PromptArmor tested its most widely circulated attack samples across multiple models and found that DeepSeek, Kimi, GLM, and Qwen models are 88% more susceptible to cyberattacks than GPT, Claude, and Gemini models. The tests covered multiple cyber risk dimensions, including susceptibility to phishing, data exfiltration, and running malware, and the authors described the differences as significant.
PromptArmor said it used multiple highly viral attack samples to test cyber risks including phishing, data exfiltration, and propensity to run malware, and that DeepSeek, Kimi, GLM, and Qwen models were 88% more likely to be attacked than GPT, Claude, and Gemini. This conclusion comes from its main tweet; the specific testing methodology and sample size were not explained in the tweet.
DeepSeek V4.1 Flash 1017 ranks first among openly available models on the Artificial Analysis Cyber Index with a score of 57%, and second on the overall leaderboard, 12 points behind GPT-6 Sol (69%), which is offered through a trusted access program. The index consists of three benchmarks: CWE-Bench-AA, DeepsecBench-AA, and CyberGym-E2E-AA, and mainly evaluates agentic cybersecurity defense tasks. Compared with the previous generation DeepSeek V4.1 Flash's 41%, the new version improves by 16 points, mainly from CyberGym-E2E-AA rising from 23% to 76%; its cost per task is $0.42, placing it on the capability-cost Pareto frontier.
DeepSeek V4.1 Flash 1017 ranks first among openly available models on the Artificial Analysis Cyber Index with a score of 57%, and second on the overall leaderboard, 12 points behind GPT-6 Sol (69%), which is offered through a trusted access program. The index consists of three benchmarks: CWE-Bench-AA, DeepsecBench-AA, and CyberGym-E2E-AA, and mainly evaluates agentic cybersecurity defense tasks. Compared with the previous generation DeepSeek V4.1 Flash's 41%, the new version improves by 16 poi
ARC Prize announces the results of DeepSeek V4.1 Flash 1017 on the ARC-AGI validation set: ARC-AGI-2 score 78.1%, cost per task $0.16; ARC-AGI-1 score 94.5%, cost per task $0.06. The best score of the DeepSeek Flash series on ARC-AGI-2 rose from 61.4% for V4 Flash 0731 to 72.9% for V4.1 Flash, and then to 78.1% for V4.1 Flash 1017.
DeepSeek Search scores 69 on the Artificial Analysis Search Index, ranking 17th among 27 search products, and is the lowest-priced paid search API on the list. It charges $0.75 per 1k searches, with a per-task cost of about $0.032, ranking 5th cheapest among 27 products. On AA-Omniscience its accuracy is 70%, ranking 10th, but on BrowseComp, which requires multi-hop reasoning, it is only 63%, ranking 25th; each search returns about 7k tokens, raising model cost to the 2nd highest on the list.
Arena.ai says DeepSeek-V4.1-Flash-20261017 (Max) has become the top-ranked open-source model in Agent Arena, ranking 11th overall, with a net improvement of 5.1% across more than 7.5K real sessions. The model's median cost per task is $0.20, roughly 37% of GPT-6.1 Sol's improvement margin and 27% of its cost; it ranks first among open-source models on all four signals—Confirmed Success, Praise vs. Complaint, Bash Reliability, and Steerability—and shows no tool-hallucination issues.
Arena says DeepSeek-V4.1-Flash-20261017 (Max) has become the top-ranked open-source model on the Code Arena WebDev leaderboard, surpassing Kimi K3 (Max) at 10 times lower cost. The model ranks 12th overall with 1,670 points, above Claude Opus 5 (High) and about 10 points behind GPT-6 Sol (Max); it is priced at $0.3/$1.2 per million input/output tokens, a 52-point improvement over the previous version DeepSeek-V4.1-Flash-20260910.
Fireworks tested DeepSeek V4 Pro 0813 on four benchmarks: SWE-Bench Verified, LiveCodeBench v6, Aider Polyglot, and Terminal-Bench 2.1. It scored 95.2% on SWE-Bench Verified, higher than Kimi K3's 92.6% and Fable 5's 85.4%, and 92.0% on LiveCodeBench v6. On cost, DeepSeek V4 Pro spent $0.309 per task solved on SWE-bench, versus $0.808 for Fable 5; on LiveCodeBench the figures were $0.040 and $0.225, respectively. Its weakness is the multilingual breadth of Aider Polyglot, where it scored only 48.9% on Java, versus 74.5% for Fable 5. After oracle routing on a combined set of 409 tasks, accuracy was 92.4%, higher than the 86.6% achieved by Fable 5 alone, at a cost of $0.279 versus $4.510 per task, with 86.3% of tasks routed to V4 Pro.
Fireworks tested DeepSeek V4 Pro 0813 on four benchmarks: SWE-Bench Verified, LiveCodeBench v6, Aider Polyglot, and Terminal-Bench 2.1. It scored 95.2% on SWE-Bench Verified, higher than Kimi K3's 92.6% and Fable 5's 85.4%, and 92.0% on LiveCodeBench v6. On cost, DeepSeek V4 Pro spent $0.309 per task solved on SWE-bench, versus $0.808 for Fable 5; on LiveCodeBench the figures were $0.040 and $0.225, respectively. Its weakness is the multilingual breadth of Aider Polyglot, where it scored only 48
