RankingEffort benchmarks

GPT-5 · Low effort benchmarks

Compare measured Low, Medium, High and other settings. Every number keeps its source.

Model

GPT-5

85 measured results · 5 reported effort labels · 23 benchmark/harness combinations. Model evidence and sources →

Capability score evidence

GPT-5 · Low: 7.8 overall comparison score. Meets the overall evidence requirements.

Which sources carry the weight?

ARC Prize · 55.7% direct comparison weight · independent
Artificial Analysis · 44.3% direct comparison weight · independent

These shares describe the comparisons entering the fit, not fractions of the final score.

How sensitive is this score?

Remove one evaluator or family and refit the graph against the same overall panel. Values use the matching dataset audited 2026-09-12. These are sensitivity checks, not confidence intervals; ranking eligibility may also change.

Removed evidence	Recomputed score	Change
Artificial Analysis	3.6	-4.2
ARC Prize	18.0	+10.2
Datacurve	6.6	-1.2
LiveCodeBench	7.7	-0.0
Scale	8.5	+0.7
Terminal-Bench	7.7	-0.0
Cognition	8.2	+0.5
LiveBench	7.9	+0.1
SWE-rebench	7.8	+0.0
Epoch AI	7.6	-0.2
Vals AI	7.4	-0.3
Provider reports · Anthropic	7.8	+0.0
Provider reports · Moonshot AI	7.8	-0.0
Provider reports · Meta	7.8	-0.0
Provider reports · Mistral	7.8	+0.0
All provider reports	7.7	-0.0

Every benchmark family

Removed evidence	Recomputed score	Change
arc-agi	17.9	+10.2
deepswe	6.6	-1.2
epoch-game-puzzles	7.2	-0.5
omniscience	7.2	-0.5
frontiercode	8.2	+0.5
hle	8.1	+0.3
mmmu-pro	8.1	+0.3
simpleqa	8.1	+0.3
ifbench	7.6	-0.2
mmlu-pro	7.9	+0.1
terminal-bench	7.9	+0.1
livebench-instructions	7.9	+0.1
multi-swe-bench	7.6	-0.1
scicode	7.9	+0.1
arc-agi-3	7.7	-0.1
frontiermath	7.9	+0.1
aa-lcr	7.9	+0.1
harvey	7.7	-0.1
livebench-language	7.8	+0.1
gdpval	7.8	+0.1
math500	7.7	-0.1
gdp-pdf	7.8	+0.1
aime	7.8	+0.1
gmmlu	7.8	+0.1
swe-atlas-qna	7.7	-0.1
apex-agents	7.8	+0.1
gpqa	7.8	+0.1
livebench-coding	7.7	-0.1
vibe-code	7.7	-0.1
critpt	7.7	-0.1
livebench-reasoning	7.7	-0.0
vals-finance-agent	7.7	-0.0
tau-bench	7.8	+0.0
swe-atlas-test-writing	7.7	-0.0
vals-code-migration	7.7	-0.0
vals-legal-research	7.7	-0.0
analyst-agent	7.8	+0.0
vals-excel-modeling	7.7	-0.0
swe-atlas-refactoring	7.8	-0.0
livebench-math	7.8	+0.0
automationbench	7.8	+0.0
livebench-data	7.8	+0.0
itbench	7.8	-0.0
enterprise-ops	7.8	+0.0
briefcase	7.8	+0.0
enigma-eval	7.8	+0.0
swe-rebench	7.8	+0.0
swe-bench-pro	7.8	+0.0
livecodebench	7.8	+0.0

What evidence is missing from the fit?

Across all collected settings for this model: 103 observations, 81 contributing, 0 matched without graph weight and 22 excluded. Counts do not establish rank eligibility.

4 · Benchmark family or source protocol has not been reviewed
12 · No reviewed effort for this catalog observation; any separately collected effort measurement is counted separately
6 · Supporting evidence outside the reviewed capability core

Inspect every observation and exclusion →

Capability profile

One configuration, seven capabilities

Comparison score · 0–100

AGPT-5 · Low3 of 7 capabilities supported

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

Agentic
A16.3Preliminary
Hard reasoning
A9.4Supported
Coding
A11.2Supported
Human pref
Unknown
Knowledge
A26.0Supported
Multimodal
A20.0Preliminary
Long context
Unknown

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.

