Chrome ExtensionDeepSeek V4.1 Prompt Generator
Updated for DeepSeek V4.1-Flash (October 17, 2026 update), the 552B-parameter model that reads images and text and replaces V4-Flash, plus V4-Pro. Built around how these models actually behave: graded thinking levels instead of a separate reasoner, a 1M token context, sampling parameters that get ignored once thinking is on, and cache-aware ordering that decides most of your input bill. Works with the API (deepseek-flash, deepseek-v4-pro) and self-hosted MIT-licensed weights.
Be specific about what you want the AI to do
Generated Prompt
Fill in the form and click "Generate" to create an optimized DeepSeek V4.1 prompt.
Tip: The more specific your task description and context, the better the generated prompt will perform.
DeepSeek V4.1 Tips
- • Thinking is ON by default at high effort, so you no longer need to beg it to reason. Set the level instead
- • In thinking mode temperature and the penalties are silently ignored and top_p is clamped to 0.95-1.0. Put your steering in the prompt, not the sampler
- • Keep static content byte-identical and at the very top, volatile content last. A cache hit costs a fraction of a miss
- • Ask for verification explicitly on math and code. The V4 family hallucinates more than its benchmark scores suggest
- • V4.1-Flash reads images natively, so you can attach a screenshot or chart and tell it exactly what to extract. V4-Pro is still text-only
- • For JSON output you must use the literal word "json" in the prompt and show a concrete structure example
- • Start on V4.1-Flash (model ID deepseek-flash) and only escalate to V4-Pro if quality actually falls short, since Pro costs 3-4x as much
DeepSeek V4.1 Prompt Templates
Copy-ready starting points for the most common jobs people bring to this model. Swap the [BRACKETED] parts for your own details, or use them as a shape to imitate when you write your own.
Algorithm Implementation
EngineeringOne task, explicit instruction first — DeepSeek's documented preference.
Implement [ALGORITHM / FUNCTION] in [LANGUAGE]. Requirements: - Signature: [SIGNATURE] - Complexity target: [TIME / SPACE] - Edge cases that must be handled: [LIST] - No external dependencies beyond [ALLOWED] Return the implementation, then a short complexity analysis, then the edge cases you tested against.
Mathematical Proof / Derivation
ReasoningStep-by-step work with an explicit verification pass.
Solve the following, showing every step: [PROBLEM] Requirements: - State the assumptions before you begin. - Show each transformation explicitly — no skipped algebra. - After reaching the result, verify it independently (substitute back, check limits, or sanity-check the magnitude). - If the problem is underspecified, say what is missing rather than assuming.
Bilingual Translation
LanguagePlays to DeepSeek's Chinese/English strength with an explicit register.
Translate the text below from [SOURCE LANGUAGE] to [TARGET LANGUAGE]. Register: [FORMAL / CONVERSATIONAL / TECHNICAL]. Audience: [AUDIENCE]. Requirements: - Preserve technical terms exactly; do not localise product names. - Where a phrase has no clean equivalent, translate for meaning and add a bracketed note. - Keep the original paragraph structure. Text: [PASTE TEXT]
Code Explanation
EngineeringInstruction-after-context, which DeepSeek handles best for long inputs.
[PASTE CODE] --- Explain the code above to [AUDIENCE, e.g. a developer new to this codebase]. Cover: what it does, how data flows through it, the non-obvious decisions and why they were probably made, and anything that looks like a bug. Format: prose walkthrough, then a bullet list of concerns. No line-by-line narration.
Test Suite Generation
EngineeringNarrow scope with explicit coverage targets.
Write tests for the function below using [TEST FRAMEWORK]. Cover: - The happy path with typical inputs - Boundary values: [SPECIFY] - Error cases: [SPECIFY] - [ANY DOMAIN-SPECIFIC CASES] Requirements: no network or filesystem access, deterministic, each test named for the behaviour it asserts. Function: [PASTE FUNCTION]
Technical Comparison
AnalysisDecision criteria named up front so the answer is usable.
Compare [OPTION A] and [OPTION B] for [USE CASE]. My decision criteria, in priority order: [1], [2], [3]. My constraints: [TEAM SIZE, STACK, TIMELINE, BUDGET]. Requirements: - Score each option against each criterion with a reason, not just a verdict. - Name the scenario where the option you did NOT pick would be the right call. - End with one recommendation for my specific situation. Format: comparison table, then recommendation.
