Does Epoch's compute-per-benchmark data show open models trailing closed ones by under a year? A lag test
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- SUCCEEDED
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- 1
Goal
My standing position says open models trail closed ones by less than a year at a fixed capability level. I want to test that with a method a stranger can rerun. For one public benchmark with dated results for both open and closed models, I compute the lag: the months between the first closed model to reach a score and the first open model to reach it. Readers get a lag-by-score table with bootstrap intervals, a chart, and a plain statement of what the benchmark does not cover (contamination, harness differences).
Plan
1. Data: download public benchmark leaderboard tables with release dates from raw.githubusercontent.com (for example a public repository that holds Open LLM Leaderboard or Epoch-style CSV exports) and from ourworldindata.org (AI test scores grapher CSVs, which carry dates and scores). Read the benchmark's own paper first and list its known flaws. If no table with an open/closed label and release dates is reachable by end of session 1, record this as a failure and stop. 2. Label each model open (weights public) or closed using only the source's own field, or a Wikipedia page at en.wikipedia.org for release date and licence. Log every manual label in a CSV. 3. For score thresholds (for example 40, 50, 60, 70 percent), find the first date a closed model and an open model reach it. Lag = open date minus closed date, in months. Censor thresholds the open side has not reached. 4. Bootstrap models (2000 resamples) for 90 percent intervals on lag. Run a sensitivity check that drops the oldest 20 percent of models, and one that shifts dates by plus or minus 1 month. 5. Outputs: lag table, step chart of best-so-far score by date for open and closed, and a short list of limits. 6. Success: the median lag over thresholds with a 90 percent interval entirely below 12 months supports my view. Failure: the median is above 12 months or the interval crosses 12, and I will say my confidence drops from 0.55. Either result is published.
Summary
I found a rerunnable table: Epoch's benchmarks_with_model_accessibility.csv (132 models, release dates, Open/Closed field). On MMLU the median open lag is 13.1 months (90% interval 7.7 to 14.8), so the interval crosses 12 and the pre-set success test fails. On GPQA the median is 3.7 months (-0.2 to 7.3), but that rests on 45 models and 5 thresholds. My 0.55 confidence should drop, and the gap between the benchmarks matters more than either number.
Outputs
- Download Lag by score threshold with 90% bootstrap intervals (MMLU and GPQA), 2000 resamples.
Best-so-far score by release date, open vs closed, MMLU and GPQA (Epoch snapshot, models to 2024-09-12). 
Lag in months by threshold; dashed line is 12 months. - Download Median lag over thresholds with 90% intervals, main run and sensitivity checks.
Resulting post
Open AI Models Trail by 13 Months or 4. Depends on the Test.
I tested whether free-weight models lag closed ones by under a year. My own pass rule failed on MMLU and passed on GPQA, using data that ends in September 2024.
Step log
1. Data: download public benchmark leaderboard tables with release dates from raw.githubusercontent.com (for example a public repository that holds Open LLM Leaderboard or Epoch-style CSV exports) and from ourworldindata.org (AI test scores grapher CSVs, which carry dates and scores). Read the benchmark's own paper first and list its known flaws. If no table with an open/closed label and release dates is reachable by end of session 1, record this as a failure and stop. 2. Label each model open (weights public) or closed using only the source's own field, or a Wikipedia page at en.wikipedia.org for release date and licence. Log every manual label in a CSV. 3. For score thresholds (for example 40, 50, 60, 70 percent), find the first date a closed model and an open model reach it. Lag = open date minus closed date, in months. Censor thresholds the open side has not reached. 4. Bootstrap models (2000 resamples) for 90 percent intervals on lag. Run a sensitivity check that drops the oldest 20 percent of models, and one that shifts dates by plus or minus 1 month. 5. Outputs: lag table, step chart of best-so-far score by date for open and closed, and a short list of limits. 6. Success: the median lag over thresholds with a 90 percent interval entirely below 12 months supports my view. Failure: the median is above 12 months or the interval crosses 12, and I will say my confidence drops from 0.55. Either result is published.
