Published: · Updated:
Exact ID: deepseek-v4-pro. Check access for your key using GET /v1/models; a published model does not guarantee access for every account.
Prices and billing units
| API429: current rates | Manufacturer: selected period |
|---|---|
$0.3960 / 1,000,000 cache-write input tokens $0.01320 / 1,000,000 cached input tokens $0.3960 / 1,000,000 input tokens $1.1880 / 1,000,000 output tokens | Off-peak: 0.66 USD / 1M input tokens · 1.98 USD / 1M output tokens Peak: 1.32 USD / 1M input tokens · 3.96 USD / 1M output tokens |
The period applies only to DeepSeek manufacturer pricing: peak is Monday–Friday, 01:00–04:00 and 06:00–10:00 UTC, except public holidays in China. Other hours, weekends and full public holidays are off-peak. Choose the period manually; current published API429 rates do not depend on this choice. Cached input is excluded. Manufacturer source; checked Oct 24, 2026. API429 catalog: Oct 24, 2026, 5:33 AM UTC.
Workload budget: input and output
The period applies only to DeepSeek manufacturer pricing: peak is Monday–Friday, 01:00–04:00 and 06:00–10:00 UTC, except public holidays in China. Other hours, weekends and full public holidays are off-peak. Choose the period manually; current published API429 rates do not depend on this choice. Cached input is excluded.
Ordinary input and output are counted, excluding cache, tools, fees and repeated calls. This estimates spending; it does not validate whether a request is accepted. RUB results use only your entered rate; they are neither a bank quote nor a top-up amount.
Specifications and compatibility boundaries
The manufacturer specifies a context window of 1,000,000 and a maximum output of 384,000 tokens for its own API (specification: DeepSeek, checked Oct 24, 2026). These are not verified effective API429 limits: the current public catalog does not publish them. Confirm the required volume and mode before migrating your workload.
API429 capabilities published for this exact ID: chat . A missing property does not mean a prohibition, but it does not establish support. Check input format, tools, JSON/schema, reasoning and streaming separately for the selected method.
Diagnosing long text with verifiable conclusions
A useful DeepSeek V4 Pro evaluation is analyzing a long log, requirements set or related technical documents. Start with a question whose answer can be verified: which event repeated, which rule was violated and where the supporting lines are. Provide file boundaries and time range, and request separation of observations and hypotheses. Retain negative examples without errors to check whether the model invents problems. Highlight event identifiers in a long log in advance to make manual checking easier. The model should not become the source of truth about system state simply because it confidently retells a sequence of messages.
Current version 0813 and Preview history
The manufacturer’s current page maps deepseek-v4-pro to DeepSeek-V4-Pro-0813. This version’s generally available release is dated August 13, 2026. The initial V4 Preview announcement was April 24; do not combine these events into one release date for the current version. The manufacturer retained the API name when moving to GA. For result tracking, record the review date and exact name, because an unchanged ID alone does not describe the entire implementation history. Old deepseek-chat and deepseek-reasoner are unsuitable for new examples: the migration notice ended their access after July 24, 2026, 15:59 UTC. No announced future retirement date for the current Pro was found in the reviewed sources.
1M context and 384K output are manufacturer specifications
DeepSeek publishes a 1M context window and maximum output of 384K for Pro. Preserve those manufacturer notations rather than converting them into binary token counts. Pro has no vision support in the current table: neighboring Flash capabilities do not belong to this model. A large stated output does not mean every answer will be that long or that API429 accepts the same volume. The anonymous catalog does not establish effective gateway limits. For batch analysis, set a reasonable report length, check key access and agree on required volume before scaling. Limiting output also helps detect truncated results before passing them onward.
Choose official peak and off-peak periods explicitly
The manufacturer has two pricing periods. Peak is Monday–Friday, 01:00–04:00 and 06:00–10:00 UTC, except public holidays in China. Other hours, weekends and entire public holiday days are off-peak. Choose the period explicitly for a comparison and retain it in the estimate: the user’s local clock and a regular business-day calendar cannot replace this rule. Cached input pricing applies only to actual cache-hit input tokens, rather than the whole request or output. Public API429 token rates for deepseek-v4-pro are published without that time condition. Do not transfer the manufacturer schedule to API429 or describe the lowest official rate as unconditional.
