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EnglishHow Does the Vals AI Industry Leaderboard Rank AI Models?
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Vals AI ranks models by a single headline number — a performance score expressed as a percentage — computed from its own benchmark suite on economically valuable tasks such as finance, software, and frontier risk areas like cybersecurity, recursive self-improvement, and mental health. The leaderboard is a comparison tool, not a certification: it tells you how models performed on Vals AI's tasks as of a given report date, and it shows only the best model from each lab by default, with a link to view full results.
What the ranking is actually measuring
Vals AI states that it runs all of its own evaluations and creates many of its benchmarks in-house. That matters for interpretation in two ways:
- The score is benchmark-specific. A model's percentage reflects performance on Vals AI's task set, not a universal capability measure. A model that leads on this leaderboard is not automatically the best choice for your particular workload.
- The suite spans several domains. The stated coverage includes finance, software, cybersecurity, recursive self-improvement, and mental health. Because the headline score aggregates across these areas, a model that is strong in one domain and weak in another can land mid-table.
The metrics shown alongside the score
The leaderboard presents more than a single ranking. Alongside performance score, it reports:
| Metric | What it tells you |
|---|---|
| Performance Score | Share of benchmark tasks the model got right, as a percentage |
| Cost | What running the evaluation cost |
| Latency | How long the model took to respond |
| Token Usage | How many tokens the model consumed |
These four dimensions are the reason the leaderboard is useful for decisions rather than just curiosity. Two models with close scores can differ substantially on cost, latency, or token consumption — and for a production use case, that gap may matter more than a point or two of accuracy.
How to read the current standings
The most recent report shown on the site is dated Oct 02, 2026, with these top scores:
| Model | Performance Score |
|---|---|
| Gemini 4 Argon | 68.90% |
| Claude Sonnet 5.5 | 67.04% |
| GPT-6 Astra | 63.13% |
| Muse Spark 1.3 Max | 58.16% |
| MiMo V2.6 Pro | 55.20% |
| Grok 4.7 | 54.95% |
| GLM 5.3 | 53.51% |
| DeepSeek V4.1 Flash | 51.32% |
| Ember-1 | 50.81% |
| Kimi K3 | 50.30% |
| Hy4 Preview | 49.94% |
| Step 5 Preview | 49.35% |
| Qwen 3.8 Max | 48.27% |
| MiniMax-M3 | 36.53% |
| Inkling | 28.67% |
| Mercury 2.5 | 8.86% |
Note the spread: the top three cluster within about six points of each other, while the bottom of the table falls off sharply. The default view shows the best model from each lab, so a lab's second-best model will not appear unless you open the full results.
How to use it to make a decision
- Start with the score band, not the exact rank. If two models are within a few points, treat them as roughly comparable on accuracy and decide on cost, latency, or token usage instead.
- Check the report date. Scores are tied to a dated evaluation. A newer model release may not yet be evaluated, and older entries may not reflect current versions.
- Match the domain to your task. If your work is in finance or software, look for the relevant benchmark report rather than relying only on the aggregate leaderboard.
- Open the full results. The default leaderboard hides all but each lab's best model, so the full view is where you find alternatives and per-benchmark detail.
Where the leaderboard is not enough
The leaderboard answers "how did these models score on Vals AI's tasks on this date." It does not answer "which model is best for my specific pipeline." For that, you need either a domain-specific report from the benchmark suite or your own evaluation on representative inputs. The leaderboard is best used as a shortlist generator: pick the top few models within a reasonable score band, then test them against your actual workload.