AI Agent Benchmark Tracker
Compare leading AI agents across key performance metrics. Select a category to see head-to-head rankings on speed, cost, accuracy, and context handling.
Best for Code Generation: Claude Sonnet 4
Fast and accurate with excellent context handling.
| Agent | Speed(tasks/hr) | Cost per Task($) | Accuracy(%) | Context Handling(/10) |
|---|---|---|---|---|
| Claude Sonnet 4 | 14.2 | $0.08 | 93.1% | 9.2 |
| Claude Opus 4 | 9.8 | $0.22 | 96.4% | 9.7 |
| GPT-4o | 12.5 | $0.11 | 91.8% | 8.5 |
| Gemini 2.5 Pro | 11.3 | $0.13 | 90.2% | 9.0 |
| DeepSeek V3 | 15.1 | $0.04 | 88.5% | 7.8 |
| Codex | 16.8 | $0.06 | 89.7% | 7.2 |
Category Leaderboards
Each agent is evaluated on a standardized set of tasks within each category. Benchmarks are run under consistent conditions with identical prompts, tool access, and timeout limits.
- Speed measures the number of tasks an agent completes per hour under standard workload, including prompt latency and tool-use overhead.
- Cost per Task captures the average API spend per completed task, including all input and output tokens plus any tool-call overhead.
- Accuracy is scored by a panel of domain experts and automated test suites, measuring correctness, completeness, and adherence to instructions.
- Context Handling rates the agent's ability to work with large, multi-file inputs, maintain coherence across long conversations, and correctly reference earlier context.
Scores are refreshed monthly. All data shown is illustrative and intended to demonstrate relative performance characteristics. Actual results may vary based on prompt design, task complexity, and API configuration.
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About This Tool
You're trying to compare GPT-4o against Claude on a coding benchmark and the numbers float around in three browser tabs. Drop them into one place, see the deltas side by side, and stop second-guessing which model actually wins on HumanEval versus MMLU.
This tracker holds the major public benchmarks — reasoning, math, code, multilingual — and lets you sort by score, by date, or by model family. Useful when you're picking a model for production work and need numbers that aren't filtered through someone's launch tweet.
New benchmark results land here as they're announced. If a result looks suspiciously high, the source link is one click away.
What's actually under the hood here is a curated table that pulls together publicly reported scores from model cards, lab papers, and a handful of independently verified runs. Each row carries a model name, a benchmark, the score, the reporting source, and a flag noting whether the number is self-reported or a third-party reproduction. Scores from labs running their own benchmarks tend to drift upward over time as evaluation methodology gets refined; that drift is part of what you're tracking when you sort by date.
If you click into a benchmark family — say MMLU — you'll find the breakdown by subject area where it exists. A model that scores 82% overall on MMLU might be 95% on history and 65% on college-level math. That breakdown is where the headline number gets useful. A frontier coding model can blow past on HumanEval and still trip on SWE-bench, which involves real GitHub issues rather than self-contained function-completion problems.
A worked example: you're picking between three models for a customer-support agent that needs both reasoning and instruction-following. You filter the table to MMLU, GPQA, and IFEval. Model A has 87/52/79. Model B has 84/58/76. Model C has 86/55/82. None dominate all three. If your workload skews toward reasoning-heavy escalation, Model B's GPQA edge probably matters more than its slightly weaker MMLU; if you mostly need clean instruction-following, Model C's IFEval lead becomes the deciding factor. The table makes those tradeoffs visible instead of buried in three separate launch posts.
The honest limitation: benchmark contamination is a real and growing problem. When the test set leaks into training data, scores rise without genuine capability gains. You won't always know it happened. A model launching with surprisingly strong scores on a benchmark older than its training cutoff deserves a skeptical eyebrow. The corrective is your own held-out eval set on tasks the model has never seen.
The about text and FAQ on this page were drafted with AI assistance and reviewed by a member of the Coherence Daddy team before publishing. See our Content Policy for editorial standards.