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 Flash API: Online text generation, tutorials and integration guide
Connect to DeepSeek V4 Flash 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 Flash API pricing and billing
Input 0-1B | Output 0-1B
Our price
- Input
- $0.3429 /M
- Output
- $1.0286 /M
- Cache input
- $0.0114 /M
DeepSeek V4 Flash 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 Flash 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 Flash
An efficient MoE language model for frequent calls
We understand DeepSeek V4 Flash as a language model that emphasizes computational efficiency without being limited to simple questions and answers. Its main model uses a mixture-of-experts architecture, or MoE, with 284B total parameters and approximately 13B active parameters per token. Rather than using all parameters for every generation, it selects some experts to participate in computation, keeping computational costs under control while retaining substantial model capacity.
What we value more is the positioning behind this design: it considers not only whether a difficult question can be answered correctly, but also whether the model is suitable for repeated calls and ongoing task processing.
An open-weight model supporting a million-token context
DeepSeek V4 Flash supports approximately one million tokens of context, and its weights are released under the MIT license. To me, this means it has two noteworthy characteristics: it can accept large amounts of text, while leaving room for self-hosting and adjusting the inference environment. For enterprise users with security requirements, self-hosting is a prerequisite for using a model.
We value long context not because it can make answers longer, but because, when analyzing a codebase or reading multiple documents, the challenge often lies in the connections between materials. A larger context window has the potential to reduce information loss caused by repeated splitting and summarization.
Post-training strengthens coding and agent capabilities
We see DeepSeek V4 Flash as an upgrade focused on task execution rather than simply increasing parameter count. DeepSeek explicitly states in its release notes that it retains the main model architecture and scale of the preview, strengthens agent task capabilities through renewed post-training, and adds native support for the Responses API format and Codex integration to its official interface.
DeepSeek V4 Flash online tools and API on RequestBeam
After signing in to RequestBeam, creating a KEY, and adding a small balance, you can test DeepSeek V4 Flash through the online tool without first completing an API integration or purchasing a package, reducing the various costs as much as possible while you evaluate the model. Using it through RequestBeam does not require a separate official DeepSeek account.
DeepSeek V4 Flash core capabilities and domain strengths
From code generation to multi-step development
We believe the most noteworthy coding capability of DeepSeek V4 Flash is not generating a single function, but advancing more complete software development tasks in a tool environment. Improvements in public terminal-operation and software-engineering evaluations for the 0731 official release make it more than just an option for "cheaper code completion". DeepSWE, for example, evaluates original, long-running software engineering tasks and verifies results through software behavior, rather than merely judging whether an answer looks reasonable. We therefore see greater value in development work with clear acceptance criteria, such as adding tests, changing features, and addressing cross-file issues.
Connecting information processing into executable workflows
When judging the tool-calling capabilities of DeepSeek V4 Flash, we look beyond whether it can generate correctly formatted call arguments and place more weight on whether it can maintain the task goal across multiple steps. Its excellent performance in this evaluation of tool-use tasks requiring extended execution in real software development environments is one of our main reasons for paying attention to it. We think the practical value of this capability is that it gives the model an opportunity to connect "reading materials, processing data, checking results, and preparing deliverables", rather than stopping at telling users what they should do.
Reducing information gaps caused by splitting materials
For long-document tasks, we place more weight on whether DeepSeek V4 Flash can connect scattered information. For example, the conditions for using an interface may be near the start of a document, exceptions near the end, and the actual implementation spread across several code files. A million-token context provides room to retain these materials together, an important capability compared with models that have smaller contexts.
Balancing everyday tasks and complex analysis
DeepSeek V4 Flash lists three reasoning levels, low, high, and max, which are settings of the same model. Tasks can be tested separately: field extraction mainly checks format and completeness, while code analysis checks diagnosis, reasoning, and modification suggestions. A level can then be selected based on quality, elapsed time, and token usage.
Our customer feedback includes users who praise its capabilities in complex projects, as well as users who report repeated investigation, detours, and substantial token consumption. Our understanding is that it has the ability to keep exploring a problem, but whether that exploration is effective also depends on task boundaries, tool configuration, and reasoning settings; thinking more does not necessarily mean finishing the work faster.
Making repetitive technical tasks economically feasible
Another aspect we value about DeepSeek V4 Flash is that it prompts developers to reconsider which tasks are worth handing over to a model. In our customer feedback, developers have reported frequent parallel usage and discussed using the model for repair suggestions in continuous integration, test generation, and log analysis. The key point about DeepSeek V4 Flash is that its lower barrier to use expands the range of tasks people are willing to try.
