- Chart data
- Meta
- Benchmark Comparison
Benchmark Comparison
The numbers behind a chart Meta published, redrawn natively. Check the original, study the data, then make your own.
Simplified redraw
Drawn by us from the numbers below. Hover, tap or use the arrow keys.A simplified redraw: it shows the numbers in our own look, not the look of the original. One accent marks what the chart is about. Values marked with ~ in the table are estimates.
The numbers
First 10 of 78 points are free| Group | Series | Value (score) | Source | Conf. |
|---|---|---|---|---|
| GDPval-AA Knowledge work | Muse Spark 1.3 (max) Meta | 1754 | Estimate | 0.50 |
| GDPval-AA Knowledge work | Muse Spark 1.2 (xhigh) Meta | 1615 | Estimate | 0.50 |
| GDPval-AA Knowledge work | GPT 5.6 Sol (max) OpenAI | 1710 | Estimate | 0.50 |
| GDPval-AA Knowledge work | Opus 5 (max) Anthropic | 1824 | Estimate | 0.50 |
| JobBench Professional tool use | Muse Spark 1.3 (max) Meta | 64.9 | Estimate | 0.50 |
| JobBench Professional tool use | Muse Spark 1.2 (xhigh) Meta | 61.6 | Estimate | 0.50 |
| JobBench Professional tool use | GPT 5.6 Sol (max) OpenAI | 45.4 | Estimate | 0.50 |
| JobBench Professional tool use | Opus 5 (max) Anthropic | 65.7 | Estimate | 0.50 |
| OSWorld 2.0 Agentic computer use | Muse Spark 1.3 (max) Meta | 66.9 | Estimate | 0.50 |
| OSWorld 2.0 Agentic computer use | Muse Spark 1.2 (xhigh) Meta | 47.6 | Estimate | 0.50 |
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| OSWorld 2.0 Agentic computer use | GPT 5.6 Sol (max) OpenAI | 62.7 | Estimate | 0.50 |
|---|---|---|---|---|
| OSWorld 2.0 Agentic computer use | Opus 5 (max) Anthropic | 68.3 | Estimate | 0.50 |
| DeepSearchQA Agentic browsing | Muse Spark 1.3 (max) Meta | 89.4 | Estimate | 0.50 |
| DeepSearchQA Agentic browsing | Muse Spark 1.2 (xhigh) Meta | 85.9 | Estimate | 0.50 |
| DeepSearchQA Agentic browsing | GPT 5.6 Sol (max) OpenAI | 93 | Estimate | 0.50 |
| DeepSearchQA Agentic browsing | Opus 5 (max) Anthropic | 90.4 | Estimate | 0.50 |
| Agentic IF Index (Internal) Instruction following | Muse Spark 1.3 (max) Meta | 57.8 | Estimate | 0.50 |
| Agentic IF Index (Internal) Instruction following | Muse Spark 1.2 (xhigh) Meta | 46.2 | Estimate | 0.50 |
| Agentic IF Index (Internal) Instruction following | GPT 5.6 Sol (max) OpenAI | 60.5 | Estimate | 0.50 |
| Agentic IF Index (Internal) Instruction following | Opus 5 (max) Anthropic | 59.1 | Estimate | 0.50 |
| AutomationBench E2E business workflows | Muse Spark 1.3 (max) Meta | 49.4 | Estimate | 0.50 |
| AutomationBench E2E business workflows | Muse Spark 1.2 (xhigh) Meta | 38.2 | Estimate | 0.50 |
| AutomationBench E2E business workflows | GPT 5.6 Sol (max) OpenAI | 46.7 | Estimate | 0.50 |
| AutomationBench E2E business workflows | Opus 5 (max) Anthropic | 50.3 | Estimate | 0.50 |
| MRCR 256K-512K Long context | Muse Spark 1.3 (max) Meta | 98.5 | Estimate | 0.97 |
| MRCR 256K-512K Long context | Muse Spark 1.2 (xhigh) Meta | 66.3 | Estimate | 0.97 |
