DeepSeek-V4.1-Flash is the smallest model in DeepSeek's new architecture family, released September 10, 2026, with native multimodal visual understanding — the production successor to both V4 Flash and the V4 Flash Vision experiment. The new architecture targets a higher capability ceiling, faster inference and higher throughput, and DeepSeek reports V4.1 Flash comprehensively surpasses V4 Pro on performance, cost, speed and total task time. It accepts text and images with text output, serves a 1M-token context window with up to 384K output tokens, and supports thinking (default, with selectable effort low / high / max) and non-thinking modes across the Chat Completions, Responses and Anthropic-compatible APIs, along with JSON output, tool calls and chat prefix completion; FIM works in non-thinking mode only. Model weights are open on Hugging Face (deepseek-ai/DeepSeek-V4.1-Flash). Official benchmarks at release: 90.6 on Terminal-Bench 2.1, 74.2 on DeepSWE v1.1, 65.4 on NL2Repo-Bench, 90.9 on GPQA Diamond, 63.9 on HLE with tools, and strong native-vision agent results (89.6 BabyVision, 78.9 Chartography with tools). Pricing was cut alongside the release: $0.15/M input (cache miss), $0.60/M output and $0.003/M on cache hits at off-peak rates, doubling during weekday peak hours (01:00-04:00 and 06:00-10:00 UTC); all other hours including weekends are off-peak.
DeepSeek V4.1 Flash is a DeepSeek model available through OrcaRouter, the OpenAI-compatible API gateway. It accepts text and image input, carries a context window of 1,048,576 tokens, and can…
DeepSeek V4.1 Flash accepts text and images as input and returns text. In practice that covers three broad patterns. The first is document understanding: dropping in screenshots of tables, scanned pages, or diagrams alongside written instructions. The second is visual grounding, where a screenshot of an interface, chart, or error state is analysed in the context of a longer conversation or specification. The third is mixed-modality reasoning, where a long textual corpus is paired with a few images that anchor the question. Output is text only, so the model describes, extracts, or explains what it sees rather than generating images. Image tokens count as input and are billed at $0.15 per 1M input tokens, the same rate as text input on OrcaRouter. For workloads where text alone is sufficient, a text-only model may be cheaper, but V4.1 Flash avoids running a separate vision pipeline for light visual tasks.
Strong fits are long-context analysis with a long answer, code comprehension across large repositories, multimodal document review, and agentic loops that need to carry a great deal of state. Because max output reaches 384,000 tokens, the model can return full reports, migration notes, or extended code rather than short summaries. Other sensible uses include transcript-to-structured-notes pipelines, contract comparison, and support workflows where the full history is retained in context. The 90.9 GPQA Diamond score suggests strength on graduate-level science questions, which points toward technical and research-oriented question answering rather than prose alone. It is less obviously suited to high-frequency, tiny-request traffic, since a short classification call does not need a million-token window, and to tasks requiring image generation, because output is text only.
Many models cap output at a few thousand tokens, which forces multi-turn stitching for anything long. A 384,000-token ceiling lets DeepSeek V4.1 Flash emit an entire artefact in one pass: a full technical specification, a long migration plan, an extended annotated diff, or a complete transcript rewrite. Single-pass generation usually preserves internal consistency better than assembling an answer from many short calls, because the model never loses the thread between turns. The trade-off is cost. Output is billed at $0.60 per 1M output tokens on OrcaRouter, four times the input rate, so a very long generation is the expensive part of a request. Use the ceiling where a coherent long answer has real value, set max_tokens to match the job, and avoid letting the model ramble when a two-paragraph answer would do.
