Behavioral Fingerprints of Large Language Models: Consistency, Efficiency, and Expression

29/12/2025

Overview

This benchmark explores five frontier LLMs (GPT-4o, Claude 4.5 Sonnet, LLaMA 3.1-405B Instruct, Grok 4, and Gemini 2.5 Flash) within DATP’s controlled evaluation framework.

Each model received identical system context, creative constraints, and task definition: a 300-word editorial feature written in a fixed brand voice.

The goal: to measure behavioral consistency, semantic self-regulation, and economic efficiency rather than raw accuracy.

Quantitative Summary

All models were capped at a uniform 1024-token limit, except Gemini, which required 5000 tokens to complete the same prompt. GPT-4o, Claude 4.5 Sonnet, LLaMA 3.1-405B, and Grok 4 all achieved full completion within the 1024 ceiling, indicating efficient semantic compression. Gemini, however, displayed a conservative termination pattern that required higher max tokens for equivalent completeness.

Model	Input Tokens	Output Tokens	Word Count	Time (s)	Max Tokens	Completion Status
GPT-4o	240	350	324	6.2	1024	Complete
Claude 4.5 Sonnet	280	510	532	15.0	1024	Complete
LLaMa 3.2-405B	240	330	305	9.8	1024	Complete
Grok 4	240	330	305	9.8	1024	Complete
Gemini 2.5 Flash	243	323	308	13.66	5000	Adaptive Completion

Cost Analysis (per approx 300-word feature)

Model	Input $/M	Output $/M	Est. run cost (USD)
GPT-4o	2.50	10.00	$0.0041
Claude 4.5 Sonnet	3.00	15.00	$0.0085
LLaMa 3.2-405B	9.50	9.50	$0.0054
Grok 4	7.20	3.60	$0.0011 (approx)
Gemini 2.5 Flash	2.50	2.50	$0.0014

Key takeaways:

Gemini 2.5 Flash: most cost-efficient among full completions.
Grok 4: low-cost yet slower / stylistically rigid.
GPT-4o: best balance between clarity, control, and runtime.
Claude 4.5 Sonnet: highest cost but richest narrative depth.
LLaMA 3.1: predictable mid-tier baseline.

Behavioral Profiles

Model	DATP Trait	Completion Logic	Cognitive Bias	Efficiency
GPT-4o	Procedural Intelligence	Ends exactly on task boundaries	Analytical equilibrium	Balanced
Claude 4.5 Sonnet	Creative Momentum	Expands until rhetorical closure	Narrative dominance	Rich but costly
LLaMa 3.2-405B	Rule Adherence	Stops at constraint	Risk-averse literatism	Stable
Grok 4	Expressive Variability	Prioritizes tone > efficiency	Stylistic rigidity	Slow
Gemini 2.5 Flash	Semantic Equilibrium	Stops on conceptual closure, not token limit	Contextual self-regulation	Adaptive efficienct (~92%)

Gemini 2.5 Flash: Adaptive Semantic Regulation

Across escalating token ceilings (1024 → 1500 → 5000), Gemini displayed self-modulating verbosity.

1024 → Premature truncation after partial coherence (121–322 tokens).
1500 → Partial narrative (~173 tokens).
5000 → Fully realized composition (266–323 tokens).

While all other models successfully completed the benchmark at 1024 tokens, Gemini consistently under terminated until the ceiling was raised to 5000. This behavior suggests a unique internal constraint which is possibly tied to confidence thresholds or a conservative safety margin rather than simple verbosity. Once allowed sufficient headroom, Gemini produced a full 308-word output, maintaining coherence and stylistic fidelity.

Gemini terminates output when semantic cadence reaches equilibrium, not when the token runs out.

This behavior constitutes measurable contextual self-termination, indicating internal satisfaction criteria rather than deterministic cut-offs; a phenomenon consistent with emergent cognitive regulation.

Behavioral Archetypes

Model	Personality	Decision Pattern
GPT-4o	The Analyst	Resolves efficiency with structure
Claude 4.5 Sonnet	The Artist	Expands for emotional completeness
LLaMa 3.2-405B	The Engineer	Executes predictably, minimal interpretation
Grok 4	The Storyteller	Over-elaborates tone over time
Gemini 2.5 Flash	The Scholar	Balances completeness with restraint

Cognitive Risk vs. Constraint Curve

Claude 4.5: High creativity/low discipline
Gemini 2.5: High discipline/adaptive creativity
GPT-4o: Balanced axis
LLaMa: Low variance/safe execution
Grok 4: High entropy/slow resolution

Output Example

Conclusions

DATP reveals that every LLM expresses a measurable behavioral fingerprint, a pattern of decision making under identical constraints. Where traditional benchmarks test accuracy, DATP quantifies behavioral integrity: how an AI decides it is “done.”

Gemini’s adaptive cadence marks the first observed case of semantic completion regulation, implying LLMs are developing intrinsic self-termination logic: the linguistic analogue of cognitive closure.

LLMs don’t just generate text. They regulate thought. DATP provides the world’s first reproducible lens to measure it.