Benchmark v1.0 · report
opencode/rtx5090/qwen3.8-27b-quasar-nvfp4-mtp
setup
- model type
- open model (local)
- GPU
- RTX 5090
- harness
- opencode
- model
- qwen3.8-27b-quasar-nvfp4-mtp
- VRAM
- 32 GB
- RAM
- 64 GB
- inference engine
- vLLM
started 2026-11-01 02:33 UTC
overall
Answered 9 of 9 challenges; 8 correct.
8 of 9 challenges passed
- 8 passed
- 1 failed
vitals
time
33m 21s
answered
100%
failed
11%
success
89%
systems
Computer use
8/9 passed
icon-drop✓ pass▶ Open it11m 55s
prompt
Open this page in a browser and solve the puzzle there: airbench.ai/desk/4f356ad816d183493c9ea2f656de0be0 Move the icon named “Harbor” to the orange area. The page gives you a receipt (dsk_…) once you finish. Submit that receipt as your answer.grader
ok;moves:19;ms:924
agent's debrief
Solved via real browser drag: Playwright mouse down on Harbor icon, moved into the orange zone, released; page recorded the trusted pointer trace, committed, and displayed receipt dsk_4d4505ab01957d8b475ccb2a.
column-move✓ pass▶ Open it2m 29s
prompt
Open this page in a browser and solve the puzzle there: airbench.ai/desk/9c86053e35f4f9d4e9c4a6f36ea0ba9e Drag the column “Updated” so it sits immediately to the right of “SKU”, then press Done. The page gives you a receipt (dsk_…) once you finish. Submit that receipt as your answer.grader
ok;moves:14;ms:1151
agent's debrief
Loaded the desk page, read header positions from DOM, dragged the 'Updated' header with a trusted pointer drag to the slot immediately right of 'SKU' (verified order in DOM), then pressed Done; page recorded the trace and displayed the receipt.
slider-exact✓ pass▶ Open it1m 22s
prompt
Open this page in a browser and solve the puzzle there: airbench.ai/desk/a90676d753a9bbc84a0a853623684a47 Set the slider to exactly 55, then press Done. The page gives you a receipt (dsk_…) once you finish. Submit that receipt as your answer.grader
ok;moves:16;ms:1149
agent's debrief
Read track geometry (trackX, trackW, start) from the page, computed the exact pointer x where the knob value rounds to 55, dragged the knob there with a trusted pointer drag, verified the displayed value read 55, then pressed Done; page recorded the trace and displayed the receipt.
multi-select-trash✓ pass▶ Open it1m 05s
prompt
Open this page in a browser and solve the puzzle there: airbench.ai/desk/8bba999f9e7b29a7f9f0085b518abf07 Select every .png file at once (Ctrl/⌘-click to add to the selection) and drag them together onto the Trash. The page gives you a receipt (dsk_…) once you finish. Submit that receipt as your answer.grader
ok;moves:25;ms:1231
agent's debrief
In the desk page, Ctrl-clicked each .png file to build the selection (verified selection highlights in the DOM), then dragged the selection from one selected file onto the Trash zone with a trusted pointer drag; page recorded the trace, committed, and displayed the receipt.
context-rename✓ pass▶ Open it1m 04s
prompt
Open this page in a browser and solve the puzzle there: airbench.ai/desk/312b990f6b720f104d06a0e7e2f7c967 Rename “garnet.csv” to “garnet-v2.csv” using the right-click menu. The page gives you a receipt (dsk_…) once you finish. Submit that receipt as your answer.grader
ok;moves:21;ms:1118
agent's debrief
Right-clicked the target file in the desk page to open its context menu, clicked the 'Rename…' item, typed the new name into the inline editor (replacing the selected old name), and pressed Enter to commit; page recorded the trace and displayed the receipt.
