Every Prompts & Optimization comparison and buyer's guide for building AI agents — 19 pieces and counting. Each is a head-to-head or a “best X for Y” roundup with a sources-backed verdict.
DeepSeek-OCR, Glyph, and AgentOCR all render text into images so a vision model can read more with fewer tokens. The compression is real — but a December rebuttal says the honest competitor isn't full text, it's just deleting the old stuff.
Every prompt tool sells the same feature — edit the prompt without shipping code. Stated precisely, that feature is: change production behavior with no PR, no eval run, and no pinned model. Here's how to keep the convenience without the shadow deploy.
They both have 'caching' in the name and both promise to slash your token spend, but they cache different things at different layers with different safety profiles. One's worst case is a cache miss. The other's worst case is a confidently wrong answer.
M3 claims to beat GPT-5.5 on SWE-bench Pro while running weights you can host yourself. The benchmark row is the least trustworthy thing in the release — and the architecture is the most.
Map-reduce's 'reduce' step quietly re-creates the exact overflow you were escaping. Refine can't parallelize. And in 2026 the fastest-improving option is often to stop summarizing and put the whole document in a million-token window — if you can pay the middle.
A chatbot's system prompt sets a personality. An agent's is control logic the model rereads on every turn of the loop. Stop writing a persona and write a policy.
Most agents summarize their context when a token counter trips. A 2026 result argues the counter is the wrong trigger — and that letting the model decide is both cheaper and more accurate.
Both kinds of cache hit read at the same discount, so cost-per-hit is the wrong thing to choose on. The real split is a guarantee you pay for versus a freebie you can't shape.
Prompt and semantic caches store the model's work and fail cheaply. Tool-result caching stores the world's — and it forces a question every agent codebase has dodged: which tools are safe to cache?
The number on the spec sheet is a memory allocation, not a comprehension score. A needle test passing at 1M tokens tells you the model can find a string — not that it can use the context. Here's the benchmark that measures the difference.
The reflex is to wrap everything in JSON because it's 'structured.' On the way into a prompt that's a token tax; on the way out it's an accuracy tax. The right answer is split, not single.
Every provider now sells the same ~90% discount on repeated context. The number on the brochure is not where the bills actually diverge — three quieter terms are.
A long-running agent fails when its window fills with stale tool output. Anthropic ships three levers for that — and the trap is treating them as competitors instead of a division of labor.
They share a word and almost nothing else. One discounts your bill, one reuses GPU memory, one can hand back the wrong answer — and teams keep enabling the one they didn't mean.
An agent that runs for a hundred turns will blow past any context window. The fix is three different mechanisms — and the order you reach for them is the opposite of most people's instinct.
GEPA optimizes prompts by reading the agent's own failure traces in plain language instead of chasing a scalar score — and reports beating an RL baseline with up to 35x fewer rollouts.
Tools that shrink a prompt by 2–20x before it hits the model promise a smaller token bill. Whether you actually save anything depends on a comparison nobody runs first — compression versus caching.
Prompt engineering optimized a string. Context engineering manages a finite, decaying budget — because the context window is not a bucket you fill, it is attention that rots as it fills.
Three Python libraries that treat your prompt as a parameter to be tuned, not a string to be hand-crafted. They disagree about what the optimizer needs from you — and that's the whole decision.
Not buyer's guides — the news, teardowns, and explainers behind this topic.