RankingEffort benchmarks

gpt-oss-20b · High

Compare models

42 measured results · 3 effort settings · 25 benchmark/harness combinations

Model evidence and sources →

Change model

Model

Current configurationSources

Compare with

Choose a second model and its effort to see both profiles and benchmark differences below.

Compare withModel and configuration

Capability profile

One configuration, seven capabilities

Comparison score · 0–100

Agpt-oss-20b · High4 of 7 capabilities supported

Gaps are unknown, not zero. Hollow points are preliminary.

Agentic
A4.6Supported
Hard reasoning
A9.1Supported
Coding
A8.3Supported
Human pref
Unknown
Knowledge
A4.5Supported
Multimodal
Unknown
Long context
A6.2Preliminary

Each profile belongs to the named configuration. Capability scores use a shared panel for all axes, distinct from the overall ranking panel; they are not accuracy percentages or direct head-to-head wins.

Capability score evidence

gpt-oss-20b · High: 1.6 overall comparison score. Meets the overall evidence requirements.

5 areas measured. The available task mix affects this estimate; missing areas remain unknown.

Which sources carry the weight?

Artificial Analysis · 75.8% direct comparison weight · independent of the model provider
Epoch AI · 24.2% direct comparison weight · independent of the model provider

Which benchmark families carry the weight?

Aime · 16.8% direct comparison weight
GPQA · 16.7% direct comparison weight
MMLU-Pro · 4.7% direct comparison weight
LiveCodeBench · 4.7% direct comparison weight
IFBench · 4.6% direct comparison weight
Other families (12) · 52.5% direct comparison weight

These shares describe direct comparison weight, not fractions of the final score or confidence. Source balancing does not give every family equal weight or remove correlations between overlapping sources.

How sensitive is this score?

The audit removes one source at a time and refits against the same overall reference panel. These published results use the matching dataset audited 2026-10-20.

Both ranks use the same surviving configurations for each removal. The cohort starts with 444 configurations supported in the original audit, including separate effort settings. These are diagnostic positions, not the default leaderboard ranks. Eligibility is not rechecked after removal.

What changes when a source is removed?
Removed source	Rank before → after	Rank movement	Cohort remaining / lost support	Score after (change)
Artificial Analysis	188 → 192	Down 4	203 remaining / 241 lost	0.7 (-1.0 points)
ARC Prize	317 → 319	Down 2	444 remaining / 0 lost	2.2 (+0.5 points)
Datacurve	317 → 317	No movement	444 remaining / 0 lost	1.3 (-0.4 points)
LiveCodeBench	317 → 317	No movement	444 remaining / 0 lost	1.6 (-0.0 points)
Scale	317 → 318	Down 1	444 remaining / 0 lost	2.0 (+0.3 points)
Terminal-Bench	317 → 317	No movement	444 remaining / 0 lost	1.6 (-0.0 points)
Cognition	317 → 316	Up 1	444 remaining / 0 lost	1.6 (-0.0 points)
LiveBench	316 → 318	Down 2	443 remaining / 1 lost	1.7 (+0.1 points)
SWE-rebench	317 → 318	Down 1	444 remaining / 0 lost	1.7 (+0.0 points)
Epoch AI	305 → 295	Up 10	431 remaining / 13 lost	2.0 (+0.4 points)
Vals AI	316 → 317	Down 1	443 remaining / 1 lost	1.6 (-0.1 points)
Provider reports · Anthropic	317 → 317	No movement	444 remaining / 0 lost	1.6 (-0.0 points)
Provider reports · Moonshot AI	317 → 317	No movement	444 remaining / 0 lost	1.6 (-0.1 points)
Provider reports · Meta	317 → 317	No movement	444 remaining / 0 lost	1.6 (-0.0 points)
Provider reports · Mistral	317 → 317	No movement	444 remaining / 0 lost	1.6 (+0.0 points)
Provider reports · OpenAI	317 → 317	No movement	444 remaining / 0 lost	1.7 (+0.0 points)
All provider reports	317 → 317	No movement	444 remaining / 0 lost	1.6 (-0.0 points)

Lost support means a configuration no longer has a comparison path to the complete reference panel. Its rank is unavailable. A surviving path alone does not establish leaderboard eligibility.

