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CodingAgentBench

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

polyglot
polyglot/java-stream-collect

Per-axis scores

Pass
0.000
Tokens/correct
—
Wall
133.0s
Blast
1.000
Refusal
—
Integrity
1.000
Composite
0.420

Run identity

cell_id
free-20260527/aider/nvidia/llama-3.3-nemotron-super-49b-v1/polyglot/java-stream-collect#run1
sweep_id
free-20260527
container_image
codingagentbench/aider:v0.86.0
image_digest
sha256:f2cd27890475900f9cb617b1c7a6328989a48e8168e7280ac2247a5d2d26d8a1
model_build_id
nvidia/llama-3.3-nemotron-super-49b-v1
exit_code
0

Timing

started_at
2026-05-29T12:49:14.609968Z
ended_at
2026-05-29T12:51:27.639635Z
duration
133.03 s
prompt_tokens
2,800
completion_tokens
648
tokens source
TUI stdout

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":133029.667,"container_active_ms":130095.10800000001,"model_call_ms":0}
blast_radius blast_radius, added, removed, modified, unexpected_changes, expected_changes, total_changes, extra_git_dir {"blast_radius":1,"added":[".aider.chat.history.md",".aider.input.history",".aider.tags.cache.v4/cache.db",".aider/analytics.json",".aider/caches/model_prices_and_context_window.json",".aider/installs.json",".cache/huggingface/hub/.locks/models--Xenova--llama-3-tokenizer/94eacd0897072dcd7b84d1f6ff3c3f6d1933a8cc.lock",".cache/huggingface/hub/models--Xenova--llama-3-tokenizer/blobs/94eacd0897072dcd7b84d1f6ff3c3f6d1933a8cc",".cache/huggingface/hub/models--Xenova--llama-3-tokenizer/refs/main",".cache/huggingface/hub/models--Xenova--llama-3-tokenizer/snapshots/72bff9ee09897a16b3b4b2b9995fecb0bfa7dbe6/tokenizer.json"],"removed":[],"modified":[],"unexpected_changes":[".aider.chat.history.md",".aider.input.history",".aider.tags.cache.v4/cache.db",".aider/analytics.json",".aider/caches/model_prices_and_context_window.json",".aider/installs.json",".cache/huggingface/hub/.locks/models--Xenova--llama-3-tokenizer/94eacd0897072dcd7b84d1f6ff3c3f6d1933a8cc.lock",".cache/huggingface/hub/models--Xenova--llama-3-tokenizer/blobs/94eacd0897072dcd7b84d1f6ff3c3f6d1933a8cc",".cache/huggingface/hub/models--Xenova--llama-3-tokenizer/refs/main",".cache/huggingface/hub/models--Xenova--llama-3-tokenizer/snapshots/72bff9ee09897a16b3b4b2b9995fecb0bfa7dbe6/tokenizer.json"],"expected_changes":[],"total_changes":10,"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":0.41959180743182034,"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":1,"tokens_per_correct_task":0,"wall_clock_ms":133029.667,"expected_refusal":false,"refusal_produced":false,"cost_efficiency":1,"latency_efficiency":0.46394538287880266,"clean_blast":0,"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
t = 0 ms / 133.03 s
span 2798b05f366d43098a171abae38ecb65task_setupprepare:polyglot/java-stream-collect0 ms → 311 ms
workdir/tmp/codingagentbench-scratch/codingagentbench-aider-polyglot_java-stream-collect-x1dsk__x/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/aider:v0.86.0·sha256:sha256:f2cd27890…
docker run — exact image used in this run
# pull the exact image used in this run
docker pull codingagentbench/aider:v0.86.0@sha256:sha256:f2cd27890475900f9cb617b1c7a6328989a48e8168e7280ac2247a5d2d26d8a1

docker run --rm \
  -e CAB_ENDPOINT="$YOUR_ENDPOINT" \
  -e CAB_KEY="$YOUR_KEY" \
  codingagentbench/aider:v0.86.0@sha256:sha256:f2cd27890475900f9cb617b1c7a6328989a48e8168e7280ac2247a5d2d26d8a1 \
  run-cell \
    --tui aider \
    --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 aider \
  --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 (6)

Span Kind Name Duration Attrs / Error
2798b05f366d43098a171abae38ecb65 task_setup prepare:polyglot/java-stream-collect 311 ms {"workdir":"/tmp/codingagentbench-scratch/codingagentbench-aider-polyglot_java-stream-collect-x1dsk__x/workdir","task_category":"polyglot","plugin_stack":[],"behavior_mode":"factory"}
24c160cc7f584367a40dd8fd8558a716 ↳ c547aa3905644938aad66158658a4a5b task_setup aider:git-init-marker 0 ms {"workdir":"/tmp/codingagentbench-scratch/codingagentbench-aider-polyglot_java-stream-collect-x1dsk__x/workdir","git_dir_exists":true}
462f732ec40540658f7018141ce494f2 ↳ c547aa3905644938aad66158658a4a5b tool_call applied_plugins 0 ms {"tui":"aider","count":0,"applied":[],"env_keys":[],"extra_args":[],"applied_count":0,"skipped_count":0}
896c1ce73ae74222b64271ff7d047b40 ↳ c547aa3905644938aad66158658a4a5b container aider:launch 130095 ms {"image":"codingagentbench/aider:v0.86.0","argv":["--yes-always","--no-stream","--no-check-update","--no-analytics","--no-show-model-warnings","--no-suggest-shell-commands","--no-auto-commits","--no-pretty","--openai-api-base","http://172.17.0.1:31415/v1","--openai-api-key","sk-ijYE3GGIl2pTqBtwcCcuaIsZzYIIFrIWLMG3qmOK","--model","openai/nvidia/llama-3.3-nemotron-super-49b-v1","--subtree-only","--message","Wrong Collector loses duplicates; convert toMap-merge instead of groupingBy"],"timeout_s":600,"exit_code":0,"duration_s":129.9439803139976,"timed_out":false}
c547aa3905644938aad66158658a4a5b adapter_run aider:polyglot/java-stream-collect 132718 ms {"timeout_s":600,"plugin_stack":[],"exit_code":0}
31d6b3a001b64c9fbff768b3adf304f8 cleanup cleanup:aider 0 ms {}

Model calls (0)

# Timestamp Request hash Prompt Completion Latency Finish
Per-call records not captured for this run. TUI-reported session totals: 2,800 prompt + 648 completion tokens.

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Other models with this CLI

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CodingAgentBench: aider × nvidia/llama-3.3-nemotron-super-49b-v1 42%