Production routeThis rate reflects platform-side availability — only confirmed server errors (HTTP 500 / empty response) count as failures. User-side issues (content moderation, invalid params, cancellation) plus rate limits, timeouts and auth errors are excluded. Before real traffic arrives, empty buckets may display as available.Live
Live
Context
1M context · 384K max output
Best For
Long-context coding and agent loops that also need to read images
Text, image and long-context tasks on one model ID, deepseek-v4.1-flash. Check current rates in Pricing and test your protocol before production.
DeepSeek V4.1 Flash
DeepSeek’s multimodal Flash model for text and image workloads
Selected
Model ID
deepseek-v4.1-flash
Best for
Coding, agent loops, document extraction and screenshot understanding with a 1M context window; evaluate quality and cost per accepted result on your own tasks.
Input
$0.442 / 1M
30 cr / 1M
Cache hit
$0.015 / 1M
1 cr / 1M
Output
$1.324 / 1M
90 cr / 1M
All rates are per 1M tokens, shown in USD and credits, and reflect your account's current pricing.
DeepSeek V4.1 Flash pricing
Estimate what one DeepSeek V4.1 Flash request costs before you integrate. The calculator uses your account's current rates, with official pricing as a reference.
DeepSeek V4.1 Flash combines native text and image input with a 1M-token context window and up to 384K output tokens. Evaluate screenshots, documents and coding tasks through the EvoLink unified API using model ID deepseek-v4.1-flash. Check current pricing and protocol requirements, then validate your workload before moving production traffic.
DeepSeek V4.1 Flash is served on EvoLink under the model ID deepseek-v4.1-flash through Chat Completions · Anthropic Messages · Responses, with the same API key and balance you use for every other model. It offers a 1M context window and up to 384K output tokens, plus long-context coding, image input, thinking mode.
DeepSeek V4.1 Flash specs and capabilities
Numbers come from the EvoLink route configuration; capabilities are what the API exposes today.
Context window
1M tokens
Max output
384K tokens
Input
Text + images
Output
Text · JSON (structured output)
Reasoning
Optional thinking mode
Prompt caching
Automatic cache reads at a lower rate
Protocols
Chat Completions · Anthropic Messages · Responses
Model ID
deepseek-v4.1-flash
Image input
PNG · JPEG · WebP · GIF
The API model ID is deepseek-v4.1-flash, written with a dot; this page’s URL uses a hyphen. DeepSeek’s direct API uses a different name, deepseek-flash.
What is the DeepSeek V4.1 Flash API best suited for?
Evaluate V4.1 Flash for coding, agent loops and tasks that combine a question with image evidence. On EvoLink, DeepSeek V4 Flash remains available as a separate text-only model, so you can test V4.1 Flash on the same tasks before moving traffic. Requests to deepseek-v4-flash-vision-exp now redirect to V4.1 Flash.
Long-context coding and review
Load related source files, specifications, and diffs into one working context and ask for a review, a patch, or a risk list. Select only relevant context and inspect reported cache hits before estimating repeated-turn cost.
Agent loops with a stable prefix
Run multi-step agents where the system prompt, tool schemas, and shared project context stay identical across turns. A reusable prefix can reduce repeated-input cost when a cache hit is reported; reuse is not guaranteed for every request. Measure the real hit rate in your own logs before projecting loop cost.
Documents and screenshots in the same request
Send a scanned form, an invoice, a dashboard screenshot, or a chart together with the question, and ask for structured fields back. Test small text, missing fields and ambiguous charts against labeled examples before automating a downstream decision.
High-volume structured extraction
Batch classification, field extraction, and summarisation at a low per-token price, with JSON-shaped output for downstream processing. Validate the schema on every response and route low-confidence results to review rather than trusting the format blindly.
What changes when you move from DeepSeek V4 Flash to V4.1 Flash?