Overlapping sources may remain correlated even with source balancing. Leave-one-source-out movement is a sensitivity diagnostic, not a spread of source-specific ranks or a confidence measure. Scoring methodology · Dated configuration audit

Every benchmark family

Removed evidence	Recomputed score	Change
arc-agi	2.1	+0.4
deepswe	1.3	-0.4
aime	1.3	-0.4
gpqa	1.9	+0.3
epoch-game-puzzles	1.4	-0.2
mmlu-pro	1.8	+0.2
critpt	1.5	-0.2
ifbench	1.5	-0.2
mmmu-pro	1.8	+0.2
hle	1.8	+0.1
gmmlu	1.8	+0.1
livecodebench	1.5	-0.1
automationbench	1.7	+0.1
simpleqa	1.7	+0.1
tau-bench	1.6	-0.1
briefcase	1.7	+0.1
omniscience	1.6	-0.1
apex-agents	1.7	+0.1
swe-rebench	1.7	+0.0
livebench-instructions	1.7	+0.0
gdp-pdf	1.7	+0.0
multi-swe-bench	1.6	-0.0
frontiercode	1.6	-0.0
livebench-language	1.7	+0.0
arc-agi-3	1.6	-0.0
terminal-bench-science	1.7	+0.0
enigma-eval	1.6	-0.0
livebench-coding	1.6	-0.0
gdpval	1.7	+0.0
swe-atlas-qna	1.6	-0.0
harvey	1.6	-0.0
aa-lcr	1.7	+0.0
terminal-bench	1.7	+0.0
analyst-agent	1.7	+0.0
itbench	1.6	-0.0
livebench-reasoning	1.6	-0.0
vibe-code	1.6	-0.0
frontiermath	1.6	-0.0
math500	1.7	+0.0
swe-atlas-test-writing	1.6	-0.0
vals-code-migration	1.6	-0.0
vals-finance-agent	1.6	-0.0
vals-legal-research	1.6	-0.0
vals-tax-agent	1.6	-0.0
vals-excel-modeling	1.6	-0.0
livebench-math	1.7	+0.0
enterprise-ops	1.6	+0.0
swe-atlas-refactoring	1.6	-0.0
swe-bench-pro	1.6	+0.0
livebench-data	1.6	-0.0

What evidence is missing from the fit?

Across all collected settings for this model: 44 observations, 39 contributing, 0 matched without graph weight and 5 excluded. Counts do not establish rank eligibility.

2 · Benchmark family or source protocol has not been reviewed
1 · Original SciCode grading defects are under review; retained as supporting evidence, excluded from aggregate and capability scores. Corrected SciCode-Verified requires a separate protocol review. Sources: https://arxiv.org/abs/2608.04975 and https://artificialanalysis.ai/evaluations/scicode
1 · Supporting evidence outside the reviewed capability core
1 · No reviewed effort for this catalog observation; any separately collected effort measurement is counted separately

Inspect every observation and exclusion →

Benchmark results

These are published benchmark scores. Supported effort estimates also appear on the Capability leaderboard. Results from different harnesses occupy different rows. Missing cells stay unknown; no scores are copied between effort levels.

