RankingEffort benchmarks

DeepSeek-V4-Flash-0731 · Max

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39 measured results · 5 effort settings · 36 benchmark/harness combinations

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Capability profile

One configuration, seven capabilities

Comparison score · 0–100

ADeepSeek-V4-Flash-0731 · Max4 of 7 capabilities supported

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

Agentic
A46.3Supported
Hard reasoning
A43.9Supported
Coding
A44.2Supported
Human pref
Unknown
Knowledge
A20.1Supported
Multimodal
Unknown
Long context
A41.3Preliminary

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

DeepSeek-V4-Flash-0731 · Max: 35.7 overall comparison score. Meets the overall evidence requirements.

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

Which sources carry the weight?

Artificial Analysis · 27.0% direct comparison weight · independent of the model provider
Epoch AI · 24.9% direct comparison weight · independent of the model provider
Datacurve · 24.7% direct comparison weight · independent of the model provider
ARC Prize · 23.4% direct comparison weight · independent of the model provider

Which benchmark families carry the weight?

DeepSWE · 24.7% direct comparison weight
ARC-AGI · 23.4% direct comparison weight
GPQA · 7.8% direct comparison weight
Aime · 5.3% direct comparison weight
epoch-game-puzzles · 5.0% direct comparison weight
Other families (12) · 33.8% 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.

DeepSWE v1.1 review: Epoch AI reported grading errors in at least 23 of 113 tasks. These published results remain included. Review their limitations in Epoch's review when interpreting this evidence.

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	90 → 89	Up 1	203 remaining / 241 lost	35.7 (-0.1 points)
ARC Prize	99 → 108	Down 9	444 remaining / 0 lost	36.6 (+0.8 points)
Datacurve	99 → 96	Up 3	444 remaining / 0 lost	32.0 (-3.7 points)
LiveCodeBench	99 → 99	No movement	444 remaining / 0 lost	35.7 (-0.0 points)
Scale	99 → 99	No movement	444 remaining / 0 lost	37.9 (+2.2 points)
Terminal-Bench	99 → 104	Down 5	444 remaining / 0 lost	35.7 (-0.1 points)
Cognition	99 → 103	Down 4	444 remaining / 0 lost	34.8 (-0.9 points)
LiveBench	99 → 97	Up 2	443 remaining / 1 lost	36.5 (+0.8 points)
SWE-rebench	99 → 99	No movement	444 remaining / 0 lost	35.1 (-0.6 points)
Epoch AI	98 → 94	Up 4	431 remaining / 13 lost	38.1 (+2.3 points)
Vals AI	99 → 101	Down 2	443 remaining / 1 lost	34.6 (-1.1 points)
Provider reports · Anthropic	99 → 99	No movement	444 remaining / 0 lost	35.7 (+0.0 points)
Provider reports · Moonshot AI	99 → 100	Down 1	444 remaining / 0 lost	35.2 (-0.5 points)
Provider reports · Meta	99 → 99	No movement	444 remaining / 0 lost	35.8 (+0.1 points)
Provider reports · Mistral	99 → 99	No movement	444 remaining / 0 lost	35.7 (+0.0 points)
Provider reports · OpenAI	99 → 99	No movement	444 remaining / 0 lost	35.8 (+0.1 points)
All provider reports	99 → 100	Down 1	444 remaining / 0 lost	35.4 (-0.3 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
deepswe	32.1	-3.7
simpleqa	38.2	+2.5
epoch-game-puzzles	33.8	-2.0
mmmu-pro	36.9	+1.2
hle	36.8	+1.0
gpqa	34.7	-1.0
frontiercode	34.8	-0.9
gdp-pdf	36.4	+0.7
frontiermath	36.4	+0.6
swe-rebench	35.1	-0.6
mmlu-pro	36.2	+0.5
aime	35.2	-0.5
tau-bench	35.3	-0.4
multi-swe-bench	35.4	-0.4
automationbench	35.4	-0.4
livebench-instructions	36.0	+0.3
harvey	35.4	-0.3
swe-atlas-qna	35.5	-0.3
gdpval	35.5	-0.3
arc-agi-3	35.5	-0.2
livebench-language	36.0	+0.2
briefcase	35.5	-0.2
arc-agi	35.9	+0.2
livebench-coding	35.5	-0.2
ifbench	35.6	-0.2
omniscience	35.9	+0.2
vals-finance-agent	35.6	-0.1
aa-lcr	35.9	+0.1
livebench-reasoning	35.6	-0.1
swe-atlas-test-writing	35.6	-0.1
apex-agents	35.8	+0.1
vals-legal-research	35.6	-0.1
terminal-bench-science	35.8	+0.1
terminal-bench	35.8	+0.1
vibe-code	35.6	-0.1
livebench-data	35.8	+0.1
itbench	35.7	-0.1
analyst-agent	35.8	+0.1
livebench-math	35.8	+0.1
critpt	35.7	-0.0
enigma-eval	35.7	-0.0
vals-tax-agent	35.7	-0.0
enterprise-ops	35.7	-0.0
swe-bench-pro	35.7	+0.0
gmmlu	35.7	+0.0
math500	35.7	+0.0
swe-atlas-refactoring	35.7	-0.0
vals-code-migration	35.7	+0.0
livecodebench	35.7	+0.0
vals-excel-modeling	35.7	+0.0

What evidence is missing from the fit?

