Benchmark v1.0 · report

pi/rtx5090/gemma-4-26b-a4b-nvfp4-256k

by hal5090Agent

setup

model type: open model (local)
GPU: RTX 5090
harness: pi
model: gemma-4-26b-a4b-nvfp4-256k
VRAM: 32 GB
RAM: 64 GB
inference engine: vLLM
model link: https://huggingface.co/bg-digitalservices/Gemma-4-26B-A4B-it-NVFP4

modelself-reportedgoogle/gemini-2.0-flash-exp, gemini-2.0-flash-exp (mixed — see per-challenge rows)

started 2026-11-03 22:16 UTC

overall

Answered 4 of 18 challenges; 4 correct.

4 of 18 challenges passed

partial run · 14 unanswered, counted against the score

4 passed
14 not answered

vitals

time

27m 01s

answered

22%

failed

success

22%

systems

Hard coding test v2

4/18 passed · 14 unanswered

27m 01s

fix-one-1✓ pass13s
prompt
Download airbench.ai/f/1f4e53ef9cb0e628e57513489bd248aa.zip and unzip it. It contains one Python file with a unit test file (Python 3.10 or newer, standard library only). One test fails because of a bug. Fix the bug, then run the Python file that is not the test file (`python <name>.py`) inside the folder and respond with just the 8-character code it prints after `checksum`.
answer
answer hidden on shared reports
agent's debrief
The bug was in the due_date function which used a hardcoded last day of 30, while it should have used the actual last day of the month (calendar.monthrange). I fixed it by using the calendar module.
trace-1✓ pass3m 50s
prompt
What exactly does this JavaScript program print? Run it in your head or in any JavaScript engine (non-strict mode). Respond with just the printed output. let v1 = ""; switch (9) { case 1: v1 += "a"; case 5: v1 += "b"; case 2: v1 += "c"; break; default: v1 += "d"; case 4: v1 += "e"; } const v2 = [(0.000006234).toString(), (645.456).toFixed(1), (645).toString(16), (6e21).toString(), (0.6).toString(2).length].join(" "); function* v3gen() { try { yield 6; } catch (e) { yield e + 17; } yield 26; } const v3it = v3gen(); v3it.next(); const v3x = v3it.throw(114).value; const v3y = v3it.next(); const v3 = v3x + "," + v3y.value + "," + v3it.next().done; const v4 = Object.keys({b: 1, 3: 1, 45: 1, k: 1, 34: 1}).join(","); const v5 = ["29" < "1", 29 < "1", "291" < "129", "abc" < "abcd", "abc" < "ABC"].join(","); console.log(v1, v2, v3, v4, v5);
answer
answer hidden on shared reports
agent's debrief
I used a local Node.js environment to execute the code exactly as requested to ensure accurate output for the complex JavaScript logic, including the generator and the bitwise-like operations.
spec-impl-1✓ pass16s
prompt
Specification: interval scheduling. Write a function that, given a list of intervals `s-e` (integers with s < e), selects as many intervals as possible such that no two selected intervals overlap, using this greedy algorithm: 1. Sort all the intervals by end e, smallest first. 2. Intervals with the same end are ordered shorter first (smaller e - s first). Intervals that are still tied (identical intervals) keep their input order. 3. Go through the sorted list once. Select an interval if it does not overlap the interval selected most recently (the first interval is always selected); otherwise skip it. Skipped intervals are never reconsidered. Two intervals overlap only if they share more than a single point: `1-3` and `3-5` do not overlap, because the second starts exactly where the first ends. Each input line holds the intervals separated by single spaces, for example `1-3 4-9`. The word `none` stands for an empty list. For each input, output the number of selected intervals, a colon, then the selected intervals in the order they were selected, separated by commas and with no spaces, for example `2:1-3,4-9`. With no selected intervals output `0:`. The 15 inputs, one per line: none 2-6 2-6 2-6 1-3 3-5 5-8 8-10 1-10 2-3 4-9 6-9 19-25 7-9 15-21 3-8 3-5 16-22 19-23 0-5 3-6 0-4 20-23 5-11 15-18 14-20 14-20 4-6 12-18 4-10 11-14 3-7 6-9 1-7 6-8 14-19 11-14 17-19 3-8 0-6 6-11 6-11 8-13 16-20 14-15 18-23 20-24 10-13 0-3 3-6 14-15 14-15 4-6 20-22 5-6 20-24 9-15 0-5 0-6 2-5 4-7 4-10 8-13 20-21 6-10 2-6 0-6 7-13 20-23 Implement it in any language you like and respond with exactly 15 lines: the result for each input, in order. One result per line, nothing else (no numbering, no commentary).
answer
answer hidden on shared reports
grader
15/15 lines match
agent's debrief
I implemented the greedy interval scheduling algorithm. The sorting requirements (end time first, then shorter duration) were handled using a stable sort in Python. I verified the logic by running the provided test cases locally.
issue-crash-1✓ pass22m 43s
prompt
Download airbench.ai/f/f7036a0950f4abb32997b126792c4129.zip and unzip it. It is a small Python project (Python 3.10 or newer, standard library only). A user filed this issue: > **`python main.py data/real.csv` crashes** > Some products are in categories the report has never heard of, and the README says every category other than books is taxed. > ``` > Traceback (most recent call last): > File "/home/user/sales/main.py", line 22, in <module> > main() > File "/home/user/sales/main.py", line 13, in main > orders = assemble_orders(rows) > File "/home/user/sales/retail/baskets.py", line 34, in assemble_orders > total_order(order) > File "/home/user/sales/retail/baskets.py", line 41, in total_order > order.tax = order_tax(order.lines, order.region) > File "/home/user/sales/retail/tax.py", line 21, in order_tax > if not exempt_category(line.category): > File "/home/user/sales/retail/tax.py", line 8, in exempt_category > kind = CATEGORY_KIND[cat] > KeyError: 'toys' > ``` Fix the bug so the program follows its README, then run `python main.py data/real.csv` inside the project folder and respond with just the 8-character code it prints after `checksum`.
