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This educational assessment method encourages students to explore the strengths and weaknesses of Claude, DeepSeek, and MiniMax, creating questions that defeat AI, thereby cultivating critical thinking and competitiveness needed in the AI era.
Discussion comparing the new Minimax M3 model to its predecessor M2.7, seeking user feedback after two weeks of release.
This article evaluates the performance of running GLM 5.2 (IQ2M quantized version) on Dual Strix Halo (256GB VRAM). The generation speed is only about 7 tokens/s, and coding tasks take twice as long as DeepSeek V4 Flash. Its cost-performance ratio is far inferior to other models, so it is not recommended for use with this hardware configuration.
A GGUF conversion of MiniMax M3's EAGLE draft model for llama.cpp is now available, enabling speculative decoding speedups on compatible hardware.
MiniMax Sparse Attention (MSA) achieves up to 28.4x reduction in attention compute at 1M tokens by adding a routing branch that selectively chooses key-value blocks for attention, enabling 14.2x faster prefill and 7.6x faster decoding on H800 GPUs while matching full attention benchmark performance.
A pull request to vLLM adds support for tensor parallelism degree 3 for MiniMax M3 with its NVFP4 quantization, enabling the model to run on 3x DGX Sparks with 87GB memory each.
This paper from Minimax introduces MiniMax Sparse Attention, which adds a tiny Index Branch to GQA to select top-k KV blocks per group, enabling GPU-native sparsity with exponential speedups on a 109B multimodal MoE.
MiniMax M3 model is now available on HuggingChat, an open source AI chat app with Artifacts support.
Minimax's M3 model requires vllm updates to support sm_120 compute capability, as the current repo only supports sm_100.
MiniMaxAI announces plans to release open weights for its upcoming M3 model on Friday, following the earlier M2.7 model.
A review of free AI tools tested this week, including Claude, MiniMax Agent, K2Think, Indic LLM Arena, and Together.ai playground, with honest assessments of their capabilities and limitations.
MiniMax's price increases and model limitations are driving users away to competitors like DeepSeek and premium options like Claude or ChatGPT, reversing its earlier reputation as a cheap, usable daily driver.
MiniMax open-sourced four AI document generation skills (PPT, PDF, Excel, Word), usable without an API key, aiming to solve issues like messy formatting and formula errors in AI-generated documents.
A comparison of four frontier AI models (Nemotron 3 Ultra, DeepSeek V4, MiniMax M3, Qwen 3.7 Max) on the same two prompts, with full results linked.
M3 achieves solid benchmark scores but impresses with its ability to perform risk assessment and pre-mortem analysis before making code changes, highlighting a more cautious and thorough approach to refactoring in messy legacy repos.
A discussion comparing DeepSeek V4 Pro, MiMo-V2.5-Pro, and MiniMax M3 for best value in local or openrouter use, with a focus on agentic and coding tasks, and mentions of Hermes Agent and Qwen 3.6 variants.
The Minimax M3 model appears to have no political censorship, standing out among Chinese LLMs in a bias benchmark.
MiniMax's new m3 model achieves the same score as Opus 4.7 on terminal-bench 2.1 while using 1/20th the compute and cost, attributed to their novel MiniMax Sparse Attention architecture.
Minimax has released its M3 model, as announced in a blog post.
MiniMax has released a detailed technical report on its M2 series and teased the upcoming M3 model, which uses a novel sparse attention mechanism to achieve up to 15.6× faster decoding at million-token contexts.