Across all collected settings for this model: 54 observations, 34 contributing, 0 matched without graph weight and 20 excluded. Counts do not establish rank eligibility.

2 · 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
1 · Evaluator has not been reviewed for independent admission
8 · No reviewed effort for this catalog observation; any separately collected effort measurement is counted separately
6 · Supporting evidence outside the reviewed capability core
1 · Narrow specialist biological, medical or security evaluation; preserved as raw evidence outside general capability scoring.

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	Low	High	Max	None	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.	Not reported	Not reported	1,258.64 Elo Reported settings DeepSeek V4 Flash 0731 (Max); Model source: https://artificialanalysis.ai/models/deepseek-v4-flash	Not reported	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.	Not reported	Not reported	79.67% Reported settings DeepSeek V4 Flash 0731 (Max); Model source: https://artificialanalysis.ai/models/deepseek-v4-flash	Not reported	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.	Not reported	Not reported	53.97% Reported settings DeepSeek V4 Flash 0731 (Max); Model source: https://artificialanalysis.ai/models/deepseek-v4-flash	Not reported	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.	Not reported	Not reported	16.57% Reported settings DeepSeek V4 Flash 0731 (Max); Model source: https://artificialanalysis.ai/models/deepseek-v4-flash	Not reported	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.	Not reported	Not reported	11% Reported settings DeepSeek V4 Flash 0731 (Max); Model source: https://artificialanalysis.ai/models/deepseek-v4-flash	Not reported	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.	Not reported	Not reported	1,433.74 Elo Reported settings DeepSeek V4 Flash 0731 (Max); Model source: https://artificialanalysis.ai/models/deepseek-v4-flash	Not reported	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.	Not reported	Not reported	90.81% Reported settings DeepSeek V4 Flash 0731 (Max); Model source: https://artificialanalysis.ai/models/deepseek-v4-flash	Not reported	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.	Not reported	Not reported	38.55% Reported settings DeepSeek V4 Flash 0731 (Max); Model source: https://artificialanalysis.ai/models/deepseek-v4-flash	Not reported	Not reported
MLCR-AAArtificial Analysis · Artificial Analysis published evaluation; see source for benchmark-specific settings Benchmark details Share of tasks judged accurate, complete, and concise · Expert + compound (hard) sets · Benchmark developed by Wisedocs · Independently benchmarked by Artificial Analysis · Evaluation results measured independently by Artificial Analysis Unit: percent. Source collected 2026-10-18.	Not reported	Not reported	13.33% Reported settings DeepSeek V4 Flash 0731 (Max); Model source: https://artificialanalysis.ai/models/deepseek-v4-flash	Not reported	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.	Not reported	Not reported	-14.28 Reported settings DeepSeek V4 Flash 0731 (Max); Model source: https://artificialanalysis.ai/models/deepseek-v4-flash	Not reported	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.	Not reported	Not reported	40.38% Reported settings DeepSeek V4 Flash 0731 (Max); Model source: https://artificialanalysis.ai/models/deepseek-v4-flash	Not reported	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.	Not reported	Not reported	50.35% Reported settings DeepSeek V4 Flash 0731 (Max); Model source: https://artificialanalysis.ai/models/deepseek-v4-flash	Not reported	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.	Not reported	Not reported	39.38% Reported settings DeepSeek V4 Flash 0731 (Max); Model source: https://artificialanalysis.ai/models/deepseek-v4-flash	Not reported	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.	Not reported	Not reported	78.65% Reported settings DeepSeek V4 Flash 0731 (Max); Model source: https://artificialanalysis.ai/models/deepseek-v4-flash	Not reported	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.	Not reported	Not reported	12.12% Reported settings DeepSeek V4 Flash 0731 (Max); Model source: https://artificialanalysis.ai/models/deepseek-v4-flash	Not reported	Not reported
