AI Model Reviewer

GLM-5.3 (fast router) in OpenCode 1.18.35

skills on · OpenCode 1.18.35 + skills on (54: 33 video + 21 web/Three.js)Kit: 33 video + 21 web/Three.js · 1 of 54 installed skills used in the session

← Open in the gallerySame prompt, all models

DNF

The model did not finish, so there is no output.

DNFWorld weather stationsn/a effortNeutral harness

Wall time 4m 35sCost $0.26 reported

Review

No verdict yet.

The full review

DNF. Harness exited after 4.6 min without writing index.html. The model's last message stopped at the output-token limit (stop reason max_tokens / length) without a tool call, so the non-interactive run ended there.

Scores

Not scored: only runs that finished on their own are scored.

Downloads

Files actually published for this run. Original output, site-made previews and post-run conversions are labelled separately.

  • Scrubbed run transcriptdash_world_weather_stations__opencode__glm-5.3-fast-skills-on__attempt1-transcript.json · 135 KB · transcript

The exact prompt

as run, 3,863 characters. Every result for this prompt

Build an interactive climate dashboard comparing six weather stations from the exact dataset below (CSV: one row per city and month with the average daily high temperature in Celsius, total rainfall in millimetres and sunshine hours). Embed the data in the page verbatim and compute every number from it; do not invent or alter any value.

city,month,avg_high_c,rain_mm,sun_hours
Reykjavik,Jan,0.4,77,54
Reykjavik,Feb,0.9,76,81
Reykjavik,Mar,3.8,67,116
Reykjavik,Apr,6.3,48,147
Reykjavik,May,10.1,32,221
Reykjavik,Jun,12.9,24,247
Reykjavik,Jul,13.2,24,274
Reykjavik,Aug,12.2,29,233
Reykjavik,Sep,9.9,37,214
Reykjavik,Oct,6.2,51,160
Reykjavik,Nov,3.1,59,103
Reykjavik,Dec,2.6,74,77
Lisbon,Jan,13.3,100,52
Lisbon,Feb,14.1,99,77
Lisbon,Mar,18.5,69,113
Lisbon,Apr,20.3,47,168
Lisbon,May,25.3,31,222
Lisbon,Jun,27.9,15,245
Lisbon,Jul,28.5,1,259
Lisbon,Aug,27.6,5,259
Lisbon,Sep,24.3,21,212
Lisbon,Oct,21.1,51,146
Lisbon,Nov,18.0,83,100
Lisbon,Dec,15.4,89,84
Singapore,Jan,29.6,254,152
Singapore,Feb,30.6,249,136
Singapore,Mar,30.4,223,151
Singapore,Apr,31.4,201,174
Singapore,May,31.6,183,132
Singapore,Jun,30.9,158,155
Singapore,Jul,31.8,164,167
Singapore,Aug,31.0,163,160
Singapore,Sep,30.8,182,130
Singapore,Oct,31.9,203,172
Singapore,Nov,30.7,235,129
Singapore,Dec,29.9,237,146
Denver,Jan,5.9,47,63
Denver,Feb,7.5,42,76
Denver,Mar,11.6,41,101
Denver,Apr,18.0,40,160
Denver,May,24.9,27,219
Denver,Jun,30.0,23,251
Denver,Jul,30.7,22,253
Denver,Aug,29.4,25,241
Denver,Sep,24.9,38,201
Denver,Oct,17.6,29,153
Denver,Nov,12.2,33,106
Denver,Dec,7.4,35,64
Cape Town,Jan,27.6,9,269
Cape Town,Feb,25.9,16,279
Cape Town,Mar,24.9,30,236
Cape Town,Apr,23.3,60,192
Cape Town,May,21.0,68,142
Cape Town,Jun,18.1,87,111
Cape Town,Jul,17.8,94,111
Cape Town,Aug,18.8,79,131
Cape Town,Sep,20.3,69,163
Cape Town,Oct,22.7,46,187
Cape Town,Nov,24.4,35,227
Cape Town,Dec,25.9,16,280
Mumbai,Jan,30.2,7,62
Mumbai,Feb,30.0,6,68
Mumbai,Mar,29.9,11,121
Mumbai,Apr,32.1,79,166
Mumbai,May,33.0,247,205
Mumbai,Jun,33.0,481,245
Mumbai,Jul,33.2,601,273
Mumbai,Aug,32.3,494,241
Mumbai,Sep,32.5,252,225
Mumbai,Oct,31.5,67,166
Mumbai,Nov,29.8,3,115
Mumbai,Dec,30.1,7,81

