Benchmarking Gemini Ultra vs Claude 4 Opus vs GPT-5 on Abstract Code Synthesis and Logical Deductions

Pagrindiniai punktai

1. Define the task precisely. Vague prompts produce variable results; specify inputs, outputs, and constraints.
2. Include edge cases. Tests that only cover the happy path miss reasoning gaps.
3. Measure reasoning, not just correctness. Ask models to explain their approach and evaluate the explanation.
4. Use held-out problems. Training data contamination invalidates results; generate novel problems.
5. Run multiple trials. Single-shot results are noisy; report pass@k and variance.
6. Control for prompt style. Different models respond differently to the same prompt; test multiple framings.
7. Evaluate logical deductions separately. Code synthesis and reasoning are different skills; test both.
8. Report confidence intervals. Point estimates without variance are misleading.

Architecture

The benchmark has three components: a problem set with hidden tests, a prompt template with multiple framings, and an evaluation harness that runs generated code and scores explanations. Results are aggregated with statistical significance testing.

Production configuration

import subprocess, json, time

def run_benchmark(model, problems, trials=3):
    results = []
    for p in problems:
        for t in range(trials):
            resp = model.generate(p.prompt, temperature=0.2)
            code = extract_code(resp)
            passed = run_tests(code, p.tests)
            reasoning = score_reasoning(resp, p.rubric)
            results.append({'problem': p.id, 'trial': t, 'passed': passed, 'reasoning': reasoning})
    return results

def generate_problem(seed):
    # avoid training data contamination
    params = random_params(seed)
    spec = f"Write a function that {params['task']}"
    tests = generate_tests(params)
    rubric = define_rubric(params)
    return Problem(spec, tests, rubric)

# ensure novelty
assert not in_training_data(problems)

import scipy.stats as stats

def analyze(results):
    for model in models:
        passes = [r['passed'] for r in results if r['model'] == model]
        mean = np.mean(passes)
        ci = stats.t.interval(0.95, len(passes)-1, loc=mean, scale=stats.sem(passes))
        print(f"{model}: {mean:.2%} (95% CI: {ci[0]:.2%}-{ci[1]:.2%})")

Edge cases from production

• Models may have seen similar problems in training; use novel, synthetic problems.
• Pass rates vary with temperature; report results at multiple temperature settings.
• Code execution environments differ; standardize the runtime and dependencies.
• Reasoning scores are subjective; use multiple evaluators and report inter-rater agreement.

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