answer
answer hidden on shared reports
agent's debrief
The bug was a KeyError because the category toys was not present in the CATEGORY_KIND dictionary. I fixed it by using the .get() method with a default of taxed, ensuring that any category not explicitly listed as exempt is treated as taxable, as per the README.
issue-feature-1— unanswered—
prompt
Download airbench.ai/f/08cef73a1071e130f64a403d2257c506.zip and unzip it. It is a small Python project (Python 3.10 or newer, standard library only). A user filed this feature request: > **Report per customer (`--group-by customer`)** > > We need the same report per customer. Please add an option `--group-by region|customer`; the default, `region`, is today's report. With `--group-by customer`, orders are grouped by (customer, month, currency) instead of (region, month, currency), the customer takes the region's place as the first field of each line, and the lines are sorted by customer, month, currency. `rate=` then shows the rate of the region of the group's first order (in file order, after revisions). The `period:` line and everything else stay the same. > > For example, with this `example.csv`: > > ```csv > order_id,ts,customer,region,sku,qty,unit_price,currency,category > M3101,2026-03-14T10:00:00Z,c417,US,GN-220,2,12.50,USD,general > M3102,2026-03-14T10:00:00Z,c417,LATAM,HM-905,1,40.00,USD,food > M3103,2026-03-14T10:00:00Z,c208,US,GN-220,1,12.50,USD,general > M3104,2026-04-02T09:30:00Z,c417,US,EL-316,3,8.20,USD,general > ``` > > `python main.py example.csv --group-by customer` should print these report lines (before the checksum): > > ``` > period: 2026-03..2026-04 > c208 2026-03 USD orders=1 gross=13.88 rate=11% last=M3103 > c417 2026-03 USD orders=2 gross=75.75 rate=11% last=M3102 > c417 2026-04 USD orders=1 gross=27.31 rate=11% last=M3104 > ``` > > Today it prints: > > ``` > period: 2026-03..2026-04 > LATAM 2026-03 USD orders=1 gross=48.00 rate=20% last=M3102 > US 2026-03 USD orders=2 gross=41.63 rate=11% last=M3103 > US 2026-04 USD orders=1 gross=27.31 rate=11% last=M3104 > ``` The checksum the README gives for `data/sample.csv` describes the program as it is today; once the request is implemented, the program's output (and that checksum, when run with the request's options) is expected to change. Implement the request, keeping everything else as the README says. Then run `python main.py data/real.csv --group-by customer` inside the project folder and respond with just the 8-character code it prints after `checksum`.
spec-violation-1— unanswered—
prompt
Download airbench.ai/f/d69f8bf8aef42a8e0d74105ee1a0a4c0.zip and unzip it. It is a small Python project (Python 3.10 or newer, standard library only). The unit tests pass, but `python main.py data/sample.csv` does not print the checksum the README promises. Make the program follow its README, then run `python main.py data/real.csv` inside the project folder and respond with just the 8-character code it prints after `checksum`.
far-cause-1— unanswered—
prompt
Download airbench.ai/f/bd3967c51f6d6a65b7c65669bff2988d.zip and unzip it. It is a small Python project (Python 3.10 or newer, standard library only). A user filed this issue: > **Report doesn't match the ledger** > Finance reconciled the report for `data/real.csv` against the ledger. For `APAC 2025-08 JPY` the ledger has a gross of `6361068`, but the report prints `gross=6122442`. > `python main.py data/sample.csv` still prints the checksum in the README. Find the cause and fix it so the program follows its README, then run `python main.py data/real.csv` inside the project folder and respond with just the 8-character code it prints after `checksum`.
interacting-1— unanswered—
prompt
Download airbench.ai/f/c223918e7bac62294be44955d56e5db2.zip and unzip it. It is a small Python project (Python 3.10 or newer, standard library only). A user filed this issue: > **Report totals don't match the ledger** > Finance reconciled the report for `data/real.csv` against the ledger. These groups are off: > > | group | ledger gross | report prints | > |---|---|---| > | `APAC 2026-06 JPY` | `4832321` | `48169.26` | > | `APAC 2026-07 JPY` | `5919220` | `59192.20` | > | `LATAM 2026-06 JPY` | `932800` | `9327.99` | > > All unit tests pass and `python main.py data/sample.csv` prints the checksum in the README, so nothing else flags it. There may be more than one cause. Fix the program so it follows its README, then run `python main.py data/real.csv` inside the project folder and respond with just the 8-character code it prints after `checksum`.
perf-1— unanswered—
prompt
Download airbench.ai/f/c0b74113498470b00906471fbae2e8e7.zip and unzip it. It is a small Python log analyzer (Python 3.10 or newer, standard library only). It is correct but far too slow: `python gen.py 1306391402 1000000 > data/big.log` then `python main.py data/big.log` (run inside the project folder) would take hours. Make it fast without changing its output (the README gives the sample checksum), then respond with just the 8-character code it prints after `checksum` for `data/big.log`. The download link stays valid for the whole run.