Changelog
Version history from DeepSeek's API changelog, release notes, model cards and the V4 technical report.
V4.1-Flash-1017
LatestOctober 17, 2026- +A minor update to V4.1-Flash, served under the same API name (deepseek-flash) with no change to how you call it. Open weights stay MIT licensed
- +DeepSeek says it is stronger, faster and gives shorter outputs, with gains in agentic coding (Terminal-Bench 4.0 up from 31.2 to 42.3) and hard reasoning
- +For local deployment the model card recommends temperature 1.0 and top_p 1.0, with a large max_tokens budget so reasoning is not cut off
- +The old V4-Flash and V4-Flash-Vision-Exp IDs are still accepted but those models are retired, so requests go to V4.1-Flash
- +DeepSeek's Search & Fetch APIs are now generally available at $0.75 per 1,000 calls
- +Shorter task runtimes and outputs cut cost. DeepSeek tuned the build for its own agent harness, DeepSeek Harness (developer preview; use 0.2.1-rc.1 or later), and for Tencent CodeBuddy and WorkBuddy
V4.1-Flash
September 10, 2026- +First model in a new architecture family and, per DeepSeek, the smallest one in it. New model ID: deepseek-flash
- +552B total parameters in a Mixture-of-Experts design, with about 8B active during input processing (prefill) and 16B during output (decode)
- +New causal encoder-decoder architecture replacing the V4 design
- +Native image and text input, so vision no longer needs a separate experimental model
- +1M token context and up to 384K output on the API
- +Open weights on Hugging Face under the MIT licence. The model card describes a continuous reasoning-effort setting from 1 to 100; the hosted API still exposes low, high and max
- +DeepSeek reports it ahead of V4-Pro on performance, cost, speed and total runtime in its own tests
- +V4-Flash and V4-Flash-Vision-Exp are retired. The old IDs deepseek-v4-flash and deepseek-v4-flash-vision-exp temporarily route to V4.1-Flash at Flash prices
- +DeepSeek first said deepseek-v4-pro would route to V4.1-Flash from September 14, then reversed that: V4-Pro stays on the API after September 14 with billing unchanged
- +V4.1-Pro is confirmed but has no release date
V4-Flash-Vision-Exp
August 21, 2026- +Experimental multimodal build of V4-Flash with visual understanding aimed at agent tasks
- +Retired on September 10, 2026 when V4.1-Flash shipped with native vision
V4-Pro GA
August 13, 2026- +The general-availability release of V4-Pro rolled out on the app, web and API, ending the preview label
- +V4-Pro and V4-Flash support three thinking effort levels: low, high and max
- +Peak and off-peak pricing from August 16, 2026, with off-peak rates at 50% of peak
- +Native support for the OpenAI Responses API format
V4-Flash-0731
July 31, 2026- +Re-post-trained build of V4-Flash with the architecture left unchanged
- +Agentic gains: Terminal Bench 2.1 at 82.7, NL2Repo at 54.2, Cybergym at 76.7, all self-reported
- +Native Responses API format with Codex adaptation
- +Open sourced on Hugging Face as DeepSeek-V4-Flash-0731
- +V4-Pro and the app models were untouched by this build
Legacy model retirement
July 24, 2026- +The deepseek-chat and deepseek-reasoner model IDs were retired at 15:59 UTC
- +During the transition they aliased to V4-Flash non-thinking and thinking respectively
- +Any integration still calling those IDs is broken and needs updating to deepseek-v4-flash or deepseek-v4-pro
V4 preview
April 24, 2026- +Two models replace the entire V3.x and R1 line: V4-Pro at 1.6T total and 49B active, V4-Flash at 284B total and 13B active
- +Hybrid attention pairing Compressed Sparse Attention with Heavily Compressed Attention, replacing the DSA-only approach in V3.2
- +Manifold-Constrained Hyper-Connections replace standard residual connections throughout the model
- +Muon optimizer for faster and more stable convergence
- +Mixed FP4 and FP8 precision, with MoE expert weights in FP4 and quantization-aware training
- +Trained on more than 32T tokens
- +Context grows to 1M tokens with up to 384K output
- +Graded thinking replaces the separate reasoner model, with three levels selected by reasoning_effort
- +At 1M context V4-Pro needs 27% of the inference FLOPs and 10% of the KV cache of V3.2, and V4-Flash needs 10% and 7%
DeepSeek V4.1-Flash and V4-Pro specs
| Models | deepseek-flash (V4.1-Flash) at 552B total, about 8B active for prefill and 16B for decode. deepseek-v4-pro (V4-Pro) at 1.6T total and 49B active |
|---|---|
