Plan a relaxed Saturday using these options: a park open 9am-6pm, a museum open 10am-5pm, and a cafe open 8am-8pm. Start at 10am, include a lunch break, and allow 30 minutes between stops. Return a timetable without inventing prices or opening hours.
DeepSeek V4 Pro API: Online text generation, tutorials and integration guide
Connect to DeepSeek V4 Pro in one click on RequestBeam. One API key connects AI and multiple data services, so you can focus on your business and explore new ideas.
DeepSeek V4 Pro API pricing and billing
Input 0-1M | Output 0-393.216K
Our price
- Input
- $1.0286 /M
- Output
- $3.0857 /M
- Cache input
- $0.0343 /M
DeepSeek V4 Pro online chat tool
Model settings
What does memory do?
It includes completed, reusable turns from this conversation in your next request, so you can ask follow-up questions without repeating the context.
What happens when it is off?Only your new message and any system prompt are sent. Existing messages stay visible; turning memory back on includes eligible earlier turns again.
Is this long-term memory?No. This is conversation context, not memory across separate conversations. Failed, stopped, truncated, or non-reusable replies are excluded. More history can use more input tokens and increase cost.
Advanced parameters
0 or more characters(0/No fixed limit)
No fixed character limit; the full request is subject to request-size and model-context limits. This is not a token count. Conversation
How can I help you today?
No response data yet Ready
DeepSeek V4 Pro prompt examples
Draft a polite customer email for this situation: an order will arrive two days late, the revised delivery date is October 15, and tracking will be sent tomorrow. Apologize without inventing compensation. Include a subject line and keep the email under 120 words.
Turn these meeting notes into an action list: Maya will revise the proposal by Friday; Leo will confirm the venue next Monday; the budget needs approval, with no owner assigned. Use columns for task, owner, deadline, and open questions. Mark missing details as unknown.
What is DeepSeek V4 Pro
A production-release model for complex reasoning and Agent tasks
We position DeepSeek V4 Pro as a reasoning model for complex text tasks, focusing on work that breaks down problems and moves forward continuously, such as code analysis, software fixes, research Q&A, and tool collaboration. DeepSeek-V4-Pro-0813, released on August 13, 2026, is its production release, with the official announcement placing particular emphasis on improvements to Agent capabilities.
An open-weight MoE model with a million-token context
DeepSeek V4 Pro uses a mixture-of-experts architecture, also known as MoE, and makes its model weights available under the MIT license; RequestBeam provides a 1M Token context, just like the previous official API. Of course, businesses and individuals with data security concerns can also consider deploying DeepSeek V4 Pro themselves.
For long-text tasks, we pay more attention to its ability to process different kinds of information together. For example, project specifications, code from multiple modules, and error logs can be supplied together so the model can analyze how they relate to the same problem.
Switch between non-thinking mode and three levels of thinking effort
DeepSeek V4 Pro supports non-thinking and thinking modes; with thinking enabled, you can choose among low, high, and max effort levels. The official documentation currently sets the default thinking effort to high. We believe the value of this control is in allocating reasoning budgets by task, providing a foundational capability for future multi-model routing.
We recommend choosing settings based on task difficulty: for format conversion and simple extraction, start by evaluating non-thinking mode or low; for everyday code analysis and multi-step tasks, start with high; for difficult problems with multiple constraints that require repeated verification, try max. The specific thinking effort should be tested against your business scenario.
RequestBeam's DeepSeek V4 Pro online tools and API
Through RequestBeam, we hope to help you understand more quickly how DeepSeek V4 Pro actually performs on your own tasks. Simply sign in, create an API Key, and add a small amount of credit to start trying the online tool, without writing API integration code first or buying a fixed plan for a short-term evaluation.
DeepSeek V4 Pro's core capabilities and domain strengths
Project coding: analysis, fixes, and refactoring within existing projects
We evaluate DeepSeek V4 Pro's coding value using real projects: asking it to understand relationships between existing modules, propose changes within constraints, and then check implementation results with tests. Most work involves continuously iterating on and maintaining projects, so this is closer to developers' actual needs.
