RankingEffort benchmarks
Qwen3.5-35B-A3B effort benchmarks
Compare measured Low, Medium, High and other settings. Every number keeps its source.
Qwen3.5-35B-A3B
32 measured results · 3 reported effort labels · 18 benchmark/harness combinations. Model evidence and sources →
Capability score evidence
Qwen3.5-35B-A3B · Reasoning: 13.4 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 | 15.8 | +2.3 |
| Datacurve | 11.7 | -1.8 |
| LiveCodeBench | 13.4 | -0.1 |
| Scale | 14.7 | +1.2 |
| Terminal-Bench | 13.5 | +0.0 |
| Cognition | 14.0 | +0.5 |
| LiveBench | 13.7 | +0.2 |
| SWE-rebench | 13.7 | +0.3 |
| Epoch AI | 13.6 | +0.1 |
| Vals AI | 13.1 | -0.4 |
| Provider reports · Anthropic | 13.4 | +0.0 |
| Provider reports · Moonshot AI | 13.4 | -0.0 |
| Provider reports · Meta | 13.4 | -0.0 |
| Provider reports · Mistral | 13.4 | +0.0 |
| All provider reports | 13.4 | -0.0 |
Every benchmark family
| Removed evidence | Recomputed score | Change |
|---|---|---|
| arc-agi | 15.7 | +2.3 |
| deepswe | 11.7 | -1.8 |
| itbench | 14.8 | +1.4 |
| ifbench | 12.5 | -1.0 |
| omniscience | 14.1 | +0.7 |
| gpqa | 12.9 | -0.6 |
| frontiercode | 14.0 | +0.5 |
| aa-lcr | 12.9 | -0.5 |
| mmmu-pro | 14.0 | +0.5 |
| epoch-game-puzzles | 12.9 | -0.5 |
| simpleqa | 13.9 | +0.5 |
| tau-bench | 13.0 | -0.4 |
| terminal-bench | 13.9 | +0.4 |
| hle | 13.8 | +0.4 |
| mmlu-pro | 13.8 | +0.3 |
| swe-rebench | 13.7 | +0.3 |
| harvey | 13.3 | -0.2 |
| multi-swe-bench | 13.3 | -0.2 |
| livebench-instructions | 13.6 | +0.2 |
| livebench-language | 13.6 | +0.1 |
| scicode | 13.6 | +0.1 |
| critpt | 13.6 | +0.1 |
| frontiermath | 13.6 | +0.1 |
| swe-atlas-qna | 13.3 | -0.1 |
| aime | 13.6 | +0.1 |
| arc-agi-3 | 13.4 | -0.1 |
| livebench-coding | 13.4 | -0.1 |
| automationbench | 13.4 | -0.1 |
| briefcase | 13.4 | -0.1 |
| apex-agents | 13.5 | +0.1 |
| vibe-code | 13.4 | -0.1 |
| livebench-reasoning | 13.4 | -0.1 |
| gdp-pdf | 13.5 | +0.1 |
| vals-finance-agent | 13.4 | -0.1 |
| vals-legal-research | 13.4 | -0.0 |
| swe-atlas-test-writing | 13.4 | -0.0 |
| enterprise-ops | 13.4 | -0.0 |
| vals-code-migration | 13.4 | -0.0 |
| math500 | 13.5 | +0.0 |
| swe-atlas-refactoring | 13.4 | -0.0 |
| livebench-data | 13.5 | +0.0 |
| livebench-math | 13.5 | +0.0 |
| vals-excel-modeling | 13.4 | -0.0 |
| gmmlu | 13.5 | +0.0 |
| analyst-agent | 13.4 | -0.0 |
| livecodebench | 13.5 | +0.0 |
| enigma-eval | 13.5 | +0.0 |
| swe-bench-pro | 13.5 | +0.0 |
| gdpval | 13.4 | +0.0 |
What evidence is missing from the fit?
Across all collected settings for this model: 34 observations, 27 contributing, 0 matched without graph weight and 7 excluded. Counts do not establish rank eligibility.
- 2 · Benchmark family or source protocol has not been reviewed
- 3 · Effort is not identified
- 2 · No reviewed effort for this catalog observation; any separately collected effort measurement is counted separately
Capability profile
One configuration, seven capabilities
- AQwen3.5-35B-A3B · Reasoning3 of 7 capabilities supported
Gaps are unknown, not zero. Hollow points are preliminary.