ARC-AGI-2ARC Prize · v2 Semi-Private Benchmark details Original public board. Configuration and task coverage may differ; inspect source. Unit: percent. Source collected 2026-10-18.	45.97% Reported settings DeepSeek V4 Flash 0731 (Low); Source model ID: deepseek-v4-flash-0731-low; 120 tasks	55.97% Reported settings DeepSeek V4 Flash 0731 (High); Source model ID: deepseek-v4-flash-0731-high; 120 tasks	61.39% Reported settings DeepSeek V4 Flash 0731 (Max); Source model ID: deepseek-v4-flash-0731-max; 120 tasks	2.08% Reported settings DeepSeek V4 Flash 0731 (None); Source model ID: deepseek-v4-flash-0731-none; 120 tasks	Not reported
DeepSWE v1.1Datacurve · mini-swe-agent Benchmark details Original public board. Configuration and task coverage may differ; inspect source. Unit: percent. Source collected 2026-10-18.	Not reported	Not reported	53.3% Reported settings deepseek-v4-flash; Reported effort: max; 113 unique tasks; 452 attempts; 4 runs	Not reported	Not reported
FrontierCode 1.1 · extended-chiselCognition · chisel Benchmark details FrontierCode 1.1; chisel. Open each result for its exact effort, task subset, metric and protocol details. Unit: percent. Source collected 2026-10-18.	Not reported	31.7% Reported settings FrontierCode 1.1; extended; 150 tasks; chisel; source effort high; weighted rubric score, not pass rate; unfair internet runs zeroed by evaluator; flag rate 0.1911	Not reported	Not reported	Not reported
FrontierCode 1.1 · main-chiselCognition · chisel Benchmark details FrontierCode 1.1; chisel. Open each result for its exact effort, task subset, metric and protocol details. Unit: percent. Source collected 2026-10-18.	Not reported	18.8% Reported settings FrontierCode 1.1; main; 100 tasks; chisel; source effort high; weighted rubric score, not pass rate; unfair internet runs zeroed by evaluator; flag rate 0.2547	Not reported	Not reported	Not reported
Epoch · Chess Puzzles · v2.0.0Epoch AI · Epoch AI Inspect; task 2.0.0; scorer exact 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	Not reported	32% Reported settings Epoch AI Inspect; task 2.0.0; scorer exact; model=deepseek-v4-flash-0731_max; run=Azz52eFkXwVJiRCGpaUBwB; mean accuracy; https://epoch.ai/data/benchmarks.csv	Not reported	Not reported
Epoch · FrontierMath Tier 4 · v2.0.0Epoch AI · Epoch AI Inspect; task 2.0.0; scorer verification_code 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. FrontierMath was developed with OpenAI funding and unequal access to part of the question set; this result is from Epoch's evaluation. Unit: percent. Source collected 2026-10-18.	Not reported	Not reported	24.39% Reported settings Epoch AI Inspect; task 2.0.0; scorer verification_code; model=deepseek-v4-flash-0731_max; run=WYs83xB6NwrkMNyQJtuMcQ; mean accuracy; https://epoch.ai/data/benchmarks.csv	Not reported	Not reported
Epoch · FrontierMath Tiers 1–3 · v2.0.0Epoch AI · Epoch AI Inspect; task 2.0.0; scorer verification_code 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. FrontierMath was developed with OpenAI funding and unequal access to part of the question set; this result is from Epoch's evaluation. Unit: percent. Source collected 2026-10-18.	Not reported	Not reported	57.54% Reported settings Epoch AI Inspect; task 2.0.0; scorer verification_code; model=deepseek-v4-flash-0731_max; run=mKhVMEicZeGjdvJdTRd7mK; mean accuracy; https://epoch.ai/data/benchmarks.csv	Not reported	Not reported
Epoch · GPQA diamond · v1.0.11Epoch AI · Epoch AI Inspect; task 1.0.11; 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.	Not reported	Not reported	91.04% Reported settings Epoch AI Inspect; task 1.0.11; scorer choice; model=deepseek-v4-flash-0731_max; run=EDFusDaaFkh5n2MqKNxMBD; mean accuracy; https://epoch.ai/data/benchmarks.csv	Not reported	Not reported
Epoch · Mystery Game Puzzles · v1.0.4 · Tool submissionEpoch AI · Epoch AI Inspect; task 1.0.4; scorer agent_submitted_next_move_scorer 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	Not reported	34% Reported settings Epoch AI Inspect; task 1.0.4; scorer agent_submitted_next_move_scorer; model=deepseek-v4-flash-0731_max; run=4S4GXbkRhLz8wWP97jYG57; mean accuracy; https://epoch.ai/data/benchmarks.csv	Not reported	Not reported
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	Not reported	94.44% Reported settings Epoch AI Inspect; task 1.1.6; scorer model_extracted_exact_match; model=deepseek-v4-flash-0731_max; run=FQ3Suh2vKacz7UNqJybTWk; mean accuracy; https://epoch.ai/data/benchmarks.csv	Not reported	Not reported

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

DeepSeek-V4-Flash-0731 · Max

Compare with

One configuration, seven capabilities

Agentic

Hard reasoning

Coding

Human pref

Knowledge

Multimodal

Long context

Capability score evidence

Benchmark results