UnifyBench

RankingEffort benchmarks

Llama-3.1-405B-Instruct

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2 measured results · 1 effort settings · 2 benchmark/harness combinations

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Capability profile

One configuration, seven capabilities

Comparison score · 0–100
Llama-3.1-405B-Instruct · Unspecified capability radarComparison scores from 0 to 100. Unknown capabilities have no point; lines stop at gaps. Hollow points are preliminary. Exact values and support labels follow the chart.AgenticHard reasoningCodingHuman prefKnowledgeMultimodalLong context50100

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

  • Agentic

    Unknown

  • Hard reasoning

    Unknown

  • Coding

    Unknown

  • Human pref

    Unknown

  • Knowledge

    Unknown

  • Multimodal

    Unknown

  • 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.

Capability score evidence

Llama-3.1-405B-Instruct · Unspecified: No connected estimate. No admitted path to the complete configuration reference panel

Which sources carry the 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.

    This configuration did not qualify for the dated stability audit.

    What evidence is missing from the fit?

    Across all collected settings for this model: 3 observations, 0 contributing, 0 matched without graph weight and 3 excluded. Counts do not establish rank eligibility.

    • 2 · Effort is not identified
    • 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 / sourceUnspecified
    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 2026-10-18.

    50.92%
    Reported settings

    Epoch AI Inspect; task 1.0.0; scorer choice; model=Llama-3.1-405B-Instruct; run=S5QYXSvQBRSbUbXSnAGbMm; 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 2026-10-18.

    9.72%
    Reported settings

    Epoch AI Inspect; task 1.0.0; scorer model_graded; model=Llama-3.1-405B-Instruct; run=2fQJP3brRApfBdQq7Zy3bm; 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 · 8,916 results across 259 models · Collected 2026-10-18