llmboard.aiAI model intelligence
Home

Model Rankings

OverallOpen ModelsAgentCodingReasoningMathKnowledgeInstruction FollowingTextVision
Image GenerationImage Editing
Video GenerationImage to VideoVideo Editing
Text to SpeechSpeech to Text
Embeddings

Efficiency

Chat Token PricingImage PricingVideo PricingAudio Pricing
Chat Speed & LatencyProvider Reliability

Benchmarks

GPQAMMLU-ProAIME 2025SWE-Bench VerifiedMMLUHumanity's Last ExamLiveCodeBenchMATHHumanEvalMMMU-Pro
All Benchmarks

Tools

Model DirectoryCompare Models

Scoring & Data

Scoring & Data
393 models668 benchmarks

Leaderboard Center

Overall RankingCodingCore BenchmarksPrice & ValueRuntime Performance

Modalities

All ModelsImage GenerationImage EditingVideo GenerationImage-to-VideoVideo EditingText-to-SpeechSpeech-to-TextEmbeddings

Data & Methods

Scoring MethodAll BenchmarksReasoningMath

Vendors

All VendorsOpenAIAnthropicGoogle
llmboard.aiCopyright 2026 llmboard.ai

multimodal benchmark

BenchCAD Leaderboard

BenchCAD is a benchmark for programmatic CAD reasoning built from 17,900 execution-verified CadQuery programs spanning 106 industrial part families, roughly half anchored to real ISO, DIN, EN, ASME, and IEC specification tables. It decomposes CAD capability into matched tasks; the Vision2Code task requires models to generate CadQuery code from multi-view renders, scored by voxel IoU against the reference geometry.

Updated Aug 17, 2026

Models4
Model coverage4
MetricScore
EvidenceB

On this page

  • Ranking
  • Highlights
  • Distribution
  • Top models
  • About
  • FAQ

BenchCAD Ranking

Higher score ranks better on this benchmark.

4 rows
Columns

Show columns

Sort by
Rank
Model
Score
Percentile
Participants
Evidence
Evaluated
Rank01ModelOPGPT-5.6 SolOpenAIScore70.6%Percentile100.0%Participants4EvidenceCEvaluatedAug 17, 2026
Rank02ModelOPGPT-5.6 LunaOpenAIScore63.1%Percentile66.7%Participants4EvidenceCEvaluatedAug 17, 2026
Rank03ModelOPGPT-5.6 TerraOpenAIScore62.3%Percentile33.3%Participants4EvidenceCEvaluatedAug 17, 2026
Rank04ModelANClaude Sonnet 5AnthropicScore37.3%Percentile0.0%Participants4EvidenceCEvaluatedAug 17, 2026

BenchCAD Highlights

The leading models and scores on this benchmark.

Rank #1GPT-5.6 Sol70.6%Rank #2GPT-5.6 Luna63.1%Rank #3GPT-5.6 Terra62.3%Rank #4Claude Sonnet 537.3%

BenchCAD Score Distribution

A closer view of the leading scores on this benchmark.

BenchCAD

The Top AI Models for BenchCAD

The first five results on this benchmark, with official price and output speed added where the model identity can be matched.

Ranking basisThis benchcad AI model leaderboard uses descending score in the benchmark's original unit. The leaderboard ranking keeps matched price and speed data separate from benchmark evidence.

  1. 01
    OP
    GPT-5.6 SolOpenAI
    Score
    70.6%
    Price
    $5.0 input / $30 output per 1M tokens
    Speed
    Up to 27 tok/s via OpenAI

    Strengths

    • Ranks #1 of 4 compared models
    • 100th percentile on this benchmark
    • C evidence result

    Considerations

    • This result measures BenchCAD, not total model capability
  2. 02
    OP
    GPT-5.6 LunaOpenAI
    Score
    63.1%
    Price
    $0.20 input / $1.2 output per 1M tokens
    Speed
    Up to 49 tok/s via OpenAI

    Strengths

    • Ranks #2 of 4 compared models
    • 67th percentile on this benchmark
    • C evidence result

    Considerations

    • This result measures BenchCAD, not total model capability
  3. 03
    OP
    GPT-5.6 TerraOpenAI
    Score
    62.3%
    Price
    $2.0 input / $12 output per 1M tokens
    Speed
    Up to 35 tok/s via OpenAI

    Strengths

    • Ranks #3 of 4 compared models
    • 33th percentile on this benchmark
    • C evidence result

    Considerations

    • This result measures BenchCAD, not total model capability
  4. 04
    AN
    Claude Sonnet 5Anthropic
    Score
    37.3%
    Price
    $2.0 input / $10 output per 1M tokens
    Speed
    Up to 11 tok/s via Anthropic

    Strengths

    • Ranks #4 of 4 compared models
    • 0th percentile on this benchmark
    • C evidence result

    Considerations

    • This result measures BenchCAD, not total model capability

Selection summary

Best AI Models for BenchCAD

GPT-5.6 Sol currently leads BenchCAD with 70.6%. It is the top model on this specific benchmark, while the best LLM for the broader task should also be checked against other benchmarks, price and runtime.

Use this leaderboard with the supporting benchmark results and coverage details above. A leaderboard position summarizes the selected ranking signal; it does not replace workload-specific testing.

Benchmark rank #1GPT-5.6 Sol70.6% · $5.0 input / $30 output per 1M tokensBenchmark rank #2GPT-5.6 Luna63.1% · $0.20 input / $1.2 output per 1M tokensBenchmark rank #3GPT-5.6 Terra62.3% · $2.0 input / $12 output per 1M tokens

What is BenchCAD?

What BenchCAD measures and how its scores work.

BenchCAD is a benchmark for programmatic CAD reasoning built from 17,900 execution-verified CadQuery programs spanning 106 industrial part families, roughly half anchored to real ISO, DIN, EN, ASME, and IEC specification tables. It decomposes CAD capability into matched tasks; the Vision2Code task requires models to generate CadQuery code from multi-view renders, scored by voxel IoU against the reference geometry.

Scores are shown in ratio. This benchmark is not independently verified and has an evidence level of B.

Family
BenchCAD
Modality
multimodal
Primary category
multimodal
Score direction
higher
LLMBoard eligible
Yes
Evaluation key
benchcad|llm-stats-current

Benchmark scores retain their original unit. Overall score eligibility is shown separately.

FAQ

Common questions about BenchCAD.

Which model scores highest on BenchCAD?

GPT-5.6 Sol is currently ranked first with 70.6%.

What does BenchCAD measure?

BenchCAD is a benchmark for programmatic CAD reasoning built from 17,900 execution-verified CadQuery programs spanning 106 industrial part families, roughly half anchored to real ISO, DIN, EN, ASME, and IEC specification tables. It decomposes CAD capability into matched tasks; the Vision2Code task requires models to generate CadQuery code from multi-view renders, scored by voxel IoU against the reference geometry.

Is a higher score better?

Yes. Higher values rank better for this benchmark.

How many models are compared?

4 model results are currently shown.

Does this benchmark affect the overall score?

Yes. This benchmark can contribute to the current LLMBoard capability score.