Big context and a high output ceiling are capabilities you pay for. If a task only needs a short prompt and a short answer, such as intent classification, entity extraction, routing, or simple rewriting, the extra window adds nothing, and a smaller model elsewhere on OrcaRouter will usually cost less per call. The same applies to high-volume batch jobs where inputs are already chunked by design. Choose DeepSeek V4.1 Flash when the job genuinely needs the window: whole documents, long code context, multi-image review, or a long single-pass answer. A useful rule is to estimate prompt size and expected output first. If both are modest, compare the total token cost against a smaller option. If the prompt runs into hundreds of thousands of tokens, the alternative is usually a retrieval pipeline, which brings its own engineering cost and information loss.
GPQA Diamond is a set of graduate-level science questions written to resist simple lookup, so scores reflect reasoning over difficult technical material rather than recall of common facts. DeepSeek V4.1 Flash scores 90.9 on this benchmark. A high result here indicates the model handles multi-step scientific reasoning and precise domain questions competently. It does not mean the model is equally strong on every task. GPQA Diamond is narrow: it says little about long-context retrieval, tone, instruction-following under messy prompts, or code quality at scale. Treat 90.9 as one data point confirming technical reasoning ability, and validate it on your own workload. Build a small evaluation set drawn from your real inputs, including long documents and image-plus-text prompts, and compare outputs on that set before committing a production route on OrcaRouter.
Latency on DeepSeek V4.1 Flash depends mostly on how much you ask it to generate. Time to first token is influenced by prompt size and provider load, while total response time grows with output length. With a 384,000-token ceiling, a maximum-length generation is inherently a long-running request. No published latency figures are available for this model on OrcaRouter, so measure with your own prompts rather than assuming a number. Practical steps: cap max_tokens for interactive paths so responses stay bounded, stream tokens so users see progress, and reserve very long generations for background jobs. Under high concurrency, expect provider throughput limits to shape your effective rate, and queue or retry accordingly. Compare against your current model using the same prompts, and track time to first token separately from total duration.
Benchmarks are useful for narrowing a field, not for ranking models in production. Each test measures a specific, constrained skill under a fixed prompt format. GPQA Diamond rewards graduate-level scientific reasoning, while a coding benchmark rewards different behaviour entirely. A strong score in one area does not transfer automatically, and small gaps between models are often within run-to-run variance. Two practices help. First, check that the benchmark's task resembles yours. A 90.9 on GPQA Diamond is meaningful for research and technical question answering, less so for chat tone or extraction. Second, test with your own data: assemble a representative sample, define a scoring rule, and measure. On OrcaRouter you can route the same request to different model ids through one OpenAI-compatible API and compare outputs directly, which is more informative than a leaderboard position alone.
Three limits are worth planning for. First, output is text only: the model takes image input, but it will not produce images. Second, long outputs cost more, and at $0.60 per 1M output tokens, four times the input rate, verbose generations are the main cost risk. Third, a large context window does not guarantee that every detail is used. Attention over a million tokens is a capability you should verify on your own documents rather than assume. There are also operational constraints. Very large prompts take longer to process and consume rate budget faster. The model is served by DeepSeek through OrcaRouter, so availability follows the provider's infrastructure and any limits they apply. Multimodal accuracy on dense charts, handwriting, or low-resolution scans varies; validate on representative samples before routing critical extraction work to it.
DeepSeek V4.1 Flash is billed at $0.15 per 1M input tokens and $0.60 per 1M output tokens. OrcaRouter passes these through at the provider rate with zero markup, so the figure you see is the provider's price rather than a resale price. Input includes everything you send: text tokens, image tokens, and the accumulated history of a conversation. Output covers everything the model generates, including tool-call arguments and any reasoning text it emits. Billing is token-based, so a cheaper request simply uses fewer tokens. No separate charge is described for the context window itself: a 1,048,576-token window is a limit, not a fee. A large prompt naturally costs more because it contains more input tokens. Track usage per route so you can attribute spend to the features that actually generate it.
