AITOPAI热报
ModelsMulti-source72°

DeepSeek-V4-Flash公开测试，代码基准1586重塑帕累托前沿

lmarena.ai@lmarena_ai
Post· 11 sources
View the post
Why we picked it

DeepSeek把V4-Flash的API放出来了，1586分的代码能力只要白菜价，还支持Codex，写前端的时候可以试试。

DeepSeek-V4-Flash-High在Frontend Code Arena中得分1586，刷新了帕累托前沿。

Body

其API定价为每百万token 0.14/0.

28美元，是同级别中性能价格比最高的模型。DeepSeek-V4-Flash官方API已进入公开测试阶段，Agent能力大幅升级，基准成绩全面超过V4-Pro-Preview。

该模型原生支持Responses API格式，并已适配Codex。

Original · lmarena.ai

Exciting news: DeepSeek-V4-Flash-High by @deepseek_ai has reshaped the Pareto Frontier in the Frontend Code Arena, with a score of 1586! Priced at $0.14/$0.28 per MToken, it’s the best performance-per-dollar of any model in its class. Congrats to the @deepseek_ai team! DeepSeek @deepseek_ai 🚀 DeepSeek-V4-Flash Official API is now LIVE in public beta! 🔷 We’ve massively upgraded its Agent capabilities—benchmark scores are now far surpassing the V4-Pro-Preview. Check out the massive performance leap below! 👇 🔷 The official V4-Flash now natively supports the Responses API format and is fully adapted for Codex! Check out the configuration details in our official API do api-docs.deepseek.com/quick_start/ag… Meiq 🔗 View Quoted Tweet📊 29 ⚡

Industry Coordinates & Source Verification

This report is categorized under AITOP's "Models" desk, focusing on China's AI industry and domestic foundation model ecosystem. Within our cross-regional analytical framework, it directly maps to AI developer toolchains, coding agents, and engineering workflows and US-China foundation model benchmarks and commercialization comparison. Key entities and technical topics under continuous tracking include DeepSeek-V4-Flash, Frontend Code Arena, coding assistants, agents.

Source attribution: curated from lmarena.ai (@lmarena_ai). Cross-source corroboration: 11 independent sources have covered this development (including AI Will, elvis, IT之家), confirming industry-wide signal strength.

#DeepSeek-V4-Flash#Frontend Code Arena#coding assistants#agents

Keep reading

Recent stories with the same tag or section
PapersBuildersSelected07:55
TRUST Gate Cuts Bad Self-Improvement Updates to 2.78%

The key insight is that most agent self-improvement loops silently accumulate harmful updates, and a simple validation-score gate cannot detect them. Developers building agent frameworks or automated tuning pipelines should consider evidence-based acceptance tests like TRUST instead of greedy score thresholds. Compared with earlier statistical gates that over-reject good updates, TRUST offers measured evidence on when and how much to require. Teams running long-horizon agent loops on models such as DeepSeek or Qwen benefit most directly.

#TRUST#DeepSeek-V4-Flash#Qwen-3.8-Flash#agents
DAIR.AI@dair_aiSource
PapersBuilders11:55
Chinese contentKV Cache 能否跨模型版本复用？DeepSeek-V4-Flash 案例研究

做推理服务优化的人该看看这篇：升级模型版本后旧 cache 别急着扔，在长前缀 QA 场景下能拿到 2 到 5 倍的首 token 加速。论文用 DeepSeek-V4-Flash 做了完整实验，还给出了 FCD 这个新指标来量化复用后行为偏移，比单纯看准确率更细。

#DeepSeek-V4-Flash#KV Cache#inference optimization#RAG
arXiv: DeepSeek@Zhikun Tao et al. (3)Source
PapersBuildersMulti-sourceSelected13:13
Ostinato speeds CPU-GPU MoE inference for local agents

Most local agent stacks today force a trade-off between snappy prefill and snappy decode; Ostinato shows the trade-off is avoidable by exploiting routing stability rather than brute-force caching. Teams running MoE models like DeepSeek-V4-Flash on consumer GPU workstations can cut per-tool-call latency by roughly half without changing model behaviour. It matters for anyone building self-hosted agent infrastructure where cloud APIs are not an option.

#Ostinato#DeepSeek-V4-Flash#MoE#llama.cpp
Event
arXiv: DeepSeek@Junning ZhuSource
PapersBuilders11:06
Chinese contentTRUST 框架为递归自我改进加统计检验门槛

智能体改自己的代码时最怕把噪声当进步，贪心门控近一半更新其实是在变差。这篇论文用在线假设检验把劣化率压到 2.78%，还给了性能增益下界证明。做自进化智能体框架的开发者值得把这套门控思路搬过去。

#TRUST#DeepSeek-V4-Flash#Qwen-3.8-Flash#agents
Event
arXiv: DeepSeek@Zihe Gao et al. (3)Source
PapersBuildersSelected11:52
LeanSparse Cuts GPU Memory for Sparse-Attention LLM Serving

The key insight is that prefill memory spikes, not just KV cache size, throttle concurrency — streaming layer histories through reusable buffers removes that admission bottleneck. Engineers running long-context inference on SGLang can adopt the design to serve more users per GPU without changing model weights. Compared with HiSparse and ECHO, the gains come from better indexing rather than more hardware.

#DeepSeek-V4-Flash#SGLang#sparse attention#inference optimization
arXiv: DeepSeek@Zaifeng Pan et al. (6)Source
PapersBuildersSelected14:29
J-PACT Detects Collusion Between LLM Agents via J-Space

Unsupervised detection matters because supervised probes need labelled collusion data that real deployments rarely have, so J-PACT lowers the barrier to auditing multi-agent systems. Teams running agent swarms — especially in finance, trading or messaging pipelines where hidden coordination is hard to spot — can use J-space signals as an oversight layer. The method worked across four open-weight model families, suggesting the collusion signature is not model-specific.

#J-PACT#DeepSeek-V4-Flash#NARCBench#agents
arXiv: DeepSeek@Marco Molinari, Gabriele BettineschiSource