In customer feedback comparing DeepSeek V4 Pro and DeepSeek V4 Flash on the same Python project, Pro was reported to cover more ground in architecture analysis and refactoring discussions. In subsequent fix tests, Pro considered the semantics of some data-merging and locking behavior more thoroughly, although it also left cross-platform compatibility issues. We therefore recommend providing explicit acceptance criteria when using DeepSeek V4 Pro to fix bugs, especially the runtime platform, data boundaries, and existing behavior.
Tool collaboration: advancing multi-step tasks based on intermediate results
DeepSeek V4 Pro supports tool calling and JSON output. We believe this gives it a foundation for participating in business automation: once an application provides the relevant tools, the model can request data queries, read the results, and decide whether the next step needs additional context or can move forward.
To build an information-organizing workflow, you need to split it into four stages: retrieval, extraction, verification, and output, allowing the model to continue processing based on tool results. Search, file access, and code execution must be provided by the application or Agent framework; you can use DeepSeek's official DeepSeek Harness or another framework with multi-Agent capabilities.
Long-document analysis: reading specifications, code, and multiple sources together
We believe DeepSeek V4 Pro's long context is worth using for comparisons and organization across multiple sources, such as checking whether an implementation meets a requirements document, comparing interface contracts across versions, or organizing changes before and after an issue from large volumes of logs. When asking questions, we recommend numbering sources, specifying the questions to answer, and requiring conclusions to reference specific paragraphs or files. This engineering-oriented approach can help you get the results you want more effectively.
In Artificial Analysis's current testing, DeepSeek V4 Pro Max scores 80% on the AA-LCR v1.1 long-context reasoning evaluation. This evaluation uses tasks with approximately 100,000 input Tokens, and we treat it as a reference for long-document analysis. For real inputs approaching a million Tokens, however, we still recommend testing in your own business scenario before use, because outputs are not at their best when model input approaches its capacity limit.
Complex reasoning: analyzing constraints, evidence, and verification
We prioritize DeepSeek V4 Pro's higher thinking effort levels for tasks that require careful consideration, such as explaining a hard-to-reproduce issue, comparing technical approaches, or checking whether a design overlooks key constraints. We recommend providing both the required context and definite final acceptance criteria so that reasoning moves toward the acceptance criteria you specify.
In our customer feedback, a customer drew on experience with their own project and found Pro's Max setting helpful for security review, while also feeling it was slower than Flash. Of course, this reflects the experience of only some developers; the main issue is the practical trade-off: whether spending more time waiting for a complex review uncovers new problems or produces new, better suggestions.
Application integration: reusing existing development workflows through compatible interfaces
DeepSeek officially provides integration formats compatible with OpenAI and Anthropic, and the production release of DeepSeek V4 Pro also added Responses API support and adaptation for Codex. RequestBeam also supports integration through the Responses API, allowing teams with existing model-calling workflows to switch seamlessly and then run experiments for their scenarios.
We recommend starting with a clearly scoped workflow for a replacement evaluation, such as code review, structured information extraction, or internal knowledge Q&A. In addition to comparing answer quality, check whether tool parameters, streaming events, and error handling meet application expectations. After these checks pass, gradually expand usage; this makes it easier to assess the model's actual business benefits.
DeepSeek V4 Pro benchmarks and performance
In this section, we distinguish official tests from independent evaluations. The publicly reported official Code Agent results use DeepSeek Harness in minimal mode, max thinking effort, temperature=1.0, and top_p=0.95; these settings apply to the corresponding code Agent tests. We retain benchmark versions and test sources so you can judge whether the data applies to your own workflow. Official test settings
Terminal tasks: 87.9% on Terminal-Bench 2.1
Pro Preview72.1%
Pro 081387.9%
DeepSeek · Pro 0813
In the same table of official DeepSeek results, Pro 0813 scores 87.9% on Terminal-Bench 2.1, compared with 72.1% for Pro Preview. The gap between them in this table is 15.8 percentage points. We see this as one of the clearer pieces of evidence of the production release's progress on terminal Agent tasks.
For work that requires running commands in a development environment, reading feedback, and continuing to make corrections, we recommend using this result as a reason to prioritize a trial. In actual evaluations, we retain command output and logs from failed steps to observe whether the model can complete the entire task, as well as how the environment is restored after failure.
