RankingEffort benchmarks

Llama-4-Scout-17B-16E-Instruct effort benchmarks

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

Model

Llama-4-Scout-17B-16E-Instruct

25 measured results · 2 reported effort labels · 25 benchmark/harness combinations. Model evidence and sources →

Capability score evidence

Llama-4-Scout-17B-16E-Instruct · Non-reasoning: 1.6 overall comparison score. Meets the overall evidence requirements.

Which sources carry the weight?

Artificial Analysis · 100.0% 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	Comparison path lost	—
ARC Prize	1.9	+0.3
Datacurve	1.3	-0.2
LiveCodeBench	1.6	-0.0
Scale	1.8	+0.2
Terminal-Bench	1.6	-0.0
Cognition	1.7	+0.1
LiveBench	1.6	+0.0
SWE-rebench	1.6	+0.0
Epoch AI	1.6	+0.0
Vals AI	1.5	-0.0
Provider reports · Anthropic	1.6	+0.0
Provider reports · Moonshot AI	1.6	-0.0
Provider reports · Meta	1.6	-0.0
Provider reports · Mistral	1.6	+0.0
All provider reports	1.6	-0.0

Every benchmark family

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

What evidence is missing from the fit?

Across all collected settings for this model: 27 observations, 22 contributing, 0 matched without graph weight and 5 excluded. Counts do not establish rank eligibility.

1 · Benchmark family or source protocol has not been reviewed
2 · Effort is not identified
2 · No reviewed effort for this catalog observation; any separately collected effort measurement is counted separately

Inspect every observation and exclusion →

Capability profile

One configuration, seven capabilities

Comparison score · 0–100

ALlama-4-Scout-17B-16E-Instruct · Non-reasoning4 of 7 capabilities supported

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

Agentic
A1.5Supported
Hard reasoning
A1.5Supported
Coding
A1.2Supported
Human pref
Unknown
Knowledge
A3.2Supported
Multimodal
A2.4Preliminary
Long context
A3.6Preliminary