The dashboard needs: small-multiple climate charts, one per city, each with monthly temperature as a line over rainfall as bars and the two axes clearly labelled; a radial (polar) chart of the selected city's year with temperature as radius and rainfall as colour; a heatmap of temperature by city and month; KPI cards per city for annual rainfall, hottest and coldest month, temperature range and total sunshine; a 'best month to visit' calculator with sliders for preferred temperature range, maximum rain and minimum sunshine that scores every city-month and lists the top five; a Celsius and Fahrenheit toggle; hover or tap tooltips with exact values; and a short auto-written comparison panel computed from the data. Make it beautiful, editorial and calm, like a data feature in a great magazine. So the page is alive without input, on load the charts draw in and a tour cycles forever: it rotates the radial chart through each city, sweeps a highlight across the heatmap month by month, adjusts the visit calculator sliders to show the ranking change, and toggles units once per loop, until the user touches a control.

Save it as index.html in the current directory: one self-contained file with inline CSS and JavaScript only, no libraries or frameworks, and no external requests of any kind (no CDNs, web fonts, images or audio files). It must work when opened directly from disk, fit any window from a 390 px wide phone to a desktop, and run without console errors. You may serve it locally and open it in headless chromium (/usr/bin/chromium, software WebGL, no GPU) to screenshot and inspect your own output, and iterate on your own before finishing. Close any browser you open as soon as you've taken your screenshot.

How it was made

Wall time 4m 35s

Step by step

The prompt, every agent turn and tool call (tool name and a one-line summary; inputs and outputs are not shown), then the final message. The full machine-format transcript is published as a scrubbed download.

Show all 4 steps

Loading the timeline…

Download the full transcript OpenCode session export, 135 KB. Scrubbed: anything that looks like a key, token, e-mail address or phone number is replaced, and account details and the test machine's file paths are removed.

Cost breakdown

Headline cost $0.26, as reported by OpenCode (its own bill for the run).

Tokens by typeTokens$ per 1MCost
Input (uncached)18,059n/an/a
Cache write0n/an/a
Cache read15,653n/an/a
Output32,368n/an/a
Total computed from tokens
unknown
Total reported by the harness
$0.2577

OpenCode 1.18.35 reported its own total; token counts x list prices shown beside it. No list price for glm-5.3-fast in config/prices.json; no estimate computed. The harness reports reasoning tokens separately from output; here output includes them (12 visible + 32,356 reasoning), and both bill at the output rate.

Run notes

one prompt, agentic harness (self-review allowed)skills=on

  • frozen prompt (written): core requirements sha256 6d5fbb9a4049, full harness prompt sha256 6d5fbb9a4049; exact prompt text used by the existing harness rows of this test on the runs branch (copied from dash_world_weather_stations__claude-code__opus-5-5-high__attempt1/meta.json, sha256 verified); Beta hal
  • no TUI: the harness ran in its non-interactive mode, so there are no TUI screenshots; the timelapse is built from version renders only
  • OpenCode config (providers only, permission allow-all except question; no agents/instructions) + both default skill kits in xdg/config/opencode/skill (skills=on)
  • infra retries: 0
  • in the published transcript the sandbox HOME directory is shown as <HOME>
  • skills=on: 54 skills installed in the harness skill directory before launch (video-33 remotion-dev/skills + heygen-com/hyperframes@0b5db9da + f; web-threejs-21 [redacted]/ai-AI Model Reviewer skills/ (alpha/mr-homebar)@7521074d98c9).
  • Mode: headless run mode (opencode run --format json), inside bubblewrap sandbox
  • Isolation audit: none
  • Skills on: the run had 54 skills installed (33 video + 21 web/Three.js), as its meta records.
  • GLM-5.3 Fast is a fast-serving router over the glm-5p3 weights (router created 2026-08-28; responses report glm-5p3)
  • GLM-5.3 (fast router) served via Fireworks (OpenCode provider fireworks-ai).
  • Same frozen prompt as the other rows on this test; configurations differ and all rows are unscored, so this is not a head-to-head result.