Deepseek V4.1 Flash 1017 beats larger rivals in agentic tasks while once again massively cutting per-task costs
DeepSeek shipped open-weight V4.1-Flash-1017, claiming better agentic coding at under $0.20 per task.
DeepSeek released DeepSeek-V4.1-Flash-1017 with open weights and API access, reporting higher scores, faster runs, and shorter outputs than V4.1-Flash. On DeepSWE it scores 77.0% at just under $0.20 per task, or about 72% near $0.10, versus 74.2% at roughly $0.50 for the predecessor. DeepSeek says it beats larger models including Claude Opus 5, GPT-5.6-Sol, Kimi K3, and GLM-5.3 on several coding and agent tests, though independent benchmarks are not yet available. It also opened its in-house search engine via Search and Fetch APIs at $0.75 per 1,000 calls.
- Open weights published on Hugging Face and ModelScope, with web, app, and API access.
- DeepSWE score of 77.0% at just under $0.20 per task, versus 74.2% at about $0.50 previously.
- Company claims it beats larger rivals on several coding and agent benchmarks; independent results are not out.
- Post-training uses reinforcement learning with self-feedback, without human evaluators or external reward models.
- Search and Fetch APIs cost $0.75 per 1,000 calls, free in some tools until October 31, 2026.
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Chinese AI company Deepseek has shipped an updated version of its Flash model with major gains in agentic and coding tasks. The company is also opening up its in-house search engine to outside developers for the first time.
Deepseek-V4.1-Flash-1017 is available now through the company's website, app, and API. Deepseek also published the model weights as open source on Hugging Face and ModelScope. Compared to its direct predecessor, the model posts better benchmark scores, finishes tasks faster, and produces shorter outputs. The company calls the update a Pareto improvement in the tradeoff between performance and cost.
For building complete software projects from scratch, Deepseek uses reinforcement learning with self-feedback (RLSF). This post-training method lets the model use its own internal confidence scores or self-generated critiques as a reward signal. That removes the need for human evaluators or external reward models. Deepseek says the approach improves end-to-end project development for tasks ranging from game programming to 3D modeling and design work. The model can also animate compact explainer videos, similar to the recently released Opus 5.5.
The model was tuned for development environments like Deepseek Harness (version v0.2.1-rc.1 or later recommended), Tencent CodeBuddy, and WorkBuddy.
The smaller Flash model beats larger rivals on coding and agent tasks
In Deepseek's own benchmarks, V4.1-Flash-1017 outperforms its predecessor V4.1-Flash. It also beats larger competing models like Claude Opus 5, GPT 5.6-Sol, Kimi K3, and GLM 5.3 in several coding and AI agent categories.
A cost chart for the DeepSWE benchmark shows how much the shorter outputs matter for efficiency. V4.1-Flash-1017 hits a top score of 77.0 percent at an average cost of just under $0.20 per task. In its low setting, it still clears 72 percent for about $0.10. The predecessor scored 74.2 percent but cost around $0.50 per run.
Rival models like GPT-6 Astra, Gemini 3.8 Flash, and Claude Opus 5 land between $2 and over $10 per task in this chart, and none of them match the smaller Deepseek model's accuracy. Independent benchmarks haven't come out yet, and benchmarks only tell half the story anyway. Developer impressions on code quality, for example, can differ wildly from benchmark results.
Deepseek turns its internal search engine into a public API
Alongside the language model, Deepseek is making its web search tools (Search & Fetch APIs) available to all developers. The company says these services run on a search engine it built internally that already handles hundreds of millions of queries per day worldwide. Setup details are in the API documentation.
Each endpoint costs $0.75 per 1,000 calls. Deepseek API users can access the features for free through server-side tools in Deepseek Harness, Claude Code, and Codex until October 31, 2026 (4:00 PM UTC).
Integrating Chinese AI models deeply into workflows did get a bit less appealing yesterday, though. The Chinese government reportedly plans to tighten rules on publishing open-weight models or even block their release entirely. One deciding factor is whether a given system ranks among the top 10 most capable models.
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