AI Model Capability Comparison
Knowledge work
Only models released within the past year are shown. Only benchmarks covering at least 3 of these models are shown. Unknown release dates are excluded; historical data is retained.
LegalBenchLegal reasoning benchmark
— means no published score for this candidate in this source, not zero.3/11 recent-year models have scores
| No. | Model | Input costInput price per million tokens | Output costOutput price per million tokens | LatencyReported latency | |
|---|---|---|---|---|---|
| 1 |  Claude Fable 5.1Claude Fable 5.1 | 88.51 | $10 | $50 | 12.4 s |
| 2 |  Gemini 4 ArgonGemini 4 Argon | 88.3 | $4 | $20 | 12.62 s |
| 3 | Claude Opus 5Claude Opus 5 | 86.97 | $5 | $25 | 4.42 s |
PandaNpc Updated Oct 21, 2026, 06:01 UTC
Source: Epoch AI, CC BY 4.0 · Vals.ai · Artificial Analysis (artificialanalysis.ai)
Column guide
- No.
- Display order only. Sorting changes this number; it is not a published capability rank.
- Model
- The evaluated model name and its provider icon. A dash in a score column means no published score is available here, not a zero.
- Input cost
- Input price per million tokens · USD / 1M tokens
- Output cost
- Output price per million tokens · USD / 1M tokens
- Latency
- Reported latency · s / min
- Vals.ai
- Costs and times are reported by Vals for this benchmark, not universal model prices or latency. CUA-bench estimated API cost covers evaluation usage and is a lower-bound estimate, not cost per task; time per action is an average, not a median. An asterisk marks launch-day prompt-cache behavior that increased cache-write costs. Missing values are unpublished, not zero.
- Vals Finance Agent v2Vals finance agent v2
- Tests financial analysis with search, financial reports and calculation tools, including cross-document data checks, company performance comparisons and valuation. The main metric is Partial Credit, averaged over three runs: answer checks receive severity-based weights, and a critical dealbreaker error makes that question score zero. Higher is better. This is distinct from the separate All-Pass metric and is not whole-question pass rate. Evaluation methodology ↗
- Harvey's Legal Agent BenchmarkHarvey legal agent benchmark
- Tests agents reading and editing files to produce multi-step legal work products. A task passes only when every grading requirement is met. The score is the average task pass rate from two judges; higher is better. It is not the percentage of individual criteria passed, and it measures legal agent workflows rather than LegalBench's legal reasoning questions. Evaluation methodology ↗
- Vals IndexVals composite index
- A composite index of professional tasks in areas such as finance, coding and law. Its percentage score combines benchmark results using the weighting method for that published version; higher means stronger overall performance, not the accuracy of a single task. Included domains and weights change across versions, so newer methodology must not be applied retrospectively to older score snapshots. Evaluation methodology ↗
- LegalBenchLegal reasoning benchmark
- Tests understanding and reasoning over legal text, including identifying legal issues, recalling and applying rules, and judging legal conclusions. The score is accuracy on legal reasoning questions; higher is better. Unlike Harvey's Legal Agent Benchmark, it does not measure an agent producing multi-step legal work products by editing files. Evaluation methodology ↗