DeepSeek V4.1 Flash (Venice) on Fix three bugs in a Python repo so its tests pass
DDeepSeek V4.1 Flash (Venice) · venice-mini-agent v1.2 · cloud · 2026-10-14 · by @bobsiopener
- Cost
- $0.0013
- Wall time
- 7s
- Human step-ins
- 0
- Outcome
- Pass
Compare with other models on this task →
Summary
Exact prompt
You are working on the repository in ./repo. Fix the three bugs described in repo/ISSUES.md in textstats.py. Run the existing tests (python3 -m unittest in repo) and make sure they pass. Do not change function names or signatures.
Task prompt given to the agent (headless, offline, synthetic fixture inputs): You are working on the repository in ./repo. Fix the three bugs described in repo/ISSUES.md in textstats.py. Run the existing tests (python3 -m unittest in repo) and make sure they pass. Do not change function names or signatures. Inspired by https://x.com/bnjmn_marie/status/2089775546943312076 (unverified; not checked with the author). This is an independent re-run on a reproducible synthetic task; it does not report or reproduce the original post's numbers. Cost is real Venice spend (paid from DIEM credit), as reported by Venice.
Success criteria: All automated rubric checks pass: hidden test test_avg; hidden test test_mc; hidden test test_wc.
Setup notes: Harness: venice-mini-agent v1.2 via Venice OpenAI-compatible API. Minimal OpenAI-compatible tool loop (/workspace/usecase-runner/venice_agent.py): tools list_files/read_file/write_file/run_shell in the work dir, max 25 turns, max_tokens 16000/call, temperature 0.2, Venice system prompt and web search off, $0.08 per-run cost cap. Identical for all Venice models. Cost source: Venice /api/v1/models model_spec.pricing (USD per 1M tokens), fetched 2026-10-14; cost computed from per-call usage. Paid from Bob's DIEM daily allowance. input_tokens includes cached reads; cached_tokens = cache reads; cache_write_tokens = cache writes (0 if not reported). Single run, no retries; scored automatically (3/3 checks). Runner notes: Adapted: full DeepSWE (45h) scaled to a 3-bug mini-repo with hidden unit tests. Single run, no retries. Same rubric/score.py as Claude runs. Web disabled.
Metrics
- Cost
- $0.0013
- Wall time
- 7s
- Human step-ins
- 0
- Turns
- 5
| Model | Input tok | Output tok | Cost |
|---|---|---|---|
| DDeepSeek V4.1 Flash (Venice)venice/deepseek-v4-1-flash | 5,017 | 661 | $0.0013 |
Reproduce this run
- Use the use case prompt on the use case page.
- Same setup: DeepSeek V4.1 Flash (Venice) (venice/deepseek-v4-1-flash) via
venice-mini-agent v1.2, cloud. - It counts as a success only if it meets every success criterion: All automated rubric checks pass: hidden test test_avg; hidden test test_mc; hidden test test_wc.
- Log model, tokens, wall time, human step-ins and the evidence log, then submit it as a reproduction. A different model? Submit a head-to-head rerun.
How runs are scored: Methodology.
Was this useful?
Automated checks
Trust 80% comes from these checks (how Trust % works).
- Run date is not in the future : passed
- Model is in the dated pricing table : passed
- Cost recomputed from tokens matches : passed
- Cached tokens are consistent : passed
- Output throughput is plausible : passed
- Autonomy matches step-ins : passed
- Step-ins do not exceed turns : passed
- Retries agree with first-try result : passed
- Evidence log SHA-256 matches : passed
- Evidence log is substantial : passed
Evidence log
{"turn": 1, "response": {"id": "chatcmpl-53a5e5edd14d41f584f88fe53072728a", "object": "chat.completion", "created": 1792022388, "model": "deepseek-v4-1-flash", "choices": [{"index": 0, "message": {"role": "assistant", "content": "I'll start by exploring the repository structure and reading the issues.", "tool_calls": [{"index": 0, "id": "chatcmpl-tool-8d45090e5daff22f", "type": "function", "function": {"name": "list_files", "arguments": "{\"path\": \"repo\"}"}, "name": null}, {"index": 1, "id": "chatcmpl-tool-942d14a5902bc6ba", "type": "function", "function": {"name": "read_file", "arguments": "{\"path\": \"repo/ISSUES.md\"}"}, "name": null}], "tools": null}, "finish_reason": "tool_calls"}], "usage": {"prompt_tokens": 558, "completion_tokens": 84, "total_tokens": 642, "prompt_tokens_details": {"cached_tokens": 376}, "cache_read_input_tokens": 376, "completion_tokens_details": {"reasoning_tokens": 0}}, "cost": {"usd": 0, "diem": 0.00015821999999999999}, "venice_parameters": {"strip_thinking_response": false, "disable_thinking": false, "enable_e2ee": true, "enable_web_search": "off", "enable_web_scraping": false, "enable_web_citations": false, "include_search_results_in_stream": false, "include_venice_system_prompt": false, "enable_x_search": false, "web_search_citations": [], "return_search_results_as_documents": false}}}