Compare this configuration →Explore the model evidence profile →

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	Minimal	Low	Medium	High	Non-reasoning
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 2026-09-12.	31.67% Reported settings GPT-5 (minimal); Model source: https://artificialanalysis.ai/models/gpt-5-minimal	83% Reported settings GPT-5 (low); Model source: https://artificialanalysis.ai/models/gpt-5-low	91.67% Reported settings GPT-5 (medium); Model source: https://artificialanalysis.ai/models/gpt-5-medium	94.33% Reported settings GPT-5 (high); Model source: https://artificialanalysis.ai/models/gpt-5	48.33% Reported settings GPT-5 (ChatGPT); Reported effort: non-reasoning; Model source: https://artificialanalysis.ai/models/gpt-5-chatgpt; Mode from evaluator isReasoning metadata; effort budget not specified
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 2026-09-12.	Not reported	Not reported	76% Reported settings GPT-5 (medium); Model source: https://artificialanalysis.ai/models/gpt-5-medium	78.2% Reported settings GPT-5 (high); Model source: https://artificialanalysis.ai/models/gpt-5	65% Reported settings GPT-5 (ChatGPT); Reported effort: non-reasoning; Model source: https://artificialanalysis.ai/models/gpt-5-chatgpt; Mode from evaluator isReasoning metadata; effort budget not specified
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 · Independently benchmarked by Artificial Analysis · Evaluation results measured independently by Artificial Analysis Unit: percent. Source collected 2026-09-12.	0% Reported settings GPT-5 (minimal); Model source: https://artificialanalysis.ai/models/gpt-5-minimal	1.14% Reported settings GPT-5 (low); Model source: https://artificialanalysis.ai/models/gpt-5-low	0% Reported settings GPT-5 (medium); Model source: https://artificialanalysis.ai/models/gpt-5-medium	5.71% Reported settings GPT-5 (high); Model source: https://artificialanalysis.ai/models/gpt-5	Not reported
GDPval-AA v2 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 a human baseline of 1,000 · Higher is better · Evaluation results measured independently by Artificial Analysis Unit: elo. Source collected 2026-09-12.	Not reported	Not reported	Not reported	1,014.92 Elo Reported settings GPT-5 (high); Model source: https://artificialanalysis.ai/models/gpt-5	Not reported
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 2026-09-12.	84.42% Reported settings GPT-5 (minimal); Model source: https://artificialanalysis.ai/models/gpt-5-minimal	Not reported	90.39% Reported settings GPT-5 (medium); Model source: https://artificialanalysis.ai/models/gpt-5-medium	90.67% Reported settings GPT-5 (high); Model source: https://artificialanalysis.ai/models/gpt-5	Not reported
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 2026-09-12.	67.27% Reported settings GPT-5 (minimal); Model source: https://artificialanalysis.ai/models/gpt-5-minimal	80.81% Reported settings GPT-5 (low); Model source: https://artificialanalysis.ai/models/gpt-5-low	84.18% Reported settings GPT-5 (medium); Model source: https://artificialanalysis.ai/models/gpt-5-medium	85.35% Reported settings GPT-5 (high); Model source: https://artificialanalysis.ai/models/gpt-5	68.59% Reported settings GPT-5 (ChatGPT); Reported effort: non-reasoning; Model source: https://artificialanalysis.ai/models/gpt-5-chatgpt; Mode from evaluator isReasoning metadata; effort budget not specified
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 2026-09-12.	6.02% Reported settings GPT-5 (minimal); Model source: https://artificialanalysis.ai/models/gpt-5-minimal	19.56% Reported settings GPT-5 (low); Model source: https://artificialanalysis.ai/models/gpt-5-low	25.39% Reported settings GPT-5 (medium); Model source: https://artificialanalysis.ai/models/gpt-5-medium	28.5% Reported settings GPT-5 (high); Model source: https://artificialanalysis.ai/models/gpt-5	6.63% Reported settings GPT-5 (ChatGPT); Reported effort: non-reasoning; Model source: https://artificialanalysis.ai/models/gpt-5-chatgpt; Mode from evaluator isReasoning metadata; effort budget not specified
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 2026-09-12.	45.58% Reported settings GPT-5 (minimal); Model source: https://artificialanalysis.ai/models/gpt-5-minimal	66.6% Reported settings GPT-5 (low); Model source: https://artificialanalysis.ai/models/gpt-5-low	70.61% Reported settings GPT-5 (medium); Model source: https://artificialanalysis.ai/models/gpt-5-medium	73.06% Reported settings GPT-5 (high); Model source: https://artificialanalysis.ai/models/gpt-5	45.03% Reported settings GPT-5 (ChatGPT); Reported effort: non-reasoning; Model source: https://artificialanalysis.ai/models/gpt-5-chatgpt; Mode from evaluator isReasoning metadata; effort budget not specified
