HF Model & Benchmark Matrix

24,078 variants · 14,984 scores · 120 benchmarks

DeepSeek-V4-Flash

deepseek-ai/DeepSeek-V4-Flash

kind
fp8
quant
fp8 (8-bit)
family
deepseek
modality
text-generation
root model
deepseek-ai/DeepSeek-V4-Flash
provider
deepseek-ai
license
mit
language
—
context
—
library
transformers
vRAM (GB)
—
size on disk
—
downloads
3,113,868
likes
1,845
created
—
last modified
2026-06-22T12:12:13+00:00 (2026)
huggingface
https://huggingface.co/deepseek-ai/DeepSeek-V4-Flash
tags
transformerssafetensorsdeepseek_v4text-generationconversationalarxiv:2606.19348license:miteval-resultsendpoints_compatible8-bitfp8deploy:azureregion:us

Benchmark scores (27)

benchmarkcategoryscorecontextconfevidence
GPQA Diamondreasoning91.3GPQA Diamond (Pass@1)medium| GPQA Diamond (Pass@1) | 91.3 | 93.0 | **94.3** | 90.5 | 86.2 | 90.1 |
GSM8Kmath91.1GSM8K (EM)medium| GSM8K (EM) | 8-shot | 91.1 | 90.8 | **92.6** |
C-Evalknowledge90.4C-Eval (EM)medium| C-Eval (EM) | 5-shot | 90.4 | 92.1 | **93.1** |
CMMLUknowledge88.9CMMLU (EM)medium| CMMLU (EM) | 5-shot | 88.9 | 90.4 | **90.8** |
LiveCodeBenchcoding88.8LiveCodeBench (Pass@1)medium| LiveCodeBench (Pass@1) | 88.8 | - | 91.7 | 89.6 | - | **93.5** |
DROPreasoning88.2DROP (F1)medium| DROP (F1) | 1-shot | 88.2 | 88.6 | **88.7** |
MMLUknowledge87.8MMLU (EM)medium| MMLU (EM) | 5-shot | 87.8 | 88.7 | **90.1** |
BBHreasoning87.6BBH (EM)medium| BBH (EM) | 3-shot | **87.6** | 86.9 | 87.5 |
MMLUknowledge87.5MMLU-Redux (EM)medium| MMLU-Redux (EM) | 5-shot | 87.5 | 89.4 | **90.8** |
HellaSwagknowledge86.4HellaSwag (EM)medium| HellaSwag (EM) | 0-shot | 86.4 | 85.7 | **88.0** |
BrowseCompagentic83.7BrowseComp (Pass@1)medium| BrowseComp (Pass@1) | 83.7 | 82.7 | **85.9** | 83.2 | 79.3 | 83.4 |
TriviaQAknowledge83.3TriviaQA (EM)medium| TriviaQA (EM) | 5-shot | 83.3 | 82.8 | **85.6** |
MGSMmath81.3MGSM (EM)medium| MGSM (EM) | 8-shot | 81.3 | **85.7** | 84.4 |
AGIEvalreasoning80.1AGIEval (EM)medium| AGIEval (EM) | 0-shot | 80.1 | 82.6 | **83.1** |
WinoGrandeknowledge78.9WinoGrande (EM)medium| WinoGrande (EM) | 0-shot | 78.9 | 79.5 | **81.5** |
SimpleQAknowledge76.4Chinese-SimpleQA (Pass@1)medium| Chinese-SimpleQA (Pass@1) | 76.4 | 76.8 | **85.9** | 75.9 | 75.0 | 84.4 |
MMLU-Proknowledge65.5MMLU-Pro (EM)medium| MMLU-Pro (EM) | 5-shot | 65.5 | 68.3 | **73.5** |
Terminal-Bench 2.0agentic-coding65.4Terminal Bench 2.0 (Acc)medium| Terminal Bench 2.0 (Acc) | 65.4 | **75.1** | 68.5 | 66.7 | 63.5 | 67.9 |
BigCodeBenchcoding63.9BigCodeBench (Pass@1)medium| BigCodeBench (Pass@1) | 3-shot | **63.9** | 56.8 | 59.2 |
HumanEvalcoding62.8HumanEval (Pass@1)medium| HumanEval (Pass@1) | 0-shot | 62.8 | 69.5 | **76.8** |
MATHmath60.5MATH (EM)medium| MATH (EM) | 4-shot | 60.5 | 57.4 | **64.5** |
Humanity's Last Examknowledge53.1HLE w/ tools (Pass@1)medium| HLE w/ tools (Pass@1) | 53.1 | 52.0 | 51.6 | **54.0** | 50.4 | 48.2 |
SimpleQAknowledge46.2SimpleQA-Verified (Pass@1)medium| SimpleQA-Verified (Pass@1) | 46.2 | 45.3 | **75.6** | 36.9 | 38.1 | 57.9 |
LongBenchlong-context40.2LongBench-V2 (EM)medium| LongBench-V2 (EM) | 1-shot | 40.2 | 44.7 | **51.5** |
Humanity's Last Examknowledge40.0HLE (Pass@1)medium| HLE (Pass@1) | 40.0 | 39.8 | **44.4** | 36.4 | 34.7 | 37.7 |
SimpleQAknowledge28.3Simple-QA verified (EM)medium| Simple-QA verified (EM) | 25-shot | 28.3 | 30.1 | **55.2** |
Terminal-Benchagentic-coding2.0mediumTerminal Bench 2.0

