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deepseek-v4-pro-0813

DeepSeek/🇨🇳 China/chat

DeepSeek v4 Pro - 1.6T total / 49B active params. Performance rivaling the world's top closed-source models.

Compare 4 providersAll models →

Which id to call

deepseek/deepseek-v4-pro-0813

This exact deployment on DeepSeek, with no routing and no failover. Send it as the model field.

Input /1M

$1.32

$0.04 cached

Output /1M

$3.96

3.0x input

Context

1M

384K output

Paid /1M

$0.13

measured, cache included

Capabilities 4/8

VisionReasoningTool callingCachingWeb searchJSON schemaComputer useImage generation

Provider rates

What deepseek-v4-pro-0813 costs

Provider prices per 1M tokens, updated September 27, 2026.more

These are the upstream provider rates. Pay as you go adds 5%, or 0% if you bring your own keys, and there is no per-request fee. Prompt caching and routing change what you pay against these rates, not the rates themselves.

Input /1M

$1.32

Output /1M

$3.96

Cache write /1M

-

Cache read /1M

$0.04

What a workload costs

100K input + 10K output
$0.17
1M input + 100K output
$1.72
10M input + 1M output
$17.16

At the rates above, before caching. A cache read costs $0.04 per 1M, so repeated context lands under these figures.

model=

Which id to call

One base url, https://router.requesty.ai/v1, and one key for every id here.

deepseek/deepseek-v4-pro-0813

This exact deployment on DeepSeek, with no routing and no failover. Send it as the model field.

OpenAI compatible

Call it in three lines

Change the base url, use your Requesty key, set the model to any id on the left. Existing OpenAI SDK code needs no other edit.

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from openai import OpenAI

client = OpenAI(
    api_key="YOUR_REQUESTY_API_KEY",
    base_url="https://router.requesty.ai/v1",
)

response = client.chat.completions.create(
    model="deepseek/deepseek-v4-pro-0813",
    messages=[
        {"role": "user", "content": "Explain quantum computing in one paragraph."},
    ],
)

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

Live from production

DeepSeek on deepseek-v4-pro-0813, measured

What this provider was measured doing on deepseek-v4-pro-0813 across Requesty traffic.more

Whole-window figures, because that is the grain published per provider. They cover DeepSeek serving this model in every region it serves it from, so a region-pinned deployment shares them with its siblings. A figure is absent where no qualifying sample exists.

First token

882ms

median

p95 wait

1.85s

slowest 5%

Output speed

36/s

median tokens

Paid /1M

$0.13

blended, cache included

Cache hit

89.1%

of input tokens

Compare these against the other 3 providers serving deepseek-v4-pro-0813.

Reference

Specs and data terms

What the catalog reports for this deployment, and what the provider does with the traffic.

Context window1M tokens
Max output384K tokens
API typechat
AddedApr 2026
Model id
Data retentionYes
Used for trainingNo
Served from🇨🇳 China
Privacy policyDeepSeek Privacy Policy →

Released 2026-08-13

Benchmark scores

Benchmarks measure the model, not this endpoint, so they are the same wherever these weights are served.

Coding Indexcoding
68.8%

Artificial Analysis Coding Index: a composite of coding evaluations including LiveCodeBench, SciCode and Terminal-Bench.

GPQA Diamondreasoning
92.8%

Graduate-level physics, chemistry & biology questions designed to resist Googling.

Intelligence Indexreasoning
36.0%

Artificial Analysis Intelligence Index: a composite of multiple evaluations measuring overall model capability.

Scores from official model cards, Artificial Analysis and public leaderboards. They measure specific skills and do not capture every aspect of model quality, so test on your own workload.

Same provider

More from DeepSeek

Newest first, on the same provider and the same key.

deepseek-v4.1-flash

1M ctx/$0.30/1M

deepseek-reasoner

1M ctx/$0.14/1M

deepseek-chat

1M ctx/$0.14/1M

Reference

deepseek-v4-pro-0813 questions

How much does deepseek-v4-pro-0813 cost?

deepseek-v4-pro-0813 is priced at $1.32 per million input tokens and $3.96 per million output tokens when accessed via Requesty. Prompt caching is supported, which can cut effective input cost by up to 90% on repeated context. Those are the upstream provider rates: pay as you go adds 5% on top, or 0% if you bring your own provider keys.

What is the context window of deepseek-v4-pro-0813?

deepseek-v4-pro-0813 has a context window of 1M tokens, with a maximum output of 384K tokens per response. That's roughly 1,333 words of input you can fit in a single prompt.

How does deepseek-v4-pro-0813 perform on benchmarks?

deepseek-v4-pro-0813 scores 92.8% on GPQA Diamond, 68.8% on Coding Index, 51.0% on SciCode. See the full benchmark chart above for results across MMLU Pro, GPQA Diamond, SWE-Bench Verified, HumanEval, MATH, AIME, MMMU, and LiveBench.

What can deepseek-v4-pro-0813 do?

deepseek-v4-pro-0813 supports tool calling, extended reasoning, prompt caching, structured outputs (JSON schema). You can call it through any OpenAI-compatible client by pointing base_url to Requesty.

How do I use deepseek-v4-pro-0813 with the OpenAI SDK?

Install the OpenAI SDK, set base_url to "https://router.requesty.ai/v1", set your API key to your Requesty key, and set the model to "deepseek/deepseek-v4-pro-0813". The Quickstart above shows Python, JavaScript and cURL snippets.

Can I run deepseek-v4-pro-0813 through Requesty?

Yes. deepseek-v4-pro-0813 runs through Requesty's OpenAI-compatible API, served from DeepSeek. You do not host the model yourself: point base_url at Requesty, set the model to "deepseek/deepseek-v4-pro-0813", and requests are routed to the upstream provider with automatic failover. The same key gives you 600+ other models too.

Call deepseek-v4-pro-0813 through one endpoint

One key for this endpoint and 600+ other models. No markup on provider prices, automatic failover, caching built in.

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