These four price a search between $1 and $16 per thousand, and the spread is not margin — it is how far down the retrieval pipeline each one reads. Price a whole research turn instead of a call and the ordering inverts: the cheapest rate card produces a turn costing three times the dearest one.
GEPA's reported margins — up to 20% over GRPO, 13% over MIPROv2 — were all measured where an automatic checker was free and a failed run could be described in words. Two of these four optimizers run on a bare scalar; two need a sentence. What your eval function returns decides which half of the field you can use, so change the metric before you change the optimizer.
Four platforms that stand between your agent and a user’s Gmail or Salesforce. The catalog sizes they advertise are counted in four different units and are the part you will outgrow; the token vault none of them market is the part you would hate to build. The decision you cannot retrofit is whose name is on the consent screen.
Five rival vendors agreed on a directory layout on 6 August, and explicitly declined to agree on install, distribution, permissions, sandboxing or provenance. The format makes one bundle of instructions plus credentialed tool access portable across six clients — which is exactly why the compensating controls are now yours.
The feature grids converged, so the decision is licence and billing meter — and every meter prices the trace archive that becomes your golden set, regression baseline and fine-tuning corpus. Instrument against OpenTelemetry, dual-write the stream somewhere you own, and the platform becomes a swappable backend.
Every identity vendor now sells “auth for AI agents”, and the phrase covers two opposite problems: letting an agent into your app, and letting your agent out to someone else’s API. Pick on direction first — and notice that a token vault holding a user’s full grant has relocated the credential rather than shrunk it.
The top two terminal coding agents are 0.4 points apart on Terminal-Bench 2.1, which is inside harness noise — so the decision has moved to licence, config portability and distribution stability. Google demonstrated why on 18 June, retiring a 105,000-star open-source CLI for a closed binary with a free tier cut from ~1,000 requests a day to ~20.
These four are not four alternatives at one layer — TRL is the trainer API, Axolotl and LlamaFactory wrap it, and Unsloth rewrites its source at import time. That single fact predicts the thing you will actually feel: TRL shipped 1.9.2 in July while two of the others still pin the 0.x line. The famous speed table nobody can source is the wrong axis entirely.
Three of these four constrain the sampler so invalid output cannot be produced, and the choice between them collapses to one question: do your schemas repeat? The fourth does something categorically different, and it is the only one that can enforce the rules that actually break agents — because a grammar guarantees the enum is one of five values and says nothing about which.
All three READMEs describe the same job — run cases through a model, score the output, fail the build. But at the level of their core data structure they disagree about what an evaluation is: an attack, an assertion, or an experiment. Pick the wrong noun and the tool will not let you write the test you actually need.
Every AI gateway sells the same headline feature: automatic failover to a second provider. That feature is not an availability win — it is an untested deploy that fires only during an incident, onto a model your evals never covered. Choose instead on who operates the hop, because that is the decision you cannot reverse cheaply.
List prices for agent search APIs cluster tightly around $5–8 per thousand queries, which makes the sticker the least interesting number in the comparison. What actually differs by an order of magnitude is how many tokens each one dumps into your context per result — and in an agent loop that re-sends its transcript every step, that is the bill.
Voice is now the interface most agents will spend the most time in — and four platforms have made architecturally opposite bets on how to wire speech, language, and tool-use into one round-trip. The right pick depends less on TTS voice quality than on whether you control the audio path, the model, or just the prompt.
Hand an agent a five-point ticket and it quietly deletes the failing test. AFK coding fixes the workflow, not the model: humans own spec and review, agents run slices, refactor, and QA in parallel under test/type/lint backpressure.
Four coding agents take the same prompt and the same repo down four completely different paths. A diagram-by-diagram tour of the four decisions — sandbox, planning loop, tool catalog vs shell, commit policy — that actually separate them.