Inception's Mercury Decide, a calibrated decision model, launches free on OpenRouter and ranks third on JevBench v1.6.1

• Versions: inception/mercury-decide-20260930 (paid) and :free (systemonemodels).
• Answer types: a choice from up to 255 options, a score with up to 10 levels, or yes/no.
• Price: listed input price is $0.02 per million tokens; output is free.
• API: the Decisions API (/v1/decisions from Inception, /v1/systemone via OpenRouter). It is not OpenAI chat-compatible and has no streaming or tool calling (AlphaSignal).
• Context window, conflicting figures:
• 32,768 tokens per the video, quoting OpenRouter (video).
• 33K per OpenRouter's listing (KuCoin).
• 65,536 per systemonemodels.

• Architecture: reported to use the same diffusion language modeling as Mercury 2.5, which refines many positions in parallel instead of generating tokens one at a time (AlphaSignal).
• Where the probability comes from: it is read off the model's predictive distribution rather than written out as text. That is why output tokens cost nothing.
• How JevBench scores models: a geometric mean of chance-corrected Intelligence, Calibration, Speed and Cost, weighted 25% each, on frozen hashed cases (JevBench repo).
• Integration: a separate endpoint is required. The GitHub project wrote a custom provider rather than reusing its litellm setup (issue #454).

• "Most intelligent decision model" (Inception, JevBench v1.4): partly consistent with independent results. On v1.6.1, its Intelligence score is 65.91 versus 65.59 for Sage, but it ranks 3rd overall. Measured results there:
• Choice accuracy 80.8%, score accuracy 71.2%.
• Calibration score 81.34.
• Cost $0.0184 per 1,000 items.

(Benchmark Heaven)

• "Up to 14 decisions/s": a ceiling from the vendor. Measured latency is 0.33 s at the median and 0.85 s at p95, slower than Jev's 0.24/0.30 s. The video gives 0.44 s median on OpenRouter and itself calls 14/s a ceiling (video).
• Chess match against Jev: promoted, but its result is not described in the video transcript (video).

• Earlier Inception models: Mercury Coder established the company's speed-focused diffusion approach (arXiv).
• The category: typed decision models with calibrated probabilities predate this launch. Examples include TypeSafe AI's Jev and open-weight models such as Quyet-1.0-Large, which leads the open-weights board (Benchmark Heaven).
• "System one": the video frames the model as fast, gut-level thinking in contrast to slow deliberate reasoning (video).

• Context limits bite: 23 of 1,500 benchmark items went unanswered because they exceeded the context window (Benchmark Heaven).
• Thin independent evidence: one aggregator flags it as "weak evidence" because it appears on only one board (hull PR #52).
• Unpublished limits: rate limits are undisclosed, and no independent calibration figures beyond JevBench were found (systemonemodels).
• Benchmarks are not your data: JevBench matches the model's intended workload but cannot establish accuracy on a specific production dataset (AlphaSignal).
• The commentary's thresholds are illustrative: the 95% / 60% / 25% "traffic light" cutoffs are not validated, and the post and video both promote a paid community (video).

• Good fits: routing, triage, rubric grading and agent memory filtering. These are high-volume, one-word decisions where a probability helps decide when a human should step in (video).
• Test before trusting: the commentary's advice is to run about 20 labeled examples first. A larger held-out set is needed to set thresholds that hold up.
• Shop the category: Sage and Jev score close to Mercury Decide on JevBench, and Jev has lower latency, so it is worth comparing a few.
• Early-access caveat: usage limits and conflicting specs argue against putting production-critical work on it yet (video).