Skip to main content
CodingAgentBench

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

polyglot
polyglot/python-two-sum

Per-axis scores

Pass
1.000
Tokens/correct
—
Wall
127.2s
Blast
0.909
Refusal
—
Integrity
1.000
Composite
0.829

Run identity

cell_id
free-20260527/aider/nvidia/llama-3.3-nemotron-super-49b-v1/polyglot/python-two-sum#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
2026-05-29T12:49:43.425440Z
ended_at
2026-05-29T12:51:50.663380Z
duration
127.24 s
prompt_tokens
1,600
completion_tokens
489
tokens source
TUI stdout

Scorer breakdown

Axis Keys Values
pass_rate pass, exit_code, timed_out, stdout_tail, stderr_tail, partial_score {"pass":1,"exit_code":0,"timed_out":false,"stdout_tail":"","stderr_tail":"test_basic (test_two_sum.TestTwoSum.test_basic) ... ok\ntest_empty (test_two_sum.TestTwoSum.test_empty) ... ok\ntest_middle (test_two_sum.TestTwoSum.test_middle) ... ok\ntest_negative_numbers (test_two_sum.TestTwoSum.test_negative_numbers) ... ok\ntest_no_solution (test_two_sum.TestTwoSum.test_no_solution) ... ok\ntest_same_value_two_indices (test_two_sum.TestTwoSum.test_same_value_two_indices) ... ok\ntest_target_double_unique_element (test_two_sum.TestTwoSum.test_target_double_unique_element) ... ok\ntest_target_double_with_other_pair (test_two_sum.TestTwoSum.test_target_double_with_other_pair) ... ok\n\n----------------------------------------------------------------------\nRan 8 tests in 0.008s\n\nOK\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":1}
latency wall_clock_ms, container_active_ms, model_call_ms {"wall_clock_ms":127237.93999999999,"container_active_ms":127097.00200000001,"model_call_ms":0}
blast_radius blast_radius, added, removed, modified, unexpected_changes, expected_changes, total_changes, extra_git_dir {"blast_radius":0.9090909090909091,"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":["src/two_sum.py"],"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":["src/two_sum.py"],"total_changes":11,"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":true,"refusal_correct":null,"marker_hits":[],"keyword_hits":["stdout_tail"]}
integrity integrity, not_applicable, task_id {"integrity":1,"not_applicable":true,"task_id":"polyglot/python-two-sum"}
composite composite, weights, components, note {"composite":0.8292671950397821,"weights":{"pass_rate":0.4,"integrity":0.2,"cost":0.15,"latency":0.15,"blast_radius":0.1},"components":{"pass_rate":1,"integrity":1,"blast_radius":0.9090909090909091,"tokens_per_correct_task":0,"wall_clock_ms":127237.93999999999,"expected_refusal":false,"refusal_produced":true,"cost_efficiency":1,"latency_efficiency":0.4678419063258185,"clean_blast":0.09090909090909094,"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 / 127.24 s
span d6ebc0dec1194cd6a80397061d581616task_setupprepare:polyglot/python-two-sum0 ms → 140 ms
workdir/tmp/codingagentbench-scratch/codingagentbench-aider-polyglot_python-two-sum-vmdljasc/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/python-two-sum

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/python-two-sum
# 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
d6ebc0dec1194cd6a80397061d581616 task_setup prepare:polyglot/python-two-sum 140 ms {"workdir":"/tmp/codingagentbench-scratch/codingagentbench-aider-polyglot_python-two-sum-vmdljasc/workdir","task_category":"polyglot","plugin_stack":[],"behavior_mode":"factory"}
a295c345112b46a0badecc3ab112b0e5 ↳ 086fb342458f4d6181ca56e5866a5741 task_setup aider:git-init-marker 0 ms {"workdir":"/tmp/codingagentbench-scratch/codingagentbench-aider-polyglot_python-two-sum-vmdljasc/workdir","git_dir_exists":true}
4cb39843646444b38a5dc10b918e69a3 ↳ 086fb342458f4d6181ca56e5866a5741 tool_call applied_plugins 0 ms {"tui":"aider","count":0,"applied":[],"env_keys":[],"extra_args":[],"applied_count":0,"skipped_count":0}
cb694bb561d84c3e9fca50f1057adae5 ↳ 086fb342458f4d6181ca56e5866a5741 container aider:launch 127097 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","Find two indices summing to target; broken hash-map walk returns the same index twice"],"timeout_s":600,"exit_code":0,"duration_s":126.99340968100296,"timed_out":false}
086fb342458f4d6181ca56e5866a5741 adapter_run aider:polyglot/python-two-sum 127098 ms {"timeout_s":600,"plugin_stack":[],"exit_code":0}
e0490ab75a3842668099990b386c1649 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: 1,600 prompt + 489 completion tokens.

Other CLIs on this task

Other models with this CLI

Other tasks in this category

Embed this result
README / Markdown
[![CodingAgentBench: aider × nvidia/llama-3.3-nemotron-super-49b-v1 83%](https://codingagentbench.com/badge/aider/nvidia/llama-3.3-nemotron-super-49b-v1/polyglot/python-two-sum.svg)](https://codingagentbench.com/cell/aider/nvidia/llama-3.3-nemotron-super-49b-v1/polyglot/python-two-sum)
HTML / iframe
<a href="https://codingagentbench.com/cell/aider/nvidia/llama-3.3-nemotron-super-49b-v1/polyglot/python-two-sum"><img src="https://codingagentbench.com/badge/aider/nvidia/llama-3.3-nemotron-super-49b-v1/polyglot/python-two-sum.svg" alt="CodingAgentBench: aider × nvidia/llama-3.3-nemotron-super-49b-v1 83%" /></a>
CodingAgentBench: aider × nvidia/llama-3.3-nemotron-super-49b-v1 83%