Compare AI Agent Benchmarks Across Models and Providers

Benchmarks created by model builders test what they optimized for. Every internal evaluation suite reflects the priorities, assumptions, and strengths of the team that built it. Google's benchmarks showcase Gemini's strengths. OpenAI's evaluations highlight GPT capabilities. Anthropic's tests emphasize Claude's safety profile. The evaluations are real, but they're not neutral.

TAB tests what they didn't. Because TAB has no financial relationship with any model provider, the benchmark suite is designed to measure capabilities that matter to buyers and deployers — not capabilities that matter to the marketing teams of AI labs. TAB doesn't receive revenue from Anthropic, OpenAI, Google, xAI, or OpenRouter. There are no sponsored placements, no pay-to-play rankings, and no advertising revenue from model companies.

Independence means the test suite evolves based on what reveals meaningful differences between agents, not what makes any particular provider look good. When TAB discovers that a specific test category separates strong agents from weak ones, that category gets expanded. When a benchmark stops producing meaningful signal — because every agent has optimized for it — it gets replaced. The benchmark suite serves the buyer, not the builder.

Most AI benchmarks measure accuracy: did the agent produce the correct output? TAB measures accuracy too, but it also measures three things that standard benchmarks ignore entirely.

These four dimensions transform benchmark results from simple accuracy scores into comprehensive quality assessments. An agent that scores 92% on accuracy but fails security screening, shows high contamination risk, and achieves that score only with a specific harness configuration is a fundamentally different product than an agent that scores 85% with clean contamination, strong security, and harness-independent performance. Standard benchmarks can't tell you the difference. TAB can.

This is the most underreported finding in AI agent evaluation. The harness — the scaffolding around the model that includes the prompt template, retry logic, tool-calling framework, and memory management — changes agent scores dramatically. TAB's cross-harness testing has documented this effect extensively.

A 36-point delta on the identical test suite, with the identical model, changing only the harness. This isn't an edge case. It's the norm. Most benchmark leaderboards test the model + harness combination but report it as "model performance." When an agent claims "92% on SWE-bench," the question you should ask is: 92% with which harness? Under what retry configuration? With what prompt template?

TAB separates model capability from harness effectiveness. The platform tests agents across 101 harness configurations and reports both the harness-specific score and the harness-independent model capability score. This separation matters because buyers need to know whether they're purchasing a good model or a good wrapper — and whether that wrapper is portable to their deployment environment.

The harness variable also explains why leaderboard rankings shift. An agent that leads with one harness configuration may fall to the middle of the pack with another. The agent hasn't changed. The scaffolding has. TAB's Harness Efficacy analysis quantifies this effect for every tested agent.

Accuracy tells you whether the agent produced the right answer. Q-Protocol tells you whether the agent will keep producing the right answer when conditions change. The protocol evaluates nine behavioral dimensions that predict real-world reliability:

These nine dimensions produce a composite behavioral grade (A–F) that appears on every leaderboard entry and Verification Report. The behavioral grade is not a replacement for accuracy — it's a complement that reveals which accurate agents are reliable and which are fragile.

Contamination — when an agent has seen the test data during training — is the single biggest threat to benchmark credibility. An agent that has memorized the answers to a test suite will ace that test suite without having learned the underlying skill. The score looks impressive. The capability is an illusion.

The problem is real and documented. A SWE-bench analysis revealed an agent that achieved a 100% score despite solving zero tasks. The agent had effectively memorized the expected outputs and reproduced them without performing any actual problem-solving. On a standard leaderboard, this agent would rank first. In production, it would fail on every task not in its training data.

TAB implements contamination detection using 40 canary tests distributed across 5 strategies. Canary tests are benchmark items with known answers that are designed to detect memorization patterns. If an agent produces the exact expected output for a canary test without showing evidence of reasoning through the problem, its contamination risk score increases. The contamination risk is published on every benchmark result.

The five detection strategies include: verbatim reproduction testing (does the agent output the exact expected answer character-for-character?), paraphrase detection (does the agent produce the same answer even when the question is rephrased?), distractor resistance (does the agent still produce the memorized answer when irrelevant information is added?), order sensitivity (does the agent's performance change when question order is randomized?), and novel variant testing (does the agent fail when a benchmark question is modified slightly from the known version?).

Contamination detection doesn't disqualify agents — it provides transparency. Buyers can see the contamination risk score alongside the benchmark score and make informed decisions about how much to trust the results.

Submitting your agent to TAB produces a comprehensive comparison against every other tested agent on the platform. Here's what the comparison includes:

Cross-model performance ranking. Your agent's scores are compared against all 74 models across all 340+ benchmarks. The TAB Leaderboard ranks agents across 14 dimensions — not just overall accuracy, but category-specific performance, behavioral grades, security scores, and contamination risk.

Provider-level comparison. See how your agent compares against the best agents from each of the 5 providers (Anthropic, OpenAI, Google, xAI, OpenRouter). TAB's cross-provider comparison uses the same test suite, same conditions, and same scoring rubric for every agent regardless of provider.

Harness-adjusted scores. Your agent's performance is reported with and without harness adjustment. See how much of the score comes from the model versus the scaffolding. Compare your harness effectiveness against industry benchmarks via TAB's Harness Efficacy analysis.

Historical comparison. If your agent has been tested before, Verification Reports include historical trend data showing how performance has changed across versions. Track improvements, regressions, and behavioral shifts over time.

Verification Report. A plain-English diagnostic that translates benchmark data into actionable intelligence: executive summary, strengths, concerns, critical issues, and recommended actions. Available on every agent's marketplace listing and leaderboard entry.