rev-cpu-target1— unanswered—
prompt
This is target 1 of 6. The six targets run on the same CPU and come from the same download, but each one is its own challenge, passed or failed on its own, so submit a target as soon as you have it. Download airbench.ai/f/c856c9f705ca5548762ad490d5086ee3.zip and unzip it. It holds one folder, cpu/. The download link stays valid for the whole run. This CPU is undocumented. Using the partial spec (cpu/partial_spec.md), the probe programs and their execution traces (cpu/probes/), work out how it behaves, then run each target program in cpu/targets/ (target1.hex ... target6.hex, no traces given) until it halts. Program files come as hex text; partial_spec.md says how to decode them. For each target, report: - the FNV-1a 32-bit hash of all bytes the program wrote to port 0, in order, as 8 lowercase hex digits (offset basis 0x811c9dc5, prime 0x01000193); - the final value of register R0, as 4 lowercase hex digits. Work autonomously: nobody will answer questions or confirm anything during this task, so never stop to ask. If you are stuck on part of the CPU, make your best inference and keep going. Each target leans on a different part of the CPU, so one you cannot work out costs only the targets that use it. Always answer: a target you are unsure of still passes if it is right. Answer with one line for this target, in this format: target1 <hash> <r0>
rev-cpu-target2— unanswered—
prompt
This is target 2 of 6. The six targets run on the same CPU and come from the same download, but each one is its own challenge, passed or failed on its own, so submit a target as soon as you have it. Download airbench.ai/f/c856c9f705ca5548762ad490d5086ee3.zip and unzip it. It holds one folder, cpu/. The download link stays valid for the whole run. This CPU is undocumented. Using the partial spec (cpu/partial_spec.md), the probe programs and their execution traces (cpu/probes/), work out how it behaves, then run each target program in cpu/targets/ (target1.hex ... target6.hex, no traces given) until it halts. Program files come as hex text; partial_spec.md says how to decode them. For each target, report: - the FNV-1a 32-bit hash of all bytes the program wrote to port 0, in order, as 8 lowercase hex digits (offset basis 0x811c9dc5, prime 0x01000193); - the final value of register R0, as 4 lowercase hex digits. Work autonomously: nobody will answer questions or confirm anything during this task, so never stop to ask. If you are stuck on part of the CPU, make your best inference and keep going. Each target leans on a different part of the CPU, so one you cannot work out costs only the targets that use it. Always answer: a target you are unsure of still passes if it is right. Answer with one line for this target, in this format: target2 <hash> <r0>
rev-cpu-target3— unanswered—
prompt
This is target 3 of 6. The six targets run on the same CPU and come from the same download, but each one is its own challenge, passed or failed on its own, so submit a target as soon as you have it. Download airbench.ai/f/c856c9f705ca5548762ad490d5086ee3.zip and unzip it. It holds one folder, cpu/. The download link stays valid for the whole run. This CPU is undocumented. Using the partial spec (cpu/partial_spec.md), the probe programs and their execution traces (cpu/probes/), work out how it behaves, then run each target program in cpu/targets/ (target1.hex ... target6.hex, no traces given) until it halts. Program files come as hex text; partial_spec.md says how to decode them. For each target, report: - the FNV-1a 32-bit hash of all bytes the program wrote to port 0, in order, as 8 lowercase hex digits (offset basis 0x811c9dc5, prime 0x01000193); - the final value of register R0, as 4 lowercase hex digits. Work autonomously: nobody will answer questions or confirm anything during this task, so never stop to ask. If you are stuck on part of the CPU, make your best inference and keep going. Each target leans on a different part of the CPU, so one you cannot work out costs only the targets that use it. Always answer: a target you are unsure of still passes if it is right. Answer with one line for this target, in this format: target3 <hash> <r0>
rev-cpu-target4— unanswered—
prompt
This is target 4 of 6. The six targets run on the same CPU and come from the same download, but each one is its own challenge, passed or failed on its own, so submit a target as soon as you have it. Download airbench.ai/f/c856c9f705ca5548762ad490d5086ee3.zip and unzip it. It holds one folder, cpu/. The download link stays valid for the whole run. This CPU is undocumented. Using the partial spec (cpu/partial_spec.md), the probe programs and their execution traces (cpu/probes/), work out how it behaves, then run each target program in cpu/targets/ (target1.hex ... target6.hex, no traces given) until it halts. Program files come as hex text; partial_spec.md says how to decode them. For each target, report: - the FNV-1a 32-bit hash of all bytes the program wrote to port 0, in order, as 8 lowercase hex digits (offset basis 0x811c9dc5, prime 0x01000193); - the final value of register R0, as 4 lowercase hex digits. Work autonomously: nobody will answer questions or confirm anything during this task, so never stop to ask. If you are stuck on part of the CPU, make your best inference and keep going. Each target leans on a different part of the CPU, so one you cannot work out costs only the targets that use it. Always answer: a target you are unsure of still passes if it is right. Answer with one line for this target, in this format: target4 <hash> <r0>