Software engineering: 62.7% on DeepSWE, 61.5% on NL2Repo
Different benchmarks, shown separately
DeepSWE62.7%
NL2Repo61.5%
DeepSeek · Pro 0813
In the official production-release results, DeepSeek V4 Pro scores 62.7% on DeepSWE and 61.5% on NL2Repo. We treat these results as different indicators for evaluating software engineering capabilities, focusing on how the model handles engineering tasks and completes project-level work from requirements.
We recommend preparing several types of real tasks for separate testing: fixing an existing issue, adding a feature constrained by an interface, and completing a small iteration from requirements. For developers, code that compiles is only the most basic requirement; we also check whether functionality is complete, existing tests still pass, and new code follows project conventions.
Tools and automation: 74.1% on Toolathlon-Verified, 31.8% on AutomationBench
Different benchmarks, shown separately
Toolathlon-Verified74.1%
AutomationBench Public31.8%
DeepSeek · Pro 0813
Official DeepSeek V4 Pro results include 74.1% on Toolathlon-Verified and 31.8% on AutomationBench Public. Our view is that different tests cover different tasks, and a high tool-use score needs to be judged alongside actual completion in your own business scenarios.
Difficult Q&A: HLE at 42.7% without tools, 60.0% with tools
Without tools42.7%
With tools60.0%
DeepSeek · Pro 0813
DeepSeek officially reports that DeepSeek V4 Pro scores 42.7% without tools and 60.0% with tools on Humanity's Last Exam, also known as HLE. We believe these results show that the engineering capabilities of tools have a very significant effect on solving complex problems.
For some scenarios, we recommend testing DeepSeek V4 Pro's capabilities with different Harnesses, which may deliver better performance in particular specialized scenarios.
Independent index and response speed: an overall score of 36, output at approximately 99 Tokens per second
- Intelligence Index v4.3.2
- 36score
- Output speed
- ~99Token/s
First Token1.67 s
First answer Token, including reasoning21.86 s
Artificial Analysis · Intelligence Index v4.3.2 · Pro MaxExternal measurements, not RequestBeam latency
Artificial Analysis's Intelligence Index v4.3.2 gives DeepSeek V4 Pro Max a score of 36. Its output speed is approximately 99 Tokens per second, with about 1.67 seconds to the first Token and about 21.86 seconds to the first answer Token when reasoning is included.
We observe the speed at which content starts returning, the wait for the final answer, and the time it takes to complete the entire task separately. For frequently interactive features, we recommend evaluating lower thinking effort first; for complex analysis, then judge whether the additional wait for Max is worthwhile.
DeepSeek V4 Pro compared with V4 Flash, Kimi K3, Claude Fable 5, and V4.1 Flash
Compared with DeepSeek V4 Flash: complex-task gains need to outweigh the speed difference
We use DeepSeek V4 Flash as a same-generation comparison. In Artificial Analysis's current tests using the same methodology, DeepSeek V4 Pro Max and DeepSeek V4 Flash Max score 36 and 34 on the overall index, respectively, and both score 80% on AA-LCR v1.1; output speeds are approximately 99 and 223 Tokens per second, respectively. Our recommendation: consider DeepSeek V4 Pro directly for complex tasks.
Compared with Kimi K3: differences in overall and long-document scores, as well as task costs
In Artificial Analysis v4.3.2's comparison at the Max setting, Kimi K3 scores 44 on the overall index and DeepSeek V4 Pro scores 36; their AA-LCR v1.1 results are 89% and 80%, respectively. Weighted costs per task in the same evaluation are $2.00 and $0.67, respectively.
For work that relies heavily on cross-document understanding, we recommend making Kimi K3 a key comparison; to expand batch usage, you should further test whether DeepSeek V4 Pro's output is acceptable for your business scenario.
