ModelsThe Qwen Family Explained: The Models You Can Own
Qwen is the tier that runs on hardware you have. What the 3.6 generation offers, why dense matters, and how to read a family with many sizes.
ReadNotes on models, agents, and what inference costs
Practical writing for developers building with large language models — how they work, how to pick one, and how to keep the bill predictable.
Picking between models — and testing them on your own work.
Models
ModelsThe Z.ai GLM Family Explained: What GLM-5.2 Changes
GLM-5.2 pairs MIT licensing with a 128K output ceiling and two reasoning modes. What the family offers, and the workloads it is uniquely good at.
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ModelsVendor-Claimed vs Independent Benchmarks: Reading Both
A launch-post score and a third-party score measure different things. Where the gap comes from, and how to normalise figures before you compare them.
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ModelsVision-Language Models: What to Test Before You Commit
Native image input is now common in open-weight models, but published multimodal benchmarks predict almost nothing. Three probes that separate them.
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ModelsWhen to Switch Models: A Threshold Worth Crossing
New models ship constantly and switching is never free. The signals that justify a migration, the ones that do not, and how to run the decision.
ReadModelsWhich Open Model Fits Your GPU: A Memory-First Guide
Total parameters set the memory floor, active parameters set the speed. How to work out what your hardware can actually serve before reading a benchmark.
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ModelsWhy Benchmarks Disagree, and Which One to Believe
Two credible evaluations can rank the same models differently without either being wrong. How to read that disagreement instead of picking the flattering one.
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ModelsBest Model for Code Review: Optimise for Precision, Not Recall
An automated reviewer that flags everything gets muted within a week. How to pick and evaluate a model on false-positive rate, and why review has odd economics.
ReadModelsBest Model for Debugging: Judge It on How It Handles Being Wrong
Debugging is a search problem, so the model that wins is the one that forms testable hypotheses and abandons them fast. How to evaluate that properly.
ReadModelsBest Model for Test Generation: Coverage Is Not the Metric
Models are very good at producing tests that pass and prove nothing. Use mutation score to pick one, and treat test writing as your cheapest high-volume task.
ReadModelsDeepSeek V4 Pro: Frontier Reasoning at Commodity Prices
A 1.6T MoE that activates 49B per token, ships under MIT, and costs $0.435 per million input tokens. Where V4 Pro is strong, where it is not, and Pro versus Flash.
ReadModelsGLM-5.1 vs GLM-5.2: Same Size, Same Price, Different Job
Z.ai kept the architecture and the price list identical and changed what the model is for. The context jump, the long-horizon gains, and the token cost nobody mentions.
ReadShowing 421–432 of 446 articles