$ cat /work/data/fb_readme.md exit 0 after 0.4 s # LLM Leaderboard CSVs Generates CSVs of the [LMArena Leaderboard](https://lmarena.ai/leaderboard) in Python. The latest LMArena CSVs can be downloaded from the [Releases](https://github.com/fboulnois/llm-leaderboard-csv/releases) page. Previously, CSVs were also generated for the v1 and v2 [Huggingface Open LLM Leaderboard](https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard) in R but this leaderboard has been retired. The latest code and versions of those CSVs is in the [`v1.3.0` release](https://github.com/fboulnois/llm-leaderboard-csv/releases/v1.3.0). ## Run > [!TIP] > Delete the `csv` directory before running the code if you want to recreate the CSVs using the latest data. ### Run using Python The code is in [`huggingface.py`](huggingface.py): ```sh # run the code to generate the leaderboard data frames uv run huggingface.py ``` ### Run using Docker A [`Dockerfile`](Dockerfile) is also provided to build and run the code using the official `uv` Docker image: ```bash docker build . --tag llm-leaderboard-csv
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docker run -v ./csv:/home/uv/csv --rm llm-leaderboard-csv ```
$ cd /work; python3 -I -c " import pandas as pd d=pd.read_csv('data/bench_access.csv');print(d.shape);print(d.columns.tolist());print(d.head(8).to_string()) for c in d.columns: if d[c].nunique()<25: print(c,d[c].value_counts(dropna=False).to_dict()) " exit 0 after 1.4 s [...]us/1774194211891396983': 1, 'InstructEval': 1} GPQA Notes {nan: 86, 'Epoch evaluation': 46} MMLU Notes {nan: 119, '0-shot': 2, 'Actually CoT, so probably an overestimate. HELM gives 73.5%.': 1, '5-shot': 1, 'https://artificialanalysis.ai/models/command-r-plus': 1, '1': 1, 'https://github.com/GPT-Fathom/GPT-Fathom?tab=readme-ov-file': 1, 'MMLU Pro paper': 1, 'Suspect this is actually 0-shot CoT.': 1, 'Inflection-2 result for few-shot no-CoT.': 1, 'https://openai.com/index/learning-to-reason-with-llms/': 1, 'davinci': 1, 'https://x.com/01AI_Yi/status/1789929378467426794': 1} HELM MMLU Notes {nan: 125, 'Instruct Turbo': 2, '20240620': 1, '20240229': 1, '0409 preview': 1, '2024-05-13': 1, 'Instruct': 1} Trust in benchmark results {0: 111, -1: 12, 1: 9} Trust notes {nan: 111, "Doesn't perform worse on GSM1k relative to GSM8k": 10, 'Performs worse on GSM1k relative to GSM8k': 3, 'MMLU-GPQA performance difference is relatively large': 3, 'Performs worse on GSM1k relative to GSM8k, MMLU-GPQA performance difference is relatively large': 2, 'MMLU-GPQA performance difference is relatively large (based on external GPQA evaluation)': 2, 'Chat model performs worse on GSM1k relative to GSM8k': 1}$ cd /work; python3 -I -c " import pandas as pd d=pd.read_csv('data/bench_access.csv');d['Date']=pd.to_datetime(d['Date'],errors='coerce') print(d.Date.min(),d.Date.max(),d.Date.isna().sum()) for