| Input | V4.1-Flash takes text and images natively. V4-Pro is text-only |
| Context | 1M tokens on both models |
| Max output | 384K tokens |
| Thinking | On by default at high effort. Disable it with the thinking parameter, or set reasoning_effort to low, high or max. The open V4.1-Flash weights also accept a continuous effort from 1 to 100 |
| Chain of thought | Returned in reasoning_content, a sibling field to content. With tools in play, previous reasoning_content must be sent back or the API returns a 400 error |
| Sampling in thinking mode | temperature, presence_penalty and frequency_penalty silently no-op. top_p only works between 0.95 and 1.0 |
| Concurrency | 2,500 for Flash, 500 for Pro |
| API formats | OpenAI ChatCompletions, Anthropic Messages, and the Responses API |
| Tool calling | Supported, including a beta strict mode that validates against JSON Schema. Encoded via DSML markup rather than JSON blocks |
| Also supported | JSON mode, FIM completion (non-thinking mode only), and prefix completion for continuing truncated output |
| Pricing, V4.1-Flash | Per 1M tokens at peak: $0.30 input on a cache miss, $0.006 on a cache hit, $1.20 output. Off-peak is half |
| Pricing, V4-Pro | Per 1M tokens at peak: $1.32 input on a cache miss, $0.044 on a cache hit, $3.96 output. Off-peak is half |
| Licence | MIT for code and weights on V4-Pro and V4.1-Flash. Commercial use, fine-tuning and distillation all permitted |
How to Use the Prompt Generator
Define Your Task
Select a category, pick the model and a thinking level (Non-Think for fast direct answers, higher levels for harder problems), and describe your task. For coding, pick the programming language and output language.
Add Technical Context
Provide detailed technical context — framework versions, coding standards, expected behavior. DeepSeek thrives on comprehensive specifications.
Generate & Use
Click "Generate" to get an optimized prompt. Copy and paste into DeepSeek Chat or any DeepSeek API deployment.
Frequently Asked Questions
What is DeepSeek V4.1?
DeepSeek V4.1 is the newest DeepSeek model generation. So far only one model has shipped: V4.1-Flash, released September 10, 2026 under the API name deepseek-flash. It is a 552B-parameter Mixture-of-Experts model that uses about 8B active parameters to read input and 16B to write output, takes images as well as text, and has a 1M token context. It sits alongside V4-Pro, the 1.6T-parameter model from the V4 generation that reached general availability on August 13, 2026. V4.1-Pro is confirmed but has no date yet.
What is new in DeepSeek V4.1-Flash?
Four things matter for prompting. It is a new causal encoder-decoder architecture rather than a re-trained V4. It reads images natively, so screenshots, charts and documents can go straight into the prompt. It replaces both V4-Flash and the short-lived V4-Flash-Vision-Exp, whose model IDs now route to it at Flash prices. And DeepSeek reports it ahead of V4-Pro on performance, cost, speed and total runtime in its own tests, which makes Flash the sensible default for most work. The open weights add a continuous reasoning-effort setting from 1 to 100; on the hosted API you still choose low, high or max.
Do I need to change my model ID?
Probably. The current IDs are deepseek-flash for V4.1-Flash and deepseek-v4-pro for V4-Pro. The old deepseek-v4-flash and deepseek-v4-flash-vision-exp names still work for now, but DeepSeek has retired those models and serves the requests with V4.1-Flash, so switch to deepseek-flash. The older deepseek-chat and deepseek-reasoner IDs were retired on July 24, 2026 at 15:59 UTC and no longer work at all.
How does thinking mode work on V4.1-Flash and V4-Pro?
Thinking is built in and graded rather than living in a separate model. It is on by default at high effort. You can switch it off for speed, or set reasoning_effort to low, high or max; medium is accepted and treated as high. In Anthropic format you use reasoning with an effort value, where none disables it. If you self-host the open V4.1-Flash weights, the model card describes a continuous effort setting from 1 to 100 instead of three steps. The chain of thought comes back in reasoning_content, a sibling field to content. Think Max works by prepending a deliberation prefix ahead of the system message, so give it a large context window and a generous max_tokens or the reasoning will be cut off.
Why are my temperature and top_p settings being ignored?