Agentic
A15.9SupportedHard reasoning
A17.7SupportedCoding
A10.7PreliminaryHuman pref
UnknownKnowledge
A16.2SupportedMultimodal
A17.7PreliminaryLong context
A26.9Preliminary
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 →
| Benchmark / source | Reasoning | Non-reasoning | Unspecified |
|---|---|---|---|
AA-LCR v1.1Benchmark detailsIndependently benchmarked by Artificial Analysis · Evaluation results measured independently by Artificial Analysis Unit: percent. Source collected 2026-09-12. | 72% Reported settingsQwen3.5 35B A3B (Reasoning); Model source: https://artificialanalysis.ai/models/qwen3-5-35b-a3b | 63% Reported settingsQwen3.5 35B A3B (Non-reasoning); Model source: https://artificialanalysis.ai/models/qwen3-5-35b-a3b-non-reasoning | Not reported |
CritPtBenchmark detailsBenchmark 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. | 0.86% Reported settingsQwen3.5 35B A3B (Reasoning); Model source: https://artificialanalysis.ai/models/qwen3-5-35b-a3b | 0.57% Reported settingsQwen3.5 35B A3B (Non-reasoning); Model source: https://artificialanalysis.ai/models/qwen3-5-35b-a3b-non-reasoning | Not reported |
GDPval-AA v2 LeaderboardBenchmark detailsElo 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 | 740.47 Elo Reported settingsQwen3.5 35B A3B (Non-reasoning); Model source: https://artificialanalysis.ai/models/qwen3-5-35b-a3b-non-reasoning | Not reported |
Global-MMLU-LiteBenchmark detailsIndependently benchmarked by Artificial Analysis · Evaluation results measured independently by Artificial Analysis Unit: percent. Source collected 2026-09-12. | 86.44% Reported settingsQwen3.5 35B A3B (Reasoning); Model source: https://artificialanalysis.ai/models/qwen3-5-35b-a3b | 82.42% Reported settingsQwen3.5 35B A3B (Non-reasoning); Model source: https://artificialanalysis.ai/models/qwen3-5-35b-a3b-non-reasoning | Not reported |
GPQA DiamondBenchmark detailsIndependently benchmarked by Artificial Analysis · Evaluation results measured independently by Artificial Analysis Unit: percent. Source collected 2026-09-12. | 84.55% Reported settingsQwen3.5 35B A3B (Reasoning); Model source: https://artificialanalysis.ai/models/qwen3-5-35b-a3b | 81.92% Reported settingsQwen3.5 35B A3B (Non-reasoning); Model source: https://artificialanalysis.ai/models/qwen3-5-35b-a3b-non-reasoning | Not reported |
Humanity's Last ExamBenchmark detailsIndependently benchmarked by Artificial Analysis · Evaluation results measured independently by Artificial Analysis Unit: percent. Source collected 2026-09-12. | 21.04% Reported settingsQwen3.5 35B A3B (Reasoning); Model source: https://artificialanalysis.ai/models/qwen3-5-35b-a3b | 13.44% Reported settingsQwen3.5 35B A3B (Non-reasoning); Model source: https://artificialanalysis.ai/models/qwen3-5-35b-a3b-non-reasoning | Not reported |
IFBenchBenchmark detailsIndependently benchmarked by Artificial Analysis · Evaluation results measured independently by Artificial Analysis Unit: percent. Source collected 2026-09-12. | 72.52% Reported settingsQwen3.5 35B A3B (Reasoning); Model source: https://artificialanalysis.ai/models/qwen3-5-35b-a3b | 44.49% Reported settingsQwen3.5 35B A3B (Non-reasoning); Model source: https://artificialanalysis.ai/models/qwen3-5-35b-a3b-non-reasoning | Not reported |
ITBench-AABenchmark detailsCombined public and private Site Reliability Engineering (SRE) tasks · Independently benchmarked by Artificial Analysis · Evaluation results measured independently by Artificial Analysis Unit: percent. Source collected 2026-09-12. | 21.52% Reported settingsQwen3.5 35B A3B (Reasoning); Model source: https://artificialanalysis.ai/models/qwen3-5-35b-a3b | Not reported | Not reported |