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 2026-09-12.	55.77% Reported settings GPT-5 (minimal); Model source: https://artificialanalysis.ai/models/gpt-5-minimal	76.3% Reported settings GPT-5 (low); Model source: https://artificialanalysis.ai/models/gpt-5-low	70.26% Reported settings GPT-5 (medium); Model source: https://artificialanalysis.ai/models/gpt-5-medium	84.55% Reported settings GPT-5 (high); Model source: https://artificialanalysis.ai/models/gpt-5	54.29% Reported settings GPT-5 (ChatGPT); Reported effort: non-reasoning; Model source: https://artificialanalysis.ai/models/gpt-5-chatgpt; Mode from evaluator isReasoning metadata; effort budget not specified
MATH-500Artificial 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 2026-09-12.	86.13% Reported settings GPT-5 (minimal); Model source: https://artificialanalysis.ai/models/gpt-5-minimal	98.73% Reported settings GPT-5 (low); Model source: https://artificialanalysis.ai/models/gpt-5-low	99.13% Reported settings GPT-5 (medium); Model source: https://artificialanalysis.ai/models/gpt-5-medium	99.4% Reported settings GPT-5 (high); Model source: https://artificialanalysis.ai/models/gpt-5	Not reported
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 2026-09-12.	80.63% Reported settings GPT-5 (minimal); Model source: https://artificialanalysis.ai/models/gpt-5-minimal	85.98% Reported settings GPT-5 (low); Model source: https://artificialanalysis.ai/models/gpt-5-low	86.74% Reported settings GPT-5 (medium); Model source: https://artificialanalysis.ai/models/gpt-5-medium	87.07% Reported settings GPT-5 (high); Model source: https://artificialanalysis.ai/models/gpt-5	82.04% Reported settings GPT-5 (ChatGPT); Reported effort: non-reasoning; Model source: https://artificialanalysis.ai/models/gpt-5-chatgpt; Mode from evaluator isReasoning metadata; effort budget not specified
MMMU-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 2026-09-12.	62.08% Reported settings GPT-5 (minimal); Model source: https://artificialanalysis.ai/models/gpt-5-minimal	73.76% Reported settings GPT-5 (low); Model source: https://artificialanalysis.ai/models/gpt-5-low	74.34% Reported settings GPT-5 (medium); Model source: https://artificialanalysis.ai/models/gpt-5-medium	74.22% Reported settings GPT-5 (high); Model source: https://artificialanalysis.ai/models/gpt-5	Not reported
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 2026-09-12.	-33.8 Reported settings GPT-5 (minimal); Model source: https://artificialanalysis.ai/models/gpt-5-minimal	-10.78 Reported settings GPT-5 (low); Model source: https://artificialanalysis.ai/models/gpt-5-low	-10.87 Reported settings GPT-5 (medium); Model source: https://artificialanalysis.ai/models/gpt-5-medium	-8.73 Reported settings GPT-5 (high); Model source: https://artificialanalysis.ai/models/gpt-5	Not reported
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 2026-09-12.	29.63% Reported settings GPT-5 (minimal); Model source: https://artificialanalysis.ai/models/gpt-5-minimal	38.12% Reported settings GPT-5 (low); Model source: https://artificialanalysis.ai/models/gpt-5-low	39.48% Reported settings GPT-5 (medium); Model source: https://artificialanalysis.ai/models/gpt-5-medium	40.32% Reported settings GPT-5 (high); Model source: https://artificialanalysis.ai/models/gpt-5	Not reported
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 2026-09-12.	66.96% Reported settings GPT-5 (minimal); Model source: https://artificialanalysis.ai/models/gpt-5-minimal	84.21% Reported settings GPT-5 (low); Model source: https://artificialanalysis.ai/models/gpt-5-low	86.55% Reported settings GPT-5 (medium); Model source: https://artificialanalysis.ai/models/gpt-5-medium	84.8% Reported settings GPT-5 (high); Model source: https://artificialanalysis.ai/models/gpt-5	0% Reported settings GPT-5 (ChatGPT); Reported effort: non-reasoning; Model source: https://artificialanalysis.ai/models/gpt-5-chatgpt; Mode from evaluator isReasoning metadata; effort budget not specified
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 2026-09-12.	Not reported	Not reported	Not reported	22.06% Reported settings GPT-5 (high); Model source: https://artificialanalysis.ai/models/gpt-5	Not reported
