ModelsSWE-bench Leaders Compared: Reading the Scores Correctly
Verified and Pro are different benchmarks and the scores are not comparable. Who leads each variant in 2026, and what the numbers do and do not tell you.
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
ModelsTerminal-Bench Explained: Scoring Agents That Run Commands
Terminal-Bench measures whether a model can be left alone in a shell and still finish the job. What it runs, what it misses, and how to read a score.
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ModelsTerminal-Bench Leaders: The Benchmark That Predicts Agents
Terminal-Bench puts a model in a real shell and scores what happens. Who leads version 2.1, why the ordering differs from SWE-bench, and how to read it.
ReadModelsThe Artificial Analysis Index Explained: What It Does Measure
A single number that ranks every model is convenient and easy to misread. What the AA Intelligence Index aggregates, and where it stops being useful.
ReadModelsThe Best Model for Students on a Real Budget
What actually matters when your AI budget is pocket money: token economics, free tiers, open weights on a laptop, and when to spend the extra.
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ModelsThe Cheapest Frontier Models, Priced Honestly
List prices across the open-weight field span more than twenty to one. Where the cheap models are genuinely sufficient, and where the gap is real.
ReadModelsThe DeepSeek V4 Family Explained: Pro, Flash and When
Two models, one release date, a 3x gap in active parameters and a 3x gap in price. How to split traffic between DeepSeek V4 Pro and Flash sensibly.
ReadModelsThe MiniMax Family Explained: M3 and Native Multimodality
MiniMax M3 pairs a 1M window with native multimodality, and its published pricing genuinely disagrees between sources. What to verify before you commit.
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ModelsThe Moonshot Kimi Family Explained: K2.6 and K3
Two very different models share the Kimi name. What separates K2.6 from K3 on scale, context and licence, and which one your workload actually wants.
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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.
ReadModelsThe 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.
ReadModelsVendor-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.
ReadShowing 85–96 of 119 articles