Compared with Claude Fable 5: first-attempt success and coverage after multiple attempts have different strengths
A first-hand experiment on August 17, 2026, compared DeepSeek V4 Pro Max and Claude Fable 5 Max on 113 DeepSWE tasks, running each task four times per model.
| Metric | DeepSeek V4 Pro Max | Claude Fable 5 Max |
|---|---|---|
| pass@1, single-attempt pass rate | 62.8% | 69.7% |
| pass@4, at least one pass within four attempts | 88.5% | 84.1% |
| Tasks passing all four attempts | 35 | 56 |
| Median time per task attempt | 35 minutes | 31 minutes |
We believe Fable 5's advantage should be understood as first-attempt success and repeatability in this experiment, while DeepSeek V4 Pro's advantage is broader task coverage when retries are allowed.
Why use DeepSeek V4 Pro through RequestBeam?
Usage-based billing and model cost optimization
Charges follow current RequestBeam rates and actual usage. Top up on demand.
Unified AI models and data APIs, with multi-model access in one place
One API key connects multiple AI models and data services, reducing integration costs and letting you switch with one click. Business applications need both AI and data.
Call logs and application access controls, with clarity on every call
Every API call is recorded in your dashboard. Create separate API keys for different applications.
Model capabilities without compromise
We do not water down our models. We cannot offer you the cheapest, but we are committed to offering you the best.
How to use DeepSeek V4 Pro on RequestBeam
After creating an API key in the console, follow the documentation to get started with DeepSeek V4 Pro.
Prepare your account and test online
Sign in, confirm your balance, and create an API key.
Try it in the Playground
Explore the model's capabilities in the Playground to find the best way to use it.
Integrate DeepSeek V4 Pro into your business
Follow our developer documentation to integrate DeepSeek V4 Pro into your business.
Future model upgrades
When a new model is released, simply replace the model name with the new one to upgrade.
DeepSeek V4 Pro API FAQ
Can the DeepSeek V4 Pro Playground accept images, PDFs, or video?
This is a text conversation tool without file or image uploads. V4 Pro is a text model, not a visual-input model. Extract relevant text from a document before submitting it; a link does not make this page download or inspect a file.
Why do I see reasoning but no final answer?
Reasoning can consume an output allowance before the final answer appears. Check the finish reason, narrow the task, or increase an explicitly set allowance. API clients should distinguish reasoning_content from content rather than treating reasoning alone as a complete answer.
Is conversation memory permanent, and why does usage grow?
Memory is context within the current conversation, not permanent memory across sessions. Enabling it sends reusable completed turns, which can increase input usage. Disabling it leaves history visible but excludes it from new requests; failed, stopped, or truncated replies are not normal reusable history.
How should I troubleshoot 401, 402, 403, or 429 errors?
Check authentication for 401, balance for 402, model access for 403, and request rate or allowance for 429. Confirm the exact model ID through the catalog for your key. A numeric response code is distinct from the HTTP status; inspect complete errors for upstream failures and interrupted streams.
Can I call DeepSeek V4 Pro using OpenAI Responses or Claude Messages?
This RequestBeam model uses the unified Chat Completions interface. Direct DeepSeek protocol options do not establish RequestBeam support. Follow the endpoint and message format in the API tab instead of only changing model in a request using another protocol.
Can I get multiple proposals or guarantee valid JSON?
Ask for several proposals within one answer, with the same evaluation criteria for each. For JSON, specify fields, types, and missing-value rules, then validate syntax, schema, and business meaning. An instruction to return JSON is not a guarantee of valid or accurate records.
Can DeepSeek V4 Pro execute code or complete business actions here?
No. This page returns text and does not run commands, change repositories, access databases, or make payments. An agent application must provide supported tools, permissions, feedback, and approval. A proposed action is not proof of completed execution.
Should I immediately retry a slow, interrupted, or stopped request?
Check existing results, request records, and usage first. Long inputs and reasoning can delay the final answer. Stopping browser waiting or losing a stream does not prove that the upstream request was unprocessed; choose retries by error type to avoid duplicate requests and charges.
Related text APIs
DeepSeek V4 Pro API specification and requests
DeepSeek V4 Pro API quickstart
deepseek-v4-proChat CompletionsPOST /v1/chat-completions
curl -X POST "https://api.requestbeam.com/v1/chat-completions" \
-H "Authorization: Bearer <API_KEY>" \
-H "Content-Type: application/json" \
-d '{"model":"deepseek-v4-pro","messages":[{"role":"user","content":"Hello, introduce yourself in one sentence."}],"stream":false}'Family and protocol reference
DeepSeek Models
Full API documentationDeepSeek V4 Flash, V4 Pro, and V4.1 Flash model IDs and Chat Completions availability.