DeepSeek V4 Flash benchmarks and performance
The following are DeepSeek evaluations of V4 Flash on public coding-agent tasks, using DeepSeek Harness in minimal mode, max reasoning, temperature=1.0, and top_p=0.95.
Terminal coding: Terminal-Bench 2.1 rises from 61.8 to 82.7
- V4 Flash preview 61.8
- V4 Flash release 82.7
In the DeepSeek official-release comparison, V4 Flash scores 82.7 on Terminal-Bench 2.1, compared with 61.8 for the preview, an increase of 20.9 points. We think these are the most direct figures for this upgrade: the improvement is not limited to written answers, with a substantial upgrade in advancing tasks through a terminal environment as well. For users who need to handle builds, tests, or development-environment issues, this direction deserves more attention than code-generation scores alone. Our interpretation is that the official V4 Flash release has stronger execution capabilities in tool environments.
Complex software engineering: DeepSWE and NL2Repo both improve
- V4 Flash preview 7.3
- V4 Flash release 54.4
- V4 Flash preview 39.4
- V4 Flash release 54.2
DeepSeek V4 Flash scores 54.4 on DeepSWE in the official comparison, versus 7.3 for the preview; NL2Repo rises from 39.4 to 54.2. Both metrics improve, so we view this as an upgrade in software development capabilities rather than the result of optimizing just one type of terminal operation. We are particularly interested in the nature of DeepSWE tasks: they require models to complete extended engineering changes in real code repositories and verify them through behavior. Such results better support the conclusion that "V4 Flash can participate in complex development work".
Multi-tool execution: Toolathlon-Verified reaches 70.3
- V4 Flash preview 49.7
- V4 Flash release 70.3
On Toolathlon-Verified, V4 Flash has an official score of 70.3, compared with 49.7 for the preview. This progress concerns not a single function call but multi-step tool use closer to real software environments. Our understanding of this result is that it is better able to use information obtained in an earlier step for subsequent actions, making it worth using for workflows that need tools, such as processing materials and analyzing data.
Independent abstract reasoning: ARC-AGI-2 reaches 61.4% at Max
- V4 Flash Low 46.0%
- V4 Flash High 56.0%
- V4 Flash Max 61.4%
- V4 Flash Max 89.0%
Independently verified results published by ARC Prize show that V4 Flash at Max scores 89.0% on the ARC-AGI-1 semi-private evaluation and 61.4% on ARC-AGI-2. On ARC-AGI-2, Low and High score 46.0% and 56.0%, respectively, showing that reasoning settings noticeably affect the results. I value these results because they offer a perspective beyond official coding evaluations from the developer. ARC-AGI-2 focuses on reasoning problems such as symbolic interpretation and rule composition, so this result supports its ability to reason about abstract rules rather than directly proving completion rates in customer support, writing, or software development.
DeepSeek V4 Flash compared with V4 Pro, GLM-5.2, Claude Opus 4.8, and Kimi K3
The following mainly uses official-release comparisons published by DeepSeek; tool frameworks and reasoning settings may differ between models, so these figures are useful for assessing the direction of their capabilities. If you need to use a model in your business, however, you should rely on tests within that business.
Compared with DeepSeek V4 Pro: Flash surpasses the old preview, but not the Pro release across the board
- V4 Flash 82.7
- V4 Pro release 87.9
- V4 Flash 54.4
- V4 Pro release 62.7
V4 Flash does surpass the V4 Pro preview in several official agent evaluations, but compared with the official V4 Pro release, Terminal-Bench 2.1 is 82.7 versus 87.9 and DeepSWE is 54.4 versus 62.7, with Pro still ahead. Our judgment is that the progress of V4 Flash is enough to change its value in everyday development, but the capability gap with the official Pro release still needs to be recognized. For frequent work with easily verified results, we place greater value on the efficiency positioning of Flash; for highly complex work where failure leads to costly rework, the higher completion rate of Pro may still make it your first choice.
Compared with GLM-5.2: Flash leads in some execution evaluations, with differences across business scenarios
- V4 Flash 70.3
- GLM-5.2 59.9
- V4 Flash 54.4
- GLM-5.2 46.2
In the public DeepSeek comparison, V4 Flash scores 70.3 on Toolathlon-Verified, versus 59.9 for GLM-5.2; their DeepSWE scores are 54.4 and 46.2, respectively. We therefore believe that the advantage of V4 Flash in this set of evaluations mainly lies in tool collaboration and software engineering execution, rather than relying only on low cost to remain competitive. In our customer feedback, some users still prefer GLM for complex coding problems, while others report that V4 Flash works well in their codebases. We therefore think their performance may be better or worse in different business scenarios. We recommend testing based on your own business scenarios.