On EvoLink both models stay available: deepseek-v4-flash continues to serve V4 Flash, and deepseek-v4.1-flash is the new model, so you can compare them on the same requests. A compatible request format does not guarantee identical outputs, thinking behavior, token use or tool execution, so check those before switching.
The model ID contains a dot, the URL does not
The API model ID is deepseek-v4.1-flash — with a dot. This page lives at /deepseek-v4-1-flash. Copy the ID from the Models card instead of deriving it from the address bar; DeepSeek’s direct API uses a different name, deepseek-flash.
Recheck rates and cost per accepted task
Use the Pricing section for current EvoLink rates and check final charges in your account. Do not infer price from the model name; reasoning, image tokens, retries and acceptance rates all change the bill.
Measure image usage before scaling
The Pricing section lists input, cached input and output rates. Inspect usage and final charges for representative images before projecting a batch cost.
Pick the protocol your stack already speaks
Chat Completions, Messages and Responses use the same model ID. Use the protocol your application already speaks, and confirm streaming events, tool handling and usage reporting on it before switching traffic.
How do you keep DeepSeek V4.1 Flash API cost predictable?
The Pricing section above holds the live rates and the request calculator. Use it as an estimate, then reconcile the reported token categories with final charges and accepted results.
Design the prefix so it caches
DeepSeek documents automatic prefix caching. Keep reusable instructions and tool definitions before changing content, and inspect the cached-token field. A changed suffix does not necessarily invalidate the entire shared prefix; cache availability and reported usage determine the actual benefit.
Treat the 1M window as capacity, not a target
A large context is useful when the task genuinely needs it, but every token you send is billed. Retrieve the files and passages that matter instead of pasting the whole repository, and the cost per completed task usually drops without hurting output quality.
Measure images on your own content
Image token counts vary with resolution and content, so a number quoted for someone else’s screenshots will not predict yours. Run a representative sample, read the input tokens reported in usage, and use that figure in the calculator above.
Set an output budget per task
The 384K output ceiling is a limit, not a default. Set a task-appropriate output limit using the selected protocol. Detect truncation and bound retries; repeating a failed task can cost more than allowing a sufficient answer on the first attempt.
Two ways to use DeepSeek V4.1 Flash: EvoLink API or Agent
Use the EvoLink API for product backends and batch jobs, or call DeepSeek V4.1 Flash from Codex, Claude, or Gemini for coding and analysis workflows. Both paths share the same EvoLink API key, balance, model ID, and request history.
Option 1
Integrate with the EvoLink API
Best for: product backends, batch jobs, automated pipelines
Send OpenAI-compatible Chat Completions (or Anthropic Messages) requests to EvoLink and control the model ID, system prompt, output budget, tools, and structured output.
1Create an EvoLink API key in the console
2Point your OpenAI or Anthropic SDK at the EvoLink base URL and select the model ID shown above
3Send one representative request and read the usage field for input, cached, and output tokens
4Set max_tokens and retries per task; keep tool-call IDs and results across turns
Best for: coding, review, and analysis tasks in Codex, Claude, and Gemini
Give the Agent the task, the inputs to include, and the acceptance criteria. It assembles the request, calls DeepSeek V4.1 Flash through EvoLink, and returns the answer with token usage.
1Set EVOLINK_API_KEY in your local environment; never put it in code or a prompt
2Describe the task, the inputs to include, and the expected output format
3Ask the Agent to call DeepSeek V4.1 Flash through EvoLink and show the request before sending
4Let the Agent report the answer, token usage, and any error body
DeepSeek V4.1 Flash API code example and error handling
This example shows the shortest runnable request: an OpenAI-compatible Chat Completions call with a system prompt, a user message, and an output budget. Open the API tab for the complete parameter and response reference.