POST
https://api.requestbeam.com/v1/chat-completionsDeepSeek models use the Common Chat Completions endpoint. To switch models, change only the model value; the endpoint and authentication stay the same.
text
POST https://api.requestbeam.com/v1/chat-completionsAvailable Models
model value | Positioning | Non-streaming | SSE streaming |
|---|---|---|---|
deepseek-v4-flash | Flash tier and a practical starting point for routine chat or batch evaluations | Yes | Yes |
deepseek-v4-pro | Pro tier for evaluations that prioritize complex reasoning and output quality | Yes | Yes |
deepseek-v4.1-flash | DeepSeek V4.1 Flash; a distinct model ID, not a V4 Flash alias | After deployment and enablement | After deployment and enablement |
V4.1 Flash becomes available only after its routing configuration is deployed and enabled. Use it only when GET https://api.requestbeam.com/v1/models returns deepseek-v4.1-flash for your API Key. This reference entry does not confirm a production rollout or a successful inference test. Existing V4 Flash and V4 Pro IDs remain unchanged.
This positioning helps you choose an initial model to test; it is not a latency, throughput, or quality SLA. Compare available models with your own prompts, output lengths, and concurrency before production use.
Authentication
http
Authorization: Bearer YOUR_API_KEY
Content-Type: application/jsonNon-streaming Request
The endpoint returns one complete Chat Completions response by default. The example includes stream: false explicitly so its behavior remains obvious when copied.
json
{
"model": "deepseek-v4-flash",
"messages": [
{
"role": "system",
"content": "You are a precise technical assistant."
},
{
"role": "user",
"content": "Explain three benefits of event-driven architecture."
}
],
"stream": false
}To use the Pro model, change only the model field:
json
{
"model": "deepseek-v4-pro"
}Streaming Request
Set stream: true explicitly for streaming and process the response as SSE events until [DONE].
json
{
"model": "deepseek-v4-pro",
"messages": [
{
"role": "user",
"content": "Create a checklist for a reversible database migration."
}
],
"stream": true
}Request Fields
modelstringrequiredUse deepseek-v4-flash, deepseek-v4-pro, or deepseek-v4.1-flash, as returned by GET https://api.requestbeam.com/v1/models.
messagesobject[]requiredOpenAI Chat Completions style messages. Each message contains role and content.
streambooleanDefault: falsetrue returns SSE; false returns one complete JSON response.
max_tokensintegerOptional maximum output tokens, as a positive integer. Omit this field to use the upstream default for the selected model. Depending on the model, the budget may include reasoning tokens as well as final text. A small budget can truncate the answer or be exhausted before final text; increasing it may increase cost.
max_completion_tokensintegerOptional compatibility alternative to max_tokens, with the same budget considerations. The selected model determines support; omit both fields to use the upstream default.
See Common Chat Completions for shared fields such as temperature, top_p, frequency_penalty, presence_penalty, stop, and n. Support and semantic effects may vary by model; this guide does not establish V4.1 Flash support for every optional field.
Success Response
json
{
"id": "chatcmpl_...",
"object": "chat.completion",
"created": 1787200000,
"model": "deepseek-v4-flash",
"choices": [
{
"index": 0,
"message": {
"role": "assistant",
"content": "1. Services can remain loosely coupled..."
},
"finish_reason": "stop"
}
],
"usage": {
"prompt_tokens": 31,
"completion_tokens": 96,
"total_tokens": 127
}
}Responses may contain additional usage details; treat them as optional.
Usage Notes
- The current public contract is Chat Completions, including non-streaming and SSE streaming.
- These models are not currently available through OpenAI Responses or Claude Messages.
- Set the request body's
modelfield to the exact ID:deepseek-v4-flash,deepseek-v4-pro, ordeepseek-v4.1-flash. - Use only model IDs returned by
GET https://api.requestbeam.com/v1/models. - Use
GET https://api.requestbeam.com/v1/modelsfor the models visible to the current API Key, and use the console for current pricing.