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RankingEffort benchmarks

Qwen3.5-4B · Non-reasoning

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25 measured results · 2 effort settings · 13 benchmark/harness combinations

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Capability profile

One configuration, seven capabilities

Comparison score · 0–100
Qwen3.5-4B · Non-reasoning 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 context50100Qwen3.5-4B · Non-reasoning · Agentic: 14.5 · PreliminaryQwen3.5-4B · Non-reasoning · Hard reasoning: 5.6 · SupportedQwen3.5-4B · Non-reasoning · Coding: 5.2 · PreliminaryQwen3.5-4B · Non-reasoning · Knowledge: 1.7 · SupportedQwen3.5-4B · Non-reasoning · Multimodal: 5.3 · PreliminaryQwen3.5-4B · Non-reasoning · Long context: 5.2 · Preliminary

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

  • Agentic

    A14.5Preliminary

  • Hard reasoning

    A5.6Supported

  • Coding

    A5.2Preliminary

  • Human pref

    Unknown

  • Knowledge

    A1.7Supported

  • Multimodal

    A5.3Preliminary

  • Long context

    A5.2Preliminary

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

Qwen3.5-4B · Non-reasoning: 1.4 overall comparison score. Meets the overall evidence requirements.

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

Which sources carry the weight?
  • Artificial Analysis · 64.7% direct comparison weight · independent of the model provider
  • Epoch AI · 35.3% direct comparison weight · independent of the model provider
Which benchmark families carry the weight?
  • Aime · 35.3% direct comparison weight
  • IFBench · 7.6% direct comparison weight
  • Humanity’s Last Exam · 7.4% direct comparison weight
  • GPQA · 7.4% direct comparison weight
  • Critpt · 7.3% direct comparison weight
  • Other families (5) · 34.9% 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 2026-10-23.

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 sourceRank before → afterRank movementCohort remaining / lost supportScore after (change)
Artificial Analysis190 → 178Up 12203 remaining / 241 lost2.1 (+0.7 points)
ARC Prize327 → 326Up 1444 remaining / 0 lost1.9 (+0.5 points)
Datacurve327 → 327No movement444 remaining / 0 lost1.1 (-0.3 points)
LiveCodeBench327 → 327No movement444 remaining / 0 lost1.4 (-0.0 points)
Scale327 → 327No movement444 remaining / 0 lost1.7 (+0.3 points)
Terminal-Bench327 → 327No movement444 remaining / 0 lost1.4 (-0.0 points)
Cognition327 → 326Up 1444 remaining / 0 lost1.4 (-0.0 points)
LiveBench326 → 325Up 1443 remaining / 1 lost1.5 (+0.1 points)
SWE-rebench327 → 327No movement444 remaining / 0 lost1.5 (+0.0 points)
Epoch AI314 → 321Down 7431 remaining / 13 lost1.3 (-0.2 points)
Vals AI326 → 326No movement443 remaining / 1 lost1.4 (-0.1 points)
Provider reports · Anthropic327 → 327No movement444 remaining / 0 lost1.4 (-0.0 points)
Provider reports · Moonshot AI327 → 327No movement444 remaining / 0 lost1.4 (-0.0 points)
Provider reports · Meta327 → 327No movement444 remaining / 0 lost1.4 (-0.0 points)
Provider reports · Mistral327 → 327No movement444 remaining / 0 lost1.4 (+0.0 points)
Provider reports · OpenAI327 → 327No movement444 remaining / 0 lost1.4 (+0.0 points)
All provider reports327 → 327No movement444 remaining / 0 lost1.4 (-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 evidenceRecomputed scoreChange
arc-agi1.8+0.4
deepswe1.1-0.3
tau-bench1.2-0.2
aime1.3-0.2
epoch-game-puzzles1.3-0.1
hle1.5+0.1
mmmu-pro1.5+0.1
ifbench1.5+0.1
simpleqa1.5+0.1
mmlu-pro1.5+0.1
omniscience1.5+0.0
terminal-bench1.4-0.0
swe-rebench1.5+0.0
livebench-instructions1.5+0.0
multi-swe-bench1.4-0.0
gdp-pdf1.5+0.0
arc-agi-31.4-0.0
livebench-language1.4+0.0
apex-agents1.4+0.0
terminal-bench-science1.4+0.0
enigma-eval1.4-0.0
livebench-coding1.4-0.0
critpt1.4+0.0
frontiercode1.4-0.0
swe-atlas-qna1.4-0.0
harvey1.4-0.0
gmmlu1.4+0.0
analyst-agent1.4+0.0
itbench1.4-0.0
livebench-reasoning1.4-0.0
briefcase1.4-0.0
vibe-code1.4-0.0
automationbench1.4-0.0
swe-atlas-test-writing1.4-0.0
vals-finance-agent1.4-0.0
vals-code-migration1.4-0.0
vals-legal-research1.4-0.0
livecodebench1.4-0.0
gdpval1.4-0.0
vals-tax-agent1.4-0.0
math5001.4+0.0
frontiermath1.4-0.0
vals-excel-modeling1.4-0.0
livebench-math1.4+0.0
gpqa1.4+0.0
aa-lcr1.4+0.0
swe-atlas-refactoring1.4-0.0
enterprise-ops1.4+0.0
swe-bench-pro1.4+0.0
livebench-data1.4+0.0
What evidence is missing from the fit?