| MRCR 256K-512K Long context | GPT 5.6 Sol (max) OpenAI | 91.5 | Estimate | 0.97 |
| MRCR 512K-1M Long context | Muse Spark 1.3 (max) Meta | 98.1 | Estimate | 0.97 |
| MRCR 512K-1M Long context | Muse Spark 1.2 (xhigh) Meta | 55.5 | Estimate | 0.97 |
| MRCR 512K-1M Long context | GPT 5.6 Sol (max) OpenAI | 73.8 | Estimate | 0.97 |
| DeepSWE v1a Long-horizon agentic coding | Muse Spark 1.3 (max) Meta | 75.4 | Estimate | 0.50 |
| DeepSWE v1a Long-horizon agentic coding | Muse Spark 1.2 (xhigh) Meta | 55 | Estimate | 0.50 |
| DeepSWE v1a Long-horizon agentic coding | GPT 5.6 Sol (max) OpenAI | 73 | Estimate | 0.50 |
| DeepSWE v1a Long-horizon agentic coding | Opus 5 (max) Anthropic | 74 | Estimate | 0.50 |
| SWEAtlas CodeBase QnA Codebase understanding | Muse Spark 1.3 (max) Meta | 59.4 | Estimate | 0.50 |
| SWEAtlas CodeBase QnA Codebase understanding | Muse Spark 1.2 (xhigh) Meta | 46.2 | Estimate | 0.50 |
| SWEAtlas CodeBase QnA Codebase understanding | GPT 5.6 Sol (max) OpenAI | 53.5 | Estimate | 0.50 |
| SWEAtlas CodeBase QnA Codebase understanding | Opus 5 (max) Anthropic | 52.7 | Estimate | 0.50 |
| Terminal-Bench 2.1 Agentic terminal coding | Muse Spark 1.3 (max) Meta | 88.8 | Estimate | 0.97 |
| Terminal-Bench 2.1 Agentic terminal coding | Muse Spark 1.2 (xhigh) Meta | 82.9 | Estimate | 0.97 |
| Terminal-Bench 2.1 Agentic terminal coding | GPT 5.6 Sol (max) OpenAI | 88.8 | Estimate | 0.97 |
| Terminal-Bench 2.1 Agentic terminal coding | Opus 5 (max) Anthropic | 86.7 | Estimate | 0.97 |
| Agent: GDPval-AA | Muse Spark 1.3 (max) | 1754 | Estimate | 0.50 |
| Agent: JobBench | Muse Spark 1.3 (max) | 64.9 | Estimate | 0.50 |
| Agent: OSWorld 2.0 | Muse Spark 1.3 (max) | 66.9 | Estimate | 0.50 |
| Agent: DeepSearchQA | Muse Spark 1.3 (max) | 89.4 | Estimate | 0.50 |
| Agent: Agentic IF Index | Muse Spark 1.3 (max) | 57.8 | Estimate | 0.50 |
| Long Context: AutomationBench | Muse Spark 1.3 (max) | 49.4 | Estimate | 0.50 |
| Coding: DeepSWE v1.1 | Muse Spark 1.3 (max) | 75.4 | Estimate | 0.50 |
| Coding: SWEAtlas | Muse Spark 1.3 (max) | 59.4 | Estimate | 0.50 |
| Coding: CodeBase QnA | Muse Spark 1.3 (max) | 88.8 | Estimate | 0.50 |
| Agent: GDPval-AA | Muse Spark 1.2 (xhigh) | 1615 | Estimate | 0.50 |
| Agent: JobBench | Muse Spark 1.2 (xhigh) | 61.6 | Estimate | 0.50 |
| Agent: OSWorld 2.0 | Muse Spark 1.2 (xhigh) | 47.6 | Estimate | 0.50 |
| Agent: DeepSearchQA | Muse Spark 1.2 (xhigh) | 85.9 | Estimate | 0.50 |
| Agent: Agentic IF Index | Muse Spark 1.2 (xhigh) | 46.2 | Estimate | 0.50 |
| Long Context: AutomationBench | Muse Spark 1.2 (xhigh) | 38.2 | Estimate | 0.50 |
| Coding: DeepSWE v1.1 | Muse Spark 1.2 (xhigh) | 55 | Estimate | 0.50 |
| Coding: SWEAtlas | Muse Spark 1.2 (xhigh) | 46.2 | Estimate | 0.50 |
| Coding: CodeBase QnA | Muse Spark 1.2 (xhigh) | 82.9 | Estimate | 0.50 |
| Agent: GDPval-AA | GPT 5.6 Sol (max) | 1710 | Estimate | 0.50 |
| Agent: JobBench | GPT 5.6 Sol (max) | 45.4 | Estimate | 0.50 |
