DeepSeek V4 Flash 0731 Released: 304B Parameters with Enhanced Agentic Capabilities and Strong Value
Score
77提供了新模型的参数、定价、评测排名及实测效果，信息密度高。
结合了权威基准与独立开发者实测，分析具体且可信。
给出了明确价格和推理参数调节建议，对开发者决策有直接帮助。
内容为最新模型发布当日的评测，时效性极强。
来源为知名AI开发者博客，并引用了Artificial Analysis等可信评测数据。
Base 85 × T2 Source weight ×0.9 = 77
Curation line 70Selected
Why selected
The release comes with both benchmark validation and independent developer testing, offering rich and directly actionable information.
AI commentary
「The real story here isn't parameter count—it's that a 304B model can beat larger rivals while being this cheap. For developers, this signals a value inflection point: building agentic apps may no longer require chasing ever-larger models. Simon's note about reasoning effort is a useful reminder that benchmark results don't automatically translate to great outputs; tuning matters before you judge a model.」
Quick overview
DeepSeek has released the latest version in its V4 series, DeepSeek-V4-Flash-0731, with 304 billion parameters and a 167GB Hugging Face checkpoint. The company says it comes with substantially enhanced agentic capabilities. Artificial Analysis ranks it ahead of the 428B-parameter MiniMax M3 on the Intelligence Index. With pricing at $0.14 per million input tokens and $0.27 per million output tokens, it currently looks like one of the best value-per-intelligence models available. Simon Willison's tests found the default reasoning level produced disappointing results for a pelican prompt, but bumping reasoning effort to high via OpenRouter yielded a much better output. The model appears to punch well above its weight, though real-world performance depends heavily on reasoning effort settings.
- DeepSeek-V4-Flash-0731 is a 304B-parameter model with a 167GB Hugging Face checkpoint, and DeepSeek highlights substantially enhanced agentic capabilities.
- Artificial Analysis ranks it ahead of the 428B-parameter MiniMax M3, suggesting it punches well above its weight.
- API pricing of $0.14 per million input tokens and $0.27 per million output tokens makes it a strong value-for-intelligence option.
- Simon Willison found default reasoning effort disappointing; raising the effort level produced significantly better results.
Full text
Showing the source text; the full translation is being generated.
deepseek-ai/DeepSeek-V4-Flash-0731
The latest release in DeepSeek's V4 family, "with substantially enhanced agentic capabilities". It's 304 billion parameters - 167GB on Hugging Face - but it appears to punch well above its weight.Artificial Analysis rank it ahead of MiniMax M3 - a 428B model. It's $0.14/million input and $0.27/million output pricing means this may currently be the best value-per-intelligence model out there. It's looking very good on the Intelligence Index vs. Cost per Intelligence Index Task chart:
I got a disappointing pelican from it using the default reasoning level via OpenRouter:
But when I bumped reasoning level up to high I got something much better:
llm -m openrouter/deepseek/deepseek-v4-flash-0731 -t pelican -o reasoning_effort high
Via Hacker News
Tags: ai, generative-ai, llms, pelican-riding-a-bicycle, deepseek, llm-release, openrouter, ai-in-china, artificial-analysis
Related items
FeaturedDeepSeek V4 Doubles Token Share on OpenRouter, Driven by Agentic Workloads
DeepSeek V4 在 OpenRouter 上的代币份额翻倍，智能体工作负载成主要驱动力
The rapid adoption of DeepSeek V4 signals a strong market preference for high-performance, low-cost inference models, especially as agentic use cases reshape AI deployment.
91
FeaturedEnabling Two Settings Triples ARC-AGI-3 Benchmark Scores
启用两项设置使ARC-AGI-3基准测试分数提升三倍
This finding is crucial! It shows that model performance depends not only on training but also on inference-time configuration. For AI engineers and researchers, this is a must-know optimization direction. Teams deploying models on resource-constrained devices in particular could find a low-cost way to boost capabilities.
91
FeaturedGPT-5.6: Fusing Frontier Intelligence with Frontier Efficiency
GPT-5.6：前沿智能与前沿效率的融合
This news is worth watching as it marks a shift from pure parameter scaling to efficiency competition. For AI developers and business decision-makers, GPT-5.6 means higher performance at lower cost, especially for long-context or agentic tasks previously hindered by inference expenses. OpenAI's move may redefine what 'frontier' means.
91
FeaturedLFM2.5-Encoders: New Models for Fast Long-Context Inference on CPU
LFM2.5-Encoder：面向CPU快速长上下文推理的新模型
This blog post catches my eye because it directly solves the pain point of running long-context LLMs on CPU. For teams or individual developers without high-end GPUs, the LFM2.5-Encoder series offers a ready-to-use open-source solution. Highly recommended for those working on RAG, document search, or code analysis—now a standard server can handle million-token documents.
91
FeaturedGPT-Red: OpenAI Automates Red Teaming via Self-Play to Boost Model Robustness
GPT-Red：OpenAI通过自我对弈实现AI红队自动化，提升模型鲁棒性
GPT-Red marks a milestone from human to automated red-teaming—essential for safety engineers and alignment researchers. While reducing cost, it also raises ethical questions about automated attacks, worth pondering for everyone in AI governance.
91
FeaturedOpenAI Launches GPT-5.5 Bio Bug Bounty Program
OpenAI推出GPT-5.5生物漏洞赏金计划
This program deserves attention as it pioneers the extension of AI bug bounty to biosecurity, offering a new research direction for the security community and a practical training ground for AI risk governance. It is particularly important for model safety researchers, biosecurity experts, and practitioners concerned about dual-use AI risks.
91
FeaturedvLLM Introduces Native-Speed Transformers Modeling Backend for Enhanced Inference Performance
vLLM推出原生速度Transformers建模后端，大幅提升推理性能
This update is critical for AI engineering teams: eliminating format conversion speeds up model iteration, and 2-5x speedup directly reduces GPU costs. If you're building LLM online services or batch inference, this is practically a must-read technical update.
90
FeaturedGoogle DeepMind Introduces SL2T, a Sign Language-to-Text Model for Deaf Users
Google DeepMind 发布手语转文本模型 SL2T，助力聋人用户
This news matters not only as a technical breakthrough but also as a sign that AI is beginning to address the needs of marginalized groups. For developers, the architecture and technical approach of SL2T merit deep study; for accessibility advocates, it's a key case for advancing industry standards.