DeepSeek V4 Flash
Q4 GGUF download: huihui-ai/Huihui-DeepSeek-V4-Flash-0731-abliterated-GGUF - 155.98 GB on disk, read 2026-09-27. VRAM figures above are estimates; this is the actual file size.
Intelligence benchmarks
Artificial Analysis indexes - compared with the best open and proprietary models
Intelligence
18.9
AA Index
Intelligence Index - DeepSeek V4 Flash vs. the field
Best open-weight models (you can run locally) and leading proprietary models for context.
Benchmark data from Artificial Analysis · updated 2026-10-19.
Arena rating (LMArena)
Human preference wins from head-to-head chat battles
| Metric | Value |
|---|---|
| Overall arena rating | 1431.8 |
| Rank (overall) | #92 |
| Battle votes | 51,987 |
Overall rating published by LMArena on 2026-10-15 - a pairwise human-preference Elo across live chat battles; higher is better. Fetched 2026-10-19. Open-weights models compete head-to-head with proprietary ones here — no benchmark prompts, just what people pick.
Standard benchmarks
Performance across standard evaluations
| Benchmark | Score |
|---|---|
| MMLU | 88.7 |
| HumanEval | 69.5 |
| GSM8K | 90.8 |
| GPQA Diamond | 71.6 |
Source: Artificial Analysis component evals - updated 2026-10-19. Other scores are carried over from the site's initial dataset - no source on record, not re-verified.
Will it run on your hardware?
Pick your GPU memory - see which quantizations fit, and the cheapest card for the rest
Need an exact figure for your context length? Use the VRAM calculator.
Run it locally
Copy-paste - running in under a minute
vllm serve deepseek-ai/DeepSeek-V4-FlashNew to this? Start with Ollama · serve to many users with vLLM.
Deep dive
Notes, sources, and the full write-up
DeepSeek V4 Flash
DeepSeek V4 Flash is the efficiency-optimized sibling of the V4 family: 284 billion total parameters with only 13 billion active per token, 1 million token context, and an MIT license. It delivers the best cost-per-intelligence ratio of any open-weight model available today, matching the reasoning performance of the Pro variant when given a larger thinking budget while using a fraction of the compute.
What makes DeepSeek V4 Flash special
- 13B active / 284B total MoE - extreme efficiency, single-host capable at FP8
- 1M token context - full-repo and multi-document support
- Hybrid Attention - Compressed Sparse Attention + Heavily Compressed Attention for long-context efficiency
- MIT license - fully open weights, commercial use allowed
- Three reasoning modes - Non-think, Think High, Think Max effort levels
- Manifold-Constrained Hyper-Connections - novel architecture improving training stability
Benchmarks
| Benchmark | DeepSeek V4 Flash | DeepSeek V4 Pro | DeepSeek V3.2 |
|---|---|---|---|
| MMLU | 88.7 | 90.1 | 87.8 |
| MMLU-Pro | 68.3 | 73.5 | 65.5 |
| GSM8K | 90.8 | 92.6 | 91.1 |
| HumanEval | 69.5 | 76.8 | 62.8 |
| MATH | 57.4 | 64.5 | 60.5 |
| LongBench-V2 | 44.7 | 51.5 | 40.2 |
Source: DeepSeek-V4 technical report. Base model evaluation at 5-shot where applicable.
VRAM requirements
| Precision | VRAM | Recommended Hardware |
|---|---|---|
| FP8 | ~140 GB | NVIDIA A6000 (x2), RTX 5090 (x4) |
| FP4+FP8 mixed | ~90 GB | RTX 5090 (x2), NVIDIA A6000 |
The model ships natively in FP4+FP8 mixed precision, making it unusually efficient for its total size. The effective VRAM footprint is dominated by the 13B active parameters, which is why it can run on as few as 2 server-class GPUs.
How to run
# Via vLLM
vllm serve deepseek-ai/DeepSeek-V4-Flash --port 8010How it compares
DeepSeek V4 Flash is the pragmatic choice for teams that need the V4 family's agentic coding capability but cannot justify the GPU budget for the full Pro model. It is significantly more capable than DeepSeek V3 while requiring less VRAM at its native FP4+FP8 precision.
Compared to Qwen3.6 27B: V4 Flash offers dramatically longer context (1M vs 262K) and stronger STEM reasoning, but Qwen3.6 27B adds vision and video modalities.
Use it with
When to use something else
If your hardware is limited to a single consumer GPU, DeepSeek V4 Flash still needs multiple server-class GPUs. For single-GPU local use, consider Qwen3.6 27B or Gemma 4 31B in Q4.
From the community
What users are saying
“Per the announcement tweet, BaseTen was the inference provider which has 20% cache cost that is typical: https://www.baseten.co/library/deepseek-v4-flash-0731/”
“In case people want to run it, it's DeepSeek-V4-Flash-284B-A13B. So it should just barely run on a single B300, and it's small enough that it'll barely run on an M5 Max too.”
“404. I believe this is the correct URL: https://artificialanalysis.ai/models/deepseek-v4-flash”
Frequently asked
Quick answers to common questions
How much VRAM does DeepSeek V4 Flash need?
DeepSeek V4 Flash with 284B parameters needs approximately 520 GB at Q4_K_M quantization. Use our VRAM calculator for an exact estimate.
Is DeepSeek V4 Flash better than other DeepSeek models?
DeepSeek V4 Flash scores 88.7 on MMLU and 69.5 on HumanEval. It has 284B parameters with 1,048,576 context - a strong choice for coding, agents, long-context.
What license is DeepSeek V4 Flash under?
DeepSeek V4 Flash is released under the MIT license, making it suitable for most commercial and personal projects.
What hardware runs DeepSeek V4 Flash well?
With 284B parameters, DeepSeek V4 Flash requires adequate VRAM. High-end GPUs like the RTX 4090 (24GB), RTX 5090 (32GB), or Mac Studio with unified memory are good options. Check our hardware directory for specific recommendations.
What is the best quantization for DeepSeek V4 Flash?
Q4_K_M is the recommended sweet spot - ~98% of FP16 quality at ~27% of the size. Step up to Q5_K_M or Q8_0 only if you have spare VRAM. Use our VRAM calculator to compare.
How long can DeepSeek V4 Flash's context window handle?
DeepSeek V4 Flash supports a 1,048,576-token context window - enough for very long documents, codebases, or multi-turn conversations. Real-world usable context may vary by implementation.
What models compete with DeepSeek V4 Flash?
DeepSeek V4 Flash competes with other 142B–426B. Browse our model directory for comparisons, benchmarks, and community reviews to find the best fit.
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