Prompting vs RAG vs fine-tuning: how to choose
Does not know something means retrieval. Does not behave right means fine-tuning. Was not told properly means prompting, and that is most cases.
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Comparisons
Head-to-head tests of AI models and tools, judged on the same tasks.
Flagship vs entry-tier AI models: when the cheap one is enough
The frontier model wins the comparison. The cheap model wins the invoice. Which matters depends entirely on your volume.
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Open-weight vs closed AI models: what each one really costs
Fixed hardware cost against per-token billing, plus deprecation risk, data residency and the licence traps hiding inside models called open.
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Reasoning vs standard AI models: when the extra cost pays
Reasoning tokens bill as output. A model thinking for 2,000 tokens before a 200-token answer charges you eleven times what you see.
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AI model pricing compared: same benchmark, five times the bill
Grok 4.6 and GPT-5.6 Sol Max tie at 61 on the AA index. One charges $6 per million output tokens, the other $30. The maths of that gap.
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