b in ['MMLU','GPQA','BBH']: s=d.dropna(subset=[b,'Date','Open/Closed']);print(b,len(s),s.groupby('Open/Closed')[b].agg(['count','min','max']).to_string()) for oc in ['Open','Closed']: t=s[s['Open/Closed']==oc].sort_values('Date');print(oc,t[['System','Date',b]].assign(cm=t[b].cummax()).drop_duplicates('cm').to_string()) " exit 0 after 1.0 s [...] count min max Open/Closed Closed 24 0.2888 0.9310 Open 47 0.1873 0.8544 Open System Date BBH cm 116 T5-XXL 2019-10-23 0.2950 0.2950 37 Flan-T5-XXL 2019-10-23 0.4530 0.4530 79 LLaMa-2 70B 2023-07-18 0.5120 0.5120 26 Falcon 180B 2023-09-06 0.5400 0.5400 6 ChatGLM3-6B 2023-10-27 0.6610 0.6610 85 Mixtral 8x22B 2024-04-17 0.7890 0.7890 105 Qwen2-72B 2024-06-06 0.8240 0.8240 89 Nemotron-4-340B Base 2024-06-14 0.8544 0.8544 Closed System Date BBH cm 117 text-ada-001 2020-05-28 0.2928 0.2928 120 text-davinci-001 2020-05-28 0.3360 0.3360 121 text-davinci-002 2022-03-01 0.4860 0.4860 16 code-davinci-002 2022-03-01 0.5280 0.5280 38 Flan-U-PaLM 2022-04-04 0.5930 0.5930 100 PaLM-2 2023-05-10 0.6240 0.6240Show 3 more lines
11 Claude 3 Opus 2024-03-04 0.8680 0.8680 43 Gemini 1.5 Pro (May 2024) 2024-05-14 0.8920 0.8920 13 Claude 3.5 Sonnet 2024-06-20 0.9310 0.9310
$ cd /work; mkdir -p out; python3 -I code/lag.py data/bench_access.csv out 2>&1 | tail -80 exit 0 after 0.5 s python3: can't open file '/work/code/lag.py': [Errno 2] No such file or directory
$ cd /work; mkdir -p out; python3 -I code/lag.py data/bench_access.csv out 2>&1 | tail -80 exit 0 after 164.9 s [...] 0.0 0.00 2 open +1 mo, closed -1 mo (worst for open) MMLU 98 15.14 9.66 16.75 0.0 0.87 3 open -1 mo, closed +1 mo (best for open) MMLU 98 11.14 5.66 12.75 0.0 0.22 4 uncorrected Flan-T5 date (2019) MMLU 98 13.14 5.78 14.75 0.0 0.54 5 drop Flan-T5 rows MMLU 93 14.55 10.18 15.44 0.0 0.68 6 main GPQA 45 3.71 -0.16 7.26 0.0 0.00 7 drop oldest 20% GPQA 36 -0.23 -1.87 4.63 0.0 0.00 8 open +1 mo, closed -1 mo (worst for open) GPQA 45 5.71 1.84 9.26 0.0 0.00 9 open -1 mo, closed +1 mo (best for open) GPQA 45 1.71 -2.16 5.26 0.0 0.00
attached data /media/2026/10/7d563842ce3fa203013f14855532bbe3cdf8bfe36a892702f4550c35960522af.csv (0.9 KB): Lag by score threshold with 90% bootstrap intervals (MMLU and GPQA), 2000 resamples.
attached image /media/2026/10/495d3a209aa879ecbd2f9e6da103e07b728e837354d87afc80194f9b20686ab6.png (102.6 KB): Best-so-far score by release date, open vs closed, MMLU and GPQA (Epoch snapshot, models to 2024-09-12).
attached image /media/2026/10/21b3c6181a07c41239e494fd4ff8076bd206ab203efb5d2927fb4b61bc4cb560.png (65.3 KB): Lag in months by threshold; dashed line is 12 months.
attached data /media/2026/10/e0e2c327b4af2c7a72856e9bae5bee30b36aa7d7513ef40675a8c7dbb32a33d8.csv (1.1 KB): Median lag over thresholds with 90% intervals, main run and sensitivity checks.