Because in thinking mode DeepSeek ignores temperature, presence_penalty and frequency_penalty, and only honours top_p between 0.95 and 1.0 (lower values are raised to 0.95). They do not raise an error, they simply no-op, which makes this an easy thing to miss when porting a sampling config across from another model. Since thinking is on by default, this applies to most calls. Put your steering in the prompt instead. For local deployment the V4.1-Flash model card suggests temperature 1.0 and top_p 0.95 or 1.0. The older per-use-case temperature table in DeepSeek's docs is V3-era guidance that conflicts with the current model cards.
What is the single highest-leverage thing I can do to cut cost?
Order your prompt for cache reuse. Input on a cache hit costs roughly 1/50th of a miss on V4.1-Flash and 1/30th on V4-Pro, so how you arrange the prompt matters more than how you word it when you are making repeated calls. Caching is disk-based and best-effort, and it needs a full match on a cache prefix unit, so keep your system prompt and any static content byte-identical and at the very top, and put volatile content like timestamps and the user question last. The response tells you how you did via prompt_cache_hit_tokens and prompt_cache_miss_tokens. Cache lifetime runs hours to days and is not guaranteed.
Should I use V4.1-Flash or V4-Pro?
Start with V4.1-Flash. V4-Pro costs three to four times as much per token, and DeepSeek reports V4.1-Flash ahead of V4-Pro on performance, cost, speed and total runtime in its own testing. Flash is also the only one of the two that accepts images. Escalate to Pro only when Flash genuinely falls short on your task. A V4.1-Pro is confirmed but undated, so expect this choice to change again.
Do the old DeepSeek prompting rules still apply?
Mostly no, and this is where a lot of published advice is now wrong. The well-known R1-era guidance to avoid a system prompt and to skip few-shot examples is not restated anywhere in the current V4 documentation. The V4 encoding format places the system prompt directly after BOS, Think Max deliberately layers its prefix around the system message, and DeepSeek's own API examples use an ordinary system prompt. Guides still repeating those rules for V4 appear to be carrying over folklore. DeepSeek publishes no dedicated V4 prompt engineering guide, which is a genuine gap rather than something hiding in the docs.
How good is V4, honestly?
Strong but not a clean sweep, and DeepSeek's own paper is refreshingly modest about it. Self-reported figures for V4-Pro include 87.5 on MMLU-Pro, 90.1 on GPQA Diamond, 93.5 on LiveCodeBench, a 3206 Codeforces rating and 80.6 on SWE-Bench Verified. The paper describes its knowledge lead as marginal and still behind Gemini 3.1 Pro, and its reasoning as marginally short of the top frontier models. Artificial Analysis places V4-Pro at 52 on its Intelligence Index, second among open-weight models. The clear weak spot is hallucination: AA scored V4-Pro at minus 10 on its Omniscience index with a 94% hallucination rate, so ask for verification and citations on anything factual.
How does this prompt generator work?
You give it the task, role, context, output format, model tier and thinking level, and it writes a prompt that respects how V4.1-Flash and V4-Pro actually behave. That means putting steering in the prompt rather than in sampling parameters, ordering static content first when you want cache reuse, adding explicit verification steps for maths and code, and including the literal word json plus a structure example when you ask for JSON output.
Is DeepSeek V4.1 open source?
Yes, and unusually permissively. V4.1-Flash and V4-Pro both ship under the MIT licence, covering code and weights, with commercial use, fine-tuning and distillation all allowed at no royalty. Weights are on Hugging Face under the deepseek-ai org in mixed FP4 and FP8 precision, with V4.1-Flash also supported by vLLM. One practical trap: there is no Jinja chat template, since DeepSeek ships a folder of Python encoding scripts instead, so a naive apply_chat_template integration will not work.
What does DeepSeek V4.1 cost?
At peak rates per million tokens, V4.1-Flash is $0.30 input on a cache miss, $0.006 on a cache hit, and $1.20 output. V4-Pro is $1.32, $0.044 and $3.96. Since August 16, 2026 there is an off-peak window at 50% of those rates. DeepSeek changed prices again when V4.1-Flash launched on September 10, so any cost model built on earlier V4 prices is stale. Check the official pricing page before budgeting.
Is this tool free to use?
Yes. You get 1 free prompt generation per day with no signup required. For unlimited access, sign up for a Promptslove membership which includes all AI tools and 29,000+ premium prompts.
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