ModelsGDPval Explained: Benchmarking Real Professional Work
GDPval scores models on deliverables from real occupations, graded by experts. What that measures, why it is not a coding benchmark, and how to read 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
ModelsGLM-5.2 vs DeepSeek V4 Flash: Ten Times the Price
Both are MIT-licensed with 1M context, but one costs ten times the other. Where the expensive model earns the gap, and where the cheap one quietly wins.
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ModelsGLM-5.2 vs MiniMax M3: Long-Horizon Code or Cheap Multimodal
Both ship 1M context and target coding. GLM-5.2 is built for unattended agent runs; M3 is multimodal and cheaper. Which one your workload actually needs.
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ModelsGLM-5.2 vs Qwen 3.6: Sparse and Huge, or Dense and Small
Both are permissively licensed and strong at code. One is a 744B mixture-of-experts, the other a dense 27B on a single GPU. The architecture is the decision.
ReadModelsHow to Read a Model Card Without Being Misled
Model cards mix hard facts, marketing and careful omissions. Which fields are reliable, which need checking, and what an absent section tells you.
ReadModelsKimi K2.6 Guide: Specs, Pricing and Where It Still Wins
Moonshot shipped K3 four months after K2.6, but the older model did not become useless. What K2.6 is, what it costs, and the jobs it is still the right pick for.
ReadModelsKimi K2.6 vs DeepSeek V4 Flash: Four Axes That Decide It
Two models released three days apart with opposite design goals. Active parameters, context length, licensing and vision decide which one fits your workload.
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ModelsKimi K2.6 vs DeepSeek V4 Pro: Sight or Depth
K2.6 sees images at 256K context under a custom licence. V4 Pro is text-only, MIT, 1M context, and reasons harder. Two models that barely overlap.
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ModelsKimi K2.6 vs GLM-5.2: Vision and 256K, or MIT and 1M
K2.6 sees images and stops at 256K context under a custom licence. GLM-5.2 is text-only, MIT, and takes 1M tokens. Two clean trade-offs, no overlap.
ReadModelsKimi K2.6 vs MiniMax M3: Two Ways to Do Multimodal
Both take images natively, but one gives you 256K of context at a known price and the other 1M at a price sources disagree on. How to pick between them.
ReadModelsKimi K2.6 vs Qwen 3.6: Hosted MoE or a Single GPU
A trillion-parameter vision model you rent against a dense 27B you can own. The comparison is about deployment shape, not a few points of benchmark difference.
ReadModelsKimi K3 vs DeepSeek V4 Flash: The Widest Gap in Open Weights
The strongest open-weight model against one of the cheapest, both with 1M context. When a twenty-fold price difference is worth paying and when it is waste.
ReadShowing 373–384 of 446 articles