Two multipliers decide your spend: how many tokens go in and how many come out. Output is the more expensive side at $0.60 per 1M tokens versus $0.15 per 1M input tokens, a four-times difference. A request that reads 200,000 tokens and writes 500 tokens is dominated by input. A request that reads 2,000 tokens and writes 20,000 is dominated by output. Knowing which pattern your product has tells you where to optimise. Three levers follow. Trim context: include only the documents and history a task needs, even though 1,048,576 tokens are available. Cap output: set max_tokens to the real requirement instead of the ceiling. And batch: fewer, larger calls avoid re-sending the same context repeatedly, which is often the quietest source of waste in long-context applications.
DeepSeek V4.1 Flash is billed by OrcaRouter at the provider rate with zero markup, so any provider-side discount or cached-input rate is passed through rather than absorbed or marked up. Whether discounted cached input is available for this model, and under what conditions, is set by DeepSeek, so confirm it in the provider's current documentation before you model savings into a budget. What you control directly is prompt design. Keep a stable prefix, such as system instructions, schema, and retrieved context, and append variable content at the end so any prefix reuse the provider supports can apply. Reuse identical context across calls in a batch instead of resending it per item. Shorten history aggressively, summarising earlier turns once they stop being needed. These habits reduce input tokens, which is where long-context workloads usually spend.
Point an OpenAI-compatible client at https://api.orcarouter.ai/v1 and set the model to deepseek/deepseek-v4.1-flash. Because OrcaRouter exposes the OpenAI chat completions interface, existing SDKs for Python, JavaScript, and other languages work with a base URL and model id change plus your OrcaRouter API key. A minimal request sends a messages array with your system and user turns, plus optional parameters such as max_tokens, temperature, and stream. Image input follows the standard multimodal content format, with image parts alongside text parts in the same user message. Responses come back in the usual shape, so parsing, streaming, and tool-call handling code carries over unchanged. Check the OrcaRouter model page for any per-model parameter notes, and log raw responses for the first few calls while you tune prompt size and output limits.
Four parameters shape most DeepSeek V4.1 Flash requests. max_tokens sets the output ceiling and is the main cost control given a maximum of 384,000 tokens; set it to what the task needs, not to the maximum. temperature governs variability, so keep it low for extraction, structured output, and code, and higher for drafting. stream lets you show progress on long generations, which matters when a response may run to tens of thousands of tokens. System instructions should carry format and safety requirements. For images, the standard multimodal content array is the mechanism to use. For long prompts, consider whether every included document is necessary, since input tokens are billed at $0.15 per 1M. If the model supports tool calling through the OpenAI-compatible schema, define narrow, well-documented tools rather than broad ones. Set a client-side timeout that matches your longest expected generation.
Migration is deliberately small. Change the base URL to https://api.orcarouter.ai/v1, replace the model string with deepseek/deepseek-v4.1-flash, and supply an OrcaRouter API key. Request and response schemas stay OpenAI-compatible, so message arrays, streaming events, and tool-call payloads need no structural rewrite. The work is in behaviour, not plumbing. A model with a 1,048,576-token window and a 384,000-token output ceiling invites prompts your previous model could not accept. Take the opportunity to raise context quality rather than blindly inflating prompt size, since input tokens cost $0.15 per 1M. Re-tune max_tokens, temperature, and retry logic, then re-run your evaluation set. Review tokeniser and prompt-splitting assumptions too: chunking logic built for a small window may now be unnecessary, and leaving it in place can fragment context.
Images are supplied as content parts in the user message, matching the OpenAI multimodal format: the message content becomes an array containing text parts and image parts, with each image given either a URL or base64 data. A typical call mixes a long text body, such as a specification, transcript, or prior conversation, with one or more screenshots or scanned pages, and asks a question that depends on both. Resize before upload. Very large images add input tokens, and input is billed at $0.15 per 1M tokens, so sending full-resolution captures of simple diagrams wastes budget without improving accuracy. Crop to the region that matters and use legible resolution for text-heavy material. If a task needs many images, group them logically in one request rather than issuing a separate call per image, which re-sends your text context each time.
