RankingEffort benchmarks

Qwen3.8-Flash-Next · Reasoning

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16 measured results · 2 effort settings · 16 benchmark/harness combinations

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Capability profile

One configuration, seven capabilities

Comparison score · 0–100

AQwen3.8-Flash-Next · Reasoning2 of 7 capabilities supported

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

Agentic
A61.8Supported
Hard reasoning
A40.7Supported
Coding
A64.6Preliminary
Human pref
Unknown
Knowledge
A12.1Preliminary
Multimodal
A42.2Preliminary
Long context
A49.9Preliminary

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.

Capability score evidence

Qwen3.8-Flash-Next · Reasoning: 40.3 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 · 100.0% direct comparison weight · independent of the model provider

Which benchmark families carry the weight?

Humanity’s Last Exam · 9.6% direct comparison weight
GPQA · 9.6% direct comparison weight
Critpt · 9.5% direct comparison weight
omniscience · 9.5% direct comparison weight
Mmmu Pro · 9.4% direct comparison weight
Other families (7) · 52.4% 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 2026-10-20.

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 source	Rank before → after	Rank movement	Cohort remaining / lost support	Score after (change)
Artificial Analysis	Comparison support lost; no rank		203 remaining / 241 lost	Unavailable
ARC Prize	86 → 84	Up 2	444 remaining / 0 lost	46.4 (+6.1 points)
Datacurve	86 → 89	Down 3	444 remaining / 0 lost	36.0 (-4.3 points)
LiveCodeBench	86 → 86	No movement	444 remaining / 0 lost	40.2 (-0.1 points)
Scale	86 → 85	Up 1	444 remaining / 0 lost	42.5 (+2.2 points)
Terminal-Bench	86 → 86	No movement	444 remaining / 0 lost	40.5 (+0.2 points)
Cognition	86 → 89	Down 3	444 remaining / 0 lost	39.0 (-1.3 points)
LiveBench	86 → 86	No movement	443 remaining / 1 lost	41.2 (+0.9 points)
SWE-rebench	86 → 84	Up 2	444 remaining / 0 lost	40.7 (+0.3 points)
Epoch AI	85 → 88	Down 3	431 remaining / 13 lost	41.0 (+0.6 points)
Vals AI	86 → 85	Up 1	443 remaining / 1 lost	39.6 (-0.7 points)
Provider reports · Anthropic	86 → 86	No movement	444 remaining / 0 lost	40.3 (-0.0 points)
Provider reports · Moonshot AI	86 → 87	Down 1	444 remaining / 0 lost	39.7 (-0.6 points)
Provider reports · Meta	86 → 86	No movement	444 remaining / 0 lost	40.2 (-0.1 points)
Provider reports · Mistral	86 → 86	No movement	444 remaining / 0 lost	40.3 (+0.0 points)
Provider reports · OpenAI	86 → 86	No movement	444 remaining / 0 lost	40.5 (+0.2 points)
All provider reports	86 → 87	Down 1	444 remaining / 0 lost	39.8 (-0.5 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 evidence	Recomputed score	Change
omniscience	48.2	+7.8
arc-agi	45.3	+5.0
deepswe	36.0	-4.3
briefcase	36.6	-3.7
gdpval	37.8	-2.5
hle	42.3	+1.9
mmmu-pro	42.0	+1.7
tau-bench	38.8	-1.5
frontiercode	39.0	-1.3
critpt	41.5	+1.2
gdp-pdf	41.5	+1.2
automationbench	39.3	-1.1
terminal-bench	39.3	-1.1
epoch-game-puzzles	39.4	-0.9
simpleqa	41.2	+0.9
gpqa	39.6	-0.7
ifbench	39.7	-0.7
multi-swe-bench	39.9	-0.4
livebench-instructions	40.7	+0.4
arc-agi-3	40.0	-0.4
mmlu-pro	40.7	+0.4
terminal-bench-science	40.7	+0.4
swe-rebench	40.7	+0.3
harvey	40.0	-0.3
livebench-language	40.6	+0.3
apex-agents	40.6	+0.3
swe-atlas-qna	40.1	-0.3
analyst-agent	40.6	+0.3
aa-lcr	40.6	+0.2
livecodebench	40.1	-0.2
livebench-coding	40.1	-0.2
aime	40.1	-0.2
enigma-eval	40.2	-0.2
vibe-code	40.2	-0.1
livebench-reasoning	40.2	-0.1
swe-atlas-test-writing	40.2	-0.1
frontiermath	40.4	+0.1
vals-legal-research	40.2	-0.1
enterprise-ops	40.4	+0.1
vals-finance-agent	40.3	-0.1
vals-tax-agent	40.3	-0.1
math500	40.3	-0.1
vals-code-migration	40.3	-0.1
itbench	40.3	-0.0
gmmlu	40.3	-0.0
swe-atlas-refactoring	40.3	-0.0
vals-excel-modeling	40.3	-0.0
livebench-math	40.3	+0.0
livebench-data	40.3	+0.0
swe-bench-pro	40.3	+0.0

What evidence is missing from the fit?

Across all collected settings for this model: 19 observations, 13 contributing, 0 matched without graph weight and 6 excluded. Counts do not establish rank eligibility.

