On the Terminal-Bench 2.1 benchmark, Reflection AI's Beam model achieved a score of approximately 80% while generating roughly 25,000 tokens per attempt, compared to Z.ai's GLM-5.2 generating roughly 80,000 tokens

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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.

Chinese AI company MiniMax released MiniMax M3.1 Flash on October 19, 2026, featuring 428 billion total parameters, about 23 billion active parameters in a mixture-of-experts architecture, and a 1,000,000-token context window.

MiniMax reported that MiniMax M3.1 Flash operates about three times faster than MiniMax M3, with median output speeds consistently exceeding 170 tokens per second.

MiniMax M3.1 Flash API pricing is set at $0.15 per million input tokens and $0.60 per million output tokens, with cached input reads priced at $0.015 per million tokens.

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.

MiniMax confirmed that an earlier build of MiniMax M3.1 Flash operated anonymously under the name Space Bunny, processing over 200 trillion tokens prior to its official launch.

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.

MiniMax reported that MiniMax M3.1 Flash can reach peak generation speeds exceeding 200 tokens per second.

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

LiteLLM, developed by BerriAI, provides a shared interface for LLM providers including OpenAI, Anthropic, and Google Gemini, receiving approximately 23 million weekly downloads.

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.

The complaints allege Meta Platforms Inc. operated a book-scanning program called Project Vellum starting around mid-2025 that purchased and scanned physical books to train its Muse Spark and Muse Glimmer AI models.