20 posts across analysis, opinion, how-to, research, and roundups.
Independent analysis, opinion, and how-to coverage of LLM tooling -- written for engineers who pay the bill. No vendor talking points, no benchmarks lifted from press kits. Every model price comes from the vendor's own page, verified the week of publication; every cost calculation shows its math. The blog sits next to the price desk and the comparisons -- prose covers the questions a table cannot answer (when does a 90% cache discount stop mattering? which workload class breaks GPT vs Claude pricing parity?). Cornerstone analysis ships first; opinion, tools how-to, research summaries, and roundups will join as the catalogue grows. Read for the math, stay for the disagreements.
Showing 8 of 20 posts — page 2 of 2
Anthropic's context editing clears stale tool results from an agent's window, cutting token use up to 84%. How it works, the config, and the prompt-cache catch.
Every legit way to get LLM compute for $0 in 2026: free API tiers, trial credits, student offers, and startup grants up to $350k — verified, with the catch on each.
Which LLM APIs are actually free to call in 2026 — not free chat apps or expiring trial credits. Verified free tiers, real rate limits, and the data catch.
The $/M input/output sticker hides cache-write premiums, a tokenizer tax, context surcharges, invisible reasoning tokens, and more. Ten verified costs, with proofs.
In 2026 the biggest lever on your LLM bill isn't which model. It's how hard the model thinks: reasoning tokens bill as output, and most models think by default.
There is no single cheapest LLM API. The winner depends on your workload shape. Verified $/M prices for classification, chat, coding, RAG, batch, and reasoning.
Caveman mode strips filler from Claude's output. The 65% headline is real for output tokens. Here's what it actually saves on your monthly bill.
Anthropic charges 10% of input for cache reads, with a 1.25x write fee. OpenAI auto-caches above 1024 tokens. The math changes which LLM is cheapest -- here is when.