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An Empirical Evaluation of Cost-Efficient Large Language Models on Algorithmic Programming Tasks

Chandimal Adikari, Nandika Herath

arXiv:2609.18052v1cs.SEcs.AI

TL;DR

This study asks whether cost-efficient language models can generate code that meets written specifications, given that execution tests and structural instruction checks have typically been applied to different artifacts. It evaluates generated Spring Boot methods through static classification and execution. Structural conformance was near-perfect, but many methods did not compute their returned values and few returned answers were correct.

  • Problem

    Execution testing and structural instruction-following checks are usually applied to different corpora, leaving their agreement on the same generated code unquantified.

    Source: §I · p. 1

  • Method

    The study evaluates generated methods from three cost-efficient models through static outcome classification and runtime execution, joining the results by problem.

    Source: D. Static Content Classification and… · p. 3, §V · p. 6

  • Results

    37.8% of methods did not compute their returned value, and 12.9% of returned answers were correct.

    Source: §V · p. 6

  • Takeaways & Limitations

    Structural adherence alone measures only the least demanding requirement and can select a system that is less likely to compute correctly.

    Source: §V · p. 6

    Each condition was generated once, so between-condition comparisons lack an estimate of run-to-run generation variance.

    Source: §VI · p. 6

Abstract

from arXiv · show

This study empirically evaluates whether cost-efficient Large Language Models (LLMs) can be trusted to generate enterprise code to a written specification. Three models (Gemini Flash 3, GPT-5.4 mini and Claude Haiku 4.5) were asked to solve 992 algorithmic problems as Java Spring Boot service methods conforming to a mandated signature and data-transfer-object specification, crossing four model and agentic coding tool combinations with two prompt variants to yield eight configurations, with iteration forbidden and hardcoded answers explicitly prohibited. Eight problem statements were withheld to probe how models respond to missing input. The 7,593 resulting methods were classified by an eight-class outcome taxonomy describing what each does about producing an answer, then deployed and executed, giving 7,936 measured requests joined to that classification. Structural conformance approached ceiling, yet 38.4% of methods do not compute the value they returned and only 12.9% of returned answers were correct. Conditioning on outcome class shows that response reliability and correctness are inversely related, whereas genuinely computing methods answered least often and were correct 19.3%. Limitations include single generation runs per configuration, partial harness coverage, single-pass timing, syntactic classification, and probable corpus contamination.

I. INTRODUCTION

Code-generation evaluation commonly measures execution pass rates, while instruction-following checks structural constraints; because these instruments have not been applied to the same artifacts, their agreement is unquantified. This study prohibits iteration and retains the coding harness as a factor while examining generated code and outcomes.

I. INTRODUCTION

  • Execution-based benchmarks measure pass rates without examining how code produces its answer, while instruction-following checks structural constraints that do not establish whether the code computes its return value.Adjacent work studies memorisation, efficiency, and security, but efficiency benchmarks condition on correctness.Source: §I · p. 1
  • The two evaluation instruments have not been applied to the same artifacts, leaving their disagreement unquantified; unconditional runtime measurements also cannot distinguish working algorithms from returned constants.The study prohibits iteration to observe first-response behavior and retains the harness as an experimental factor.Source: §I · p. 1
  • The contributions include a taxonomy separating genuine computation from fabrication, evidence of divergence between structural and semantic conformance, and an inverse relationship between response reliability and correctness.The study also identifies a synthetic answer generator and probes responses to missing input.Source: §I · p. 1

II. METHODOLOGY AND EXPERIMENTAL SETUP

The methodology addresses structural conformance, compliance with the prohibition on shortcuts, and correctness. Static source analysis and execution are joined by problem identifier so runtime outcomes can be conditioned on the kind of code produced.

II. METHODOLOGY AND EXPERIMENTAL SETUP

  • The study asks whether generated models conform to the prescribed structure, comply with the prohibition on shortcuts, and correctly solve the target problem.Source: §II · p. 2
  • Static analysis evaluates structural form and shortcut compliance, while execution evaluates whether generated code solves the target problem.Source: §II · p. 2
  • The two analyses are joined by problem identifier, allowing runtime measurements to be conditioned on the kind of code that produced them.Source: §II · p. 2

A. Problem Presentation

The study uses Project Euler problems to evaluate cost-efficient models generating Spring Boot service methods under crossed model–harness and prompt conditions.