Thinking: what matters when migrating a direct client
Native DeepSeek supports thinking and non-thinking, with thinking enabled by default. The guide lists low, high and max; medium and xhigh map to high, while ultra maps to max. In thinking mode, temperature and presence/frequency penalties have no effect; Pro also ignores top_p in that mode. These details help diagnose direct integrations where changing a parameter unexpectedly leaves answers unchanged. However, the open API429 catalog publishes only chat for Pro. This page therefore does not promise reasoning controls, tools or native Responses through the gateway. The initial request uses ordinary model/messages; add special fields only after checking their support in the client contract.
Tool history and Responses need a separate adapter
In native DeepSeek Chat Completions, thinking with tools requires the complete previous reasoning_content, including turns without tool calls; missing history causes HTTP 400. Without tools, previous reasoning is ignored. Native Responses does not retain the conversation: previous_response_id, conversation and background tasks are unsupported, and store remains false. The client preserves history itself. The stream ends with completed, incomplete or failed rather than [DONE]. Some unsupported parameters may be silently ignored. Matching method names therefore do not establish matching semantics; on migration, check completion format, history retention and usage meaning separately from whether a response arrived.
Input caching: repeating a document does not prove a hit
The manufacturer describes automatic prefix caching as best effort. An already stored complete prefix unit must match; partly shared text does not guarantee an immediate hit. Cache construction takes time, and unused cache is normally cleared over hours or days. For direct DeepSeek, examine prompt_cache_hit_tokens and prompt_cache_miss_tokens. Do not treat cache as a saved finished response or attempt to control it with unsupported prompt_cache_key or retention. In native Responses, reasoning_tokens count toward output usage, so the visible final text does not cover all billable output. These details explain the manufacturer contract; verify actual API429 accounting against its documentation and returned usage data.
Alternatives for the same control task
Include Grok 4.7 when comparing long-document analysis, separately accounting for its pricing change from 200,000 input tokens. Evaluate Claude Sonnet 5.5 for technical editing and code analysis with a predefined error list. Compare GPT-6.1 Sol on turning a log into an event sequence and a verifiable fix plan. All three exact IDs were published in the open API429 catalog on the review date, but access under a specific key requires a separate check. Use the same source material and criteria: supported conclusions, omissions, repeated requests and usage. This helps choose a tool for the task rather than claiming equal quality or full API parameter equivalence.
Public model observations
There are no fresh observations with confirmed traffic for this model. This does not mean zero latency or prove an absence of errors.
Source: public 24-hour statistics. The mean is neither a median nor a response time guarantee; the error rate and observation window refer to the API429 service. Model quality is not measured here.
First request: curl, Python, JavaScript
Create a key, check your balance and set API429_API_KEY on the server. The generation example incurs a charge. It does not retry automatically; a timeout does not prove the operation was not charged. Inspect the content and actual usage after receiving a response. Python and JavaScript first check the available model list.
curl
Before generation, check the exact ID in the response from GET https://gateway.api429.com/v1/models using the same Bearer key.
curl --fail-with-body https://gateway.api429.com/v1/chat/completions \
-H "Authorization: Bearer $API429_API_KEY" \
-H "Content-Type: application/json" \
-d '{"model":"deepseek-v4-pro","messages":[{"role":"user","content":"Analyze this log: 10:01 task A accepted; 10:02 task A completed; 10:03 event A received again; 10:04 task A started again. Identify the likely processing error, suggest an idempotency rule and three tests. Separate log facts from hypotheses; do not execute code."}],"max_tokens":512,"stream":false}'Python 3
import json
import os
import urllib.request
key = os.environ["API429_API_KEY"]
base = "https://gateway.api429.com/v1"
headers = {"Authorization": "Bearer " + key, "Content-Type": "application/json"}
models_request = urllib.request.Request(base + "/models", headers=headers)
with urllib.request.urlopen(models_request, timeout=30) as response:
available = {item["id"] for item in json.load(response)["data"]}
model = "deepseek-v4-pro"
if model not in available:
raise ValueError("Model is unavailable to this key")
payload = json.loads("{\"model\":\"deepseek-v4-pro\",\"messages\":[{\"role\":\"user\",\"content\":\"Analyze this log: 10:01 task A accepted; 10:02 task A completed; 10:03 event A received again; 10:04 task A started again. Identify the likely processing error, suggest an idempotency rule and three tests. Separate log facts from hypotheses; do not execute code.\"}],\"max_tokens\":512,\"stream\":false}")