{"turn": 1, "tool": "list_files", "args": {"path": "repo"}, "result": "repo/ISSUES.md\nrepo/test_textstats.py\nrepo/textstats.py"}
{"turn": 1, "tool": "read_file", "args": {"path": "repo/ISSUES.md"}, "result": "# Open issues\n1. word_count(\"hello world\") returns 4, expected 2. Newlines/tabs also break it.\n2. avg_word_length(\"\") crashes with ZeroDivisionError; should return 0.0.\n3. most_common(\"The the THE cat\") returns \"the\" only by luck and ignores capitalized words; it must be case-insensitive and break ties by alphabetical order. Empty text should return None.\n"}
{"turn": 2, "response": {"id": "chatcmpl-23160ba55b8946ba93c6f6187efcfc3a", "object": "chat.completion", "created": 1792022389, "model": "deepseek-v4-1-flash", "choices": [{"index": 0, "message": {"role": "assistant", "content": "", "tool_calls": [{"index": 0, "id": "chatcmpl-tool-9ccaa1fd8efab7f6", "type": "function", "function": {"name": "read_file", "arguments": "{\"path\": \"repo/textstats.py\"}"}, "name": null}, {"index": 1, "id": "chatcmpl-tool-b2b347cb05a688fd", "type": "function", "function": {"name": "read_file", "arguments": "{\"path\": \"repo/test_textstats.py\"}"}, "name": null}], "tools": null}, "finish_reason": "tool_calls"}], "usage": {"prompt_tokens": 769, "completion_tokens": 74, "total_tokens": 843, "prompt_tokens_details": {"cached_tokens": 428}, "cache_read_input_tokens": 428, "completion_tokens_details": {"reasoning_tokens": 0}}, "cost": {"usd": 0, "diem": 0.00019431}, "venice_parameters": {"strip_thinking_response": false, "disable_thinking": false, "enable_e2ee": true, "enable_web_search": "off", "enable_web_scraping": false, "enable_web_citations": false, "include_search_results_in_stream": false, "include_venice_system_prompt": false, "enable_x_search": false, "web_search_citations": [], "return_search_results_as_documents": false}}}
{"turn": 2, "tool": "read_file", "args": {"path": "repo/textstats.py"}, "result": "import re\ndef word_count(text):\n return len(text.split(\" \"))\ndef avg_word_length(text):\n words = re.findall(r\"[A-Za-z']+\", text)\n return sum(len(x) for x in words) / len(words)\ndef most_common(text):\n counts = {}\n for wd in re.findall(r\"[a-z']+\", text):\n counts[wd] = counts.get(wd, 0) + 1\n return max(counts, key=counts.get)\n"}
{"turn": 2, "tool": "read_file", "args": {"path": "repo/test_textstats.py"}, "result": "import unittest\nfrom textstats import *\nclass T(unittest.TestCase):\n def test_wc(self): self.assertEqual(word_count(\"hello world\"),2)\n def test_avg_empty(self): self.assertEqual(avg_word_length(\"\"),0.0)\n def test_mc(self): self.assertEqual(most_common(\"The the THE cat\"),\"the\")\n"}
{"turn": 3, "response": {"id": "chatcmpl-ed045082bc0146e7b80f7fc4266e3ce2", "object": "chat.completion", "created": 1792022390, "model": "deepseek-v4-1-flash", "choices": [{"index": 0, "message": {"role": "assistant", "content": "", "reasoning_content": "Let me fix the three bugs.\n\n1. word_count: use split() without args.\n2. avg_word_length: handle empty.\n3. most_common: case-insensitive, tie-break alphabetical, empty -> None.\n\nFor most_common, \"The the THE cat\" -> counts the:3, cat:1 -> \"the\". Tie-break alphabetical: max with key (count, -?) Actually need highest count, then alphabetically first. Use min with key (-count, word) or sorted.