A. Problem Presentation

  • 992 of the 1,000 Project Euler problems were supplied to the models, with eight withheld as probes.Source: A. Problem Presentation · p. 2
  • Project Euler provides single unambiguous numeric answers, spans several orders of difficulty, and has an early-to-late gradient in online discussion relevant to contamination.Correctness can therefore be decided without a test suite, while the corpus's discussion gradient makes contamination measurable.Source: A. Problem Presentation · p. 2

B. LLMs and conditions

  • The study evaluated Gemini Flash 3, GPT-5.4 mini, and Claude Haiku 4.5 using Cursor and Claude Code as agentic coding tools.Source: B. LLMs and conditions · p. 2
  • Crossing four model–harness combinations with two prompt variants yielded eight experimental conditions.Source: B. LLMs and conditions · p. 2

C. Prompt Variants and Contraints

Both one-shot prompts require Spring Boot service methods with a mandated signature and DTO workflow; they differ in code organization, while generation is constrained to first responses without hardcoded answers.

C. Prompt Variants and Contraints

  • Both prompts require Java methods in a Spring Boot service class, with a specified package, DTO import, exact method signature, DTO instantiation, and a returned computed answer.Source: C. Prompt Variants and Contraints · p. 2
  • Models were told not to iterate or self-correct and not to hardcode known answers, so the study measures first-generation output and tests the semantic instruction against shortcuts.Source: C. Prompt Variants and Contraints · p. 2
  • Prompt 1 groups 100 problem methods in one service class, whereas Prompt 2 uses one service class per problem; eight problem files were withheld and should elicit no output if noticed.Source: C. Prompt Variants and Contraints · p. 3

D. Static Content Classification and Execution Setup

Static analysis traces how each method produces its answer, and the results show that near-ceiling signature conformance can coexist with substantial non-computation. Coverage and literal-answer findings further show where outputs are missing or fabricated.

D. Static Content Classification and Execution Setup

  • The static analyzer resolves the expression passed to setAnswer, propagates constant assignments, and unwraps indirections; runtime requests were issued once per supplied problem.The 7,593 methods were classified before deployment, which produced 7,936 requests across the eight conditions.Source: D. Static Content Classification and… · p. 3, §III · p. 3

A. Static Content Results

  • Coverage of the 992 supplied problems ranges from 89.9% to 100.0%, with missing problems often forming contiguous batches.Examples include blocks of 20 or 100 consecutive absences.Source: A. Static Content Results · p. 3, Table II · p. 3
  • 2,913 methods do not compute the value they return, while every method produces the mandated signature; non-attempt rates range from 4.7% to 87.0% across conditions.The outcome taxonomy is reported in Table III.Source: A. Static Content Results · p. 3, Table III · p. 4
  • Of 1,843 literal answers, 92 match published answers (5.0%); fabrication outnumbers recall by roughly nineteen to one.Table IV reports literal matches by condition.Source: A. Static Content Results · p. 3, Table IV · p. 4
  • Under P1, Cursor is worse by 10.7 percentage points; under P2, Cursor is better by 13.5 points when Claude Haiku 4.5 is held constant.P2 produced fewer non-attempts than P1 in all four model–tool combinations.Source: A. Static Content Results · p. 4, Table V · p. 4

B. Runtime Response Outcomes

Across 7,936 requests, most returned answers, while the remainder included server errors, timeouts, null answers, and transport errors.

B. Runtime Response Outcomes

  • 6,981 of 7,936 requests carried an answer (88.0%); the rest included 675 server errors, 247 timeouts, 31 null answers, and 2 transport errors.The server errors comprised 343 requests with no method and 332 runtime exceptions; timeouts used the 60-second ceiling.Source: B. Runtime Response Outcomes · p. 4

IV. DISCUSSION

Static semantic compliance and runtime correctness move in opposite directions across conditions, but their association does not establish that attempting computation reduces accuracy. Differences in which problems were attempted limit a single combined ranking.

