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Covered in Models & BenchmarksAI EngineeringAgents & Protocols, where the background and the sources for this subject live.
Claude Opus 5.5 lists 20 percent below Claude Opus 5 on input and output, at $4 and $20 per million tokens. Its cache reads fall 60 percent, to $0.20, because the multiplier changed from 0.1x to 0.05x. Thinking cannot be disabled, and 25 percent more output tokens erases the output cut.
4 min read
TypeSafe publishes four use cases for Jev and two rate limits: 250,000 tokens per second and 1,200 requests a minute. Below 12,500 tokens per request the request limit binds first, so a small-state workload reaches a fraction of the token ceiling. Map-reducing a petabyte stays out of reach.
4 min read
Jev posts state and a map of questions to one endpoint and returns typed answers carrying probabilities. TypeSafe claims 0% type errors, which is a guarantee about schema conformance rather than about correctness. A noul of 0.95 is a well-formed number that can still be wrong about the thing it scored.
3 min read
Jev answers noul, choice and score questions. Choice caps at 255 options and score at 10 levels, and both return a confidence derived from their probability distribution. Noul returns no confidence field at all, because the probability it hands back already describes the uncertainty. Modelling the problem means picking among those three.
4 min read
TypeSafe AI launched Jev on 15 September 2026 as a System One model, priced at $0.042 per million input tokens with output free, and reports 70ms to 500ms against 3 to 329 seconds for frontier models. The economics are the strong part of the claim. The psychology metaphor carries less weight.
4 min read
Claude now has two compaction features with the same name. Threshold compaction, beta compact_20260112, leaves its summary block after the messages it summarises. On-demand compaction, beta compact-2026-09-04, returns a signed block that replaces them and must be sent first, or the request returns a 400.
6 min read
MCP revision 2026-07-28 requires ttlMs and cacheScope on every list result and tells servers to return tools in a deterministic order. Both are filed as minor changes. They are the most direct cost change in the revision, because tool definitions sit in the cacheable prefix of every agent step.
3 min read
Anthropic measured one Drive to Salesforce transfer falling from 150,000 tokens to 2,000 by writing code against MCP servers instead of calling tools directly. That is a code execution result rather than an argument for deleting MCP. The Agent Skills spec puts numbers on the alternative: about 100 tokens per skill at rest.
4 min read
Prompt caching stores a prefix of your prompt so later requests reuse it instead of reprocessing it. Reads are far cheaper than base input tokens, but writing the cache costs a premium and entries expire. It pays whenever a large stable prefix is reused several times inside the TTL, and loses on one-shot traffic.
6 min read