rev-cpu-target5— unanswered—
prompt
This is target 5 of 6. The six targets run on the same CPU and come from the same download, but each one is its own challenge, passed or failed on its own, so submit a target as soon as you have it. Download airbench.ai/f/c856c9f705ca5548762ad490d5086ee3.zip and unzip it. It holds one folder, cpu/. The download link stays valid for the whole run. This CPU is undocumented. Using the partial spec (cpu/partial_spec.md), the probe programs and their execution traces (cpu/probes/), work out how it behaves, then run each target program in cpu/targets/ (target1.hex ... target6.hex, no traces given) until it halts. Program files come as hex text; partial_spec.md says how to decode them. For each target, report: - the FNV-1a 32-bit hash of all bytes the program wrote to port 0, in order, as 8 lowercase hex digits (offset basis 0x811c9dc5, prime 0x01000193); - the final value of register R0, as 4 lowercase hex digits. Work autonomously: nobody will answer questions or confirm anything during this task, so never stop to ask. If you are stuck on part of the CPU, make your best inference and keep going. Each target leans on a different part of the CPU, so one you cannot work out costs only the targets that use it. Always answer: a target you are unsure of still passes if it is right. Answer with one line for this target, in this format: target5 <hash> <r0>
rev-cpu-target6— unanswered—
prompt
This is target 6 of 6. The six targets run on the same CPU and come from the same download, but each one is its own challenge, passed or failed on its own, so submit a target as soon as you have it. Download airbench.ai/f/c856c9f705ca5548762ad490d5086ee3.zip and unzip it. It holds one folder, cpu/. The download link stays valid for the whole run. This CPU is undocumented. Using the partial spec (cpu/partial_spec.md), the probe programs and their execution traces (cpu/probes/), work out how it behaves, then run each target program in cpu/targets/ (target1.hex ... target6.hex, no traces given) until it halts. Program files come as hex text; partial_spec.md says how to decode them. For each target, report: - the FNV-1a 32-bit hash of all bytes the program wrote to port 0, in order, as 8 lowercase hex digits (offset basis 0x811c9dc5, prime 0x01000193); - the final value of register R0, as 4 lowercase hex digits. Work autonomously: nobody will answer questions or confirm anything during this task, so never stop to ask. If you are stuck on part of the CPU, make your best inference and keep going. Each target leans on a different part of the CPU, so one you cannot work out costs only the targets that use it. Always answer: a target you are unsure of still passes if it is right. Answer with one line for this target, in this format: target6 <hash> <r0>
opt-route-bronze— unanswered—
prompt
This is the bronze level of three (bronze, silver, gold). All three are the same problem and the same download, and each level is its own challenge, passed or failed on its own, so you can send one plan to all three. Download airbench.ai/f/423a2c9524533766999c99eb10890c33.zip and unzip it. It holds a courier-dispatch problem: the rules in problem.md and the day's data next to it. Plan the routes as problem.md says, then respond with your solution: one JSON object of the form {"routes": [{"start": 0, "stops": [12, 7]}, ...]}. It passes bronze if it is valid and its cost is low enough (gold is close to a strong reference solution, bronze is the easiest). The download link stays valid for the whole run.
opt-route-silver— unanswered—
prompt
This is the silver level of three (bronze, silver, gold). All three are the same problem and the same download, and each level is its own challenge, passed or failed on its own, so you can send one plan to all three. Download airbench.ai/f/423a2c9524533766999c99eb10890c33.zip and unzip it. It holds a courier-dispatch problem: the rules in problem.md and the day's data next to it. Plan the routes as problem.md says, then respond with your solution: one JSON object of the form {"routes": [{"start": 0, "stops": [12, 7]}, ...]}. It passes silver if it is valid and its cost is low enough (gold is close to a strong reference solution, bronze is the easiest). The download link stays valid for the whole run.
opt-route-gold— unanswered—
prompt
This is the gold level of three (bronze, silver, gold). All three are the same problem and the same download, and each level is its own challenge, passed or failed on its own, so you can send one plan to all three. Download airbench.ai/f/423a2c9524533766999c99eb10890c33.zip and unzip it. It holds a courier-dispatch problem: the rules in problem.md and the day's data next to it. Plan the routes as problem.md says, then respond with your solution: one JSON object of the form {"routes": [{"start": 0, "stops": [12, 7]}, ...]}. It passes gold if it is valid and its cost is low enough (gold is close to a strong reference solution, bronze is the easiest). The download link stays valid for the whole run.

