Technical specifications of DeepSeek-V4-Flash
| Item | Details |
|---|---|
| Model | DeepSeek-V4-Flash |
| Provider | DeepSeek |
| Family | DeepSeek-V4 preview series |
| Architecture | Mixture-of-Experts (MoE) |
| Total parameters | 284B |
| Activated parameters | 13B |
| Context length | 1,000,000 tokens |
| Precision | FP4 + FP8 mixed |
| Reasoning modes | Non-think, Think, Think Max |
| Release status | Preview model |
| License | MIT License |
What is DeepSeek-V4-Flash?
DeepSeek-V4-Flash is DeepSeek’s efficiency-focused preview model in the V4 series. It is built as a Mixture-of-Experts language model with a relatively small active footprint for its size, which helps it stay responsive while still supporting a very large 1M-token context window.
Main features of DeepSeek-V4-Flash
- Million-token context: The model supports a 1,000,000-token context window, which makes it suitable for very long documents, large codebases, and multi-step agent sessions.
- Efficiency-first MoE design: It uses 284B total parameters but only 13B activated parameters per request, a setup aimed at faster and more efficient inference.
- Three reasoning modes: Non-think, Think, and Think Max let you trade speed for deeper reasoning when the task gets harder.
- Strong long-context architecture: DeepSeek says the V4 series combines Compressed Sparse Attention and Heavily Compressed Attention to improve long-context efficiency.
- Competitive coding and agent behavior: The model card reports strong results on coding and agentic benchmarks, including HumanEval, SWE Verified, Terminal Bench 2.0, and BrowseComp.
- Open weights and local deployment: The release includes model weights, local inference guidance, and an MIT License, which makes self-hosting and experimentation practical.
Benchmark performance of DeepSeek-V4-Flash
Selected results from the official model card show that DeepSeek-V4-Flash improves over DeepSeek-V3.2-Base on several core benchmarks:
| Benchmark | DeepSeek-V3.2-Base | DeepSeek-V4-Flash-Base | DeepSeek-V4-Pro-Base |
|---|---|---|---|
| AGIEval (EM) | 80.1 | 82.6 | 83.1 |
| MMLU (EM) | 87.8 | 88.7 | 90.1 |
| MMLU-Pro (EM) | 65.5 | 68.3 | 73.5 |
| HumanEval (Pass@1) | 62.8 | 69.5 | 76.8 |
| LongBench-V2 (EM) | 40.2 | 44.7 | 51.5 |
In the reasoning-and-agent table, the Flash variant also posts solid results on terminal and software tasks, with Flash Max reaching 56.9 on Terminal Bench 2.0 and 79.0 on SWE Verified, while still trailing the larger Pro model on the hardest knowledge-heavy and agentic tasks.
DeepSeek-V4-Flash vs DeepSeek-V4-Pro vs DeepSeek-V3.2
| Model | Best fit | Tradeoff |
|---|---|---|
| DeepSeek-V4-Flash | Fast, long-context work, coding assistants, and high-throughput agent flows | Slightly behind Pro on pure knowledge and the most complex agentic tasks |
| DeepSeek-V4-Pro | Highest-capability tasks, deeper reasoning, and harder agent workflows | Heavier and less efficiency-oriented than Flash |
| DeepSeek-V3.2 | Older baseline for comparison and migration planning | Lower benchmark performance than V4-Flash on the official tables |
Typical use cases for DeepSeek-V4-Flash
- Long-document analysis for contracts, research packs, support knowledge bases, and internal wikis.
- Coding assistants that need to inspect big repos, follow instructions across many files, and keep context alive.
- Agent workflows where the model needs to reason, call tools, and iterate without losing the thread.
- Enterprise chat systems that benefit from a very large context window and low-friction deployment.
- Prototype local deployments for teams that want to evaluate DeepSeek-V4 behavior before production hardening.
How to access and use Deepseek v4 Flash API
Step 1: Sign Up for API Key
Log in to cometapi.com. If you are not our user yet, please register first. Sign into your CometAPI console. Get the access credential API key of the interface. Click “Add Token” at the API token in the personal center, get the token key: sk-xxxxx and submit.
Step 2: Send Requests to deepseek v4 flash API
Select the “deepseek-v4-flash” endpoint to send the API request and set the request body. The request method and request body are obtained from our website API doc. Our website also provides Apifox test for your convenience. Replace <YOUR_API_KEY> with your actual CometAPI key from your account. Where to call it: Anthropic Messages format and Chat format.
Insert your question or request into the content field—this is what the model will respond to . Process the API response to get the generated answer.
Step 3: Retrieve and Verify Results
Process the API response to get the generated answer. After processing, the API responds with the task status and output data.Enable features such as streaming, prompt caching, or long-context handling via standard parameters.
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