ModelsChoosing a Model for Non-English Codebases
Comments, identifiers and issues in another language change tokenisation, cost and accuracy. What to test and how to pick a model that handles it.
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
ModelsContext Windows Compared: Input, Output and What It Costs
Filling a million-token window costs between fourteen cents and three dollars depending on the model. The full comparison, including output ceilings.
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ModelsCost-Adjusted Model Scoring: Ranking by Value, Not Capability
Capability leaderboards ignore price entirely. How to build a score that reflects what a model costs to run on your workload, and where it misleads.
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ModelsDeepSeek V4 Flash vs MiniMax M3: Cheapest Against Multimodal
Two budget models with 1M context. One is cheaper and text-only under MIT, the other sees images. The choice is almost entirely about input type.
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ModelsDeepSeek V4 Flash vs Qwen 3.6: Rent Cheap or Own It
A 13B-active MoE at fourteen cents per million tokens against a dense 27B you can run yourself. The crossover point is lower than most teams assume.
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ModelsDeepSeek V4 Flash: 1M Context at 13B Active Parameters
The cheap half of the DeepSeek V4 family keeps the million-token window and drops active parameters to 13B. What that trade buys, and where it stops working.
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ModelsDeepSeek V4 Pro vs Qwen 3.6: 1.6T or 27B, for 3 Points
V4 Pro reports 80.6 percent on SWE-bench Verified, Qwen 3.6 27B reports 77.2. One is sixty times larger. What that tells you about model size in 2026.
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ModelsDeepSeek V4 Pro vs V4 Flash: Same Window, Different Brain
Both ship 1M context and an MIT licence. Pro activates 49B parameters per token, Flash 13B. Where that single difference decides which one you should run.
ReadModelsDense vs Mixture-of-Experts: Which You Can Actually Run
MoE models are cheap to compute and expensive to hold. Dense models are the reverse. The architecture decides your deployment more than your benchmark scores.
ReadModelsEvaluating Models on Your Own Codebase
Public benchmarks rank a sample that is not your repository. How to choose tasks, source ground truth and size a set that actually predicts your work.
ReadModelsEvery 1M-Context Model Compared: Which Window Is Real
Five open-weight models now advertise 1M tokens. Advertised context and usable context are different things. How to tell which window actually holds up.
ReadModelsFallback Model Selection: Choosing Your Second Model Well
A fallback that behaves nothing like your primary turns an outage into a quality incident. How to pick a second model and prove it works.
ReadShowing 361–372 of 446 articles