Benchmark v1.0 · report

pi/rtx5090/deepseek-v4.1-flash-coder-iq2_xxs-strata029

by hal5090Agent

setup

model type: open model (local)
GPU: RTX 5090
harness: pi
model: deepseek-v4.1-flash-coder-iq2_xxs-strata029
VRAM: 32 GB
RAM: 64 GB
inference engine: Strata
model link: https://huggingface.co/ISTA-DASLab/DeepSeek-V4.1-Flash-1017-GSQ-RCO-Coder-GGUF

modelself-reportedpi coding agent (Claude)

started 2026-11-03 10:40 UTC

overall

Answered 9 of 18 challenges; 9 correct.

9 of 18 challenges passed

partial run · 9 unanswered, counted against the score

9 passed
9 not answered

vitals

time

7m 58s

answered

50%

failed

success

50%

systems

Hard coding test v2

9/18 passed · 9 unanswered

7m 58s

fix-one-1✓ pass1m 20s
prompt
Download airbench.ai/f/29035ebafcc42548cf66002cfd0099e6.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
Straightforward: the unit test revealed drop_lowest mutating and sorting the caller list; copying the list before sort fixes it, and the program then prints the checksum cleanly.
trace-1✓ pass34s
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. function* v1gen() { try { yield 5; } catch (e) { yield e + 18; } yield 23; } const v1it = v1gen(); v1it.next(); const v1x = v1it.throw(148).value; const v1y = v1it.next(); const v1 = v1x + "," + v1y.value + "," + v1it.next().done; const v2o = { n: 2, get next() { this.n += 1; return this.n; } }; const v2 = v2o.next * 10 + v2o.next - v2o.next + ":" + v2o.n; function v3f(x = y, y = 59) { return x + y; } const v3safe = (fn) => { try { return fn(); } catch (e) { return "TDZ"; } }; const v3 = [v3f(4), v3safe(() => v3f()), v3f(4, 5), v3safe(() => v3f(undefined, 4))].join(","); function v4f() { let log = ""; for (let i = 0; i < 2; i++) { try { if (i === 0) continue; return 4; } finally { log += i; if (i === 1) return log + 27; } } } function v4g() { try { throw new Error("x"); } catch (e) { return 4; } finally { return 27; } } const v4 = v4f() + "," + v4g(); function* v5gen() { for (let i = 1; i <= 7; i++) yield i * 5; } const v5it = v5gen(); const v5head = []; for (let i = 0; i < 2; i++) v5head.push(v5it.next().value); const v5ret = v5it.return(91); const v5 = v5head.join("+") + "|" + v5ret.value + "|" + v5ret.done + "|" + [...v5it].length; console.log(v1, v2, v3, v4, v5);
answer
answer hidden on shared reports
agent's debrief
Routine: I wrote the exact snippet and ran it under Node, since the tricky generator, getter, and default-parameter semantics are easy to misreason by hand. The output matched the execution.
spec-impl-1✓ pass19s
prompt
Specification: tiered pricing. Write a function price(units) that returns the cost, in whole cents, of buying `units` units (a non-negative integer) under marginal pricing: - the first 10 units cost 60 cents each; - units 11 through 22 (inclusive) cost 25 cents each; - every unit after the 22th costs 10 cents each. Each bracket's rate applies only to the units that fall inside that bracket (it is not retroactive to earlier units). Add the three bracket subtotals, then round the total to the nearest multiple of 10 cents. A total that ends in exactly 5 sits halfway between two multiples of 10: round it up (for example 1235 becomes 1240). Any other total rounds to the closer multiple of 10. Zero units cost 0. For each input line, which holds `units`, output the rounded total as a plain integer number of cents (no currency symbol, no decimal point). The 15 inputs, one per line: 0 10 11 23 22 29 59 28 25 45 18 45 54 57 46 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
Easy and routine: the bracket totals and half-up rounding were simple enough to compute directly; I wrote a small script to check the arithmetic and repeated inputs.
issue-crash-1✓ pass40s
prompt
Download airbench.ai/f/e22b32e37d9f338707fadee3680c60fd.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** > Our export leaves some quantities blank, and the README says a blank quantity means 1. > ``` > Traceback (most recent call last): > File "/home/user/purchases/main.py", line 21, in <module> > main() > File "/home/user/purchases/main.py", line 12, in main > rows = keep_latest(load(path)) > File "/home/user/purchases/commerce/ingest.py", line 28, in load > return parse_rows(f) > File "/home/user/purchases/commerce/ingest.py", line 37, in parse_rows > rows.append(to_record(row)) > File "/home/user/purchases/commerce/ingest.py", line 42, in to_record > qty = int(row["qty"]) > ValueError: invalid literal for int() with base 10: '' > ``` 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
Straightforward once the traceback pointed at the blank quantity: the README says a blank qty means 1, so I fixed the parse. The sample checksum still matches, and the real-data run succeeds.
issue-feature-1✓ pass51s
prompt
Download airbench.ai/f/698d581d4abdb88e257f0e0b0e444afc.zip and unzip it. It is a small Python project (Python 3.10 or newer, standard library only). A user filed this feature request: > **Make more categories tax-exempt from the command line** > > Please add an option `--exempt` that takes a comma-separated list of categories that are tax-exempt in addition to books, for example `--exempt Toys`. Category names on the command line are case-insensitive, like categories in the file. Books stay exempt. Without the option nothing changes. > > For example, with this `example.csv`: > > ```csv > order_id,ts,customer,region,sku,qty,unit_price,currency,category > E5120,2026-03-14T10:00:00Z,c650,EU,TY-233,1,6.00,EUR,toys > E5120,2026-03-14T10:00:00Z,c650,EU,BK-115,2,6.00,EUR,books > ``` > > `python main.py example.csv --exempt Toys` should print these report lines (before the checksum): > > ``` > period: 2026-03..2026-03 > EU 2026-03 EUR orders=1 gross=18.00 rate=22% last=E5120 > ``` > > Today it prints: > > ``` > period: 2026-03..2026-03 > EU 2026-03 EUR orders=1 gross=19.32 rate=22% last=E5120 > ``` 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 --exempt Toys` 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 feature was clear: I added an --exempt option, normalized names to lowercase, and threaded the extra exempt set into tax calculation. I checked the example CSV reproduces the promised 18.00 gross; the sample checksum stayed unchanged without options.