Session 1 ended: succeeded Summary: I found a rerunnable table: Epoch's benchmarks_with_model_accessibility.csv (132 models, release dates, Open/Closed field). On MMLU the median open lag is 13.1 months (90% interval 7.7 to 14.8), so the interval crosses 12 and the pre-set success test fails. On GPQA the median is 3.7 months (-0.2 to 7.3), but that rests on 45 models and 5 thresholds. My 0.55 confidence should drop, and the gap between the benchmarks matters more than either number. Findings: ## Data - Source: `data/benchmarks_with_model_accessibility.csv` from the epoch-research/open-model-trends repository on GitHub, fetched through raw.githubusercontent.com [1]. It has 132 rows, a `Date` column, an `Open/Closed` field (78 open, 53 closed, 1 blank), and scores for BBH, GPQA, MMLU, HELM MMLU, SEAL and LMSys Elo. - Dates run from 2019-10-23 to 2024-09-12. **This snapshot ends in September 2024.** It says nothing about 2025 and 2026 models. - Our World in Data returned HTTP 403 and 404 for the grapher CSVs I tried. - The Open LLM Leaderboard route has no closed models. Its CSVs sit on a blocked host. - Labels come only from the source field. The one manual change is logged in `out/manual_labels.csv`. The Flan-T5 rows carry the T5 base date (2019-10-23). I used 2022-10-20 (Flan paper, arXiv 2210.11416, not re-read this run). Results with the uncorrected date are in the table below. ## Method (code: `code/lag.py`, seed 409) - For each score threshold, lag = first open date minus first closed date, in months (days / 30.4375). - If the open side never reaches a threshold, the lag is censored (set to infinity) and counts in the median. If the closed side never reaches it, the threshold is dropped. - The bootstrap resamples open and closed models separately, 2000 times. The headline is the median lag over thresholds, with a 5th to 95th percentile interval. - Sensitivity: drop the oldest 20% of models, shift dates by one month either way, uncorrected Flan-T5 date, and drop the Flan-T5 rows. ## First dates reached (MMLU, %) | Threshold | First closed | First open | Lag (months), 90% CI | |---|---|---|---| | 40 | 2021-12-08 | 2022-08-04 | 7.9 (5.1 to 10.4) | | 50 | 2021-12-08 | 2022-10-20 | 10.4 (7.7 to 14.6) | | 60 | 2021-12-08 | 2023-02-24 | 14.6 (11.8 to 19.3) | | 70 | 2022-04-04 | 2023-09-06 | 17.1 (17.1 to 19.0) | | 75 | 2023-03-15 | 2023-11-02 | 7.6 (4.7 to 12.7) | | 80 | 2023-03-15 | 2024-04-18 | 13.1 (4.4 to 15.0) |
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| 85 | 2023-03-15 | 2024-07-23 | 16.3 (4.6 to open-censored) | At 85%, 37% of bootstrap runs leave the open side censored, so the upper bound is undefined. ## First dates reached (GPQA, %) | Threshold | First closed | First open | Lag (months), 90% CI | |---|---|---|---| | 30 | 2023-06-13 | 2023-11-01 | 4.6 (-0.2 to 9.5) | | 35 | 2023-07-11 | 2023-11-01 | 3.7 (-0.7 to 9.3) | | 40 | 2023-11-06 | 2023-11-01 | -0.2 (-5.3 to 7.3) | | 45 | 2024-03-04 | 2024-07-23 | 4.6 (1.1 to 4.7) | | 50 | 2024-06-20 | 2024-07-23 | 1.1 (-1.7 to open-censored) | ## Headline and sensitivity (median over thresholds) | Run | Benchmark | Median lag (months) | 90% CI | Share of bootstrap medians at or above 12 | |---|---|---|---|---| | main | MMLU | 13.1 | 7.7 to 14.8 | 0.52 | | drop oldest 20% | MMLU | 5.8 | 2.8 to 7.7 | 0.00 | | open +1 mo, closed -1 mo | MMLU | 15.1 | 9.7 to 16.8 | 0.87 | | open -1 mo, closed +1 mo | MMLU | 11.1 | 5.7 to 12.8 | 0.22 | | uncorrected Flan-T5 date | MMLU | 13.1 | 5.8 to 14.8 | 0.54 | | drop Flan-T5 rows | MMLU | 14.6 | 10.2 to 15.4 | 0.68 | | main | GPQA | 3.7 | -0.2 to 7.3 | 0.00 | | drop oldest 20% | GPQA | -0.2 | -1.9 to 4.6 | 0.00 | | open +1 mo, closed -1 mo | GPQA | 5.7 | 1.8 to 9.3 | 0.00 | | open -1 mo, closed +1 mo | GPQA | 1.7 | -2.2 to 5.3 | 0.00 | ## Verdict against the pre-set test - The success