Frontier-class models often lead on the hardest reasoning and coding benchmarks, but they typically charge substantially more per token and may cap output far below what DeepSeek V4.1 Flash allows. This model's 90.9 on GPQA Diamond puts it in credible territory for graduate-level science reasoning, while pricing of $0.15 per 1M input tokens and $0.60 per 1M output tokens keeps long-context work affordable. The choice usually comes down to three questions. How hard is the reasoning task, and does the accuracy gap matter for it? How long is the input, and how much does a 1,048,576-token window save in pipeline complexity? How long is the output, given the 384,000-token ceiling? For document-heavy analysis, long single-pass generation, and multimodal review at volume, V4.1 Flash is often the practical pick. For the most difficult reasoning steps, consider routing those calls to a costlier model on OrcaRouter.
OrcaRouter exposes many models behind one OpenAI-compatible API, so comparison is a matter of switching the model id rather than integrating a second vendor. Within the DeepSeek family, the meaningful axes are price, context window, and output ceiling. V4.1 Flash pairs a 1,048,576-token window with a 384,000-token output limit at $0.15 per 1M input and $0.60 per 1M output tokens. Smaller or cheaper models make sense when prompts and answers are short and the extra window adds no value. Vision-capable alternatives matter when you need image output rather than image input. Specialised code or reasoning models may win on narrow tasks. Because OrcaRouter prices at the provider rate with zero markup, the comparison is apples-to-apples on tokens: run identical prompts through both ids, measure cost and quality, and route per task.
Pick something else when the task shape does not match the model's strengths. Short, high-frequency classification, routing, or extraction jobs gain nothing from a 1,048,576-token window and are cheaper on a smaller model. Tasks requiring generated images need a different class of model entirely. If a single hard reasoning step dominates quality, such as theorem-style maths or subtle algorithm design, a frontier model used only for that step may be worth the higher rate. It is also reasonable to combine models. Use DeepSeek V4.1 Flash to read long inputs, gather evidence, and draft extended output at $0.15 per 1M input and $0.60 per 1M output tokens, then escalate specific decisions to a costlier model for verification. OrcaRouter's OpenAI-compatible API makes that routing a configuration change rather than a new integration.
OpenAI-compatible — keep the SDK you already use
https://api.orcarouter.ai/v1https://api.orcarouter.aiimport os
from openai import OpenAI
client = OpenAI(
base_url="https://api.orcarouter.ai/v1",
api_key=os.environ["ORCAROUTER_API_KEY"],
)
response = client.chat.completions.create(
model="deepseek/deepseek-v4.1-flash",
messages=[{"role": "user", "content": "Hello"}],
)
print(response.choices[0].message.content)include_reasoninglogprobsmax_tokensreasoningreasoning_effortresponse_formatstopstreamstream_optionstemperaturethinkingtool_choicetoolstop_logprobstop_puser_id| Input / 1M tokens · Off-peak | $0.150 |
|---|---|
| Output / 1M tokens · Off-peak | $0.600 |
| Cache read / 1M · Off-peak | $0.0030 |
| Peak hours | 01:00–04:00, 06:00–10:00 ×2 (UTC) |
| Input / 1M tokens · ×2 | $0.300 |
| Output / 1M tokens · ×2 | $1.20 |
| Cache read / 1M · ×2 | $0.0060 |
| Currency | USD |
Estimate based on list price
Estimate only — actual token counts depend on the provider's tokenizer.
What developers are saying this week
GET /api/public/models/deepseek/deepseek-v4.1-flashOpen @misc{orcarouter_deepseek_v4_1_flash,
title = {DeepSeek V4.1 Flash API},
author = {DeepSeek},
year = {2026},
howpublished = {OrcaRouter},
url = {https://www.orcarouter.ai/models/deepseek/deepseek-v4.1-flash}
}DeepSeek. (2026). DeepSeek V4.1 Flash API. OrcaRouter. https://www.orcarouter.ai/models/deepseek/deepseek-v4.1-flash