Subject · tested 27 Oct 2026

Best AI models for engineering

Scores on the 15 engineering questions in the suite (14 from SuperGPQA, 1 from MMLU-Pro). That is a small sample: the bars show each score's 95% interval, and overlapping bars mean the models can't be told apart.

Questions15of 80 in the suite
Top score13/15Claude Fable 5.1, Gemini 4 Argon, Claude Sonnet 5.5
Tied with the leader42overlapping 95% intervals
Median score40.0%of 72 models

Engineering: score by model

15 questions · the bar shows each score's 95% interval

Engineering leaderboard
#ModelCorrectScore95% interval
1Claude Fable 5.1Anthropic13 / 1586.7%62–96%
1Gemini 4 ArgonGoogle · statistically tied with #113 / 1586.7%62–96%
1Claude Sonnet 5.5Anthropic · statistically tied with #113 / 1586.7%62–96%
4Claude Opus 5.5Anthropic · statistically tied with #112 / 1580.0%55–93%
4Gemini 3.8 FlashGoogle · statistically tied with #112 / 1580.0%55–93%
6Muse WonderMeta · statistically tied with #111 / 1573.3%48–89%
6Grok 4.7SpaceXAI · statistically tied with #111 / 1573.3%48–89%
6GPT-5.6 TerraOpenAI · statistically tied with #111 / 1573.3%48–89%
6GPT-5.3-CodexOpenAI · statistically tied with #111 / 1573.3%48–89%
6Muse Spark OpenMeta · statistically tied with #111 / 1573.3%48–89%
6Kimi K3.1 ProMoonshotAI · statistically tied with #111 / 1573.3%48–89%
12GPT-6 AstraOpenAI · statistically tied with #110 / 1566.7%42–85%
12GPT-6.1 Terra ProOpenAI · statistically tied with #110 / 1566.7%42–85%
12DeepSeek V4 Flash Vision ExpDeepSeek · statistically tied with #110 / 1566.7%42–85%
12Gemini 3.5 Flash LiteGoogle · statistically tied with #110 / 1566.7%42–85%
12Gemini 3.1 Pro PreviewGoogle · statistically tied with #110 / 1566.7%42–85%
12GLM 5.3Z.ai · statistically tied with #110 / 1566.7%42–85%
12GPT-6.1 SolOpenAI · statistically tied with #110 / 1566.7%42–85%
12GPT-5.4OpenAI · statistically tied with #110 / 1566.7%42–85%
12Claude Haiku 5.5Anthropic · statistically tied with #110 / 1566.7%42–85%
21GPT-6.1 TerraOpenAI · statistically tied with #19 / 1560.0%36–80%
21DeepSeek V4.1 Flash 1017DeepSeek · statistically tied with #19 / 1560.0%36–80%
21GPT-6 LunaOpenAI · statistically tied with #19 / 1560.0%36–80%
21Qwen3.8 Max (0902)Qwen · statistically tied with #19 / 1560.0%36–80%
25o3OpenAI · statistically tied with #18 / 1553.3%30–75%
25GLM 5.3 FlashZ.ai · statistically tied with #18 / 1553.3%30–75%
25DeepSeek V4 Pro 0813DeepSeek · statistically tied with #18 / 1553.3%30–75%
25GPT-6 SolOpenAI · statistically tied with #18 / 1553.3%30–75%
25MiMo-V2.6-ProXiaomi · statistically tied with #18 / 1553.3%30–75%
30Kimi K2.7 CodeMoonshotAI · statistically tied with #17 / 1546.7%25–70%
30MiMo-V2.6-FlashXiaomi · statistically tied with #17 / 1546.7%25–70%
32Llama 4 MaverickMeta · statistically tied with #16 / 1442.9%21–67%
33InklingThinking Machines · statistically tied with #16 / 1540.0%20–64%