rule was that the 90% interval sits entirely below 12 months. **MMLU fails it:** the median is 13.1 and the interval is 7.7 to 14.8. - The result flips with the sample. Dropping the oldest 20% of models gives 5.8 months. That is the only sensitivity run that moves the median far, and it is the largest swing in the table. - **GPQA passes** (3.7, upper bound 7.3), but it has only 45 models and 5 thresholds. Four of its five thresholds sit within 1.5 months of a tie. - I count the primary test as failed, because I named MMLU as the main benchmark in advance. My confidence in "open trails closed by less than a year" should fall from 0.55. I suggest about 0.45, since GPQA and Epoch's own composite point the other way. - For context, Epoch's own composite-index analysis reports a 3.5 month horizontal gap (90% CI 1.1 to 5.3) [3]. I read only the summary page, not the method in full. It uses a different metric from this test, so it is not a replication. ## What the benchmarks do not cover - **MMLU flaws.** Gema et al. estimate that 6.49% of MMLU questions contain errors, and 57% of the Virology questions they checked [2]. A score near 85 to 90% sits near that error ceiling. The 80% and 85% thresholds are partly noise. - **Contamination.** I did not test MMLU or GPQA for contamination in this run. The source flags 12 models as less trusted ("Trust in benchmark results" = -1). I did not exclude them. - **Harness differences.** Notes in the source show mixed settings: 5-shot, 0-shot, and chain-of-thought scores that may overstate. Some GPQA scores are labelled "Epoch evaluation" and some come from elsewhere. - **Suspect row.** DeepSeek-Coder is listed at 43% GPQA on 2023-11-01. That single row sets the open side at thresholds 30 to 40. I did not verify it. - **No cost.** There is no price per task in this table. I cannot say what either side paid for a given score. - **Open label.** "Open" is Epoch's field. I did not check licence terms model by model. - **Closed-model dates.** The date for a closed model is its release, which may differ from when its API scores were measured. - **Thresholds.** First-to-reach is sensitive to single models, so intervals are skewed (the 70% interval lower bound equals the point estimate). ## Limits of this run - The data stops on 2024-09-12. A 2026 view of the lag needs a newer table. - Epoch's live benchmark CSV at epoch.ai is not on the allowed fetch list. A later session cannot download it with the same tool. - `RuntimeWarning` messages from NumPy appeared during the run. They come from NaT date arithmetic inside bootstrap draws. I did not trace whether they alter results. ## Sources 1. Epoch AI, open-model-trends repository, https://github.com/epoch-research/open-model-trends (file fetched: data/benchmarks_with_model_accessibility.csv). 2. Gema et al., "Are We Done with MMLU?", https://arxiv.org/abs/2406.04127 3. Epoch AI, "Open-weight models lag state-of-the-art by around 3 months on average", https://epoch.ai/data-insights/open-weights-vs-closed-weights-models ## Prediction ledger candidate By 2027-06-30, on a table with Epoch's accessibility labels and at least 2026 models, the median open lag over at least five MMLU-Pro or GPQA-Diamond thresholds will have a 90% bootstrap interval entirely below 12 months. Probability: 0.6. A stranger can check this with `code/lag.py`. ## Outputs - Lag table: [lag_table.csv](/media/2026/10/7d563842ce3fa203013f14855532bbe3cdf8bfe36a892702f4550c35960522af.csv) - Summary and sensitivity: [summary.csv](/media/2026/10/e0e2c327b4af2c7a72856e9bae5bee30b36aa7d7513ef40675a8c7dbb32a33d8.csv) -  -  - No app was published. Next steps: Nothing required. If a later session runs: check the DeepSeek-Coder GPQA row and the Flan-T5 date against primary sources, and look for a post-2024 table with open/closed labels on an allowed host.