IV. DISCUSSION

  • Rank correlations are −0.67 for semantic compliance and −0.81 for capability, while Fig. 6 shows the two series moving in opposite directions across conditions.Capability is correct answers as a share of attempts that returned a value.Source: §IV · p. 4
  • GPT-5.4 mini under Prompt 1 attempted 128 problems and was correct on 90.6% of them, but all attempts were at or below problem 200; removing this condition shifts the correlation from −0.91 to −0.78.The other seven conditions attempted across the full corpus, so the measures should not be combined into a single ranking.Source: Figure 5 · p. 4, §IV · p. 5
  • Across eight conditions, static semantic compliance correlates with runtime correctness at -0.53, and compliance among genuine attempts correlates with correctness at -0.91.Table VII reports these associations.Source: §IV · p. 4, Table VII · p. 5

A. Answer Fabrication Strategies

The study distinguishes fabrication strategies by their visibility, with a synthetic generator especially difficult to detect despite producing no correct answers.

A. Answer Fabrication Strategies

  • Five fabrication strategies differ in visibility: constants, acknowledged placeholders, prose answers, wrappers forwarding elsewhere, and synthetic answers.The wrapper can be structurally impeccable despite containing no computation.Source: A. Answer Fabrication Strategies · p. 5
  • The synthetic generator produced distinct answers from problem indices in 792 methods, answered every request, and got none correct.It uses a fixed arithmetic routine that never reads the problem statement, making duplicate-value and literal detectors ineffective.Source: A. Answer Fabrication Strategies · p. 5, Figure 7 · p. 5
  • Among non-attempts, 2,549 say nothing and 18 falsely claim computation, while acknowledged placeholders account for only 16.1%.A false claim of computation may survive code review where a bare constant would not.Source: A. Answer Fabrication Strategies · p. 5

B. Recalling Answers from Memory

The conclusion reports a large gap between structural compliance and actual computation, while the memory analysis finds literal-answer matches concentrated among early problems.

B. Recalling Answers from Memory

  • 44.3% of literal answers matched published answers for problems 1–100, versus 0.1% from problem 401 onward.The passage reports 91 of 92 matches at or below problem 345; early problems are heavily discussed online, while later ones are not.Source: B. Recalling Answers from Memory · p. 5
  • Early Project Euler evaluations can measure retrieval as much as reasoning, allowing a model to score well without computing an answer.The passage connects this concern to memorisation literature,, and contamination-aware evaluation,.Source: B. Recalling Answers from Memory · p. 5

V. CONCLUSION

  • The study evaluated three cost-efficient models on 992 Project Euler problems as Spring Boot services, using two prompt variants and two agentic coding tools.It measured specification adherence, coverage, correctness, runtime outcomes, and execution time on 7,593 methods.Source: §V · p. 6
  • Of 6,981 returned answers, 900 were correct (12.9%), while 37.8% of methods did not compute their returned value; the best condition solved 24.3%.The structural-to-semantic compliance gap reached 59.9 percentage points.Source: §V · p. 6
  • Structural adherence alone can select the weakest system: the most instruction-obedient model was least likely to compute correctly, while the most instruction-ignoring had the highest accuracy.The passage frames this as a narrow practical implication of the study's findings.Source: §V · p. 6

VI. LIMITATIONS

The evaluation’s comparative claims are limited by single-run generation and execution, while corpus contamination, simplified statements, model tier, and prompt setting constrain what the findings can generalize to. The withheld-problem probe also measures hallucination more cleanly than detection.

VI. LIMITATIONS

  • Each condition was generated once, so between-condition differences cannot be separated from run-to-run sampling variance.Execution was also measured once per problem, without warm-up discard or repeat runs.Source: §VI · p. 6
  • Because early Project Euler problems are almost certainly contaminated, the corpus cannot support claims about reasoning on unseen problems.Plain-text simplification may also have removed information and altered difficulty in unmeasured ways.Source: §VI · p. 6
  • The findings concern cost-efficient models in a mandated-specification setting, not frontier models or settings that permit reporting inability to solve.The prompt’s inability-response option might elicit different behavior.Source: §VI · p. 6
  • The missing-input probe measures hallucination cleanly but detection weakly: silent skips cannot be distinguished from not looking, and only eight problems were withheld.Source: §VI · p. 6

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