MMMU-ProBenchmark detailsIndependently benchmarked by Artificial Analysis · Evaluation results measured independently by Artificial Analysis Unit: percent. Source collected 2026-09-12. | 72.66% Reported settingsQwen3.5 35B A3B (Reasoning); Model source: https://artificialanalysis.ai/models/qwen3-5-35b-a3b | 69.19% Reported settingsQwen3.5 35B A3B (Non-reasoning); Model source: https://artificialanalysis.ai/models/qwen3-5-35b-a3b-non-reasoning | Not reported |
AA-Omniscience IndexBenchmark detailsAA-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. | -48.1 Reported settingsQwen3.5 35B A3B (Reasoning); Model source: https://artificialanalysis.ai/models/qwen3-5-35b-a3b | -63.02 Reported settingsQwen3.5 35B A3B (Non-reasoning); Model source: https://artificialanalysis.ai/models/qwen3-5-35b-a3b-non-reasoning | Not reported |
AA-Omniscience AccuracyBenchmark detailsAA-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. | 20.13% Reported settingsQwen3.5 35B A3B (Reasoning); Model source: https://artificialanalysis.ai/models/qwen3-5-35b-a3b | 15.72% Reported settingsQwen3.5 35B A3B (Non-reasoning); Model source: https://artificialanalysis.ai/models/qwen3-5-35b-a3b-non-reasoning | Not reported |
𝜏²-Bench TelecomBenchmark detailsIndependently benchmarked by Artificial Analysis · Evaluation results measured independently by Artificial Analysis Unit: percent. Source collected 2026-09-12. | 89.18% Reported settingsQwen3.5 35B A3B (Reasoning); Model source: https://artificialanalysis.ai/models/qwen3-5-35b-a3b | 86.26% Reported settingsQwen3.5 35B A3B (Non-reasoning); Model source: https://artificialanalysis.ai/models/qwen3-5-35b-a3b-non-reasoning | Not reported |
𝜏³-BankingBenchmark detailsBenchmark 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 | 4.95% Reported settingsQwen3.5 35B A3B (Non-reasoning); Model source: https://artificialanalysis.ai/models/qwen3-5-35b-a3b-non-reasoning | Not reported |
Terminal-Bench HardBenchmark detailsIndependently benchmarked by Artificial Analysis · Evaluation results measured independently by Artificial Analysis Unit: percent. Source collected 2026-09-12. | 26.52% Reported settingsQwen3.5 35B A3B (Reasoning); Model source: https://artificialanalysis.ai/models/qwen3-5-35b-a3b | 10.61% Reported settingsQwen3.5 35B A3B (Non-reasoning); Model source: https://artificialanalysis.ai/models/qwen3-5-35b-a3b-non-reasoning | Not reported |
Terminal-Bench v2.1Benchmark detailsBenchmark 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 | 40.82% Reported settingsQwen3.5 35B A3B (Non-reasoning); Model source: https://artificialanalysis.ai/models/qwen3-5-35b-a3b-non-reasoning | Not reported |
Epoch · Chess Puzzles · v1.1.6Benchmark detailsEpoch 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 | 10% Reported settingsEpoch AI Inspect; task 1.1.6; scorer model_extracted_exact_match; model=qwen3.5-35b-a3b_none; run=jzqDbWHvQXhMkpYHfTjnRE; mean accuracy; https://epoch.ai/data/benchmarks.csv | 1% Reported settingsEpoch AI Inspect; task 1.1.6; scorer model_extracted_exact_match; model=qwen3.5-35b-a3b; run=KxxB8kDa9obqrPuec4PEiv; mean accuracy; https://epoch.ai/data/benchmarks.csv |
Epoch · GPQA diamond · v1.0.11Benchmark detailsEpoch 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 | 81.19% Reported settingsEpoch AI Inspect; task 1.0.11; scorer choice; model=qwen3.5-35b-a3b_none; run=KKm5DbPUgXo88eYr64g4SK; mean accuracy; https://epoch.ai/data/benchmarks.csv | 83.46% Reported settingsEpoch AI Inspect; task 1.0.11; scorer choice; model=qwen3.5-35b-a3b; run=RWjuCuVGvaRd4vDjJJ6aP2; mean accuracy; https://epoch.ai/data/benchmarks.csv |
Epoch · OTIS Mock AIME 2024-2025 · v1.1.6Benchmark detailsEpoch 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 | 70% Reported settingsEpoch AI Inspect; task 1.1.6; scorer model_extracted_exact_match; model=qwen3.5-35b-a3b_none; run=h9aoDxyWEhybvARHpyuoco; mean accuracy; https://epoch.ai/data/benchmarks.csv | 54.44% Reported settingsEpoch AI Inspect; task 1.1.6; scorer model_extracted_exact_match; model=qwen3.5-35b-a3b; run=9WS6kHXT8XFtJWtE2wWLAo; 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