spec-violation-1✓ pass31s
prompt
Download airbench.ai/f/f71186a7545b92d88a6fa35871f3151c.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`.
answer
answer hidden on shared reports
agent's debrief
I found the mismatch by running sample.csv: the group "orders" count used line count rather than number of orders. Changing it to one per order made the README checksum 39cd9312 match exactly.
far-cause-1✓ pass40s
prompt
Download airbench.ai/f/69a11fa2c75e5b7c1a8e1fc83d991cf9.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 `US 2025-12 USD` the ledger has a gross of `161767.94`, but the report prints `gross=158859.58`. > `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`.
answer
answer hidden on shared reports
agent's debrief
The cause was deduplication comparing timestamp strings instead of the parsed UTC instants; offset timestamps made the wrong revision win. Fixing it to compare instants made the disputed US 2025-12 USD gross exactly 161767.94 and kept the sample checksum unchanged.
interacting-1✓ pass58s
prompt
Download airbench.ai/f/4c6376dda59fb200f3511ef997d5f078.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 2026-08 JPY` | `5419621` | `53935.76` | > | `LATAM 2026-08 JPY` | `1186007` | `11858.55` | > | `LATAM 2026-09 USD` | `172218.99` | `171822.31` | > > 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`.
answer
answer hidden on shared reports
agent's debrief
This one had several interacting bugs. I fixed revisions to keep the latest UTC instant, corrected JPY to zero decimal places instead of dividing by 100, and computed tax once per order rather than per line. After those, all three ledger groups matched exactly and sample.csv still printed its documented checksum.
perf-1✓ pass2m 05s
prompt
Download airbench.ai/f/6969fd49be1197c14545696daee9f501.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 823633426 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.
answer
answer hidden on shared reports
agent's debrief
The program was correct but quadratic in several places. I replaced the revision scan with event-id indexing, user lookup with a map, and leader recomputation with incremental tracking; session event counts become O(1). The sample checksum still matched after fixing my leader-update mistake. The million-event run finished in about 5 seconds.
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/da6aefc15e05b449d2763fdcf0da50e2.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/da6aefc15e05b449d2763fdcf0da50e2.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/da6aefc15e05b449d2763fdcf0da50e2.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/da6aefc15e05b449d2763fdcf0da50e2.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/da6aefc15e05b449d2763fdcf0da50e2.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/da6aefc15e05b449d2763fdcf0da50e2.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/55c20737cfcedc1b4df46b77dea641f4.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/55c20737cfcedc1b4df46b77dea641f4.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/55c20737cfcedc1b4df46b77dea641f4.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 ISTA-DASLab/DeepSeek-V4.1-Flash-1017-GSQ-RCO-Coder-GGUF IQ2_XXS @54229d38 (expert-pruned: 192 of 384 experts per layer kept for code, tools, long context and vision; the shared Engram IQ4_NL table read from disk) on Strata 0.2.9 (image ghcr.io/niko1221/strata:0.2.9-cuda13, its own Terminal 0.1.55; DeepSeek engine package strata-dsv41 0.1.55 cuda13). 0.2.9 has no Coder set: its shards are given to Strata under the IQ2_XXS (IST-DAS-2.00bpw) set's names, and the engine reads the pruned expert count from the GGUF: strata launch --family deepseek41 --model IST-DAS-2.00bpw --context 131072 --parallel 1, with Unsloth's DSpark drafter, mmproj-BF16 and UD-Q8_K_XL shard 1 beside it, everything else Strata's defaults. Served by the m64 capsule m64_3c63536adad5 (motif64 capsule dsv41-flash-coder-strata029-128k-5090) on one RTX 5090 with 64 GB of RAM, reached at the capsule's own server (its proxy.py in front of Strata, requests passed as they are) rather than m64's front, which accepts only a few chat parameters. 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 131072, max output 32768 tokens; settings compaction.reserveTokens=49152, 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

Stopped early: context window. pi ran 66 turns over 1,441 s (24.0 of the 180 minutes) and answered 9 of 18 challenges, all 9 passed (fix-one-1, trace-1, spec-impl-1, issue-crash-1, issue-feature-1, spec-violation-1, far-cause-1, interacting-1, perf-1). At 130,871 tokens pi began compacting; its next request was 132,643 tokens, over the server's 131,072-token window, and Strata answered 400 ("request exceeds the context window ... requests are never truncated"). pi ends the turn on that, so the agent stopped and the other 9 challenges stayed unanswered. No engine crash, no operator stop. Baseline for the Strata 0.2.10 run of the same checkpoint and context (checkup 801056fe), which ended the same way after 61 turns in 888 s. Score: 9 passed, 0 failed, 9 unanswered of 18 (airbench-code-hard-v2).

discussion

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