Artificial Analysis

Paid

Artificial Analysis is a benchmarking platform for research work. It measures AI models and API hosts so you can pick what fits your budget and speed needs.

You get side-by-side comparisons of cost, latency, output speed, and test scores. The numbers come from live API calls, not spec sheets.

It is built for people shipping production systems. It is not a place to chat with models or test prompts.

Open it if you need hard numbers before signing an API contract. Skip it if you want to try models by typing to them.

It saves time when these are the questions you are stuck on.

• Developers choosing models for production applications: You see which model hits your latency budget. You avoid over-buying on intelligence you do not need.
• QA engineers evaluating AI for test automation workflows: You compare speed and cost across providers. You pick the host that keeps your pipeline fast.
• Product teams making cost-performance trade-offs: You view scatter plots of price versus quality. You spot the Pareto frontier where no cheaper option beats the quality.
• Technical architects planning AI infrastructure: You check context windows and throughput side by side. You build capacity plans from measured data.
• Teams comparing multiple API providers for same model: You see the same weights served by different hosts. You find speed gaps that marketing hides.
• CTOs making model procurement decisions based on measured numbers: You cross-check vendor claims against independent runs. You negotiate rates with fresh data.
• Teams wanting to cross-check marketing claims: You verify "fastest" and "cheapest" on your own workload. You catch daylight between promise and delivery.
• Anyone following frontier model releases on launch day: You get benchmarks within 24 hours of launch. You stop waiting for third-party reviews.
• Procurement teams negotiating API contracts: You arm yourself with price-per-token across inputs and outputs. You spot hidden costs in the fine print.
• Enterprise technology buyers evaluating AI systems: You see how models handle real deliverables, not just exam questions. You reduce hallucination risk in regulated deployments.

These are why I keep opening it.

• Same model, different hosts: It shows Llama-70B on Groq versus Fireworks versus Together. You find speed swings of 3–5× for the same weights.
• Live speed measurements: It pings APIs eight times daily. You trust the latency numbers are current, not cached.
• Price broken out by token type: It splits input, output, and cache costs. You model bills accurately instead of guessing averages.
• Intelligence versus cost view: You see which models are overpriced for their score. You avoid buying a racecar for grocery runs.
• Fast updates on new models: You get benchmarks within a day of release. You stop refreshing Twitter for numbers.
• No paid placements: The independence policy means no one bought their spot. You trust the comparison is not an ad.

The flow is narrow but fast.

1. Open the site and pick a model category: text, image, video, or speech.
2. Filter by what matters to you—cost, speed, latency, or intelligence score.
3. Add providers to your comparison set and view the matrix side by side.
4. Export the data or API key for your own dashboards if you paid for access.

Skip it when these limits block your work.

• English-only coverage: The Intelligence Index tests English tasks only. Your multilingual needs are not measured here.
• Dense interface: You stare at four-variable matrices early on. It takes a few visits to read fluently.
• Averaged speed numbers: Regional latency varies. Your users may see different response times than the benchmark region.
• No custom prompt testing: You cannot drop in your own inputs. You still need internal evals on real data.