| Agent: OSWorld 2.0 | GPT 5.6 Sol (max) | 62.7 | Estimate | 0.50 |
| Agent: DeepSearchQA | GPT 5.6 Sol (max) | 93 | Estimate | 0.50 |
| Agent: Agentic IF Index | GPT 5.6 Sol (max) | 60.5 | Estimate | 0.50 |
| Long Context: AutomationBench | GPT 5.6 Sol (max) | 46.7 | Estimate | 0.50 |
| Coding: DeepSWE v1.1 | GPT 5.6 Sol (max) | 73 | Estimate | 0.50 |
| Coding: SWEAtlas | GPT 5.6 Sol (max) | 53.5 | Estimate | 0.50 |
| Coding: CodeBase QnA | GPT 5.6 Sol (max) | 88.8 | Estimate | 0.50 |
| Agent: GDPval-AA | Opus 5 (max) | 1824 | Estimate | 0.50 |
| Agent: JobBench | Opus 5 (max) | 65.7 | Estimate | 0.50 |
| Agent: OSWorld 2.0 | Opus 5 (max) | 68.3 | Estimate | 0.50 |
| Agent: DeepSearchQA | Opus 5 (max) | 90.4 | Estimate | 0.50 |
| Agent: Agentic IF Index | Opus 5 (max) | 59.1 | Estimate | 0.50 |
| Long Context: AutomationBench | Opus 5 (max) | 50.3 | Estimate | 0.50 |
| Coding: DeepSWE v1.1 | Opus 5 (max) | 74 | Estimate | 0.50 |
| Coding: SWEAtlas | Opus 5 (max) | 52.7 | Estimate | 0.50 |
| Coding: CodeBase QnA | Opus 5 (max) | 86.7 | Estimate | 0.50 |
Max subscribers get the same data by API. API docs
API · Max
Use this chart via API
On the Max plan you can pull this chart's numbers with the provenance of each one, as JSON or CSV, with its redraw spec, or call it from an agent over MCP. Chart id d9ad65f6a9e4.
| Path | Returns | Plan | |
|---|---|---|---|
| GET | /api/v1/charts/d9ad65f6a9e4 | Title, publisher, date, form, point count; with a Max key, the data link | Open |
| GET | /api/v1/charts/d9ad65f6a9e4/data | Every point with its provenance and confidence, plus the redraw spec. Add ?format=csv for CSV | Max |
| GET | /api/v1/charts/d9ad65f6a9e4/render?view=1 | The draw-only view this page uses | Open |
| POST | /mcp | Tool get_chart_data with this id, for Claude, Cursor and other agents | Max |
curl -s https://viral-chart.com/api/v1/charts/d9ad65f6a9e4/data \
-H "Authorization: Bearer $VIRALCHART_KEY"claude mcp add --transport http viralchart https://viral-chart.com/mcp \
--header "Authorization: Bearer $VIRALCHART_KEY"60 requests a minute per key; over that you get 429 with Retry-After. Up to 5 keys per account, made on your account page and shown once. Without Max, the data route answers 402 with a link to the plans. The numbers describe a chart its publisher owns, and every file carries a license note.
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How accurate are these numbers?
They are estimated from the image by two independent passes, with confidence 0.56 on a 0 to 1 scale. Printed numbers read well; values read off an axis can be a few percent off.
Can I reuse this data?
The numbers describe a chart that Meta published. The image, layout and styling belong to them. Use the numbers with a credit to the publisher, and make your picture from your own data.
How do I get the file?
The JSON, CSV and agent skill file are under the email gate on this page. In this prototype they are static files at /api/charts/d9ad65f6a9e4.json and /api/charts/d9ad65f6a9e4.skill.md.