Three companies launched decision-model APIs in nine days. OpenAI charges $0.10 per million input tokens. Perplexity charges $0.02. Microsoft sits in the middle at $0.042. All three return probabilities instead of prose, all three bill $0 for output — and Perplexity's weights are open source. Here's the full scoreboard, what the benchmarks actually say, and which one to prototype with.
What a decision model is (30 seconds)
A decision model doesn't write text. You send it a state (text, JSON, images) plus one or more typed questions, and it returns structured answers: a yes/no probability, a choice from your list with confidence, or an ordered score. The category got its name from TypeSafe's Jev, launched September 2026. Since October 1, it's been launch week every week.
The scoreboard
| Model | Launched | Input price | Output price | Weights | Context |
|---|---|---|---|---|---|
| OpenAI Decisions API (gpt-6-luna) | Oct 6 | $0.10 / 1M | $0 | Closed | undisclosed |
| Perplexity pplx-decider-v1-27b | Oct 1 | $0.04 / 1M | $0 | Apache 2.0 | 262k |
| Perplexity pplx-decider-v1.1-27b | Oct 7 | $0.02 / 1M | $0 | Apache 2.0 | 262k |
| Microsoft-Decision-1 | Oct 9 | $0.042 / 1M | $0 | undisclosed | undisclosed |
| TypeSafe Jev | Sep 2026 | varies | varies | — | — |
| Cloudflare Clef / Clef-flash | Oct 1 | undisclosed | undisclosed | open | — |
| AWS Strands Decider 2B | Oct 1 | undisclosed | undisclosed | — | — |
Seven launches in about five weeks. The pricing spread is 5x from cheapest to most expensive — for APIs that do structurally the same thing.
Why is Perplexity so much cheaper?
Two reasons, both verifiable. First, the model is small: 27B parameters, fine-tuned from Qwen3.8-27B, versus whatever frontier-scale model sits behind OpenAI's endpoint. Smaller model, cheaper inference — the math is straightforward.
Second, Perplexity open-sourced the weights under Apache 2.0. You can self-host v1.1 if you have a GPU with roughly 49 GiB of VRAM. That puts a ceiling on what they can charge: anyone with hardware can run it for the cost of electricity. OpenAI's closed model has no such ceiling.
The honest caveat: cheaper per token is not cheaper per correct decision. A $0.02 model that's wrong 15% of the time costs more than a $0.10 model that's wrong 5% of the time — once you price in the cost of being wrong.
What do the benchmarks actually say?
Perplexity's own numbers put pplx-decider-v1 at 85.71% versus Jev's 84.51% across an 11-benchmark, 7,210-sample panel, with the widest gap on RAGTruth (88.80% vs 77.27%). Jev still leads on 4 of the 11 tests, including WinoGrande and BBH.
Treat every number in that paragraph as a company claim. Perplexity's docs publish no benchmark table — the figures come from their announcement, not an independent rerun. The community Decision Index leaderboard (a Hugging Face project tracking open Jev reproductions) shows Perplexity at 62.75 vs Jev at 60.11 under its v0.3 scoring — but the formula changed between editions, so even those two numbers aren't directly comparable.
An independent evaluation by SAS (about 50 examples per dataset, three runs each) measured v1 at 0.95 accuracy on hand-written cases but 0.80 on public datasets, with calibration error 0.06–0.09 and median latency around 0.3s. Small sample, but it's the closest thing to a third-party number available.
Bottom line: Perplexity's model looks competitive with Jev on Perplexity's tests. Nobody has published a head-to-head of all seven against each other. The benchmark war is marketing until independents run the same suite on every model.
How fast are they?
Perplexity publishes latency figures from its own tests (September 30): under 2 seconds for a few hundred input tokens, ~5s at 90k tokens, ~14s at 190k, ~23s just under the 262k input ceiling. OpenAI publishes a "10x faster than the Responses API" claim with no benchmark attached. Microsoft and the others publish nothing.
Latency scales with input length on every one of these — a decision over a 200k-token document is a different beast than a decision over a tweet. Test on your actual input sizes.
Which one should you prototype with?
If you want cheapest right now: Perplexity v1.1 at $0.02/M. Half the price of v1, open weights as a fallback, no per-request fee.
If you want open weights: Perplexity, full stop. It's the only decision model in the table you can self-host today.
If you're already on OpenAI infra: the Decisions API keeps you in one billing account and one SDK. You're paying 5x the per-token rate for that convenience — fine at prototype scale, worth re-examining at volume.
If you need a second opinion on accuracy: run the same 200 labeled examples through two of them and compute a confusion matrix before committing.
The pattern underneath
This is the same unbundling I wrote about earlier: the industry spent years selling one primitive (generate text) and developers built classifiers out of prose plus parsing. Now classification is its own product category with its own price war — seven entrants in five weeks, prices already down 5x from top to bottom.
Expect the same thing to happen to every other "LLM wearing a costume" workload. Reranking, extraction, and moderation are next.
TL;DR
- 7 decision-model launches in ~5 weeks: OpenAI ($0.10), Perplexity v1 ($0.04) and v1.1 ($0.02), Microsoft ($0.042), plus Jev, Cloudflare Clef, AWS Strands Decider
- Perplexity is cheapest AND open-weight (Apache 2.0, self-hostable at ~49 GiB VRAM)
- All benchmarks cited are company-provided; no independent head-to-head exists yet
- Prototype on the cheapest, validate accuracy on your own labeled data, then decide
Sources: Perplexity docs and timeline (Oct 1–7, 2026), OpenAI Decisions guide, tsnmedia.org comparison (Oct 2026), explainx.ai decision-models guide, AI Weekly alert, SAS Model Card evaluation, OpenRouter pricing pages. Benchmark figures are vendor-provided unless noted; no independent cross-model benchmark was available at writing time.
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