Model · Image and text to text
MiniMax
MiniMax-M3.1-Flash is a native multimodal mixture-of-experts model for coding, agentic workflows, computer use, and cybersecurity. It supports text, image, and video inputs with a context window of up to 1M tokens. Building on MiniMax-M3, M3.1-Flash improves task performance and inference efficiency with sparse attention throughout the model, quantization-aware training, and DSpark speculative decoding. - Agents’ Last Exam: Official Overall setting; full-set results from the official ALE leaderboard only. Blank = no official result (CLI-only excluded), not zero. Results are for the official weights; third-party quantizations may differ. M3.1-Flash retains M3’s core mixture-of-experts…
Open weights
other
220.5B parameters
1,048,576 tokens
transformers
Model · Image and text to text
Z.ai
Join our WeChat or Discord community. Check out the GLM-5.3-Flash blog and GLM-5 Technical report. Use GLM-5.3-Flash API services on Z.ai API Platform. We introduce GLM-5.3-Flash, the first natively multimodal model in the GLM-5 series. With 320B total parameters and just 18B active parameters, it outperforms GLM-5.2 across benchmarks and real-world workloads at one-tenth the price, while approaching Claude Opus 4.8 on coding and agentic benchmarks. GLM-5.3-Flash starts from a newly trained base model, with its architecture and training recipe redesigned around capability and efficiency. For the first time in the GLM series, we introduce a hybrid architecture combining sparse and linear…
Open weights
mit
321.3B parameters
1,048,576 tokens
transformers
Join our WeChat or Discord community. Check out the GLM-5.3-Flash blog and GLM-5 Technical report. Use GLM-5.3-Flash API services on Z.ai API Platform. We introduce GLM-5.3-Flash, the first natively multimodal model in the GLM-5 series. With 320B total parameters and just 18B active parameters, it outperforms GLM-5.2 across benchmarks and real-world workloads at one-tenth the price, while approaching Claude Opus 4.8 on coding and agentic benchmarks. GLM-5.3-Flash starts from a newly trained base model, with its architecture and training recipe redesigned around capability and efficiency. For the first time in the GLM series, we introduce a hybrid architecture combining sparse and linear…
Open weights
mit
321.3B parameters
1,048,576 tokens
transformers
Model · Image and text to text
Qwen
As the frontier of foundation models pushes toward ever-larger parameter counts and ever-longer context windows, the question is no longer just how much we can scale, but how efficiently we can do so. Sustainable progress toward artificial general intelligence (AGI) that benefits everyone demands architectural innovation. Today, we are sharing a concrete step in that direction: Qwen3.8-Flash-Next. This experimental preview of the architecture that will underpin Qwen4 is built around a fundamental rethinking of how the core components of modern large language models (LLMs) interact at scale. The first open-weight release under this architecture is Qwen3.8-Flash-Next, which introduces: For…
Open weights
other
180B parameters
262,144 tokens
transformers
This model has had its safety alignment substantially removed via abliteration (orthogonalizing the refusal direction out of the residual stream). It will comply with harmful, unethical, or illegal requests the original Qwen3.8-Flash-Next would refuse. Released strictly for legitimate research — interpretability, AI-safety / refusal-mechanism study, red-teaming, and robustness evaluation. You assume full responsibility for how you use it and everything it generates; add your own safety and moderation layers before any deployment. Use must comply with the Apache 2.0 License inherited from the base model and all applicable law. The authors accept no liability for misuse. - A Blackwell GPU…
Access requested at publisher
apache-2.0
180B parameters
transformers
flagship of the Darwin family — #1 on AIME 2026, HMMT Feb 2026, GPQA Diamond, MMLU-Pro, MMMU-Pro, LEXam and LEXam-hard, and a model that gets better by learning from its own verified work. Scores as listed on the Hugging Face official benchmark leaderboards (self-reported by each model's publisher). = #1 on that leaderboard. "—" = not reported. Darwin is VIDRAFT's measurement-driven reasoning model family — 50+ official models, 400+ community derivatives, and now two places in the GPQA Diamond top 3 (Darwin-180B-RSI #1 · Darwin-397B-ZTC #3). Darwin treats a strong open model as a parent. It measures where the parent is weak, and strengthens exactly those parts — instead of re-training…
Open weights
other
180B parameters
262,144 tokens
transformers