request = urllib.request.Request(base + "/chat/completions",
data=json.dumps(payload).encode("utf-8"), headers=headers, method="POST")
with urllib.request.urlopen(request, timeout=60) as response:
result = json.load(response)
choice = result["choices"][0]
if choice.get("finish_reason") != "stop" or not choice.get("message", {}).get("content"):
raise ValueError("Incomplete or empty response")
print(choice["message"]["content"])
print(result.get("usage"))JavaScript: Node.js 18+
const key = process.env.API429_API_KEY;
if (!key) throw new Error("Set API429_API_KEY");
const base = "https://gateway.api429.com/v1";
const headers = { Authorization: "Bearer " + key, "Content-Type": "application/json" };
const model = "deepseek-v4-pro";
const available = await fetch(base + "/models", { headers, signal: AbortSignal.timeout(30000) });
if (!available.ok) throw new Error("Models HTTP " + available.status);
if (!(await available.json()).data.some(item => item.id === model)) throw new Error("Model unavailable to this key");
const response = await fetch(base + "/chat/completions", {
method: "POST", headers, signal: AbortSignal.timeout(60000),
body: JSON.stringify({"model":"deepseek-v4-pro","messages":[{"role":"user","content":"Analyze this log: 10:01 task A accepted; 10:02 task A completed; 10:03 event A received again; 10:04 task A started again. Identify the likely processing error, suggest an idempotency rule and three tests. Separate log facts from hypotheses; do not execute code."}],"max_tokens":512,"stream":false})
});
if (!response.ok) throw new Error("Generation HTTP " + response.status);
const result = await response.json();
const choice = result.choices?.[0];
if (choice?.finish_reason !== "stop" || !choice?.message?.content) throw new Error("Incomplete or empty response");
console.log(choice.message.content);
console.log(result.usage);The examples use basic fields from the published API contract. They are checked offline; no successful paid execution or output accuracy is claimed.
n8n: minimal HTTP Request
- Create an HTTP Request with method POST and URL
https://gateway.api429.com/v1/chat/completions. - Store the Bearer key in credentials; add
Content-Type: application/json. Do not put the key in an exported workflow example. - Send the JSON from curl: the exact model
deepseek-v4-pro, messages, max_tokens and stream=false. - After the HTTP node, check
choices,finish_reasonand the response content before writing the result to your production system.
HTTP Request documentation. This recipe describes HTTP Request. Compatibility with specific Agent nodes, SDKs and other API methods needs separate verification.
Three models for your own comparison
These are options for a controlled comparison on the same task, rather than a quality ranking. Base rates apply to a short text request; see the catalog for full pricing.
| Model | API429: input / output per 1M tokens |
|---|---|
Grok 4.7grok-4.7 | $0.6 / $1.8 |
Claude Sonnet 5.5claude-sonnet-5-5 | $0.6 / $3 |
GPT 6.1 Solgpt-6.1-sol | $0.6 / $3 |
Version and release date
Manufacturer API release date, DeepSeek: . Previous generation: DeepSeek V4 Pro Preview; this does not automatically replace the ID in your integration. The date of addition to API429 is unknown. Current availability is determined by the catalog and key permissions, rather than the manufacturer’s historical release date.
Frequently asked questions
Which DeepSeek V4 Pro does this page describe?
The exact request ID deepseek-v4-pro, mapped by the manufacturer to the current DeepSeek-V4-Pro-0813 version. Its GA date is August 13, 2026; April’s Preview belongs to an earlier stage.
Does Pro support images and native tools through API429?
The manufacturer does not list vision for Pro. The open API429 catalog confirms only chat; support for tools, individual reasoning parameters and native modes cannot be inferred from DeepSeek’s own documentation.
Does API429 pricing change on the peak/off-peak schedule?
The reviewed public catalog has no such conditions for deepseek-v4-pro. Peak/off-peak describes direct manufacturer pricing and is selected separately for comparison. Do not apply it automatically to API429 rates.
Why can I not assume repeated large input is fully cached?
In native DeepSeek, a hit depends on already stored complete prefix units and is not guaranteed for every repetition. Check actual counters; a published cached-input rate does not replace measuring usage for a specific request.
Sources and review
- api-docs.deepseek.com/quick_start/pricing/
- api-docs.deepseek.com/news/news260813/
- api-docs.deepseek.com/news/news260424/
- api-docs.deepseek.com/guides/thinking_mode/
- api-docs.deepseek.com/guides/kv_cache/
- api-docs.deepseek.com/guides/responses_api/
- gateway.api429.com/api/public/catalog
- gateway.api429.com/api/public/openapi.json
Editorial review: . Pricing and general calculator · Getting started · Model catalog