Compared with Claude Opus 4.8: close terminal scores, a more pronounced development gap
- V4 Flash 82.7
- Claude Opus 4.8 85.0
- V4 Flash 54.2
- Claude Opus 4.8 69.7
V4 Flash and Claude Opus 4.8 score 82.7 and 85.0, respectively, on Terminal-Bench 2.1 in the official comparison; on NL2Repo, their scores are 54.2 and 69.7. For us, the closeness in terminal evaluations supports including V4 Flash among candidates for everyday development tools; the development gap reminds me not to assign V4 Flash to every complex engineering task. It is a model worth serious consideration, but a single score does not establish it as a comprehensive replacement.
Compared with Kimi K3: capability gaps remain in complex engineering and difficult reasoning
- V4 Flash 54.4
- Kimi K3 67.5
- V4 Flash 37.8
- Kimi K3 43.5
In the DeepSeek comparison table, Kimi K3 scores 67.5 on DeepSWE, above the 54.4 of Flash 0731; their HLE scores without tools are 43.5 and 37.8, respectively. These results incline me to believe that Kimi K3 retains an advantage in these complex engineering and difficult reasoning evaluations. At the same time, Kimi officially states that some of its coding results were evaluated using the Kimi Code framework, reminding us that delivered results depend not only on the model but also on the execution framework. Our overall judgment is that V4 Flash cannot replace every stronger model; rather, its response speed and cost make this combination of capability and efficiency especially worth attention for tasks with clear acceptance criteria that require repeated calls.
Why use DeepSeek V4 Flash 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 Flash on RequestBeam
After creating an API key in the console, follow the documentation to get started with DeepSeek V4 Flash.
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 Flash into your business
Follow our developer documentation to integrate DeepSeek V4 Flash 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 Flash API FAQ
Can the DeepSeek V4 Flash Playground accept images, PDFs, or video?
This page provides text conversations without file or image upload controls. It does not download linked documents or automatically browse the web. Extract relevant text before submitting it. The direct-service V4.1 redirect and its visual capabilities do not establish that this RequestBeam text tool accepts media.
Why is the answer incomplete, or why do I see only reasoning?
Inspect finish_reason and the requested output allowance. Depending on the serving model, reasoning can consume output budget before final text, and length indicates truncation rather than a complete answer. Narrow the task or adjust an explicit allowance. Parse content separately from reasoning_content when present; reasoning is not the final result.
Is conversation memory permanent, and why does usage grow?
Memory here is current conversation context, not persistent memory across sessions. Enabling it includes reusable completed turns, which can increase input usage. Disabling it leaves history visible but excludes it from new requests. Failed, stopped, truncated, or non-reusable replies are not appended as normal history.
How should I troubleshoot 401, 402, 403, or 429 errors?
For 401, check the key and Bearer format; for 402, check balance; for 403, check model permissions; for 429, check request rate or allowance. Verify deepseek-v4-flash against the catalog for your key. A numeric response code is not the HTTP status. Diagnose 502, 503, 504, or in-stream SSE errors using the complete error details before choosing a retry.
Can I call DeepSeek V4 Flash using OpenAI Responses or Claude Messages?
This RequestBeam catalog model currently uses the unified Chat Completions interface with non-streaming and SSE streaming. It is not listed for OpenAI Responses or Claude Messages. DeepSeek direct-service protocol support is a separate contract; follow the RequestBeam endpoint and messages format rather than only changing model in another protocol.
Can one request return multiple alternatives or guarantee valid JSON?
You can ask for several alternatives within one answer. DeepSeek documentation lists n among shared accepted fields, but that does not establish identical semantics or behavior for every serving revision. Start with one result. For JSON records, define fields and missing values, then validate syntax, schema, and source meaning before importing them.
Can DeepSeek V4 Flash execute code, use tools, or update records here?
This page returns text. It does not run code, modify repositories, access databases, or make payments. An agent application needs execution tools, permissions, result feedback, and approval controls, with confirmed support on the target interface. An extracted order or proposed patch is not evidence that a business operation occurred.
Should I immediately retry a slow response, interrupted stream, or stopped request?
Check existing results, request records, and usage first. Input size, requested output, reasoning, and service conditions can affect duration. Interrupted streams and stopping browser waiting do not prove that the upstream request was unprocessed. Resolve outstanding results and use bounded retries appropriate to the error to avoid duplicate requests and charges.
Related text APIs
DeepSeek V4 Flash API specification and requests
DeepSeek V4 Flash API quickstart
deepseek-v4-flashChat 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-flash","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.