News & guides
Terminal-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.
· 9 min read
Terminal-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.
· 9 min read
The 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.
· 9 min readThe 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.
· 9 min read
The 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.
· 9 min readThe 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.
· 9 min readThe 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.
· 9 min read
The 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.
· 9 min read
The 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.
· 9 min readThe 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.
· 9 min readVendor-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.
· 9 min readVision-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.
· 9 min readReady to try it?
Every model behind one key. Start free and upgrade when it sticks.