deepseek-v4-pro deep verification model-consistency report
Report ID MZZR2026071505265320B992Generated at 2026/07/15 13:26
Doubtful
deepseek-v4-pro deep verification result: Doubtful (73/100). Main deductions: Logic-grid reasoning was unstable; Model-specific boundary response was unclear; Brand-boundary response was unclear.
73Consistency score
主要扣分项:逻辑网格推理不稳定;模型专属边界表达不清;品牌边界表达不清。
EvaluatorMozhenzhen
Tested modelDeepSeek V4 Pro
ProvidersInfistar
Samples30 probes
Duration2 min 17 sec
Verification versionV11
Identity verdictNot verified
Verification basisMozhenzhen versioned baseline
Token usage
Measured API usage is shown first; official baselines or test estimates are used only when measured values are unavailable.
Measured token usage1.4MFrom the response usage fields; this is the report's primary usage measure.Test-suite estimate1.7MPrecisely estimated from this test suite when no official baseline is available.Variance assessment16% belowMeasured token usage is below the estimate, possibly due to tokenizer, compression, or usage-reporting differences.Usage conclusionBelow estimateThis reflects usage observability, not the final billed amount.
Token cache usage test
Observes token usage, cache fields, and reuse across rounds; 0% means no cached-token field was observed.
Tested 5 times
Observed cache share60.4%
Low cache share
Cached tokens were observed, but the share is low. Review API parameters, context reuse, and billing methodology.
Observed tokens13.6KCached tokens8.2KNon-cached tokens5.4KAverage per run2.7K
Cache share by testRepresents the API-reported cache share, not the final billing discount.
Long-context test
Shows stable responses at different context lengths and the model center reference window.
7 samples
Verified up to 1M
All tested context tiers (32K / 64K / 100K / 200K / 400K / 800K / 1M) returned reliably.
Reference windowReference window 1MModel-center context window; available lengths may vary by provider.Tested context lengths32K / 64K / 100K / 200K / 400K / 800K / 1MTests begin at the longest generated context and step down to verify stable, correct responses.
Model authenticity score
Review API availability, model identity, response completeness, and other checks separately.
deepseek-v4-pro deep verification result: Doubtful (73/100). Main deductions: Logic-grid reasoning was unstable; Model-specific boundary response was unclear; Brand-boundary response was unclear.
EvaluatorMozhenzhen
Tested modelDeepSeek V4 Pro
ProvidersInfistar
Samples30
Duration2 min
Test versionV11
Identity verdictNot verified
Verification basisMozhenzhen versioned baseline
Token usage
Measured API usage is shown first; official baselines or test estimates are used only when measured values are unavailable.
Measured token usage1.4MFrom the response usage fields; this is the report's primary usage measure.Test-suite estimate1.7MPrecisely estimated from this test suite when no official baseline is available.Variance assessment16% belowMeasured token usage is below the estimate, possibly due to tokenizer, compression, or usage-reporting differences.Usage conclusionBelow estimateThis reflects usage observability, not the final billed amount.
Token cache usage test
Observes token usage, cache fields, and reuse across rounds; 0% means no cached-token field was observed.
Tested 5 times
Observed cache share60.4%
Low cache share
Cached tokens were observed, but the share is low. Review API parameters, context reuse, and billing methodology.
Observed tokens13.6KCached tokens8.2KNon-cached tokens5.4KAverage per run2.7K
Cache share by testRepresents the API-reported cache share, not the final billing discount.
Long-context test
Shows stable responses at different context lengths and the model center reference window.
7 samples
Verified up to 1M
All tested context tiers (32K / 64K / 100K / 200K / 400K / 800K / 1M) returned reliably.
Reference windowReference window 1MModel-center context window; available lengths may vary by provider.Tested context lengths32K / 64K / 100K / 200K / 400K / 800K / 1MTests begin at the longest generated context and step down to verify stable, correct responses.
Model authenticity score
Review API availability, model identity, response completeness, and other checks separately.