MiniMax’s M2.5 GGUF Model Performs Poorly Across Benchmarks in Tests

MiniMax’s latest quantized releases have sparked a brief but intense testing sprint among independent researchers, revealing a stark performance gap that challenges the prevailing assumption that “Q4‑level quantization is universally safe.” According to a series of posts by Benjamin Marie on X (formerly Twitter), the company’s M2.5 GGUF models—spanning the Q4, Q3, Q2 and Q1 quantization levels—consistently fell short of the baseline unquantized model across every benchmark examined. Marie’s charts show accuracy and perplexity scores that are markedly worse than those of the original architecture, and he notes that even the most aggressive Q1‑0 configuration, which retained a modest 0‑bit precision, “held up well enough” in his separate tests of the competing Qwen 3.5 GGUF, underscoring that MiniMax’s models are an outlier rather than the rule (Marie, X post).

The testing process itself was labor‑intensive, further highlighting the practical costs of poor quantization. Marie reports that each model required between ten and twenty hours of compute on an NVIDIA H200 GPU, and that the degraded outputs often devolved into nonsensical text that ran to the maximum sequence length before terminating. He estimates the total effort at “over a week” for the full suite of models, a timeline that would be prohibitive for most enterprise teams seeking rapid deployment of quantized LLMs. These findings echo a broader industry caution: while quantization can slash inference latency and hardware expenses, the robustness of a model’s architecture under reduced precision varies dramatically (Marie, X post).

MiniMax’s results contrast sharply with recent quantization successes from other vendors. For example, Baidu’s newly released ERNIE‑4.5‑21B‑A3B‑Thinking model, announced in VentureBeat, is being offered under an Apache 2.0 license and is touted for its “increased efficiency” despite still lagging behind the top U.S. offerings such as OpenAI’s GPT‑5 (VentureBeat). Similarly, open‑source projects like Qwen 3.5 have demonstrated that even low‑bit formats (e.g., TQ1‑0) can preserve functional performance when the underlying model is designed with quantization in mind. The disparity suggests that MiniMax’s engineering pipeline may lack the fine‑tuned calibration steps that other teams have integrated, a gap that could erode confidence among developers who rely on off‑the‑shelf quantized models for edge deployments.

Analysts observing the episode point to a broader market implication: quantization is no longer a one‑size‑fits‑all solution, and vendors must provide transparent robustness metrics before enterprises commit to large‑scale rollouts. Marie’s “lesson” posts—“Models aren’t equally robust, even under otherwise very good quantization algorithms” and “‘Just take Q4, it’ll be fine’ is a rule of thumb that doesn’t generalize”—serve as a cautionary checklist for procurement teams. If MiniMax cannot demonstrate that its M2.5 line meets baseline accuracy thresholds, the company risks losing traction to competitors that can guarantee both efficiency and fidelity, especially as cloud providers like Google and Adobe continue to bundle AI capabilities with tighter performance guarantees (VentureBeat).

In the short term, MiniMax is likely to face pressure from its user community to either release updated weights that survive low‑bit compression or to provide detailed guidance on which workloads can tolerate the observed degradation. Given the week‑long compute effort required for each test, the company may also need to invest in more automated benchmarking pipelines to avoid repeating Marie’s manual, time‑consuming methodology. Until such steps are taken, the M2.5 GGUF suite will remain a cautionary footnote in the fast‑moving quantization landscape, reminding the industry that efficiency gains must be balanced against the fundamental reliability of the underlying language model.