Blueprint-Bench 2
Agents turn about 20 interior photos of each of 50 apartments into 2D floor plans. Scores compare room connectivity with ground truth, normalized against random.
The leader now ties the human baseline, which was measured on only 12 apartments.
- Level
- Everyday Most adults, without training
- Scored
- Checked automatically A program checks the result against an answer key, unit tests or the expected final state.
- Answers
- Open-ended Free-form text, documents, designs or media judged as a whole.
Gemini 4 Carbon leads with 58.6%
Best score per model from the benchmark's own leaderboard, read on 2 Nov 2026. Where a model ran with several settings or harnesses, we keep its best.
- 58.6
Gemini 4 Carbon
- 54.4
Gemini 4 Argon
- 51.2 Claude Opus 5.5
- 49.7 GPT-6 Astra
- 42.1 GPT-6.1 Terra
- 41.9 Claude Fable 5.1
- 39.3 Claude Sonnet 5.5
- 38.6 Claude Fable 5
- 38.6
Gemini 3.8 Flash
- 36.9 GPT-6 Sol
- 36.2 GPT-5.5
- 33.6
Gemini 3.5 Flash
- 33.6 GPT-5.6 Sol
- 33.2 Grok 4.6
- 33.0 Claude Haiku 5.5
- 32.5 Grok 4.7
- 31.2
Gemini 3.6 Flash
- 31.2 GPT-6 Luna
- 30.8 GPT-5.6 Terra
- 30.4 Claude Opus 5
- 29.5 Kimi K3
- 27.3 Grok 4.5
- 27.1 GPT-5.4
- 26.5
Gemini 3.1 Pro
- 24.9 Claude Sonnet 5
- 24.5 Claude Opus 4.7
- 22.6 GPT-5.6 Luna
- 14.5 Claude Opus 4.8
- 6.70 Claude Sonnet 4.6
- 3.90 Kimi K2.6
- 0.0 Claude Haiku 4.5
- 0.0
Gemini 3 Flash
- 0.0 Grok 4.20
- 0.0
Gemini Robotics-ER 1.6
- 0.0 Grok 4.3
- 1
Gemini 4 Carbon 58.6%
- 2
Gemini 4 Argon 54.4%
- 3 Claude Opus 5.5 51.2%
- 4 GPT-6 Astra 49.7%
- 5 GPT-6.1 Terra 42.1%
- 6 Claude Fable 5.1 41.9%
- 7 Claude Sonnet 5.5 39.3%
- 8 Claude Fable 5 38.6%
- 9
Gemini 3.8 Flash 38.6%
- 10 GPT-6 Sol 36.9%
- 11 GPT-5.5 36.2%
- 12
Gemini 3.5 Flash 33.6%
- 13 GPT-5.6 Sol 33.6%
- 14 Grok 4.6 33.2%
- 15 Claude Haiku 5.5 33%
- 16 Grok 4.7 32.5%
- 17
Gemini 3.6 Flash 31.2%
- 18 GPT-6 Luna 31.2%
- 19 GPT-5.6 Terra 30.8%
- 20 Claude Opus 5 30.4%
- 21 Kimi K3 29.5%
- 22 Grok 4.5 27.3%
- 23 GPT-5.4 27.1%
- 24
Gemini 3.1 Pro 26.5%
- 25 Claude Sonnet 5 24.9%
- 26 Claude Opus 4.7 24.5%
- 27 GPT-5.6 Luna 22.6%
- 28 Claude Opus 4.8 14.5%
- 29 Claude Sonnet 4.6 6.70%
- 30 Kimi K2.6 3.90%
- 31 Claude Haiku 4.5 0%
- 32
Gemini 3 Flash 0%
- 33 Grok 4.20 reasoning 0%
- 34
Gemini Robotics-ER 1.6 0%
- 35 Grok 4.3 0%
| # | Model | Score | Setting | Date | Source |
|---|---|---|---|---|---|
| 1 | | 58.6% | Oct 2026 | Official leaderboard | |
| 2 | | 54.4% | Sep 2026 | Official leaderboard | |
| 3 | Claude Opus 5.5 | 51.2% | Sep 2026 | Official leaderboard | |
| 4 | GPT-6 Astra | 49.7% | Sep 2026 | Official leaderboard | |
| 5 | GPT-6.1 Terra | 42.1% | Oct 2026 | Official leaderboard | |
| 6 | Claude Fable 5.1 | 41.9% | Sep 2026 | Official leaderboard | |
| 7 | Claude Sonnet 5.5 | 39.3% | Sep 2026 | Official leaderboard | |
| 8 | Claude Fable 5 | 38.6% | Jun 2026 | Official leaderboard | |
| 9 | | 38.6% | Sep 2026 | Official leaderboard | |
| 10 | GPT-6 Sol | 36.9% | Sep 2026 | Official leaderboard | |
| 11 | GPT-5.5 | 36.2% | Apr 2026 | Official leaderboard | |
| 12 | | 33.6% | May 2026 | Official leaderboard | |
| 13 | GPT-5.6 Sol | 33.6% | Jul 2026 | Official leaderboard | |
| 14 | Grok 4.6 | 33.2% | Aug 2026 | Official leaderboard | |
| 15 | Claude Haiku 5.5 | 33% | Oct 2026 | Official leaderboard | |
| 16 | Grok 4.7 | 32.5% | Sep 2026 | Official leaderboard | |
| 17 | | 31.2% | Jul 2026 | Official leaderboard | |
| 18 | GPT-6 Luna | 31.2% | Sep 2026 | Official leaderboard | |
