codex × nvidia/llama-3.3-nemotron-super-49b-v1

polyglot

polyglot/java-stream-collect

Per-axis scores

Pass

0.000

Tokens/correct

—

Wall

25.5s

Blast

0.000

Refusal

—

Integrity

1.000

Composite

0.547

Run identity

cell_id: expansion-20260616/codex/nvidia/llama-3.3-nemotron-super-49b-v1/polyglot/java-stream-collect
sweep_id: expansion-20260616
container_image: codingagentbench/codex:v0.140.0
image_digest: sha256:52116224fbc698acc448343890c4542efcc101a950ef8c8de1eca3e051f228bc
model_build_id: nvidia/llama-3.3-nemotron-super-49b-v1
exit_code: 0

Timing

started_at: 2026-06-17T00:59:57.623481Z
ended_at: 2026-06-17T01:00:23.151492Z
duration: 25.53 s
tokens: not recorded

Scorer breakdown

Axis	Keys	Values
pass_rate	pass, exit_code, timed_out, stdout_tail, stderr_tail, partial_score	{"pass":0,"exit_code":1,"timed_out":false,"stdout_tail":"","stderr_tail":"Exception in thread \"main\" java.lang.IllegalStateException: Duplicate key a (attempted merging values 1 and 1)\n\tat java.base/java.util.stream.Collectors.duplicateKeyException(Collectors.java:133)\n\tat java.base/java.util.stream.Collectors.lambda$uniqKeysMapAccumulator$1(Collectors.java:180)\n\tat java.base/java.util.stream.ReduceOps$3ReducingSink.accept(ReduceOps.java:169)\n\tat java.base/java.util.Spliterators$ArraySpliterator.forEachRemaining(Spliterators.java:948)\n\tat java.base/java.util.stream.AbstractPipeline.copyInto(AbstractPipeline.java:484)\n\tat java.base/java.util.stream.AbstractPipeline.wrapAndCopyInto(AbstractPipeline.java:474)\n\tat java.base/java.util.stream.ReduceOps$ReduceOp.evaluateSequential(ReduceOps.java:913)\n\tat java.base/java.util.stream.AbstractPipeline.evaluate(AbstractPipeline.java:234)\n\tat java.base/java.util.stream.ReferencePipeline.collect(ReferencePipeline.java:578)\n\tat WordCounter.countWords(WordCounter.java:8)\n\tat Tests.main(Tests.java:16)\n","partial_score":null}
tokens	prompt_tokens, completion_tokens, total_tokens, model_calls, tokens_per_correct_task, pass_used_for_division	`{"prompt_tokens":0,"completion_tokens":0,"total_tokens":0,"model_calls":0,"tokens_per_correct_task":0,"pass_used_for_division":0}`
latency	wall_clock_ms, container_active_ms, model_call_ms	`{"wall_clock_ms":25528.011,"container_active_ms":25515.082,"model_call_ms":0}`
blast_radius	blast_radius, added, removed, modified, unexpected_changes, expected_changes, total_changes, extra_git_dir	`{"blast_radius":0,"added":[],"removed":[],"modified":[],"unexpected_changes":[],"expected_changes":[],"total_changes":0,"extra_git_dir":true}`
refusal	refusal, not_applicable, expected_refusal, refusal_produced, refusal_correct, marker_hits, keyword_hits	`{"refusal":0,"not_applicable":true,"expected_refusal":false,"refusal_produced":false,"refusal_correct":null,"marker_hits":[],"keyword_hits":[]}`
integrity	integrity, not_applicable, task_id	`{"integrity":1,"not_applicable":true,"task_id":"polyglot/java-stream-collect"}`
composite	composite, weights, components, note	{"composite":0.5467385628864301,"weights":{"pass_rate":0.4,"integrity":0.2,"cost":0.15,"latency":0.15,"blast_radius":0.1},"components":{"pass_rate":0,"integrity":1,"blast_radius":0,"tokens_per_correct_task":0,"wall_clock_ms":25528.011,"expected_refusal":false,"refusal_produced":false,"cost_efficiency":1,"latency_efficiency":0.644923752576201,"clean_blast":1,"refusal_factor":1},"note":"Single-cell composite — sweep-level normalisation lives in the runner. MANIFESTO #6: per-axis numbers are canonical; composite is derivative."}

Trace replay

space play/pause · ←/→ step span · ,/. step 100 ms

speedt = 0 ms / 25.53 s

span 2711d1eaf43145d7acba46cf87d0050atask_setupprepare:polyglot/java-stream-collect0 ms → 13 ms

workdir/home/andrew/.codingagentbench-scratch/codingagentbench-codex-polyglot_java-stream-collect-rgbxf4rg/workdir
task_categorypolyglot
plugin_stack[]
behavior_modefactory

Terminal playback

replay of the run — opencode cells show genuine step timing; others show a span summary

Run it yourself

Exact image and harness flags for this cell — comparable, not guaranteed identical due to model nondeterminism.

