Model

TL;DR Composer 2.5 is Cursor’s most capable in-house coding model yet, built on Moonshot’s open-source Kimi K2.5 checkpoint with about 85% of total training compute spent on Cursor’s own continued pretraining and RL The model is purpose-built for the agent loop inside Cursor - long-horizon tasks, hundreds of tool calls, multi-step instructions - rather than as a general-purpose chat model Cursor claims parity with Claude Opus 4.7 and GPT-5.5 on its own CursorBench v3.1 (63.2%) and a strong 79.8% on SWE-Bench Multilingual Pricing is dramatically lower: $0.50 / $2.50 per million input/output tokens on the default variant, with included usage doubled for the first week Together with SpaceXAI, Cursor is now training a much larger successor model from scratch on Colossus 2 with around 10x the compute - so 2.5 is a waypoint, not the endgame For a while, Cursor was an IDE wrapped around someone else’s models - Claude, GPT, Gemini. That story has shifted. With Composer 2.5, released this week, Cursor has shipped its most capable first-party coding model yet, and it is a serious enough piece of work that it deserves real consideration as a daily driver rather than a budget fallback. ...

For most of the LLM era the open-weight story was framed as a trailing one. Open models were cheaper, smaller, and a generation behind. That framing has not survived 2026. The gap between the best open-weight model and the best closed model is now narrow enough on most workloads that the choice is no longer “settle for less” - it is “decide what you actually need.” TL;DR Open weights have closed the headline gap. Top open-weight models are within striking distance of closed frontier models on reasoning, coding, and general knowledge benchmarks. The economics changed first. DeepSeek’s R1 made it credible that a frontier model could be trained for tens of millions, not billions - and that the weights could be released for free. Llama, Mistral, Qwen, and DeepSeek lead on different axes: Llama for broad ecosystem support, Mistral for European deployment and tool use, Qwen for multilingual and long-context work, DeepSeek for raw reasoning. Inference flexibility is the underrated win. Open weights mean you can run on your own hardware, fine-tune freely, and avoid surprises from a closed provider’s roadmap. The remaining closed-model advantages are real but narrowing - agentic depth, multimodal performance, and the polished tool-use stacks around them. Where the gap actually is in 2026 Benchmarks are imperfect, but the picture they sketch is consistent. On standard reasoning suites - MMLU, GPQA, MATH - open-weight models are within a few percentage points of the closed frontier. On coding - HumanEval, SWE-Bench - the gap is similar. On long-context retrieval, the gap is mostly gone. ...

TL;DR Ollama is a lightweight tool for running open-source language models locally with no cloud costs, rate limits, or data leaving your machine Models are managed with simple commands (ollama pull, ollama run) and can be queried via a local HTTP API on localhost:11434 Popular models include Mistral 7B for speed, Meta’s Llama 3 and Llama 4 lineups for all-around performance, and OpenClaw for code and reasoning tasks Running models locally delivers privacy, zero per-token cost, lower latency, and full offline capability You don’t need a GPU to start - a 7B model runs on 8GB of RAM, and Ollama automatically uses 4-bit quantization for larger models Running AI Models Locally with Ollama: From Setup to OpenClaw Ollama has quietly become the go-to tool for developers who want to run large language models on their own machines without relying on APIs. No cloud costs, no rate limits, no sending your prompts to third-party servers. Just you, your hardware, and a surprisingly capable AI model running locally. ...