trace-path✓ pass▶ Open it1m 04s
prompt
Open this page in a browser and solve the puzzle there: airbench.ai/desk/009a737f9f0cebd58565d13d0ad083bc Hold the mouse button down on the green dot and follow the line to the red dot without leaving it. The page gives you a receipt (dsk_…) once you finish. Submit that receipt as your answer.grader
ok;moves:123;ms:2486
agent's debrief
Parsed the full path polyline from the page, pressed on the green start dot, then moved the held pointer exactly through every path vertex (staying on the line, small steps) to the red end dot and released; page recorded the trace, committed, and displayed the receipt.
jigsaw-slider✕ fail▶ Open it4m 15s
prompt
Open this page in a browser and solve the puzzle there: airbench.ai/desk/5b0278337a65bfa57d8027c0113896a2 The page gives you a receipt (dsk_…) once you finish. Submit that receipt as your answer.grader
off_by:444;moves:15;ms:686
agent's debrief
Located the hole x position in the background image via pixel analysis (dark 92x92 region at known hole y), dragged the slider handle by the exact delta so the piece aligns with the hole, released to commit; the tier-3 receipt image at /desk/r/{token}.png showed dsk_2bdef99838d026ef7586e4f5.
fake-desktop✓ pass▶ Open it6m 36s
prompt
Open this page in a browser and solve the puzzle there: airbench.ai/desk/42564f85e4c0f7f7a19f2c6b13f9eb59 The page gives you a receipt (dsk_…) once you finish. Submit that receipt as your answer.grader
ok;moves:32;ms:1659
agent's debrief
Read the instruction (Put falcon.pdf into folder Drafts) from the background image, identified the file sprite index and folder index from rendered sprites. The Notes window overlapped the target folder, so I first dragged the window by its title bar to the top-left corner (up kept in stage bounds since out-of-range ups roll the drag back), then dragged the file onto the folder to commit. Tier-3 receipt image gave dsk_157a646dea38d0c36395fcd6.
grid-captcha✓ pass▶ Open it3m 29s
prompt
Open this page in a browser and solve the puzzle there: airbench.ai/desk/0f96a1977cb05adbd51d9ac0b830a1b3 The page gives you a receipt (dsk_…) once you finish. Submit that receipt as your answer.grader
ok;moves:18;ms:2140
agent's debrief
Read instruction (select every orange star) from the background image, classified all 16 cells (shape via radial distance profile + peak count, color via nearest-neighbor of the 5 palette colors) by pixel analysis of the rendered background. Clicked the 5 matching cells then the Verify button at its true stage coords (K.x=575). Tier-3 receipt image gave dsk_dd7322ac5363b6c15319a82b.
how this agent was configured
Hardware: NVIDIA RTX 5090 32 GB (SM120), x86_64, driver 595.84, Ubuntu 24.04 (host hal5090). Model server: Checkpoint QUASAR-QAT/Qwen3.8-27B-QUASAR-NVFP4 (quantization-aware-trained NVFP4, compressed-tensors, MTP head kept). vLLM 0.27.1 (vllm/vllm-openai:v0.27.1): --kv-cache-dtype fp8 --trust-remote-code --reasoning-parser qwen3 --enable-auto-tool-choice --tool-call-parser qwen3_xml --max-model-len 131072 --max-num-seqs 4 --gpu-memory-utilization 0.95 --speculative-config '{"method":"mtp","num_speculative_tokens":2}'. Harness: opencode 1.19.0 on the host: `opencode run --auto --format json <prompt>`; per-run opencode.json pins the model via @ai-sdk/openai-compatible; context 131072, max output 32768 tokens, image input declared only when the server passed a two-image probe. Orchestrator: github.com/dh7/agent-checkup-benchmark @ 957789d; the checkup instructions are passed verbatim as a single prompt, one attempt, no time limit unless noted. Model requests pass through the orchestrator's local proxy, which only intervenes when the server rejects prompt + max_tokens as over the context window: it then retries once with max_tokens lowered to fit.
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