curl -X POST https://api.evolink.ai/v1/chat/completions \
-H "Authorization: Bearer $EVOLINK_API_KEY" \
-H "Content-Type: application/json" \
-d '{
"model": "deepseek-v4.1-flash",
"messages": [
{ "role": "system", "content": "You are a senior engineer reviewing a pull request." },
{ "role": "user", "content": "Review this diff and list every risky change with file and line:\n<diff>" }
],
"max_tokens": 4096,
"temperature": 0.2
}'
# Anthropic Messages (/v1/messages) and Responses (/v1/responses) accept the
# same model ID.
# The response includes choices[0].message and a usage object (prompt,
# cached, and completion tokens).
Invalid request or unsupported parameter
Check the model ID, the messages array, and parameter ranges against the API reference; remove fields this route does not support.
Authentication or balance issue
Check the Authorization bearer token and confirm the available balance in the console.
Context length exceeded
Prompt tokens exceed the DeepSeek V4.1 Flash context window. Trim or retrieve only the relevant evidence and reuse cached prefixes.
Rate limited (429)
Back off and retry with jitter; batch or queue requests instead of sending parallel bursts.
Content or tool call rejected
Review sensitive content, malformed tool-call arguments, and JSON schema mismatches before retrying.
How do you send your first DeepSeek V4.1 Flash request?
Four steps from an API key to production traffic. The API link opens EvoLink documentation; V4.1 Flash protocol examples will be added there.
01
Copy the exact model ID
Use deepseek-v4.1-flash, with the dot, in your model configuration. The copy button on the Models card above gives you the exact string; do not substitute the page slug or the official direct-API name for the EvoLink ID.
Model configuration
02
Send a text request on your existing protocol
Use the current EvoLink request example for your selected protocol with an active key. Confirm endpoint, authentication, response parsing and usage on a small text request before adding images or tool calls.
First request
03
Add image content and read the usage back
Check the enabled image format in the selected protocol: image_url for Chat, an image block for Messages, or input_image for Responses. After a successful image request, compare usage and final charge with the corresponding text-only task.
Image input
04
Stabilise the prefix, then scale up
Keep reusable instructions and tool definitions stable, measure cache hits instead of assuming them, and increase traffic after quality, latency and final charges pass your acceptance criteria.
Gradual rollout
Cache hits are billed at a fraction of fresh input
DeepSeek documents automatic prefix caching. Put reusable instructions and tool definitions first, then measure the cache-hit tokens reported in usage. Cache reuse can lower repeated-input cost, but every request is not guaranteed to hit the cache.
1M-token context with a 384K output ceiling
Keep related files, specifications, and agent state in one working context, but treat the ceiling as capacity rather than a target: retrieve what matters, keep the prefix stable so it caches, and set an output budget per task since output is the most expensive of the three billed dimensions.
Compare DeepSeek V4.1 Flash with Qwen 3.8 Max and GLM-5.3
EvoLink
Compare input/output rates, context, caching, and workload fit. Benchmark the same requests before choosing a route; prices below follow your account’s current rates.
Coding, agent loops, document extraction and screenshot understanding with a 1M context window; evaluate quality and cost per accepted result on your own tasks.
Coding agents, long task chains, tool-heavy workflows
DeepSeek V4.1 Flash
Input / output$0.442 / $1.324
Context1M
CachingAutomatic cache hits
Best forCoding, agent loops, document extraction and screenshot understanding with a 1M context window; evaluate quality and cost per accepted result on your own tasks.
Best forCoding agents, long task chains, tool-heavy workflows
What should you confirm before putting DeepSeek V4.1 Flash on production traffic?
Verify output quality, supported controls and actual charges. On EvoLink you can replay the same real requests through V4 Flash and V4.1 Flash and compare them side by side; if you are moving from Vision Exp, compare against results you saved before the redirect.
The model ID and protocol are wired correctly
Confirm the request sends deepseek-v4.1-flash, that responses come back on the protocol you expect, and that the usage object reports input, cached, and output tokens separately. Without that split you cannot attribute cost or verify that caching is working.
