eval-harness

arm

triage_mistral_large_4_v2

One config on orrery_triage_v1_labelled, scored one way; its repeats averaged per item.

dataset
orrery_triage_v1_labelled
scored against
gold/orrery_triage_v1_labelled gold
goal
Triage decisions on Orrery's error signals
runs
1
headline
triage_cost 0.800
last run
2026-10-31 16:14
re-scored
the goals read its re-score, runs-rescored/5f2c057cdaa5/runs/orrery_triage_v1_labelled/triage_mistral_large_4_v2

Inspect itWith the 317 arms scored the same way

01metrics

metricmeanitemsbetter
triage_cost0.80065lower
correct0.50865higher
cost_usd$0.00694865lower
deferred0.00065lower
escalated0.46265lower
false_alarm0.71031lower
false_dismiss0.17634lower
latency_ms20489.065lower
model_correct0.44665higher
model_escalated0.00065lower
model_false_alarm0.29031lower
model_false_dismiss0.67634lower
model_triage_cost1.90865lower
n_probes2.53865higher
no_decision0.00065lower
table_miss0.00065lower
tokens_in5176.665lower
tokens_out1583.265lower
triage_cost_p101.26265lower
triage_cost_p30.61565lower

02cost and speed

cost per item
$0.006948 · 6,760 tok
speed per item
p50 19,158 · p95 35,678 ms

Per item, over its items: cost as dollars and tokens (a local model is free but still uses tokens), speed as the median and the slow tail (p95). A goal that names this arm weighs them in its decision.

03runs

runmadeitemscosthow
triage_mistral_large_4_v2_20261031T155150Z2026-10-31 16:1465$0.451636measured

04config

config triage_mistral_large_4_v2 · adapter examples/triage-fleet/adapter.py · deployment hosted · credentials are never shown

max_tokens
4096
model
eval-mistral-large-4
provider
gateway
reasoning_effort
low
usd_per_mtok_in
0.7
usd_per_mtok_out
2.1

05conditions

data.items_sha256
d428aca49adc8e762dcbbcad6c9a1dba1ac4e4e3315ff26025af975faba1176a
deployment.machine
homelab LiteLLM (the fleet's gateway)
deployment.model
eval-mistral-large-4
deployment.source
record
fingerprint.version
5
harness.code_sha256
216b9a5df01c9986c4f863acb6f4d79c9796becf8a6944ba1e82f747844cd244
harness.version
0.5.0
library.anthropic
2.3.0
library.numpy
2.5.4
library.openai
3.41.0
library.rouge-score
0.1.2
machine.cpu_count
32
machine.platform
Linux/x86_64
machine.python
3.14.7
machine.release
7.2.5-3-omarchy
reference.sha256
f617ad3bb9bb9f5feb8ffe54e0ef1b9b05dfb316fac13f63a925f9d35fb191cd
scorer.id
examples/triage-fleet/adapter.py
scorer.sha256
3d3a6afdb978f2db2d6c6f732d5842d09c3df0eca24ef724171a162b4b937527

06the same config, scored other ways

scored against silverscored by a judgewrote the reference it is scored againstscored against a label set, not a reference setunmarked rows are scored against gold

07rounds