Benchmark / source	Low	Medium	High
AA-Briefcase EloArtificial Analysis · Artificial Analysis published evaluation; see source for benchmark-specific settings Benchmark details AA-Briefcase v1.1 is an agentic knowledge work benchmark developed by Artificial Analysis. AA-Briefcase Elo is a combined metric that aggregates rubric pass rate, analytical quality Elo and presentation Elo · Higher is better · Evaluation results measured independently by Artificial Analysis Unit: elo. Source collected 2026-10-18.	Not reported	Not reported	0 Elo Reported settings gpt-oss-20b (High); Model source: https://artificialanalysis.ai/models/gpt-oss-20b
AIME 2025Artificial Analysis · Artificial Analysis published evaluation; see source for benchmark-specific settings Benchmark details Independently benchmarked by Artificial Analysis · Evaluation results measured independently by Artificial Analysis Unit: percent. Source collected 2026-10-18.	62.33% Reported settings gpt-oss-20b (Low); Model source: https://artificialanalysis.ai/models/gpt-oss-20b-low	Not reported	89.33% Reported settings gpt-oss-20b (High); Model source: https://artificialanalysis.ai/models/gpt-oss-20b
APEX-Agents-AAArtificial Analysis · Artificial Analysis published evaluation; see source for benchmark-specific settings Benchmark details Independently benchmarked by Artificial Analysis · Evaluation results measured independently by Artificial Analysis Unit: percent. Source collected 2026-10-18.	Not reported	Not reported	0.74% Reported settings gpt-oss-20b (High); Model source: https://artificialanalysis.ai/models/gpt-oss-20b
AA-LCR v1.1Artificial Analysis · Artificial Analysis published evaluation; see source for benchmark-specific settings Benchmark details Independently benchmarked by Artificial Analysis · Evaluation results measured independently by Artificial Analysis Unit: percent. Source collected 2026-10-18.	31% Reported settings gpt-oss-20b (Low); Model source: https://artificialanalysis.ai/models/gpt-oss-20b-low	Not reported	34.67% Reported settings gpt-oss-20b (High); Model source: https://artificialanalysis.ai/models/gpt-oss-20b
AutomationBench-AAArtificial Analysis · Artificial Analysis published evaluation; see source for benchmark-specific settings Benchmark details Share of task objectives completed with no guardrail violations · Higher is better · Benchmark developed by Zapier · Independently benchmarked by Artificial Analysis · Evaluation results measured independently by Artificial Analysis Unit: percent. Source collected 2026-10-18.	Not reported	Not reported	0.19% Reported settings gpt-oss-20b (High); Model source: https://artificialanalysis.ai/models/gpt-oss-20b
CritPtArtificial Analysis · Artificial Analysis published evaluation; see source for benchmark-specific settings Benchmark details Benchmark developed by Argonne and UIUC, with contributions from 60+ researchers globally. CritPt is under review by its authors following external feedback · Independently benchmarked by Artificial Analysis · Evaluation results measured independently by Artificial Analysis Unit: percent. Source collected 2026-10-18.	0% Reported settings gpt-oss-20b (Low); Model source: https://artificialanalysis.ai/models/gpt-oss-20b-low	Not reported	1.43% Reported settings gpt-oss-20b (High); Model source: https://artificialanalysis.ai/models/gpt-oss-20b
GDP.pdf: All-pass RateArtificial Analysis · Artificial Analysis published evaluation; see source for benchmark-specific settings Benchmark details Share of attempts where every atomic criterion passed · Independently benchmarked by Artificial Analysis · Evaluation results measured independently by Artificial Analysis Unit: percent. Source collected 2026-10-18.	Not reported	Not reported	2% Reported settings gpt-oss-20b (High); Model source: https://artificialanalysis.ai/models/gpt-oss-20b
GDPval-AA v2.1 LeaderboardArtificial Analysis · Artificial Analysis published evaluation; see source for benchmark-specific settings Benchmark details Elo rating for performance on real-world work tasks · Anchored to DeepSeek V4.1 Flash (max) at 1600 · Higher is better · Evaluation results measured independently by Artificial Analysis Unit: elo. Source collected 2026-10-18.	Not reported	Not reported	339.96 Elo Reported settings gpt-oss-20b (High); Model source: https://artificialanalysis.ai/models/gpt-oss-20b
Global-MMLU-LiteArtificial Analysis · Artificial Analysis published evaluation; see source for benchmark-specific settings Benchmark details Independently benchmarked by Artificial Analysis · Evaluation results measured independently by Artificial Analysis Unit: percent. Source collected 2026-10-18.	72.85% Reported settings gpt-oss-20b (Low); Model source: https://artificialanalysis.ai/models/gpt-oss-20b-low	Not reported	75.16% Reported settings gpt-oss-20b (High); Model source: https://artificialanalysis.ai/models/gpt-oss-20b
