タグ: Open-source model

In late May 2026, MiniMax’s Head of Engineering, Skyler Miao, broke cover with the first technical preview of MiniMax M3, the Shanghai lab’s next flagship language model. The pitch is sharp: 9.7× faster prefill and 15.6× faster decoding at 1-million-token context versus the current MiniMax-M2.7. The trick is reintroducing sparse attention, an architecture MiniMax explicitly killed in its own M2 generation a year earlier. That reversal is the story. In this preview we cover what’s confirmed, what’s still teased, when M3 is likely to ship, and how the speed claims hold up. We also look at what it means for the race against Claude, GPT, Gemini, and DeepSeek, and what […]

Kimi K2.6 is the new open-source AI model from China’s Moonshot AI, released on April 20, 2026. It just tied GPT-5.5 on the toughest coding benchmark in the industry, at roughly 80% less per million tokens. With 1 trillion total parameters, 32 billion active per token, and a 256K-token context window, K2.6 is now the #1 open-weight model on the Artificial Analysis Intelligence Index. In this article you will find what Kimi K2.6 actually does well, where it still loses to Claude Opus 4.7 and GPT-5.5, exactly how much it costs, and the simplest ways to use it on a Mac without renting your own GPU. We will also cover […]

The five open-source AI models worth running on an M5 Mac in April 2026 are Mistral 7B (16 GB), Qwen 3.5 9B (24 GB), DeepSeek R1 Distill Qwen 14B (24 GB), Gemma 4 26B-A4B (32 GB), and Qwen 3.5 35B-A3B (32 GB+). Minimum unified memory is 16 GB, the sweet spot is 24 GB, and anything above 30B parameters needs 32 GB or more. Frontier open-source models like GLM-5 or the full DeepSeek R1 need hundreds of GBs of VRAM, so skip those entirely. The five below are what actually fits. Apple’s own MLX benchmarks show the M5 chip delivering a 3 to 4 times speedup on time-to-first-token compared to […]

MiniMax M2.5 scores 80.2% on SWE-Bench Verified, runs at 100 tokens/sec, and costs $1/hour. Here’s what we know — and what’s still unverified.

On November 6, 2025, Alibaba-backed Moonshot AI released Kimi K2 Thinking, its most advanced open-source model yet. It’s the first reasoning-focused variant in the Kimi K2 family and marks a major step forward in long-context, multi-step reasoning and autonomous tool use. Kimi K2 Thinking immediately made headlines for its performance: it set new state-of-the-art scores on several open benchmarks, including Humanity’s Last Exam (HLE) and BrowseComp, where it outperformed closed models like GPT-5and Claude Sonnet 4.5. Unlike its competitors, K2 Thinking is fully open-weight, offering public access to its architecture, weights, and API, with only minimal license restrictions. Its release marks a key moment in the open vs. closed model race. While U.S. labs like OpenAI, Anthropic, and xAI keep their top […]

Moonshot AI, Chinese startup backed by Alibaba and Tencent, is rolling out an upgraded version of its open‑weight open-source large language model, Kimi K2. The new build Kimi K2‑0905, introduces a 256,000-token context window, improved coding performance, and retains its permissive modified MIT license. Although early beta access was briefly mentioned on the company’s Discord before details were removed, the model is now fully available to the public. Weights and a complete model card are live on Hugging Face, making it easy for developers to download, run, and fine-tune the model locally. Moonshot’s Meteoric Rise Founded in March 2023, Moonshot AI has grown at breakneck speed. In just over a year, the startup […]

OpenAI just dropped its first open-weight models since GPT-2 — a big move that shifts its usual strategy. Announced on August 5, the new models, called gpt-oss, are fully open-source under the Apache 2.0 license. That means anyone can download them, tweak them, and run them locally without any restrictions. This release comes as open-source AI is gaining momentum, with players like Meta and Mistral pushing strong alternatives. gpt-oss includes two models: Both are optimized to run directly on local hardware — no internet, no API needed — and still support features like chain-of-thought reasoning, tool use, and large context windows. While OpenAI continues to build out its paid API offerings, […]