Benchmarks · Independent lab note · Published September 2, 2026

What changed when Gemini could inspect video agentically?

We tested Gemini 3.7 Flash in agentic and static video-processing modes on the same synthetic long-form editing tasks. Agentic found more brief events. Static was faster and won several broad-retrieval measures. The honest result is mixed.

6 × 10-minute videos5 valid matched pairsprotocol v0.4exploratory

The short version

Across five valid pairs, agentic processing found 18 of 20 brief events; static found 15. Agentic also improved edit-decision classification, but static retrieved broad moments slightly better, localized boundaries more tightly, answered more frozen evidence checks, while agentic took 81% longer.

18/20
brief events found by agentic
agentic +3 events
15/20
brief events found by static
same model
81.32%
more planning latency for agentic
static faster
1 of 6
agentic outputs rejected by the frozen JSON contract
no repair or retry

Automated scorecard

MetricAgenticStaticObserved edge
Moment-retrieval F10.26670.3000Static +0.0333
Short-event recall0.90000.7500Agentic +0.1500
Boundary error, median1.20 s1.00 sStatic by 0.20 s
Boundary error, p904.00 s2.80 sStatic by 1.20 s
Frozen-rule evidence accuracy0.83330.9000Static +0.0667
Edit-decision macro F10.68070.5481Agentic +0.1326
Automated brief pass rate4/54/5Tie
Total accounted tokens392,902289,108Static used 26.42% fewer
Paid-equivalent API cost$0.330778$0.254673Static cost 23.01% less
Total planning latency243.608 s134.352 sStatic was 44.85% faster

Percentages in the table describe the reduction from the agentic total to the static total. The paired aggregate can also be stated as agentic using 35.90% more tokens, costing 29.88% more, and taking 81.32% longer.

What the ten metrics mean

1. Moment retrieval

Whether the response found the expected broad highlights in each video.

2. Short-event recall

Whether it caught two-second-or-shorter visual and audio events that are easy to miss.

3. Boundary accuracy

How closely predicted start and end times matched the frozen ground truth.

4. Evidence accuracy

A deterministic concept-and-time-overlap check—not a human semantic judgment.

5. Edit-decision quality

Macro F1 across the frozen edit labels expected for each candidate moment.

6. Story coherence

Not measured. Requires rendered cuts and blinded editor ratings.

7. Brief compliance

Whether the proposed edit satisfied deterministic duration and structure constraints.

8. Cut-seam defects

Not measured. Requires shared renders, automatic QA, and blinded review.

9. Compute efficiency

Accounted tokens and paid-equivalent API cost for the five valid pairs.

10. Planning latency

End-to-end model planning time recorded for each valid response.

Per-video results

FixtureModeMoment F1Short recallEvidenceEdit F1BriefLatency
screen-01Agentic0.16671.000.83330.6741pass45.016 s
screen-01Static0.16670.750.83330.3333pass33.286 s
screen-02Agentic0.16671.000.83330.2222pass51.779 s
screen-02Static0.33330.750.91670.3333pass26.724 s
solo-01Agentic0.16671.000.91670.7407pass48.165 s
solo-01Static0.16671.001.00000.4074pass23.481 s
podcast-01Agentic0.50000.500.75000.8333pass39.201 s
podcast-01Static0.66670.500.83331.0000pass25.869 s
podcast-02Agentic0.33331.000.83330.9333fail: 126 s59.447 s
podcast-02Static0.16670.750.91670.6667fail: 130 s24.992 s
solo-02Agenticinvalid JSON-only output; excluded without repair or retry102.614 s
solo-02Staticnot run by the frozen conditional gate

How we ran it

  1. Generated six 600-second, 960×540 videos locally from programmatic graphics, system-synthesized voices, and generated tones.
  2. Hashed every video and froze its ground truth, evidence rules, prompt, schema, and editing brief before the request.
  3. Used Gemini 3.7 Flash with low thinking, an 8,192-token output ceiling, store: false, and either agentic or static video processing.
  4. Required a provider processing call to verify the agentic arm rather than trusting a label in the response.
  5. Rejected malformed output without repair or unequal retry. Solo-02 failed this gate, so its static arm did not run.
  6. Scored the ten valid outputs with committed deterministic scripts. Earlier protocol versions were excluded from quality aggregation.

Limits and open questions

Small, synthetic sample

Five valid pairs are not enough for significance testing and may not represent real creator footage.

One-sided attrition

One agentic result was malformed. The protocol correctly excluded it, but the comparison ends with only five pairs.

No human viewing panel

Story coherence and cut quality cannot be inferred from JSON planning outputs alone.

No PaperEdits arm

A future comparison needs a frozen PaperEdits commit and configuration applied to the identical fixtures.

Total paid-equivalent spend across every protocol version and invalid attempt was $0.826280. The final paired economic totals above include only the five valid pairs. This evaluation was conducted independently by the PaperEdits team and was not sponsored or endorsed by Google.

Inspect the evidence

The protocol, prompts, ground truth, model responses, deterministic scorers, failure record, and aggregate are committed publicly. Reproducibility matters more than a polished headline.