GPQA DiamondArtificial Analysis · Artificial Analysis published evaluation; see source for benchmark-specific settings Benchmark details Independently benchmarked by Artificial Analysis · Evaluation results measured independently by Artificial Analysis Unit: percent. Source collected 2026-10-18.	61.11% Reported settings gpt-oss-20b (Low); Model source: https://artificialanalysis.ai/models/gpt-oss-20b-low	Not reported	68.79% Reported settings gpt-oss-20b (High); Model source: https://artificialanalysis.ai/models/gpt-oss-20b
Humanity's Last ExamArtificial Analysis · Artificial Analysis published evaluation; see source for benchmark-specific settings Benchmark details Independently benchmarked by Artificial Analysis · Evaluation results measured independently by Artificial Analysis Unit: percent. Source collected 2026-10-18.	5.28% Reported settings gpt-oss-20b (Low); Model source: https://artificialanalysis.ai/models/gpt-oss-20b-low	Not reported	10.98% Reported settings gpt-oss-20b (High); Model source: https://artificialanalysis.ai/models/gpt-oss-20b
IFBenchArtificial Analysis · Artificial Analysis published evaluation; see source for benchmark-specific settings Benchmark details Independently benchmarked by Artificial Analysis · Evaluation results measured independently by Artificial Analysis Unit: percent. Source collected 2026-10-18.	57.82% Reported settings gpt-oss-20b (Low); Model source: https://artificialanalysis.ai/models/gpt-oss-20b-low	Not reported	65.1% Reported settings gpt-oss-20b (High); Model source: https://artificialanalysis.ai/models/gpt-oss-20b
LiveCodeBenchArtificial Analysis · Artificial Analysis published evaluation; see source for benchmark-specific settings Benchmark details Independently benchmarked by Artificial Analysis · Evaluation results measured independently by Artificial Analysis Unit: percent. Source collected 2026-10-18.	65.19% Reported settings gpt-oss-20b (Low); Model source: https://artificialanalysis.ai/models/gpt-oss-20b-low	Not reported	77.67% Reported settings gpt-oss-20b (High); Model source: https://artificialanalysis.ai/models/gpt-oss-20b
MLCR-AAArtificial Analysis · Artificial Analysis published evaluation; see source for benchmark-specific settings Benchmark details Share of tasks judged accurate, complete, and concise · Expert + compound (hard) sets · Benchmark developed by Wisedocs · Independently benchmarked by Artificial Analysis · Evaluation results measured independently by Artificial Analysis Unit: percent. Source collected 2026-10-18.	Not reported	Not reported	0.56% Reported settings gpt-oss-20b (High); Model source: https://artificialanalysis.ai/models/gpt-oss-20b
MMLU-ProArtificial Analysis · Artificial Analysis published evaluation; see source for benchmark-specific settings Benchmark details Independently benchmarked by Artificial Analysis · Evaluation results measured independently by Artificial Analysis Unit: percent. Source collected 2026-10-18.	71.8% Reported settings gpt-oss-20b (Low); Model source: https://artificialanalysis.ai/models/gpt-oss-20b-low	Not reported	74.77% Reported settings gpt-oss-20b (High); Model source: https://artificialanalysis.ai/models/gpt-oss-20b
AA-Omniscience IndexArtificial Analysis · Artificial Analysis published evaluation; see source for benchmark-specific settings Benchmark details AA-Omniscience Index (higher is better) measures knowledge reliability and hallucination. It rewards correct answers, penalizes hallucinations, and has no penalty for refusing to answer. Scores range from -100 to 100, where 0 means as many correct as incorrect answers, and negative scores mean more incorrect than correct. · Evaluation results measured independently by Artificial Analysis Unit: index. Source collected 2026-10-18.	-58.55 Reported settings gpt-oss-20b (Low); Model source: https://artificialanalysis.ai/models/gpt-oss-20b-low	Not reported	-63.05 Reported settings gpt-oss-20b (High); Model source: https://artificialanalysis.ai/models/gpt-oss-20b
AA-Omniscience AccuracyArtificial Analysis · 6000 questions; 1 attempt; correct-answer share; GPT-5.6 Luna medium grader Benchmark details AA-Omniscience Accuracy (higher is better) measures the proportion of correctly answered questions out of all questions, regardless of whether the model chooses to answer · Evaluation results measured independently by Artificial Analysis Unit: percent. Source collected 2026-10-18.	15.08% Reported settings gpt-oss-20b (Low); Model source: https://artificialanalysis.ai/models/gpt-oss-20b-low	Not reported	16% Reported settings gpt-oss-20b (High); Model source: https://artificialanalysis.ai/models/gpt-oss-20b
SciCodeArtificial Analysis · Artificial Analysis published evaluation; see source for benchmark-specific settings Benchmark details Benchmark curated by scientists across 16 disciplines. SciCode is under review following an independent audit of the dataset · Independently benchmarked by Artificial Analysis · Evaluation results measured independently by Artificial Analysis Unit: percent. Source collected 2026-10-18.	Not reported	Not reported	38.89% Reported settings gpt-oss-20b (High); Model source: https://artificialanalysis.ai/models/gpt-oss-20b
