Kimi K3.1 Pro scored 65.7% on Terminal-Bench 4.0 coding, trailing Claude Opus 5.5 at 66.4%, while scoring 38.1 on Terminal-Bench Science 0.1 compared to 68.1 for OpenAI's GPT-6 Astra

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On the SWE-Marathon coding benchmark, Kimi K3.1 Pro scored a self-reported 51.7, leading Anthropic's Claude Opus 5.5 score of 46.3

On the internal Kimi Hallu Bench Hard stress test, claim-level hallucinations for Kimi K3.1 Pro fell to 8.3% from 23.4% in K3, compared to 5.5% for GPT-6 Astra

On financial benchmarks, Kimi K3.1 Pro reported a score of 64.4 on OfficeQA Pro v2 and 58.6 on Frontier Finance

Artificial Analysis reported that MiniMax M3.1 Flash demonstrated a 93 percent hallucination rate on the AA-Omniscience benchmark because the model attempted nearly every question rather than acknowledging uncertainty.

Artificial Analysis found MiniMax M3.1 Flash's Intelligence Index task evaluation cost dropped 57 percent to $0.22 from MiniMax M3's $0.51, compared to $0.26 for DeepSeek V4.1 Flash-1017, $0.28 for GLM-5.3 Flash, and $1.24 for Gemini 3.8 Flash.

MiniMax M3.1 Flash achieved an overall AA-Omniscience benchmark score of 16 despite correctly answering 56 percent of factual questions, compared to 43 percent for DeepSeek V4.1 Flash-1017 and 28 percent for GLM-5.3 Flash.

Artificial Analysis awarded MiniMax M3.1 Flash a score of 42 on its Intelligence Index, an increase of 13 points over the MiniMax M3 model's score of 29.

Artificial Analysis reported that DeepSeek V4.1 Flash-1017 scored 45 on its Intelligence Index, while Gemini 3.8 Flash scored 41.

MiniMax reported that all five reasoning effort levels for MiniMax M3.1 Flash stayed below $0.05 per task on its Automation-Bench chart.

Artificial Analysis reported that DeepSeek V4.1 Flash-1017 generated approximately 224 million output tokens during its Intelligence Index evaluation.

Artificial Analysis found MiniMax M3.1 Flash generated approximately 189 million output tokens during its Intelligence Index evaluation, compared to roughly 324 million for GLM-5.3 Flash and 334 million for Gemini 3.8 Flash.

Benchmark analysis from Artificial Analysis identified a "DeepSeek death zone" describing market pressure on artificial intelligence competitors unable to match DeepSeek's performance or offer lower prices

Researchers at Mass General Brigham developed the FAHR-Face artificial intelligence foundation model by training the algorithm on over 40 million facial photographs across diverse ages and ethnic backgrounds.

Mass General Brigham study findings indicated that the FAHR-FaceAge model maintained consistent performance across photo variations in pose, lighting, expression, and makeup, with rhinoplasty causing subjects to evaluate slightly older.

In a study of 122 cancer patients categorized as high mortality risk by FAHR-FaceSurvival, 52.5 percent died within 90 days, compared to approximately two percent of 975 low-risk patients.

Independent experts noted that while Mass General Brigham's findings show strong proof-of-concept associations, FAHR-Face tools require prospective clinical trials across diverse populations before clinical deployment.

Frenos researchers tested six frontier models, two U.S. open-weight models, and two foreign open-weight models by providing them target details and asset information to assemble attack steps, without disclosing model names.

The Frenos study evaluated 10 artificial intelligence models across 16,980 total tests conducted between October 2025 and September 2026, including 13,884 decision attempts, 2,520 planning attempts, and 576 simulated attack runs.

In the Frenos study, the strongest foreign open-weight model achieved a 36.2% success rate in attack planning, outperforming the top U.S. open-weight model at 5.2% and three U.S. frontier models scoring between 17.9% and 31.9%.

The three leading frontier models tested by Frenos produced attack plans that passed every repeat test in 60%, 62.9%, and 74.3% of attempts, showing inconsistency and omitting essential steps or assuming unestablished access.