1 · Benchmark family or source protocol has not been reviewed
1 · Original SciCode grading defects are under review; retained as supporting evidence, excluded from aggregate and capability scores. Corrected SciCode-Verified requires a separate protocol review. Sources: https://arxiv.org/abs/2608.04975 and https://artificialanalysis.ai/evaluations/scicode
1 · Effort is not identified
2 · Supporting evidence outside the reviewed capability core
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 / source	Reasoning	Unspecified
AA-Briefcase EloArtificial Analysis · Artificial Analysis published evaluation; see source for benchmark-specific settings Benchmark details AA-Briefcase v1.1 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 2026-10-18.	1,579.93 Elo Reported settings Qwen3.8-Flash-Next; Reported effort: reasoning; Model source: https://artificialanalysis.ai/models/qwen3-8-flash-next; 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 2026-10-18.	79.67% Reported settings Qwen3.8-Flash-Next; Reported effort: reasoning; Model source: https://artificialanalysis.ai/models/qwen3-8-flash-next; 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 2026-10-18.	55.91% Reported settings Qwen3.8-Flash-Next; Reported effort: reasoning; Model source: https://artificialanalysis.ai/models/qwen3-8-flash-next; 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. 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.	11.14% Reported settings Qwen3.8-Flash-Next; Reported effort: reasoning; Model source: https://artificialanalysis.ai/models/qwen3-8-flash-next; Mode from evaluator isReasoning metadata; effort budget not specified	Not reported
GDP.pdf: All-pass RateArtificial 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 2026-10-18.	15.6% Reported settings Qwen3.8-Flash-Next; Reported effort: reasoning; Model source: https://artificialanalysis.ai/models/qwen3-8-flash-next; Mode from evaluator isReasoning metadata; effort budget not specified	Not reported
GDPval-AA v2.1 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 DeepSeek V4.1 Flash (max) at 1600 · Higher is better · Evaluation results measured independently by Artificial Analysis Unit: elo. Source collected 2026-10-18.	1,627.86 Elo Reported settings Qwen3.8-Flash-Next; Reported effort: reasoning; Model source: https://artificialanalysis.ai/models/qwen3-8-flash-next; 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 2026-10-18.	92.32% Reported settings Qwen3.8-Flash-Next; Reported effort: reasoning; Model source: https://artificialanalysis.ai/models/qwen3-8-flash-next; 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 2026-10-18.	38.04% Reported settings Qwen3.8-Flash-Next; Reported effort: reasoning; Model source: https://artificialanalysis.ai/models/qwen3-8-flash-next; 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 2026-10-18.	79.77% Reported settings Qwen3.8-Flash-Next; Reported effort: reasoning; Model source: https://artificialanalysis.ai/models/qwen3-8-flash-next; 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 2026-10-18.	-9.72 Reported settings Qwen3.8-Flash-Next; Reported effort: reasoning; Model source: https://artificialanalysis.ai/models/qwen3-8-flash-next; 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 2026-10-18.	24.5% Reported settings Qwen3.8-Flash-Next; Reported effort: reasoning; Model source: https://artificialanalysis.ai/models/qwen3-8-flash-next; 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 Benchmark curated by scientists across 16 disciplines. SciCode is under review following an independent audit of the dataset · Independently benchmarked by Artificial Analysis · Evaluation results measured independently by Artificial Analysis Unit: percent. Source collected 2026-10-18.	50.58% Reported settings Qwen3.8-Flash-Next; Reported effort: reasoning; Model source: https://artificialanalysis.ai/models/qwen3-8-flash-next; 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 2026-10-18.	45.36% Reported settings Qwen3.8-Flash-Next; Reported effort: reasoning; Model source: https://artificialanalysis.ai/models/qwen3-8-flash-next; 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 2026-10-18.	86.14% Reported settings Qwen3.8-Flash-Next; Reported effort: reasoning; Model source: https://artificialanalysis.ai/models/qwen3-8-flash-next; 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 2026-10-18.	25.25% Reported settings Qwen3.8-Flash-Next; Reported effort: reasoning; Model source: https://artificialanalysis.ai/models/qwen3-8-flash-next; Mode from evaluator isReasoning metadata; effort budget not specified	Not reported
LMArena Agent ArenaLMArena · Arena Agent Mode. The board does not name a per-model scaffold. Benchmark details Aggregate Agent Arena IPS net improvement, stored as the board's printed percentage points. Arena Agent Mode does not name a per-model scaffold. Display only. Excluded from weighted Overall. Unit: index. Source collected 2026-10-18.	Not reported	-0.73 Reported settings Qwen3.8 Flash Next; Arena Agent Mode; IPS net improvement -0.73 points ±0.63. rank 34; observations 11249221; sessions 83198; published 2026-10-02. The board does not name a per-model scaffold. Display only. Excluded from weighted Overall.

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 · 8,915 results across 259 models · Collected 2026-10-18

Qwen3.8-Flash-Next · Reasoning

Compare with

One configuration, seven capabilities

Agentic

Hard reasoning

Coding

Human pref

Knowledge

Multimodal

Long context

Capability score evidence

Benchmark results