Leaderboard alternative
Artificial Analysis Alternative: BenchLM Rankings
Artificial Analysis runs its own model benchmarking and publishes composite indices. BenchLM is the alternative built on published, citable results: every sourced score links to where it was reported, a verified lane excludes anything uncited, and daily-synced pricing sits next to every ranking. Start at /best/overall.
Artificial Analysis and BenchLM sit in the same aisle — independent sites helping you choose between LLMs — but they take opposite approaches to where the numbers come from. Artificial Analysis generates evaluation data by running models itself; BenchLM aggregates published benchmark results and makes their provenance the product. BenchLM even tracks the Artificial Analysis Intelligence Index as a display-only external reference, so you can see both signals side by side.
This page lays out, as of July 2026, what each approach is good for and what switching to (or adding) BenchLM gets you.
What Artificial Analysis does well
Artificial Analysis has earned its reputation as an independent benchmarking and analysis site. Running evaluations itself means consistent conditions across models and coverage that does not depend on what labs choose to publish — new releases get measured quickly, on the same footing as everything else. Its composite indices condense that work into memorable single numbers that the industry widely quotes as a market snapshot, and its analysis extends beyond quality into the operational dimensions of speed and cost. That combination — independence, consistency, and fast coverage of new models — is why BenchLM itself tracks the Artificial Analysis Intelligence Index as an external reference signal rather than ignoring it.
When you want an alternative
A composite index answers "roughly how good is this model" in one number, and that is exactly its limitation when you need to defend a decision. If a stakeholder asks why you picked a model, "it scores 80.2 on SWE-bench Verified, reported here" is a different class of answer from "it ranks highly on an index." You may want scores that trace to specific published results, an explicit split between verified and provisional data, visibility into how much sourced coverage backs each headline number, and raw data you can export and re-weight with your own priorities. If per-benchmark provenance and open data are what you are missing, that is the specific gap BenchLM is built to fill.
Artificial Analysis vs BenchLM
| Dimension | Artificial Analysis | BenchLM |
|---|---|---|
| Ranking method | Independent benchmarking and analysis, summarized in composite indices such as its Intelligence Index | Weighted category scores built from published per-benchmark results, so you can always drill from the headline number to the individual scores behind it |
| Verification & provenance | Runs and publishes its own evaluations | Separate verified (sourced-only, exact citations) and provisional lanes, plus 1–3 confidence dots per model showing sourced coverage |
| Pricing data | Publishes model pricing and performance analysis | Direct provider list prices per 1M tokens, synced daily, with eight provider pricing hubs and price-vs-performance rankings |
| Open data access | — | CSV/JSON exports of the full leaderboard on /data, plus llms.txt and llms-full.txt for LLM crawlers |
Competitor cells stay at the level of its publicly known approach; "—" means we do not make claims about that dimension. BenchLM's cells are documented on the methodology page.
What you get on BenchLM
The main leaderboard and /best/overall rank every tracked model with two lanes: verified, built only from scores with exact citations, and provisional, which admits uncited-but-public numbers until citations are attached. Confidence dots on every model show at a glance how much sourced coverage backs its score, and each of the benchmark pages — including the Artificial Analysis Intelligence Index page itself — documents what is measured and where each score came from.
Around that core: daily-synced pricing with provider hubs (Anthropic, OpenAI, Google, DeepSeek), speed and latency snapshots, and fully open data — CSV/JSON exports, llms.txt, and llms-full.txt — so you can rebuild or re-weight the ranking yourself.
Artificial Analysis alternative FAQ
Is BenchLM better than Artificial Analysis?
They are different instruments. Artificial Analysis runs its own consistent evaluations and condenses them into widely cited indices; BenchLM aggregates published benchmark results and makes every sourced score auditable. If you want fast, independent measurement of new releases, Artificial Analysis does that well. If you want per-benchmark provenance, a verified-only lane, and exportable data, that is BenchLM. Using both is reasonable — BenchLM even displays the AA Intelligence Index as a reference.
Does BenchLM use Artificial Analysis data?
BenchLM tracks the Artificial Analysis Intelligence Index as a display-only external reference — useful as a market snapshot, but it is not a weighted input to BenchLM's own scores, which are built from published per-benchmark results as described in the methodology.
Why do the two sites rank models differently?
Different inputs and different weights. Artificial Analysis scores come from its own evaluation runs combined into composite indices; BenchLM scores come from published results across dozens of benchmarks, with agentic, coding, and reasoning work weighted most heavily. A model with strong lab-reported benchmark results but a middling index score (or the reverse) will land in different places — comparing the two is often more informative than either alone.
Is BenchLM free and open?
Yes. The rankings, benchmark pages, and pricing hubs are free, and the underlying dataset is published for reuse via CSV/JSON exports, llms.txt, and llms-full.txt.
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