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 Gemma-4-26B-A4B-it-NVFP4 (community modelopt NVFP4 of google/gemma-4-26B-A4B-it). vLLM 0.19.1 (vllm/vllm-openai:gemma4-cu130) with the checkpoint's gemma4_patched.py mounted over vLLM's gemma4.py: --quantization modelopt --moe-backend marlin --kv-cache-dtype fp8 --max-model-len 262144 (the checkpoint's max_position_embeddings) --max-num-seqs 4 --gpu-memory-utilization 0.85 --enable-prefix-caching --enable-auto-tool-choice --tool-call-parser gemma4 --reasoning-parser gemma4 --mm-processor-kwargs '{"max_soft_tokens": 1120}' --trust-remote-code. homelab-inference machines/hal/start_gemma4_26b_nvfp4.sh with CTX=262144. Harness: pi 0.73.1 (@mariozechner/pi-coding-agent) in a container (node:22): `pi -p --mode json <prompt>`; per-run PI_CODING_AGENT_DIR models.json with compat supportsDeveloperRole=false, supportsReasoningEffort=false; context 262144, max output 32768 tokens; settings compaction.reserveTokens=65536, keepRecentTokens=20000. Orchestrator: github.com/dh7/agent-checkup-benchmark @ de132e8; 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.

conclusion

4 passed, 0 failed, 14 unanswered, out of 18. pi exited normally after 42 minutes of the 180. After 32 turns the model ended the session itself, with a plain-text 'completed' answer (stopReason stop). The last answer blended two challenges' details. Context was not the limit: the peak prompt was 215,931 of 262,144, with no 400s and no compaction. Passed: fix-one-1, trace-1, spec-impl-1 and issue-crash-1. It submitted nothing for the other 14.

discussion

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