ModelsLatency-Adjusted Model Scoring: When Fast Beats Smart
Benchmarks measure what a model answers, never how long it took. How to weight latency into model selection for interactive and agentic workloads.
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
ModelsMiniMax M2.7 vs M3: How to Judge an In-Family Upgrade
Deciding whether to move from one generation of a model to the next, using what is actually verifiable about MiniMax M3 rather than a spec-sheet duel.
ReadModelsMiniMax M2.7: Should You Still Be Running It?
M2.7 predates M3 and its published specs are inconsistent across sources. How to decide whether a superseded model is still the right one for your workload.
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ModelsMIT-Licensed Models: What You Can Actually Build On
GLM-5.2 and both DeepSeek V4 variants ship under MIT. Kimi does not. Why the licence column decides more architecture than any benchmark score.
ReadModelsModel Deprecation and Migration: Planning for the Sunset
Every model you depend on will be retired. How to track deprecation notices, size the migration and move without a quality cliff on the cutover day.
ReadModelsModel Drift in Production: Detecting Quality Change With No Error
Model output quality can degrade without a single failed request. The signals that move first, how to instrument them, and what to do when one shifts.
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ModelsModel Release Cadence in 2026: Building for a Moving Target
Four major open-weight releases in four months. What that pace does to your evaluation harness, your version pins and your migration budget.
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ModelsModel Selection for Cost Ceilings: Working Backwards From a Budget
When the budget is fixed before the model is chosen, selection inverts. How to derive a per-request token allowance and pick what fits inside it.
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ModelsModels That Fit on One GPU: What You Can Actually Run
The single-accelerator threshold decides your whole deployment. Which 2026 models clear it, how to do the memory arithmetic, and what you give up.
ReadModelsMulti-Model Strategies: Splitting Work Across Two or Three Models
One model for everything is simple and usually wasteful. How to split a workload by difficulty, and when the routing complexity is not worth it.
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ModelsOpen Model Pricing Compared: What the Per-Token Rate Hides
Input and output rates across the open-weight field, and why the cheapest model per token is frequently not the cheapest per completed task.
ReadModelsOpen-Weight Model Licences Compared: What You Can Ship
MIT, custom terms with revenue thresholds, and everything between. Which 2026 open-weight models you can build a product on without conditions.
ReadShowing 61–72 of 119 articles