Cloud baseline

Mistral Large 4 — 0.5462 on Gauntlet 7.2

mistralai/mistral-large-4-0 · scorer sb-7.2

Mistral Large 4, single model via OpenRoutermistralai/mistral-large-4-0 · single agentOct 19, 2026
Overall
0.5462
Excellent
no
Benchmark
Gauntlet 7.2
Model build
143m 59s
Prompt tok
19.4M
Gen tok
574.0k

Recorded inference cost

$4.93

From the saved OpenRouter generation billing summary (156 recorded requests). Includes auxiliary model calls made during the build. Scoring reuses the saved build; this amount describes the original inference spend.

Tier breakdown

A · structure & runtime1.00
B · behaviour0.91
C · vendor contract0.81
D · finesse0.90

Scoring detail

How this score was built

Per-check scorer output, as posted by the app. Expand a section to inspect its checks.

Earned credit before admission

(0.88 × 0.9277 core + 0.12 × 0.92 gate × 0.85 excellence) × 0.6000 critical = 0.5462

Critical defects compound a multiplier on the whole score (pre-severity 0.9104):

b_money_rendered 0.00 → ×0.60

The core (88% of the total) is the weighted mean of the thirteen measured tiers. The last 12% is the excellence slice: it unlocks in proportion to the perfection conditions below (14 of 16 met here), then pays out at the excellence tier's own measured mean. Core 0.8164 + excellence 0.0940.

j_first_use 1.00j_workflow_journey 1.00j_error_state 1.00j_empty_state 1.00console_clean 1.00v_responsive_375 1.00v_dates_readable 1.00t_scene_binding 1.00x_conservation_residual 1.00r_no_row_loss 1.00p_drag_frames 1.00p_idle_flatness 1.00p_stream_apply 0.75p_under_stream 1.00p_api_latency 1.00p_sync_wall 1.00

Earned score 0.546 · Admission ceiling 0.709 · Final score 0.546

Final score is the lower of earned credit and the admission ceiling. Passing admission adds no points.

Visible: passedMatching: passedGood: not metVisual excellence: not metBackend excellence: passed

3D interaction and overview legibility: t_pick_buffer, t_coast_identity, t_coast_reality, q_overview_legibility (maximum 0.709)

Event animation and backend recovery: m_committed_event_replay (maximum 0.899)

Token rates

Measured by the engine itself, one record per completed model call: prefill rate is prompt tokens over time-to-first-token, generation rate is completion tokens over the decode window. Medians per node.

NodeCallsPrompt tokGen tokPrefill tok/sGen tok/s
gemini4390,364146,00228104.1261.1
mistral15019,036,980427,99852696.789.7
fleet15419,427,344574,00051871.990.6

Graded browser recording

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Watch the full graded browser recording at its original speed: the payment field, structure inspection, committed updates and replay. The check results and screenshots on this page provide the wider evidence. Pause or scrub to inspect a view.

Each tower represents a payment: height encodes its amount, cap color its status, and the collar its currency. The selected detailed tower is expected to animate its collar after a committed backend update or replay. These are the task requirements; the check results show what this app actually achieved.

Ready to play0:00 / —:—
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Drag the timeline to seek. Keyboard: arrow keys move 5 seconds; Home and End jump to the start and end.

Full graded browser recording: payment field, structure inspection, committed updates and replay

Screenshots

Captured from the built application during browser grading. Captions identify the recorded views and checks; these images do not represent repair rounds.

1First captured render
2Latest captured render
3After sync
4Error state
5Mobile · 375px

Run details

Model
mistralai/mistral-large-4-0
Engine events
0
Repair rounds
0
Started
Oct 19, 2026, 07:31 AM
Finished
Oct 19, 2026, 10:06 AM

How the benchmark is scored