 per 1M tokens. A 1M context window, vision input, and pricing built for batch workloads.

## 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

## Background

On October 17, 2026, DeepSeek refreshed the V4.1 Flash line to the 1017 checkpoint, keeping the 552B-parameter MoE architecture and folding vision understanding, deep reasoning, prefix completion, and context caching into one model line. That model already beats the previous-generation V4-Pro flagship on several benchmarks, and it is the value-oriented tier of DeepSeek's new architecture family.

The economy edition is the first time this line has been sold as "same weights, different terms." The vendor's description is unusually blunt: **it loads exactly the same weights as the standard edition, with identical capabilities and output quality, and only generates more slowly**. It is priced at 80% of the standard edition within the same window, and the vendor positions it for offline batch processing, background agents, data synthesis, and other large-scale workloads that are latency-tolerant but cost-sensitive.

Where that 20% actually comes from, **the vendor does not say**. As of writing, only three facts can be checked: the same weights, slower generation, and a price at 80% of the standard edition. The spec sheet also rules out the usual cost-cutters — quantization, distillation, a pruned parameter count — because context length, output cap, and vision support match the standard edition item for item. We do not invent a mechanism on the vendor's behalf; what matters is whether your workload tolerates roughly 15 ms per output token.

For a team cleaning a hundred thousand records overnight, a few extra seconds per record barely moves the delivery date, but it moves the invoice.

The APIYI path is the **BytePlus official channel with full weights**: requests land on BytePlus directly, loading the complete 552B model with no quantization, distillation, or parameter cuts. The model ID is `deepseek-v4-1-flash-economy-261017`, it is open in the `Default` and `SVIP` groups, and the call shape is identical to the standard edition — switching is a one-line `model` change.

## In Depth

### Slower, not dumber

On the vendor's comparison table, both columns read the same thing under model quality, and the economy column carries an extra `Identical` badge:

| Metric | Economy edition | Standard edition |
| - | - | - |
| Model quality | Same weights | Same weights |
| Time to first token (TTFT) | \~2 s | Faster |
| Time per output token (TPOT) | \~15 ms (\~67 tokens/s) | Faster |
| Minimum cache-hit length | 128 tokens | 128 tokens |
| Billing | Peak / off-peak, 80% of standard | Peak / off-peak |

Both speed figures carry an `Est.` label, which means **estimates** based on typical long-context, high-cache-hit scenarios. Real numbers vary with input length and cache hit rate, and they are not a service guarantee — do not use them as the deadline for a batch job.

For calibration, \~67 tokens/s is "normal, not aggressive" for a Flash-tier model. It is fine for document summarization, structured extraction, and synthetic data generation. If you are building a real-time chat surface or an agent front end that must produce a complete answer within two seconds, the standard edition is the better fit.

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

Upstream, the economy edition matches the standard edition item for item:

| Spec | Value |
| - | - |
| Context length | 1,024K tokens |
| Max input | 1,024K tokens |
| Max output | 384K tokens |
| Max reasoning (CoT) | 128K tokens |
| Default `max_tokens` | 128K (reasoning included) |
| Image understanding | Supported (50 MB per image) |
| Structured output / function calling / MCP | Supported |
| Prefix completion, context caching | Supported |
| Upstream rate limits | 3,000 RPM, 5,000K TPM |

A few details are worth calling out:

* **`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

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

The vendor publishes a two-column table. Peak hours are **Monday to Friday, 09:00–12:00 and 14:00–18:00 (UTC+8)**; everything else, including all weekend hours, is off-peak:

| Line item (per 1M tokens) | Upstream off-peak | Upstream peak | On APIYI |
| - | - | - | - |
| Input (cache miss) | \$0.12 | \$0.24 | **\$0.24** |
| Input (cache hit) | \$0.002 | \$0.005 | **\$0.006** |
| Output | \$0.48 | \$0.96 | **\$0.96** |

APIYI offers **one peak-price tier**: \$0.24 input / \$0.96 output per 1M tokens, flat across the clock. The upside is a predictable bill — daytime, overnight, and weekend runs all cost the same per token, so you never schedule work around a window. The upstream off-peak tier is not offered separately.

To push beyond that 20%, use recharge bonuses: stacked on the peak price, APIYI recharge promotions lower the effective per-token cost further, and that is currently our main discount channel. See "Pricing and Availability" below.

One neutral cost detail: the cache-read price is \$0.006 on both editions, so for cache-heavy workloads the gap shows up mainly on the uncached portion of the input. Workloads dominated by fresh input and heavy output capture the full 20%.

