DeepSeek released R1 alongside a family of distilled smaller models — ranging from 1.5B to 70B parameters — specifically so practitioners could run powerful reasoning locally without a data centre. The choice between 14B, 32B, and 70B is not simply “bigger is better”: it involves your hardware, your tolerance for latency, and how much of … Read more
Test-time compute scaling is the observation that spending more compute during inference — having a model think longer rather than answer immediately — improves output quality in ways that parallel the improvements from spending more compute during training. It is the key insight behind reasoning models: instead of making the model larger or training it … Read more
Budget forcing is a technique for constraining how much a reasoning model thinks before answering. It works by explicitly limiting the number of thinking tokens the model can use — either by setting a hard token cap, by injecting a prompt at a specific point in the thinking trace, or by using a model API’s … Read more
Reasoning models perform substantially better than standard language models on hard coding tasks. On SWE-bench Verified — a benchmark that evaluates models on real GitHub issues from major open-source repositories, where the model must write code changes that make tests pass — the performance gap is striking. Standard GPT-4o class models score around 33–38%; o3 … Read more
When DeepSeek released R1 in January 2025, it was the first open-weights reasoning model to match OpenAI o1’s benchmark performance on mathematics and coding — and it arrived at a fraction of the training cost, with weights anyone could download and run locally. The comparison between DeepSeek R1 and OpenAI o1 is significant not just … Read more
OpenAI’s o-series models replaced the traditional “scale up the base model” approach with an explicit reasoning step: the model thinks through a problem before producing an answer, spending more compute at inference time rather than training time. o3 and o4-mini are the current generation of this series. They differ not just in size but in … Read more
Reasoning models are language models that think before they answer. Instead of predicting the next token immediately from the input, they generate an extended internal monologue — working through a problem step by step, checking their own logic, backtracking when something does not hold, and only then producing a final response. This is what “chain … Read more
Reasoning models and standard language models are not competing products — they are different tools with different strengths, costs, and failure modes. Choosing between them is a matter of matching the tool to the task, not a one-time decision about which technology is superior. The practical question is: given a specific task, does it benefit … Read more
Chain-of-thought and zero-shot prompting sit at opposite ends of the reasoning depth spectrum. Zero-shot asks a model for a direct answer with no examples or reasoning scaffolding. Chain-of-thought prompts the model to work through its reasoning before producing a final answer. For standard language models, the choice between them has a measurable impact on accuracy … Read more
QwQ is Alibaba’s open-weights reasoning model, released in late 2024 as part of the Qwen model family. It is a 32B parameter model trained specifically to reason through hard problems using extended chain-of-thought, and it achieved benchmark scores competitive with OpenAI o1-preview and DeepSeek R1 at the time of release. QwQ is notable for a … Read more