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	Non-reasoning	Unspecified
AA-Briefcase EloArtificial Analysis · Artificial Analysis published evaluation; see source for benchmark-specific settings Benchmark details AA-Briefcase 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 2026-09-12.	0 Elo Reported settings Llama 4 Scout; Reported effort: non-reasoning; Model source: https://artificialanalysis.ai/models/llama-4-scout; Mode from evaluator isReasoning metadata; effort budget not specified	Not reported
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.	14% Reported settings Llama 4 Scout; Reported effort: non-reasoning; Model source: https://artificialanalysis.ai/models/llama-4-scout; Mode from evaluator isReasoning metadata; effort budget not specified	Not reported
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.	27.7% Reported settings Llama 4 Scout; Reported effort: non-reasoning; Model source: https://artificialanalysis.ai/models/llama-4-scout; Mode from evaluator isReasoning metadata; effort budget not specified	Not reported
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 2026-09-12.	0.19% Reported settings Llama 4 Scout; Reported effort: non-reasoning; Model source: https://artificialanalysis.ai/models/llama-4-scout; Mode from evaluator isReasoning metadata; effort budget not specified	Not reported
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 Llama 4 Scout; Reported effort: non-reasoning; Model source: https://artificialanalysis.ai/models/llama-4-scout; Mode from evaluator isReasoning metadata; effort budget not specified	Not reported
GDP.pdf: All-passArtificial 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 2026-09-12.	0% Reported settings Llama 4 Scout; Reported effort: non-reasoning; Model source: https://artificialanalysis.ai/models/llama-4-scout; Mode from evaluator isReasoning metadata; effort budget not specified	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.	59.71 Elo Reported settings Llama 4 Scout; Reported effort: non-reasoning; Model source: https://artificialanalysis.ai/models/llama-4-scout; Mode from evaluator isReasoning metadata; effort budget not specified	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.	74.14% Reported settings Llama 4 Scout; Reported effort: non-reasoning; Model source: https://artificialanalysis.ai/models/llama-4-scout; Mode from evaluator isReasoning metadata; effort budget not specified	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.	58.69% Reported settings Llama 4 Scout; Reported effort: non-reasoning; Model source: https://artificialanalysis.ai/models/llama-4-scout; Mode from evaluator isReasoning metadata; effort budget not specified	Not reported
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.	3.78% Reported settings Llama 4 Scout; Reported effort: non-reasoning; Model source: https://artificialanalysis.ai/models/llama-4-scout; Mode from evaluator isReasoning metadata; effort budget not specified	Not reported
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.	39.52% Reported settings Llama 4 Scout; Reported effort: non-reasoning; Model source: https://artificialanalysis.ai/models/llama-4-scout; Mode from evaluator isReasoning metadata; effort budget not specified	Not reported
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.	29.95% Reported settings Llama 4 Scout; Reported effort: non-reasoning; Model source: https://artificialanalysis.ai/models/llama-4-scout; Mode from evaluator isReasoning metadata; effort budget not specified	Not reported
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.	84.4% Reported settings Llama 4 Scout; Reported effort: non-reasoning; Model source: https://artificialanalysis.ai/models/llama-4-scout; Mode from evaluator isReasoning metadata; effort budget not specified	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.	75.17% Reported settings Llama 4 Scout; Reported effort: non-reasoning; Model source: https://artificialanalysis.ai/models/llama-4-scout; Mode from evaluator isReasoning metadata; effort budget not specified	Not reported
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.	52.95% Reported settings Llama 4 Scout; Reported effort: non-reasoning; Model source: https://artificialanalysis.ai/models/llama-4-scout; Mode from evaluator isReasoning metadata; effort budget not specified	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.	-52.15 Reported settings Llama 4 Scout; Reported effort: non-reasoning; Model source: https://artificialanalysis.ai/models/llama-4-scout; Mode from evaluator isReasoning metadata; effort budget not specified	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.	15.17% Reported settings Llama 4 Scout; Reported effort: non-reasoning; Model source: https://artificialanalysis.ai/models/llama-4-scout; Mode from evaluator isReasoning metadata; effort budget not specified	Not reported
SciCodeArtificial 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.	21.3% Reported settings Llama 4 Scout; Reported effort: non-reasoning; Model source: https://artificialanalysis.ai/models/llama-4-scout; Mode from evaluator isReasoning metadata; effort budget not specified	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.	15.5% Reported settings Llama 4 Scout; Reported effort: non-reasoning; Model source: https://artificialanalysis.ai/models/llama-4-scout; Mode from evaluator isReasoning metadata; effort budget not specified	Not reported
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.	3.3% Reported settings Llama 4 Scout; Reported effort: non-reasoning; Model source: https://artificialanalysis.ai/models/llama-4-scout; Mode from evaluator isReasoning metadata; effort budget not specified	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.	1.52% Reported settings Llama 4 Scout; Reported effort: non-reasoning; Model source: https://artificialanalysis.ai/models/llama-4-scout; Mode from evaluator isReasoning metadata; effort budget not specified	Not reported
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.	3.75% Reported settings Llama 4 Scout; Reported effort: non-reasoning; Model source: https://artificialanalysis.ai/models/llama-4-scout; Mode from evaluator isReasoning metadata; effort budget not specified	Not reported
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 2026-09-12.	0% Reported settings Llama 4 Scout; Reported effort: non-reasoning; Model source: https://artificialanalysis.ai/models/llama-4-scout; Mode from evaluator isReasoning metadata; effort budget not specified	Not reported
Epoch · GPQA diamond · v1.0.0Epoch AI · Epoch AI Inspect; task 1.0.0; 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 2026-09-12.	Not reported	51.83% Reported settings Epoch AI Inspect; task 1.0.0; scorer choice; model=Llama-4-Scout-17B-16E-Instruct; run=MsXAXDEKP3xBKtiA7aTBBG; mean accuracy; https://epoch.ai/data/benchmarks.csv
Epoch · OTIS Mock AIME 2024-2025 · v1.0.0Epoch AI · Epoch AI Inspect; task 1.0.0; scorer model_graded 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 2026-09-12.	Not reported	7.78% Reported settings Epoch AI Inspect; task 1.0.0; scorer model_graded; model=Llama-4-Scout-17B-16E-Instruct; run=T3X5DeC47iqCaoRYXu5k8L; 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 · 7,424 results across 240 models · Collected 2026-09-12

Llama-4-Scout-17B-16E-Instruct effort benchmarks