Discussions (50)

titleauthorstatuscommentstype
Delete inference/README.mdPq234open0disc
Update README.mdPq234open0disc
什么时候满血版v4,梁圣!AISkywalkeropen0disc
fastllm 启动deepseek-v4-flash 回答问题乱码zll3053open0disc
Run DeepSeek-V4-Flash locally! 🐳danielhanchenopen0disc
Add SkillsBench v1.1 evaluation resultbingran-youopen0disc
Listed on OpenModelMapduola15open0disc
Listed on OpenModelMapduola15open0disc
Since the DSA indexer selects the top‑k tokens with respect to k, is it really necessary to multiply all tokens by a softmax_scale scalar?jinluyangopen0disc
Deepseek-v4-flash image supportsongwang41open0disc
DeepSeek-V4-Flash fine-tuning: can a merged model keep the same FP4+FP8 format as the base model, or is BF16 the only supported merge output?zhangshuai121open0disc
Reproducing benchmark results for DeepSeek-v4-Flash — sampling parameters?rasmuskhclosed0disc
TemporalMesh Transformer: 29.4 PPL at 48% compute — beats Mamba, new open-source architecturevigneshwar234open0disc
I'm confused (v4 Flash vs v3.2)MateoTeoopen0disc
Number of layers in inference mock codeNotTeteopen0disc
🚀 ms-swift Provides DeepSeek-V4 Fine-tuning Practicestudy-hjtopen0disc
DeepSeek Training SupportSuperXropen0disc
官方是否有兴趣帮忙改进完善一下Apple MLX框架的适配?hehua2008open0disc
DeepSeek-V4帮我破了《真探3》的案子Mephisto1484open0disc
Add Claw-Eval evaluation resultsSaylorTwiftopen0disc
Add Claw-Eval evaluation results (general + multi_turn)SaylorTwiftclosed0disc
use vLLM with 2 DGX Sparks , response brokeneasonchow0419open0disc
Running models with vLLM on the RTX Pro 6000 - SM120liku2001open0disc
model anwser with <ds_safety>rick-jiangopen0disc
Claude 4.7 Reasoning into DeepSeek-V4-Flash for Open-Source Edge Intelligencemuneebkhanniaziopen0disc
How to run deepseek on Ada GPUs?Mine is L20.XiaoZaiyiopen0disc
How can I use this with llama.cpp?KeilahEllaopen0disc
Should the "index_topk" be updated to 1024 just like the Pro model?jfcherngclosed0disc
Questions on MoE Hash Routingmattduerrmeieropen0disc
120B model?jacek2024open0disc
Run DeepSeek-V4-Flash on more hardware: FP8/BF16 adapted versions for 8 AI chips (ready to download)Yonghuaopen0disc
[适配请求] 建议 API 兼容 OpenAI 新规范中的 developer 角色 (It is recommended that the API be compatible with the developer role in the new OpenAI specificationxusu616open0disc
Update README.md to add SGLang deployment optionzjxiaopen0disc
Is 158B or 284b params ?celsowmopen0disc
Add chat templateRocketknight1open0disc
Unable to run on 2x RTX Pro 6000 (DEEP_GEMM problem)stev236open0disc
bsarpelbsarpeldraft0disc
Local Installation Video and Testing - Step by Stepfahdmirzacopen0disc
Too big to run locally.Dampfinchenopen0disc
Add community evaluation results for GPQA, MMLU-PRO, SWE-BENCH_VERIFIED, HLE, TERMINAL-BENCH-2.0nielsropen0disc
May I ask if there is a deployment document?jerryliujiaweiopen0disc
Will there be a smaller model like Qwen3.5 122 or Nemotron 3 supermayankiit04open0disc
Maximum Thinking Token High vs Max?Miracle12345closed0disc
终于来了~zhubao315open0disc
量化相关Paulzhouopen0disc
起立,立正!lizhoohopen0disc
前排前排!合影hakuproopen0disc
SickGreen-eyedDevilopen0disc
support inference by sglang or vllm ?howtainopen0disc
Here comes the big onehuggingfacessopen0disc
Hugging Face model & benchmark data scraped & matrix-ified. 24,078 variants · 14,984 scores · 120 benchmarks.