Chrome ExtensionDeepSeek V4 Prompt Generator
Generate prompts for DeepSeek V4, the model family that replaced the entire V3 and R1 line in April 2026. Built around how V4 actually behaves: three 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 V4-Pro, V4-Flash, 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 prompt.
Tip: The more specific your task description and context, the better the generated prompt will perform.
DeepSeek V4 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, top_p and the penalties are silently ignored. 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. V4 hallucinates more than its benchmark scores suggest
- • For JSON output you must use the literal word "json" in the prompt and show a concrete structure example
- • Start on V4-Flash and only escalate to V4-Pro if quality actually falls short, since Pro costs about 3x
Changelog
Version history from DeepSeek's API changelog, release notes and the V4 technical report.
V4-Flash-0731
LatestJuly 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 specs and configs
| Models | deepseek-v4-pro at 1.6T total and 49B active. deepseek-v4-flash at 284B total and 13B active |
|---|---|
| Context | 1M tokens, the default across all official DeepSeek services |
| Max output | 384K tokens |
| Thinking | Three levels: Non-Think, Think High (default) and Think Max, set via reasoning_effort |
| Chain of thought | Returned in reasoning_content, a sibling field to content |
| Ignored in thinking mode | temperature, top_p, presence_penalty and frequency_penalty all silently no-op |
| 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 on Flash, and prefix completion for continuing truncated output |
| Pricing, Flash | $0.14 per 1M input on a cache miss, $0.0028 on a cache hit, $0.28 output |
| Pricing, Pro | $0.435 per 1M input on a cache miss, $0.003625 on a cache hit, $0.87 output |
| Licence | MIT for code and weights, both models. Commercial use, fine-tuning and distillation all permitted |
How to Use the Prompt Generator
Define Your Task
Select a category, toggle Reasoning Mode (Off for fast direct answers, On to make DeepSeek show its steps and verify), 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?
DeepSeek V4 is the current flagship from DeepSeek AI, released as a preview on April 24, 2026. It replaces the whole V3.x and R1 line with two models: V4-Pro at 1.6T total parameters with 49B active, and V4-Flash at 284B total with 13B active. Both carry a 1M token context and up to 384K output. The separate reasoner model is gone, replaced by graded thinking built into both models. V4-Pro is still labelled preview; V4-Flash reached its official build on July 31, 2026.
Do I need to change my model ID?
Almost certainly yes. The deepseek-chat and deepseek-reasoner IDs were retired on July 24, 2026 at 15:59 UTC. During the transition they aliased to V4-Flash in non-thinking and thinking mode, but that alias is gone. Any integration still pointing at those names is broken. Use deepseek-v4-flash or deepseek-v4-pro instead.
How does thinking mode work in V4?
Thinking is built in and graded rather than living in a separate model. There are three levels: Non-Think for speed, Think High which is the default, and Think Max for the deepest reasoning. In OpenAI format you set reasoning_effort to low, high or max. In Anthropic format you use reasoning with an effort value, where none disables it. 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 V4 ignores temperature, top_p, presence_penalty and frequency_penalty entirely. 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 model card suggests temperature and top_p both at 1.0. The older per-use-case temperature table in DeepSeek's docs is V3-era guidance that conflicts with the V4 model card.
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 both models, 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-Flash or V4-Pro?
Start with Flash. Pro costs about three times as much per token and the gap on published benchmarks is narrower than the price difference suggests, with Flash-0731 scoring 50 on the Artificial Analysis Intelligence Index against 52 for Pro. Escalate to Pro only when Flash genuinely falls short on your task. One catch to know about: on V4-Pro both high and max currently map to actual effort high, so asking for max does not get you max. DeepSeek documented that as changing in early August 2026, so it is worth re-checking.
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 actually behaves. 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 open source?
Yes, and unusually permissively. Both V4-Pro and V4-Flash 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, supported by Transformers, vLLM and SGLang. 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 cost?
Per million tokens, V4-Flash is $0.14 input on a cache miss, $0.0028 on a cache hit, and $0.28 output. V4-Pro is $0.435, $0.003625 and $0.87. There are no off-peak discounts on V4, since the old 50 and 75 percent discount window died with the V3 and R1 retirement. Two warnings: V4-Pro prices were cut by roughly 75% on May 31, 2026, so any cost model quoting $1.74 or $3.48 is stale, and DeepSeek has said publicly that it plans a significant overall price rise, so do not treat current pricing as fixed.
Is this tool free to use?
Yes. You get 3 free prompt generations per day with no signup required. For unlimited access, sign up for a Promptslove membership which includes all AI tools and 20,000+ premium prompts.
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