### Where it sits in the V4 family

| Model | Input / 1M | Output / 1M | Cache hit / 1M | Groups |
| - | - | - | - | - |
| `deepseek-v4-1-flash-economy-261017` | \$0.24 | \$0.96 | \$0.006 | Default, SVIP |
| `deepseek-v4.1-flash` (standard) | \$0.30 | \$1.20 | \$0.006 | Default, SVIP, ClaudeCode, CodexResponses |
| `deepseek-v4-flash-ga-260731` | \$0.44 | \$1.32 | \$0.01364 | Default, SVIP, CodexResponses |
| `deepseek-v4-pro` | \$1.32 | \$3.96 | \$0.044 | Default, SVIP, ClaudeCode |

At the economy edition's input price you pay roughly 18% of V4-Pro, and about 24% on output. Against the closest previous-generation release, `deepseek-v4-flash-ga-260731`, input is about 45% cheaper and output about 27% cheaper — while the economy edition runs the 552B V4.1 architecture rather than the older 284B Flash.

## Practical Use

### Recommended scenarios

> [card] Good fit
>
> * Offline batch work: summarization, classification, cleanup, translation
> * Background agents: long, schedulable, non-interactive jobs
> * Data synthesis: generating training or evaluation samples at scale
> * Whole-corpus analysis where a 1M context window matters
> * Cost-sensitive pipelines that can run overnight

> [card] Poor fit
>
> * Real-time chat that needs sub-second first tokens
> * Interactive coding assistants (use the standard edition)
> * Claude Code integration (no Anthropic endpoint)
> * Repeated queries where nearly all input hits cache

### Code sample

The most important step in batch work is turning reasoning off. The default is `high`, so even a simple extraction can carry hundreds of reasoning tokens:

```python theme={null}
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)
```

When you do need reasoning, cap the output — `max_tokens` includes the reasoning chain, so a tight limit truncates the thinking first:

```python theme={null}
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,
)
```

For bulk runs, pace yourself instead of treating upstream rate limits as a free safety net:

```python theme={null}
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))
```

### Best practices

**1. Turn reasoning off by default**

`reasoning_effort="none"` is the highest-value parameter on this model. Summarization, extraction, and
classification do not need a reasoning chain, and turning it off often cuts output tokens by an order of magnitude.

**2. Keep the prefix byte-identical**

Put the system prompt, instructions, and few-shot examples first and keep them unchanged; move volatile
content (IDs, timestamps, the document body) to the end. The vendor's comparison lists a 128-token
minimum shared prefix for cache hits, and cached input bills at \$0.006 per 1M tokens.

**3. Set max\_tokens explicitly**

The 128K default includes the reasoning chain. Set a value that matches the expected record length
(512 or 1024), which prevents runaway generations and unnecessary output spend.

**4. Schedule for your own load, not for price**

The gateway prices everything at the peak tier, so shifting work to the night flattens your own
concurrency rather than waiting out a price window.

**5. Switch back when you need the standard toolchain**

For Claude Code, the Anthropic format, or the Codex Responses path, use `deepseek-v4.1-flash`,
which is available in those groups.

## Pricing and Availability

### Pricing

| Model | Input | Output | Cache hit (input / read) |
| - | - | - | - |
| `deepseek-v4-1-flash-economy-261017` | \$0.24 / 1M tokens | \$0.96 / 1M tokens | \$0.006 / 1M tokens |

Available groups: `Default` and `SVIP`. Endpoint: the OpenAI-compatible `/v1/chat/completions`; the Anthropic format and the Responses endpoint are not enabled for this model. Billing uses the peak-price tier and does not move with the clock.

> [!NOTE]
>
> Pricing is aligned with the official list and may change; the table is for reference only. The **Model Pricing** tab in the top navigation is authoritative: [Model Pricing](https://docs.apiyi.com/en/models/index.md).

### Stacking recharge promotions

Discounts are delivered mainly through recharge bonuses: stacked on the peak price, APIYI recharge promotions lower the effective per-token cost further. See [recharge promotions](https://docs.apiyi.com/en/faq/recharge-promotions.md).

## Summary

The economy edition is not a reduced model: **BytePlus official channel, full weights**, output quality identical to the standard edition, 20% lower price, and the only real cost is slower generation. For workloads that tolerate latency but run at a scale where the bill matters — offline batch, background agents, data synthesis — that is the most direct cost optimization available, and it requires no code change beyond the `model` field.

How to choose:

* **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

> [!NOTE]
>
> Sources and verification date: BytePlus ModelArk pricing and model list (updated 2026-11-09, checked 2026-11-11),
> DeepSeek platform model and pricing page (checked 2026-11-11), and APIYI's live pricing API plus model page.
> The TTFT and TPOT figures are vendor estimates marked `Est.`; they are not service guarantees, and we have not
> run independent load tests. The vendor does not explain where the 20% comes from, and we do not speculate.
> Specs and prices track upstream, so validate with a small workload first.


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