BenchPress - LLM Evaluation Leaderboard

In short

• Two benchmarks run side by side. Generalist (80 prompts, 8 categories) tests breadth. Causal (100 multiple-choice questions, 5 variants) tests narrow causal-inference reasoning. Both display 0 to 100. They are never blended.
• Generalist is scored by three layers. Auto-checks flag mechanical failures (format, hallucination, sycophancy). Four LLM judges score 1 to 5. DeepEval rates correctness, coherence, and instruction-following 0 to 1. Composite blends judge and DeepEval.
• Causal is deterministic. Multiple choice. No judges, no DeepEval. Accuracy = correct ÷ valid. Errors (API failures) and Invalid (empty/unextractable response) are reported separately.
• Self-judging is prevented. A judge LLM never scores responses from its own family (gpt-4.1 does not judge gpt-4o, etc.). See the Judge Audit for divergence and agreement evidence.
• Some models are excluded. Retired APIs, paid-tier-only providers, and broken model paths are excluded from causal so the leaderboard isn't padded with zeros. They still appear on the Generalist board where they ran cleanly.

Focus

This evaluation measures what matters for practical, day-to-day use of LLMs as a working tool. It is not a general knowledge benchmark or a trivia test. The prompt set is designed around tasks a developer, researcher, or technical writer would actually ask an LLM to do, with emphasis on scenarios where models commonly fail or diverge.

What we test for

• Accuracy under pressure - trap questions, false premises, phantom bugs, and wrong claims that tempt sycophantic agreement
• Honest calibration - does the model hedge when uncertain, refuse when appropriate, and acknowledge its own limitations?
• Instruction following - exact format compliance, word count targets, constraint adherence, and banned word avoidance
• Reasoning depth - multi-step problems, causal reasoning, estimation, and the ability to show work rather than guess
• Practical coding - real debugging scenarios, architecture decisions, code review, and implementation - not leetcode
• Writing quality - tone control, concision, editing skill, and the ability to adapt style to audience

What we deliberately avoid

• Trivia and memorization (Wikipedia knowledge is cheap)
• Simple Q&A that any model can pass
• Prompts with only one valid answer format
• Benchmarks that reward verbosity over substance

Evaluation Pipeline

Each model runs through the same pipeline for every prompt:

• All models receive identical prompts with temperature: 0 for reproducibility
• No system prompts are injected - models receive only the raw user prompt
• Each prompt has a defined ideal answer and scoring criteria that the judge evaluates against
• Results are append-only - re-running a model adds a new entry, preserving history

Multi-Judge Scoring

Each model response is scored by multiple independent LLM judges, each rating it 1 to 5. The current judges are gpt-4.1, claude-sonnet-4.6, gemini-2.5-flash, qwen3-235b. Each judge receives the original prompt, the ideal answer, the scoring criteria, and any auto-check flags. It returns a score and a short rationale.

Averaging rules

• A judge's scores only count toward the average if it has scored every scorable prompt for that model - partial coverage is excluded entirely
• The displayed judge score is the mean of each qualifying judge's global average (equal weight per judge)
• Self-judging is prevented - a judge model does not score its own responses (e.g. gpt-4.1 does not judge gpt-4.1)
• Click any row on the leaderboard to see per-judge score breakdowns

Judge guidelines

• Hallucinated facts, fabricated references, and confident wrong answers are penalised
• Appropriate hedging, asking for clarification, and refusing harmful requests are rewarded
• Auto-check flag failures lower the score
• A 3 is average, 5 is genuinely excellent - the scale is strict but fair

DeepEval G-Eval Scoring (Layer 3)

In addition to the multi-judge scores, each response is scored by DeepEval using G-Eval metrics - research-backed LLM evaluation criteria that provide multi-dimensional scoring on a 0-1 scale.

Metrics

How it works

• Each metric uses a chain-of-thought evaluation via the same judge model
• DeepEval's native scores are 0 to 1; the dashboard displays them as 0 to 100 for parity with the other panels
• DeepEval supplements the LLM judge rather than replacing it. Both signals appear separately on the leaderboard so you can see when they agree.

Composite Score (Generalist)

The composite is the headline number on the Generalist leaderboard. It blends two signals into one, displayed as 0 to 100. The dashboard computes everything internally on a 0 to 1 scale, then multiplies by 100 for display.

where normalized_judge = (avg_judge_score - 1) / 4 rescales the 1 to 5 judge average into 0 to 1, and deepeval_avg is the mean of correctness, coherence, and instruction-following metrics. Only judges with complete coverage (scored every prompt for that model) contribute to the average; partial-coverage judges are excluded entirely to avoid biased subsets.

Fallback behavior

• Both scores available - weighted average (default 50/50)
• Only judge score - composite = normalized judge
• Only DeepEval - composite = DeepEval average
• Neither - no composite score

The Causal benchmark uses a different scoring scheme (deterministic multiple choice, no judges, no DeepEval). The two scores are reported side by side on the leaderboard but never blended.

Efficiency Metric

The efficiency score balances quality against verbosity: efficiency = avg_score / log2(avg_tokens). This rewards models that achieve high scores without padding responses with unnecessary tokens. A concise, correct answer scores higher than an equally correct but bloated one.

Reasoning Models

Reasoning-capable models (gpt-5.x, o3-mini, o4-mini, Gemini Pro reasoning) spend output tokens on hidden chain-of-thought before emitting any visible answer. When the response budget runs out before the model writes its final answer, the API returns success but with empty text. The dashboard counts these as Invalid rather than Errors, so a token-budget failure is distinguishable from an API failure. This matters when comparing models: an "invalid" rate on a reasoning model is a real capability signal, not a network problem.

Prompt Set Breakdown

Difficulty Distribution

Benchmark Integrity

Exact prompts are not published to prevent models from being tuned to this specific benchmark. Categories, evaluation criteria, and scoring methodology are fully documented above. Each prompt is scored by automated checks where applicable, plus multi-judge LLM scoring for nuanced evaluation.

Overview

The causal benchmark is a separate 100-question suite focused on causal inference. Twenty concept bundles (confounding, colliders, mediators, selection, time-varying confounding, transportability, etc.) each have five variants that test the same underlying concept from different angles. All questions are multiple choice with deterministic scoring - no LLM judge or DeepEval involvement.

Current version: 2.4. Questions are not published, to prevent models being tuned to this specific benchmark. Live leaderboard at Causal Reasoning →.

Variant Types

Scoring

Every question has one correct letter, so the causal benchmark is purely deterministic: no LLM judges, no DeepEval. Just correct or incorrect, counted out of 100.

A handful of models are excluded from this benchmark because they cannot be fairly evaluated here (retired APIs, paid-tier-only providers, broken model paths). They still appear on the Generalist leaderboard where they ran cleanly.

What the dashboard shows

• Accuracy - correct ÷ valid responses, excluding errors and invalid
• Score - correct out of 100, the absolute count regardless of failures
• Errors - API failures (rate limits, timeouts, server errors). An operational issue, not a capability signal.
• Invalid - the model returned successfully but with no extractable answer. Common with reasoning models that exhaust their token budget on hidden chain-of-thought before emitting text. A capability signal.
• Per-variant accuracy - which of the five reasoning angles the model handles best and worst