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 2026-09-12.	18.18% Reported settings GPT-5 (minimal); Model source: https://artificialanalysis.ai/models/gpt-5-minimal	26.52% Reported settings GPT-5 (low); Model source: https://artificialanalysis.ai/models/gpt-5-low	37.88% Reported settings GPT-5 (medium); Model source: https://artificialanalysis.ai/models/gpt-5-medium	32.58% Reported settings GPT-5 (high); Model source: https://artificialanalysis.ai/models/gpt-5	12.88% Reported settings GPT-5 (ChatGPT); Reported effort: non-reasoning; Model source: https://artificialanalysis.ai/models/gpt-5-chatgpt; Mode from evaluator isReasoning metadata; effort budget not specified
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 2026-09-12.	Not reported	Not reported	Not reported	35.21% Reported settings GPT-5 (high); Model source: https://artificialanalysis.ai/models/gpt-5	Not reported
ARC-AGI-2ARC Prize · v2 Semi-Private Benchmark details Original public board. Configuration and task coverage may differ; inspect source. Unit: percent. Source collected 2026-09-12.	0% Reported settings GPT-5 (Minimal); Source model ID: gpt-5-2025-08-07-minimal; 120 tasks	1.94% Reported settings GPT-5 (Low); Source model ID: gpt-5-2025-08-07-low; 120 tasks	7.49% Reported settings GPT-5 (Medium); Source model ID: gpt-5-2025-08-07-medium; 120 tasks	9.86% Reported settings GPT-5 (High); Source model ID: gpt-5-2025-08-07-high; 120 tasks	Not reported
SWE-rebench · 2025-08 · toolsSWE-rebench · SWE-rebench standardized ReAct tools Benchmark details Average resolved rate across repeated runs, not best-of-N success. Disjoint complete calendar months within each model’s tested range; standardized harness and all-language task set. Unit: percent. Source collected 2026-09-12.	33.46% Reported settings SWE-rebench standardized ReAct tools; swe-rebench-2025-08-tools; gpt-5-2025-08-07-minimal; checkpoint=gpt-5	Not reported	45.38% Reported settings SWE-rebench standardized ReAct tools; swe-rebench-2025-08-tools; gpt-5-2025-08-07-medium; checkpoint=gpt-5	46.54% Reported settings SWE-rebench standardized ReAct tools; swe-rebench-2025-08-tools; gpt-5-2025-08-07-high; checkpoint=gpt-5	Not reported
SWE-rebench · 2025-09 · toolsSWE-rebench · SWE-rebench standardized ReAct tools Benchmark details Average resolved rate across repeated runs, not best-of-N success. Disjoint complete calendar months within each model’s tested range; standardized harness and all-language task set. Unit: percent. Source collected 2026-09-12.	30.61% Reported settings SWE-rebench standardized ReAct tools; swe-rebench-2025-09-tools; gpt-5-2025-08-07-minimal; checkpoint=gpt-5	Not reported	38.78% Reported settings SWE-rebench standardized ReAct tools; swe-rebench-2025-09-tools; gpt-5-2025-08-07-medium; checkpoint=gpt-5	36.33% Reported settings SWE-rebench standardized ReAct tools; swe-rebench-2025-09-tools; gpt-5-2025-08-07-high; checkpoint=gpt-5	Not reported
SWE-rebench · 2025-10 · toolsSWE-rebench · SWE-rebench standardized ReAct tools Benchmark details Average resolved rate across repeated runs, not best-of-N success. Disjoint complete calendar months within each model’s tested range; standardized harness and all-language task set. Unit: percent. Source collected 2026-09-12.	25.97% Reported settings SWE-rebench standardized ReAct tools; swe-rebench-2025-10-tools; gpt-5-2025-08-07-minimal; checkpoint=gpt-5	Not reported	42.35% Reported settings SWE-rebench standardized ReAct tools; swe-rebench-2025-10-tools; gpt-5-2025-08-07-medium; checkpoint=gpt-5	38.82% Reported settings SWE-rebench standardized ReAct tools; swe-rebench-2025-10-tools; gpt-5-2025-08-07-high; checkpoint=gpt-5	Not reported
SWE-rebench · 2025-11 · toolsSWE-rebench · SWE-rebench standardized ReAct tools Benchmark details Average resolved rate across repeated runs, not best-of-N success. Disjoint complete calendar months within each model’s tested range; standardized harness and all-language task set. Unit: percent. Source collected 2026-09-12.	34.89% Reported settings SWE-rebench standardized ReAct tools; swe-rebench-2025-11-tools; gpt-5-2025-08-07-minimal; checkpoint=gpt-5	Not reported	58.72% Reported settings SWE-rebench standardized ReAct tools; swe-rebench-2025-11-tools; gpt-5-2025-08-07-medium; checkpoint=gpt-5	55.74% Reported settings SWE-rebench standardized ReAct tools; swe-rebench-2025-11-tools; gpt-5-2025-08-07-high; checkpoint=gpt-5	Not reported

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 · 7,424 results across 240 models · Collected 2026-09-12

GPT-5 · Low effort benchmarks

GPT-5

Capability score evidence

One configuration, seven capabilities

Agentic

Hard reasoning

Coding

Human pref

Knowledge

Multimodal

Long context