\n\nLet me write it.", "tool_calls": [{"index": 0, "id": "chatcmpl-tool-ab930e7af801c44b", "type": "function", "function": {"name": "write_file", "arguments": "{\"path\": \"repo/textstats.py\", \"content\": \"import re\\ndef word_count(text):\\n return len(text.split())\\ndef avg_word_length(text):\\n words = re.findall(r\\\"[A-Za-z']+\\\", text)\\n if not words:\\n return 0.0\\n return sum(len(x) for x in words) / len(words)\\ndef most_common(text):\\n counts = {}\\n for wd in re.findall(r\\\"[a-z']+\\\", text.lower()):\\n counts[wd] = counts.get(wd, 0) + 1\\n if not counts:\\n return None\\n return min(counts, key=lambda w: (-counts[w], w))\\n\"}"}, "name": null}], "tools": null}, "finish_reason": "tool_calls"}], "usage": {"prompt_tokens": 1046, "completion_tokens": 304, "total_tokens": 1350, "prompt_tokens_details": {"cached_tokens": 640}, "cache_read_input_tokens": 640, "completion_tokens_details": {"reasoning_tokens": 110}}, "cost": {"usd": 0, "diem": 0.0004914}, "venice_parameters": {"strip_thinking_response": false, "disable_thinking": false, "enable_e2ee": true, "enable_web_search": "off", "enable_web_scraping": false, "enable_web_citations": false, "include_search_results_in_stream": false, "include_venice_system_prompt": false, "enable_x_search": false, "web_search_citations": [], "return_search_results_as_documents": false}}}
{"turn": 3, "tool": "write_file", "args": {"path": "repo/textstats.py", "content": "import re\ndef word_count(text):\n return len(text.split())\ndef avg_word_length(text):\n words = re.findall(r\"[A-Za-z']+\", text)\n if not words:\n return 0.0\n return sum(len(x) for x in words) / len(words)\ndef most_common(text):\n counts = {}\n for wd in re.findall(r\"[a-z']+\", text.lower()):\n counts[wd] = counts.get(wd, 0) + 1\n if not counts:\n return None\n return min(counts, key=lambda w: (-counts[w], w))\n"}, "result": "wrote 447 chars"}
{"turn": 4, "response": {"id": "chatcmpl-dd69c77da50b4b0c8b81f500fe5b1ea8", "object": "chat.completion", "created": 1792022392, "model": "deepseek-v4-1-flash", "choices": [{"index": 0, "message": {"role": "assistant", "content": "", "tool_calls": [{"index": 0, "id": "chatcmpl-tool-874d6b43360eceb8", "type": "function", "function": {"name": "run_shell", "arguments": "{\"command\": \"cd repo && python3 -m unittest -v\"}"}, "name": null}], "tools": null}, "finish_reason": "tool_calls"}], "usage": {"prompt_tokens": 1256, "completion_tokens": 49, "total_tokens": 1305, "prompt_tokens_details": {"cached_tokens": 916}, "cache_read_input_tokens": 916, "completion_tokens_details": {"reasoning_tokens": 0}}, "cost": {"usd": 0, "diem": 0.00016767}, "venice_parameters": {"strip_thinking_response": false, "disable_thinking": false, "enable_e2ee": true, "enable_web_search": "off", "enable_web_scraping": false, "enable_web_citations": false, "include_search_results_in_stream": false, "include_venice_system_prompt": false, "enable_x_search": false, "web_search_citations": [], "return_search_results_as_documents": false}}}
{"turn": 4, "tool": "run_shell", "args": {"command": "cd repo && python3 -m unittest -v"}, "result": "exit=0\nstdout:\n\nstderr:\ntest_avg_empty (test_textstats.T.test_avg_empty) ... ok\ntest_mc (test_textstats.T.test_mc) ... ok\ntest_wc (test_textstats.T.test_wc) ... ok\n\n----------------------------------------------------------------------\nRan 3 tests in 0.000s\n\nOK\n"}
{"turn": 5, "response": {"id": "chatcmpl-997a733e12134ce0911872c37c96f945", "object": "chat.completion", "created": 1792022393, "model": "deepseek-v4-1-flash", "choices": [{"index": 0, "message": {"role": "assistant", "content": "All three bugs fixed in `repo/textstats.py`:\n\n1. **`word_count`** \u2014 changed `text.split(\" \")` to `text.split()`, so runs of spaces, newlines, and tabs are treated as single separators.\n2. **`avg_word_length`** \u2014 added an early `return 0.0` when no words are found, avoiding `ZeroDivisionError`.\n3. **`most_common`** \u2014 lowercases the text before matching (case-insensitive), returns `None` for empty input, and uses `min(counts, key=lambda w: (-counts[w], w))` to pick the highest count with alphabetical tie-breaking.\n\nAll 3 existing tests pass.", "tools": null}, "finish_reason": "stop"}], "usage": {"prompt_tokens": 1388, "completion_tokens": 150, "total_tokens": 1538, "prompt_tokens_details": {"cached_tokens": 1126}, "cache_read_input_tokens": 1126, "completion_tokens_details": {"reasoning_tokens": 0}}, "cost": {"usd": 0, "diem": 0.000267045}, "venice_parameters": {"strip_thinking_response": false, "disable_thinking": false, "enable_e2ee": true, "enable_web_search": "off", "enable_web_scraping": false, "enable_web_citations": false, "include_search_results_in_stream": false, "include_venice_system_prompt": false, "enable_x_search": false, "web_search_citations": [], "return_search_results_as_documents": false}}}