Across all collected settings for this model: 26 observations, 23 contributing, 0 matched without graph weight and 3 excluded. Counts do not establish rank eligibility.

  • 2 · Benchmark family or source protocol has not been reviewed
  • 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 / sourceReasoningNon-reasoning
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.

63%
Reported settings

Qwen3.5 4B (Reasoning); Model source: https://artificialanalysis.ai/models/qwen3-5-4b

34.67%
Reported settings

Qwen3.5 4B (Non-reasoning); Model source: https://artificialanalysis.ai/models/qwen3-5-4b-non-reasoning

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

Qwen3.5 4B (Reasoning); Model source: https://artificialanalysis.ai/models/qwen3-5-4b

0%
Reported settings

Qwen3.5 4B (Non-reasoning); Model source: https://artificialanalysis.ai/models/qwen3-5-4b-non-reasoning

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.

77.07%
Reported settings

Qwen3.5 4B (Reasoning); Model source: https://artificialanalysis.ai/models/qwen3-5-4b

71.21%
Reported settings

Qwen3.5 4B (Non-reasoning); Model source: https://artificialanalysis.ai/models/qwen3-5-4b-non-reasoning

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.

9.92%
Reported settings

Qwen3.5 4B (Reasoning); Model source: https://artificialanalysis.ai/models/qwen3-5-4b

7.97%
Reported settings

Qwen3.5 4B (Non-reasoning); Model source: https://artificialanalysis.ai/models/qwen3-5-4b-non-reasoning

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.

51.97%
Reported settings

Qwen3.5 4B (Reasoning); Model source: https://artificialanalysis.ai/models/qwen3-5-4b

33.27%
Reported settings

Qwen3.5 4B (Non-reasoning); Model source: https://artificialanalysis.ai/models/qwen3-5-4b-non-reasoning

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 2026-10-18.

65.38%
Reported settings

Qwen3.5 4B (Reasoning); Model source: https://artificialanalysis.ai/models/qwen3-5-4b

62.08%
Reported settings

Qwen3.5 4B (Non-reasoning); Model source: https://artificialanalysis.ai/models/qwen3-5-4b-non-reasoning

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.

-58.35
Reported settings

Qwen3.5 4B (Reasoning); Model source: https://artificialanalysis.ai/models/qwen3-5-4b

-74.73
Reported settings

Qwen3.5 4B (Non-reasoning); Model source: https://artificialanalysis.ai/models/qwen3-5-4b-non-reasoning

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.

15.12%
Reported settings

Qwen3.5 4B (Reasoning); Model source: https://artificialanalysis.ai/models/qwen3-5-4b

11.22%
Reported settings

Qwen3.5 4B (Non-reasoning); Model source: https://artificialanalysis.ai/models/qwen3-5-4b-non-reasoning

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.

92.11%
Reported settings

Qwen3.5 4B (Reasoning); Model source: https://artificialanalysis.ai/models/qwen3-5-4b

87.72%
Reported settings

Qwen3.5 4B (Non-reasoning); Model source: https://artificialanalysis.ai/models/qwen3-5-4b-non-reasoning

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.

6.8%
Reported settings

Qwen3.5 4B (Reasoning); Model source: https://artificialanalysis.ai/models/qwen3-5-4b

4.33%
Reported settings

Qwen3.5 4B (Non-reasoning); Model source: https://artificialanalysis.ai/models/qwen3-5-4b-non-reasoning

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.

18.18%
Reported settings

Qwen3.5 4B (Reasoning); Model source: https://artificialanalysis.ai/models/qwen3-5-4b

11.36%
Reported settings

Qwen3.5 4B (Non-reasoning); Model source: https://artificialanalysis.ai/models/qwen3-5-4b-non-reasoning

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.

25.84%
Reported settings

Qwen3.5 4B (Reasoning); Model source: https://artificialanalysis.ai/models/qwen3-5-4b

21.35%
Reported settings

Qwen3.5 4B (Non-reasoning); Model source: https://artificialanalysis.ai/models/qwen3-5-4b-non-reasoning

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.

Not reported
55.83%
Reported settings

Epoch AI Inspect; task 1.1.6; scorer model_extracted_exact_match; model=qwen3.5-4b_none; run=SAZy8rk2HgyPjA8XZJFUmR; 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