33Step 5 PreviewStepFun · statistically tied with #16 / 1540.0%20–64%
33Mercury 2.5Inception · statistically tied with #16 / 1540.0%20–64%
33Phi 4Microsoft · statistically tied with #16 / 1540.0%20–64%
33Hy3Tencent · statistically tied with #16 / 1540.0%20–64%
33Qwen3 Coder NextQwen · statistically tied with #16 / 1540.0%20–64%
33Inkling SmallThinking Machines · statistically tied with #16 / 1540.0%20–64%
33Qwen3.8 27BQwen · statistically tied with #16 / 1540.0%20–64%
33Nemotron 3 UltraNVIDIA · statistically tied with #16 / 1540.0%20–64%
33MiniMax M3.1 FlashMiniMax · statistically tied with #16 / 1540.0%20–64%
33gpt-oss-120bOpenAI · statistically tied with #16 / 1540.0%20–64%
44Qwen3.8 2.4T A95BQwen5 / 1533.3%15–58%
44MiniMax M3MiniMax5 / 1533.3%15–58%
46Qwen3.7 PlusQwen4 / 1526.7%11–52%
46Nemotron 3.5 Super VLNVIDIA4 / 1526.7%11–52%
46gpt-oss-20bOpenAI4 / 1526.7%11–52%
46Ministral 3 8B 2512Mistral4 / 1526.7%11–52%
46Nemotron 3.5 LightningNVIDIA4 / 1526.7%11–52%
46Granite 4.2 8BIBM4 / 1526.7%11–52%
52Command ACohere3 / 1520.0%7–45%
52Mistral Large 4Mistral3 / 1520.0%7–45%
54Llama 4 ScoutMeta2 / 1513.3%4–38%
54Ministral 3 3B 2512Mistral2 / 1513.3%4–38%
54Solar Mini 4Upstage2 / 1513.3%4–38%
54Ministral 3 14B 2512Mistral2 / 1513.3%4–38%
54Llama 3.3 70B InstructMeta2 / 1513.3%4–38%
54Qwen3.6 35B A3BQwen2 / 1513.3%4–38%
60Ling 3.1 FlashinclusionAI1 / 156.7%1–30%
60Granite 4.0 MicroIBM1 / 156.7%1–30%
60Nova Micro 1.0Amazon1 / 156.7%1–30%
60Solar Pro 4Upstage1 / 156.7%1–30%
60LongCat 2.0Meituan1 / 156.7%1–30%
65Mistral Medium 3.5Mistral0 / 150.0%0–20%
65Reka Flash 3rekaai0 / 150.0%0–20%
65Gemma 4 26B A4BGoogle0 / 150.0%0–20%
65Gemma 4 31BGoogle0 / 150.0%0–20%
65Qwen3.5 397B A17BQwen0 / 150.0%0–20%
65Hermes 4 405BNous0 / 150.0%0–20%
65Mistral Small 4Mistral0 / 150.0%0–20%
65Trinity Large ThinkingArcee AI0 / 150.0%0–20%

How hard are these questions?

Questions by the share of 72 scored models that got them right

  1. 1No model right1 question
  2. 2Under 25% of models3 questions
  3. 325–50% of models5 questions
  4. 450–75% of models5 questions
  5. 575% or more, not all1 question
  6. 6Every model right0 questions

Science →Medicine →Economics →Law →Humanities & social science →

Engineering: frequently asked questions

Which AI model is best at engineering?
Claude Fable 5.1 and Gemini 4 Argon and Claude Sonnet 5.5 answered the most engineering questions correctly: 13 of 15. With only 15 questions, 42 other models are statistically tied with the leader, so treat this ranking as a rough guide.
Where do the engineering questions come from?
14 from SuperGPQA (ODC-BY 1.0) and 1 from MMLU-Pro (MIT). Every question is multiple choice with ten options and one correct answer.
How hard are the engineering questions?
1 of the 15 questions were answered correctly by none of the 72 scored models, and 0 by all of them.