Benchmark Almanac

Web Clone Bench

Claude Opus 5.5 34.2% leads since Oct 2026 Not in model cards

Agent gets a running reference app (Gmail, Jira, Figma, etc.) and 8 hours to build a clone. Expert-written workflows are replayed as end-to-end tests.

9 apps. Shipping the reference's own bundles scores zero. Environments are available through partnership.

Made by Plato Released Oct 2026 Building whole projects Tasks kept private Last model added Oct 2026 Scores refreshed 2 Nov 2026
Level
Research frontier Researchers, on open problems or tasks that take days
Scored
Checked automatically A program checks the result against an answer key, unit tests or the expected final state.
Answers
Code A program or patch run against tests.
9
Models scored on its own leaderboard

Claude Opus 5.5 leads with 34.2%

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. Every run is in the next chart.

  1. 1 Claude Opus 5.5 xhigh 34.2%
  2. 2 GPT-6 Astra max 17.5%
  3. 3 Claude Fable 5.1 high 13.3%
  4. 4 GPT-6.1 Sol max 12.6%
  5. 5 Gemini 4 Argon medium 8.60%
  6. 6 Muse Wonder high 3.70%
  7. 7 Grok 4.7 low 2.80%
  8. 8 Muse Spark 1.3 medium 1.50%
  9. 9 Gemini 3.8 Flash medium 1.00%
#ModelScoreSettingDateSource
1 Claude Opus 5.5 34.2% xhigh Sep 2026 Official leaderboard
2 GPT-6 Astra 17.5% max Sep 2026 Official leaderboard
3 Claude Fable 5.1 13.3% high Sep 2026 Official leaderboard
4 GPT-6.1 Sol 12.6% max Sep 2026 Official leaderboard
5 Gemini 4 Argon 8.60% medium Sep 2026 Official leaderboard
6 Muse Wonder 3.70% high Oct 2026 Official leaderboard
7 Grok 4.7 2.80% low Sep 2026 Official leaderboard
8 Muse Spark 1.3 1.50% medium Sep 2026 Official leaderboard
9 Gemini 3.8 Flash 1.00% medium Sep 2026 Official leaderboard

The best score, 34.2%, costs $652 a task

Runs on the cost frontier score higher than any run that's cheaper. The costs are the leaderboard's own estimates, so compare them only within this benchmark.

Google DeepMind Meta OpenAI Anthropic Cost frontier One model at each effort level
0% 10% 20% 30% 40% $1.00 $10 $100 $1,000 Cost per task, log scale Claude Opus 5.5, xhigh Claude Opus 5.5, high Claude Opus 5.5, medium GPT-6.1 Sol, max GPT-6.1 Sol, high GPT-6.1 Sol, medium GPT-6.1 Sol, low 0% 10% 20% 30% 40% $1.00 $10 $100 $1,000 Cost per task, log scale Claude Opus 5.5, xhigh Claude Opus 5.5, high
ModelEffortHarnessScoreCost per taskOutput tokens
Claude Opus 5.5 xhigh 34.2% $652
Claude Opus 5.5 max 32.3% $719
Claude Opus 5.5 high 30.5% $547
Claude Opus 5.5 medium 18.6% $404
GPT-6 Astra max 17.5% $207
Claude Fable 5.1 high 13.3% $272
Claude Fable 5.1 xhigh 13% $314
GPT-6.1 Sol max 12.6% $25
GPT-6 Astra xhigh 11.8% $117
Claude Fable 5.1 max 11.6% $228
GPT-6 Astra high 10% $75
GPT-6.1 Sol high 9.90% $14
GPT-6 Astra medium 9.60% $46
Claude Fable 5.1 low 9.20% $175
Claude Fable 5.1 medium 9.10% $207
GPT-6.1 Sol xhigh 8.60% $17
Gemini 4 Argon medium 8.60% $89
GPT-6 Astra low 7.80% $31
GPT-6.1 Sol medium 6.90% $5.80
Gemini 4 Argon high 6.30% $49
GPT-6.1 Sol low 4.60% $2.49
Muse Wonder high 3.70% $8.60
Gemini 4 Argon low 3.30% $39
Muse Wonder xhigh 2.90% $6.81
Grok 4.7 low 2.80% $18
Grok 4.7 high 2.70% $19
Claude Opus 5.5 low 2.40% $17
Grok 4.7 medium 2.10% $16
Muse Wonder max 2.00% $14
Muse Spark 1.3 medium 1.50% $5.46
Gemini 3.8 Flash medium 1.00% $2.01
Muse Spark 1.3 low 0.90% $1.89
Gemini 3.8 Flash high 0.50% $2.72
Gemini 3.8 Flash low 0.20% $1.66
Muse Spark 1.3 high 0.20% $4.20

Number one over time

Lab claims count from the day the card came out, independent runs from the model’s release.

Anthropic
Own leaderboard 1 leader

Scores over time

All the scores we have, one dot each, so a model tested at several settings appears more than once.

On the benchmark’s own leaderboard Best so far
0% 20% 40% 60% 80% 100% Near the ceiling (85%) Claude Opus 5.5 GPT-6 Astra Claude Fable 5.1 0% 20% 40% 60% 80% 100% Near the ceiling (85%) Claude Opus 5.5 GPT-6 Astra

No lab model card reports it yet

We read the headline results of 479 launch posts from 51 labs, and none of them list this benchmark.

More in Coding

All 243 in Coding
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  • SWE-bench Multilingual In use 300 GitHub-issue tasks across nine programming languages in SWE-bench format. Patch graded by repository tests. Claude Opus 5.5 93.9%
  • LiveCodeBench v6 In use Competitive programming problems collected after model cutoffs from LeetCode, AtCoder, Codeforces. Scored pass@1 on hidden tests. DeepSeek-V4-Pro 93.5%
  • DeepSWE In use 113 original long-horizon engineering tasks across 91 repos and five languages, written from scratch. Hand-written verifiers accept alternative solutions. GPT-6 Astra 74.1%
  • SciCode In use Scientist-curated research coding problems split into subproblems across natural sciences. Code checked against test cases. Seed2.1 Pro 59.8%
  • Codeforces rating In use Model solves recent Codeforces contest problems. An Elo-style rating is computed from simulated contest performance. DeepSeek V4.1 Flash 3,471