Output quality holds on your existing prompts
Replay a fixed set of real requests through deepseek-v4-flash and deepseek-v4.1-flash on EvoLink and compare the results side by side. Check instruction following, format validity, tool outcomes and thinking behavior rather than assuming prompts tuned for V4 Flash carry over.
Image results meet your acceptance bar
If you are adding image input, build a fixed evaluation set of the screenshots, forms, and charts you actually process. Check field accuracy, missing values, small-text handling, and invented details, and decide which confidence levels go to human review.
Latency and error handling behave under load
Observe response time on representative traffic and prepare for rate limits, timeouts, and invalid structured output. Keeping the model name in configuration rather than in code makes switching back to a verified route a config change instead of a deploy.
Begin with representative tasks and a small traffic share. Expand when quality, latency and cost per accepted result meet your bar. For text-only tasks, keep deepseek-v4-flash as a fallback: on EvoLink it still serves V4 Flash. It is text-only, so image tasks need another model that passed the same visual evaluation, or a human-review path. deepseek-v4-flash-vision-exp redirects to V4.1 Flash and is not a fallback.
DeepSeek V4 Model Family
Same API key and balance — switch tiers without changing your integration.
DeepSeek V4 Flash Vision
Legacy Vision Exp ID, now routed to DeepSeek V4.1 Flash
The EvoLink model ID is deepseek-v4.1-flash, with a dot; this page uses /deepseek-v4-1-flash. The official DeepSeek direct API uses deepseek-flash. Use the identifier documented for your provider and endpoint.
How is V4.1 Flash different from V4 Flash?
V4.1 Flash accepts image input natively; V4 Flash is text-only. Both have a 1M-token context window and a 384K output ceiling. On EvoLink, deepseek-v4-flash is not affected by DeepSeek’s retirement of the original model on its direct API and still serves V4 Flash, so you can run the two side by side.
Does it cost more than V4 Flash?
Compare the rates shown in each model page’s Pricing section and confirm with your final account charges; do not infer price from the model name. Task cost also depends on output length, thinking, images, cache hits and retries. Official DeepSeek peak/off-peak prices are a separate price schedule.
Which protocols can I call it on?
Use the same model ID with Chat Completions (/v1/chat/completions), Messages (/v1/messages) or Responses (/v1/responses). Validate your chosen protocol with your key, including streaming, tools and usage, before production; image fields differ by protocol.
How are images billed?
Check the Pricing section for input, cached input and output rates, then measure representative image requests and reconcile the reported usage with your final charge before projecting batch cost.
How does caching work?
DeepSeek documents automatic prefix caching. Put reusable context first and inspect reported cache hits. Reusing a prompt is not a guarantee of a cache hit, and changing a suffix does not necessarily discard all cached prefix tokens.
Is thinking mode on by default?
DeepSeek documents thinking as enabled by default on its direct API, with a non-thinking mode available. Confirm the EvoLink default and supported controls on the protocol you use; optional support does not mean thinking is off by default. Measure its effect on output usage.
Can I migrate from V4 Flash by changing the model name?
The model name is the main code change, but not the whole migration. On EvoLink, keep deepseek-v4-flash running, replay the same requests through deepseek-v4.1-flash, compare outputs, tool results and usage, then move traffic gradually.
How much context should I actually send?
Send the evidence the task needs and set an appropriate output limit. Measure accepted results, cache reuse and total charge across representative context sizes rather than filling the 1M window on every request.
What should I keep as an alternative route?
Keep a route that still serves a model you have verified for the same workload, and separate text from image tasks. For text, deepseek-v4-flash and deepseek-v4-pro on EvoLink are not affected by DeepSeek’s direct-API changes and remain different models; different models do not guarantee independent failures, so check whether they share a provider or quota. For images, both are text-only, so use another model that passed the same visual evaluation, or a human-review path. deepseek-v4-flash-vision-exp now redirects to V4.1 Flash, so it is not a fallback.