𝜏²-Bench TelecomArtificial Analysis · Artificial Analysis published evaluation; see source for benchmark-specific settings Benchmark details Independently benchmarked by Artificial Analysis · Evaluation results measured independently by Artificial Analysis Unit: percent. Source collected 2026-10-18.	50.29% Reported settings gpt-oss-20b (Low); Model source: https://artificialanalysis.ai/models/gpt-oss-20b-low	Not reported	60.23% Reported settings gpt-oss-20b (High); Model source: https://artificialanalysis.ai/models/gpt-oss-20b
𝜏³-BankingArtificial Analysis · Artificial Analysis published evaluation; see source for benchmark-specific settings Benchmark details Benchmark developed by Sierra Research · Independently benchmarked by Artificial Analysis · Evaluation results measured independently by Artificial Analysis Unit: percent. Source collected 2026-10-18.	Not reported	Not reported	7.01% Reported settings gpt-oss-20b (High); Model source: https://artificialanalysis.ai/models/gpt-oss-20b
Terminal-Bench HardArtificial Analysis · Artificial Analysis published evaluation; see source for benchmark-specific settings Benchmark details Independently benchmarked by Artificial Analysis · Evaluation results measured independently by Artificial Analysis Unit: percent. Source collected 2026-10-18.	4.55% Reported settings gpt-oss-20b (Low); Model source: https://artificialanalysis.ai/models/gpt-oss-20b-low	Not reported	10.61% Reported settings gpt-oss-20b (High); Model source: https://artificialanalysis.ai/models/gpt-oss-20b
Terminal-Bench v2.1Artificial Analysis · Artificial Analysis published evaluation; see source for benchmark-specific settings Benchmark details Benchmark developed by the Laude Institute and open-source contributors · Independently benchmarked by Artificial Analysis · Evaluation results measured independently by Artificial Analysis Unit: percent. Source collected 2026-10-18.	Not reported	Not reported	13.86% Reported settings gpt-oss-20b (High); Model source: https://artificialanalysis.ai/models/gpt-oss-20b
Terminal-Bench v4.0Artificial Analysis · mini-SWE-agent v2.4.6; 66 tasks; pass@1 averaged over 3 repeats; 500 steps; task timeout up to 8 hours Benchmark details Benchmark developed by the Laude Institute, Stanford, and open-source contributors · Independently benchmarked by Artificial Analysis · Evaluation results measured independently by Artificial Analysis Unit: percent. Source collected 2026-10-18.	Not reported	Not reported	0% Reported settings gpt-oss-20b (High); Model source: https://artificialanalysis.ai/models/gpt-oss-20b
Epoch · GPQA diamond · v1.0.11Epoch AI · Epoch AI Inspect; task 1.0.11; scorer choice Benchmark details Epoch AI internal evaluation. Mean accuracy under the published setting; exact task version and scorer stay separate. Related tasks share a family budget. Data by Epoch AI, CC BY. Unit: percent. Source collected 2026-10-18.	53.16% Reported settings Epoch AI Inspect; task 1.0.11; scorer choice; model=gpt-oss-20b_low; run=DCJQMqipndGxwMXe8BDGeg; mean accuracy; https://epoch.ai/data/benchmarks.csv	60.8% Reported settings Epoch AI Inspect; task 1.0.11; scorer choice; model=gpt-oss-20b_medium; run=GEwyLqZT93Hp55fdtHrJhm; mean accuracy; https://epoch.ai/data/benchmarks.csv	45.96% Reported settings Epoch AI Inspect; task 1.0.11; scorer choice; model=gpt-oss-20b_high; run=hYATFPeUnMq7Std2nLUVAJ; mean accuracy; https://epoch.ai/data/benchmarks.csv
Epoch · OTIS Mock AIME 2024-2025 · v1.1.6Epoch AI · Epoch AI Inspect; task 1.1.6; scorer model_extracted_exact_match Benchmark details Epoch AI internal evaluation. Mean accuracy under the published setting; exact task version and scorer stay separate. Related tasks share a family budget. Data by Epoch AI, CC BY. Unit: percent. Source collected 2026-10-18.	40.28% Reported settings Epoch AI Inspect; task 1.1.6; scorer model_extracted_exact_match; model=gpt-oss-20b_low; run=cMwETYmC6b4yKupyJfEHv2; mean accuracy; https://epoch.ai/data/benchmarks.csv	65.28% Reported settings Epoch AI Inspect; task 1.1.6; scorer model_extracted_exact_match; model=gpt-oss-20b_medium; run=DB8ja6pEZWGocQbdciyisL; mean accuracy; https://epoch.ai/data/benchmarks.csv	50.83% Reported settings Epoch AI Inspect; task 1.1.6; scorer model_extracted_exact_match; model=gpt-oss-20b_high; run=GMngscbSmqoPbGZWNv2Jvw; mean accuracy; https://epoch.ai/data/benchmarks.csv

What these comparisons mean

Each source reports its own effort labels. Matching labels do not establish equal compute budgets or identical fallback behavior. Open Reported settings to inspect the exact model name, agent and task coverage. Multiple results at one level remain visible rather than selecting the best. Source-reported None or Non-reasoning labels are not treated as evidence that an API supports those settings.

These measurements use their own dated source collection. The leaderboard fits identified configurations jointly; it contains no pooled model ratings. Explicit thinking budgets are separate configurations. Read the methodology.

Download all effort benchmark results · 8,915 results across 259 models · Collected 2026-10-18

gpt-oss-20b · High

Compare with

One configuration, seven capabilities

Agentic

Hard reasoning

Coding

Human pref

Knowledge

Multimodal

Long context

Capability score evidence

Benchmark results