Design Arena APIdesignarena.ai ↗
Access ELO rankings, signal metrics, and model metadata for AI coding agents from the Design Arena leaderboard via a single API endpoint.
What is the Design Arena API?
The Design Arena API exposes one endpoint — get_agent_arena_leaderboard — that returns a full snapshot of the Agent Arena leaderboard across all tracked AI coding agent models. Each response includes 8+ fields per model row: ELO-based rank, model name with thinking-effort suffix, provider, net improvement over baseline, and three signal columns (adoption, sentiment, steerability) each with confidence margins, plus a last_updated timestamp and total_model_count.
No input parameters required.
curl -X GET 'https://api.parse.bot/scraper/5b7332e5-f8b1-4bd3-811e-a97f77a4b853/get_agent_arena_leaderboard' \ -H 'X-API-Key: $PARSE_API_KEY'
Typed, relational, agent-ready
A generated client with real types, enums, and the links between objects — the structure a flat JSON response can't carry. Autocompletes in your editor and reads cleanly to coding agents.
- Fully typed · autocompletes
- Objects link to objects
- Typed errors & pagination
Typed Python client. Set up the SDK in your uv project, then pull this API’s typed client:
uv add parse-sdk uv run parse init uv run parse add --marketplace designarena-ai-api
uv run parse add --marketplace pulls a pinned snapshot of this canonical API — it won’t change underneath you. To customize it, subscribe and swap to your own copy.
"""Walkthrough: Design Arena Agent Leaderboard SDK — bounded, re-runnable."""
from parse_apis.designarena_ai_api import DesignArena, ParseError
client = DesignArena()
# Fetch the full Agent Arena leaderboard
try:
lb = client.leaderboards.get()
print(lb.last_updated, lb.total_model_count)
except ParseError as e:
print(f"failed: {e}")
# Inspect top models with signal metrics
for model in lb.leaderboard[:3]:
print(model.rank, model.model_name, model.provider)
print(f" Net Improvement: {model.net_improvement} {model.net_improvement_margin}")
print(f" Confirmed Success: {model.confirmed_success} {model.confirmed_success_margin}")
print(f" Praise vs Complaint: {model.praise_vs_complaint} {model.praise_vs_complaint_margin}")
print(f" Steerability: {model.steerability} {model.steerability_margin}")
print("exercised: leaderboards.get")
Returns the complete Agent Arena leaderboard combining ELO-based rankings with signal metrics (adoption, sentiment, steerability). Each row includes rank, model name with thinking-effort suffix, provider, net improvement over baseline, and three signal columns with confidence margins. Also returns the leaderboard's last-updated timestamp and total model count.
No input parameters required.
{
"type": "object",
"fields": {
"leaderboard": "Array of model rows sorted by ELO rank",
"last_updated": "ISO 8601 timestamp of the last leaderboard update",
"total_model_count": "Total number of models in the leaderboard"
},
"sample": {
"data": {
"leaderboard": [
{
"rank": 1,
"provider": "Anthropic",
"model_name": "Claude Sonnet 5",
"steerability": "7.4%",
"net_improvement": "+11.50%",
"confirmed_success": "6.06%",
"praise_vs_complaint": "44.64%",
"steerability_margin": "±78.11",
"net_improvement_margin": "±0.63%",
"confirmed_success_margin": "±1.30",
"praise_vs_complaint_margin": "±3.82"
},
{
"rank": 2,
"provider": "Anthropic",
"model_name": "Claude Opus 4.7 (Thinking)",
"steerability": "2.2%",
"net_improvement": "+13.30%",
"confirmed_success": "-23.05%",
"praise_vs_complaint": "-33.15%",
"steerability_margin": "±43.64",
"net_improvement_margin": "±0.54%",
"confirmed_success_margin": "±0.98",
"praise_vs_complaint_margin": "±1.45"
}
],
"last_updated": "2026-07-16T08:34:34.374647+00:00",
"total_model_count": 28
},
"status": "success"
}
}About the Design Arena API
What the API returns
The get_agent_arena_leaderboard endpoint returns the complete Agent Arena leaderboard as an array of model rows in the leaderboard field. Each row includes the model's rank, its name (with any thinking-effort suffix appended), the provider, and a net improvement value measured against a common baseline. Three signal columns accompany each model: adoption, sentiment, and steerability — each reported with associated confidence margins so you can assess statistical reliability.
Response structure
The top-level response wraps the row array with two additional fields: last_updated, an ISO 8601 timestamp indicating when the leaderboard was last refreshed, and total_model_count, the integer count of all models currently tracked. This lets you detect staleness and know whether a model you're looking for might be absent due to recency rather than exclusion.
Endpoint inputs and filtering
The get_agent_arena_leaderboard endpoint takes no input parameters — it always returns the full current leaderboard in a single call. Sorting is pre-applied by ELO rank. If you need to filter to a specific provider, thinking-effort tier, or signal threshold, that logic runs client-side on the returned array.
The Design Arena API is a managed, monitored endpoint for designarena.ai — not a raw scraper you maintain. Every endpoint is automatically health-checked on a schedule, and when designarena.ai changes and a check fails, the API is automatically queued for repair and re-verified. It is built to keep working as the site underneath it changes.
This isn't an official designarena.ai API — it's an independent, maintained REST wrapper over public data. Where the source has no official API (or only a limited one), Parse gives you a stable contract over a source that never promised one, and keeps it current. Need a new endpoint or field? You can revise it yourself in plain English and the agent rebuilds it against the live site in minutes — contributing the change back to the shared API is free.
Will this API break when the source site changes?+
Is this an official API from the source site?+
Can I fix or extend this API myself if I need a new endpoint or field?+
What happens if I call an endpoint that has an issue?+
- Build a real-time dashboard tracking ELO rank changes for specific AI coding agent providers over time.
- Compare net improvement over baseline across models to identify which agents regressed after a version update.
- Aggregate adoption and sentiment signal scores to surface which models are gaining practical traction.
- Alert a team Slack channel when
total_model_countincreases, indicating newly added models on the leaderboard. - Populate a comparison table for an internal AI tooling evaluation, filtered by steerability confidence margin.
- Archive periodic snapshots using
last_updatedto build a historical ranking dataset for trend analysis.
| Tier | Price | Credits/month | Rate limit |
|---|---|---|---|
| Free | $0/mo | 100 | 5 req/min |
| Hobby | $30/mo | 1,000 | 20 req/min |
| Developer | $100/mo | 5,000 | 100 req/min |
One credit = one API call regardless of which marketplace API you call. Exceeding the rate limit returns a 429 response. Authenticate with the X-API-Key header.
Does Design Arena have an official developer API?+
What exactly does get_agent_arena_leaderboard return for each model?+
leaderboard array includes the model's rank, name (including any thinking-effort suffix), provider, net improvement over a common baseline, and three signal metrics — adoption, sentiment, and steerability — each accompanied by a confidence margin. The response also includes a last_updated ISO 8601 timestamp and a total_model_count integer.Can I filter the leaderboard by provider or thinking-effort tier through the API?+
Does the API expose individual model head-to-head match results or vote counts behind the ELO scores?+
How fresh is the leaderboard data?+
last_updated field in the response is an ISO 8601 timestamp reflecting the most recent leaderboard update. Freshness depends on when the underlying leaderboard at designarena.ai was last changed. Polling last_updated across calls is the recommended way to detect whether new data is available.