| 19 | GPT-5.6 Terra | 30.8% | Jul 2026 | Official leaderboard | |
| 20 | Claude Opus 5 | 30.4% | Jul 2026 | Official leaderboard | |
| 21 | Kimi K3 | 29.5% | Jul 2026 | Official leaderboard | |
| 22 | Grok 4.5 | 27.3% | Jul 2026 | Official leaderboard | |
| 23 | GPT-5.4 | 27.1% | Mar 2026 | Official leaderboard | |
| 24 | | 26.5% | Feb 2026 | Official leaderboard | |
| 25 | Claude Sonnet 5 | 24.9% | Jun 2026 | Official leaderboard | |
| 26 | Claude Opus 4.7 | 24.5% | Apr 2026 | Official leaderboard | |
| 27 | GPT-5.6 Luna | 22.6% | Jul 2026 | Official leaderboard | |
| 28 | Claude Opus 4.8 | 14.5% | May 2026 | Official leaderboard | |
| 29 | Claude Sonnet 4.6 | 6.70% | Feb 2026 | Official leaderboard | |
| 30 | Kimi K2.6 | 3.90% | Apr 2026 | Official leaderboard | |
| 31 | Claude Haiku 4.5 | 0% | Oct 2025 | Official leaderboard | |
| 32 | | 0% | Dec 2025 | Official leaderboard | |
| 33 | Grok 4.20 | 0% | reasoning | Feb 2026 | Official leaderboard |
| 34 | | 0% | Apr 2026 | Official leaderboard | |
| 35 | Grok 4.3 | 0% | Apr 2026 | Official leaderboard |
Epoch AI has scores for 35 models. Labs report scores for 3 models in their cards. We plot every source in the charts below.
7 models have held number one since its release in May 2026
Lab claims count from the day the card came out, independent runs from the model’s release. We draw one row each for the benchmark’s own leaderboard, Epoch AI’s runs and what labs claim in their cards.
Scores over time
All the scores we have, one dot each, so a model tested at several settings appears more than once.
2 labs put it in a launch post in the last six months
Launch posts from every lab that has listed it, one tick per post. Tall colored ticks include it, and short gray ones leave it out.
| Date | Launch post | Score | As the post writes it |
|---|---|---|---|
| 28 Oct 2026 | 58.6 | Blueprint-Bench 2 (Similarity score) | |
| 9 Jun 2026 | Claude Fable 5 | 38.6 | Spatial reasoning Blueprint-Bench 2 |
| 19 May 2026 | 33.6 | Blueprint-Bench 2 (Normalized score) |
2 system cards report it, from 2 labs
Besides launch posts, we read the system cards, model cards and technical reports labs publish with a release. They report far more, including safety and frontier-risk tests.
| Date | Document | Score | As the document writes it | Section |
|---|---|---|---|---|
| 9 Jun 2026 | Claude Fable 5 system card | 38.6 | Blueprint-Bench 2, normalized composite score on 50 apartments, run by Andon Labs, max effort with coding tools (Table 8.1.A, 8.16.2) | Capabilities |
| 19 May 2026 | | 33.6 | Blueprint-Bench 2, normalized score (results table, p.4) | Capabilities |
Versions
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- MMMU-Pro In use Harder MMMU: college-level multimodal questions with more options and vision-only inputs to remove text shortcuts. Kimi K3.1 Pro 84.1%
- CharXiv In use Chart understanding on real arXiv figures. Reasoning questions require synthesizing information from complex charts. Claude Fable 5 93.5%
- MATH-Vision (MathVision) In use 3,040 competition math problems with visual context. Answer accuracy.
Qwen 3.8 Max 97.7%
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Command A Vision 90.9%
- DocVQA In use Models answer 50,000 questions about 12,000 scanned document images by reading their text and layout. Scoring is ANLS, a string similarity to reference answers.
Seed1.5-VL 96.9%