Imagecodingagentbench/codex:v0.140.0·sha256:sha256:52116224f…

docker run — exact image used in this run

# pull the exact image used in this run
docker pull codingagentbench/codex:v0.140.0@sha256:sha256:52116224fbc698acc448343890c4542efcc101a950ef8c8de1eca3e051f228bc

docker run --rm \
  -e CAB_ENDPOINT="$YOUR_ENDPOINT" \
  -e CAB_KEY="$YOUR_KEY" \
  codingagentbench/codex:v0.140.0@sha256:sha256:52116224fbc698acc448343890c4542efcc101a950ef8c8de1eca3e051f228bc \
  run-cell \
    --tui codex \
    --model nvidia/llama-3.3-nemotron-super-49b-v1 \
    --task polyglot/java-stream-collect

Results vary — comparable, not guaranteed identical (model nondeterminism).

Explore with your own model

Substitute your endpoint, key, and model ID. Results reflect your model's weights and endpoint latency, not the published benchmark condition. Endpoint must speak OpenAI-compatible /v1/chat/completions.

aider — BYO endpoint wiring

# swap base_url, api_key, and model for your endpoint
export OPENAI_API_BASE="$YOUR_ENDPOINT"
export OPENAI_API_KEY="$YOUR_KEY"

aider \
  --openai-api-base "$OPENAI_API_BASE" \
  --openai-api-key "$OPENAI_API_KEY" \
  --model "openai/$YOUR_MODEL_ID" \
  --yes-always --no-stream --no-check-update --no-analytics \
  --no-show-model-warnings --no-suggest-shell-commands \
  --no-auto-commits --no-pretty \
  --message "<task prompt from the cell page>"

# Published cell used model: nvidia/llama-3.3-nemotron-super-49b-v1

Zero-install preview — npx CLI (provisional, not for citation)

npx @codingagentbench/cli check

npx @codingagentbench/cli check \
  --tui codex \
  --model nvidia/llama-3.3-nemotron-super-49b-v1 \
  --endpoint "$YOUR_ENDPOINT" \
  --key "$YOUR_KEY" \
  --task polyglot/java-stream-collect
# provisional score — not for citation

Provisional score — not for citation. Use the Docker or Harness CLI path for citable results.

Trace spans (5)

Span	Kind	Name	Duration	Attrs / Error
2711d1eaf43145d7acba46cf87d0050a	task_setup	prepare:polyglot/java-stream-collect	13 ms	`{"workdir":"/home/andrew/.codingagentbench-scratch/codingagentbench-codex-polyglot_java-stream-collect-rgbxf4rg/workdir","task_category":"polyglot","plugin_stack":[],"behavior_mode":"factory"}`
eecceb5a7ed148e2b6f9ef8b7f3b6e9c ↳ d5c862bcf6d04e1c8bc8c5fda732e271	tool_call	applied_plugins	0 ms	`{"tui":"codex","count":0,"applied":[],"env_keys":[],"extra_args":[],"applied_count":0,"skipped_count":0}`
410883cdb5a44e2ea7a3a0c690e1149d ↳ d5c862bcf6d04e1c8bc8c5fda732e271	container	codex:launch	25515 ms	{"image":"codingagentbench/codex:v0.140.0","argv":["exec","--dangerously-bypass-approvals-and-sandbox","--skip-git-repo-check","--ephemeral","--ignore-user-config","-m","nvidia/llama-3.3-nemotron-super-49b-v1","-c","model_providers.nim={ name = 'nim', base_url = 'http://172.17.0.1:31415/v1', wire_api = 'responses', env_key = 'OPENAI_API_KEY' }","-c","model_provider=\"nim\"","--disable","multi_agent","--disable","browser_use","--disable","browser_use_external","--disable","computer_use","--disable","apps","Wrong Collector loses duplicates; convert toMap-merge instead of groupingBy"],"timeout_s":600,"exit_code":0,"duration_s":25.499908089987002,"timed_out":false}
d5c862bcf6d04e1c8bc8c5fda732e271	adapter_run	codex:polyglot/java-stream-collect	25515 ms	`{"timeout_s":600,"plugin_stack":[],"exit_code":0}`
417f630d116d492eb326596c861cff73	cleanup	cleanup:codex	0 ms	`{}`

Model calls (0)

#	Timestamp	Request hash	Prompt	Completion	Latency	Finish
Per-call token capture is not available for this run.

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