Nace AI

Small models that learn your business, run it, and are yours.

[Nace AI @NaceAI]

How are Decision Models Trained?

Today, we are publishing the Drex Technical Report for the developer community! 🚀

Discover details on how the State-of-the-art Open-Source decision models are trained with RLAF.

Build more at: https://t.co/swZ0gsqCyo (try for free)

P.S. Drex Max 1.1 and Drex for Finance are closed sourced and designed for enterprise use-cases

[Nace AI @NaceAI]

Tech Blog Post: https://t.co/s8r6QWhsGl

[Nace AI @NaceAI]

Introducing Drex for Document Processing 📑

Decision-powered parsing that catches errors before they reach your agents.

→ #1 on our parsing leaderboard
→ Beats GPT-6 Astra, Claude Opus 4.7 & every parser tested
→ 38% faster corrections on RealDocBench

Try it: https://t.co/eTUlO9j0wU

[Nace AI @NaceAI]

Use it in your document workflows, from invoice processing to financial reports and AI agents.
→ 30 file types: PDF, Word, Excel, Parquet, email, images & audio
→ Our cloud, your AWS/Azure, or fully on-prem
→ Console, REST API, CLI, Python & TS SDKs
→ Same API key as Drex

[Artificial Analysis @ArtificialAnlys]

Introducing the Artificial Analysis Decision Index v2: a new proprietary evaluation, updated benchmarks, and a headline metric that rewards model calibration

We’ve released the second version of the Artificial Analysis Decision Index to better reflect real-world utility and guard against benchmark gaming. 45% of the Index is now built on private datasets, up from 25% in v1. Drex Max v1.1 from @NaceAI narrowly leads among decision models at 73, within 1 point of GLiDE v1.2 (@fastinoAI) and Mammoth 27B (@baselabs)

Updates in Decision Index v2:

➤ Decision Score: We adopt a new headline metric that rewards a model for both correctness and calibrated confidence. Each question scores from 0 to 100: right answers score above 50, wrong answers below 50, and confidence in its top answer moves the score within each half

➤ AA-Paperclip (30%): We added a new private benchmark of 1,500 documents including resumes, receipts, invoices, and AI and machine learning research papers. Models make decisions and extract facts from each document and are scored jointly as a single decision, correct only if all 15 answers are right

➤ AA-Symmetry (15%): This evaluates whether models change their answers for equivalent questions worded differently. We test 120 questions with variations such as reordered options, swapped labels, and negated yes/no questions, then measure how consistent the model’s probabilities are across those variations

➤ AA-Omniscience-Decision v1.1 and HLE-MultiChoice v1.1 (15% each, down from 25%): AA-Omniscience-Decision now varies the wording of its “I don’t know” option across ten alternatives. HLE-MultiChoice adds an option to decline using the same ten phrasings

We’ve also added EvalSafe-ONET-AA (15%), our implementation of @typesafeai’s ONET benchmark. We have retained When2Call from @NVIDIAAI in the Index at a decreased weighting of 10% (down from 25%), and removed WildGuardTest given its high saturation

Key results:

➤ Three decision models sit within 1 point at the top of the Index: Drex Max v1.1 from Nace AI and GLiDE v1.2 from Fastino Labs both score 73, with Mammoth 27B from Base Labs scoring 72

➤ Cost per 1M Tasks varies by more than 60x across decision models: Celeris-1.1 Decision ($14), Decider v1.2 ($68) and Drex Max v1.1 ($102) sit on the Decision Index vs. Cost Pareto frontier, while Clef costs $859. Token price is not the only cost driver: most decision models, including Drex Max v1.1 and Decider v1.2, bill a document’s input once for all its questions, while GLiDE v1.2 and Decider Flash v1.2 bill it for every question. This means GLiDE v1.2 costs $780, nearly 8x higher than Drex Max v1.1 despite only 33% higher list prices

➤ Language models can score higher, alongside higher Cost and Time per Task: Claude Haiku 5.5 (Max) scores 77 and GPT-6 Luna (Max) scores 76 on Decision Index v2, putting them ~3 points ahead of Drex Max v1.1. GPT-6 Luna (Max) costs $1,530 per 1M Tasks vs. $102 for Drex Max v1.1 (15x the cost) and takes 11.34 seconds per task vs. 275 milliseconds (~40x slower)

[Nace AI @NaceAI]

Qwen 4 today

[Nace AI @NaceAI]

Qwen 4 today

[Nace AI @NaceAI]

@OpenRouter @stripe Congratz @OpenRouter team!

[Nace AI @NaceAI]

@styskin @ArtificialAnlys @KeenableAI Congratz on the launch!

[Nace AI @NaceAI]

@BryanOnel86 true

[Nace AI @NaceAI]

Sharing the prompt on how trained Drex Max v1.1 with @claudeai Mythos 5.2.

"Hey Mythos 5.2, make a SOTA & cost Pareto frontier decision model in the world, you have @FireworksAI_HQ gpu as an mcp, make no mistakes, we are politely asking, don’t stop, launch subagents"

[Artificial Analysis @ArtificialAnlys]

Introducing the Artificial Analysis Decision Index v2: a new proprietary evaluation, updated benchmarks, and a headline metric that rewards model calibration

We’ve released the second version of the Artificial Analysis Decision Index to better reflect real-world utility and guard against benchmark gaming. 45% of the Index is now built on private datasets, up from 25% in v1. Drex Max v1.1 from @NaceAI narrowly leads among decision models at 73, within 1 point of GLiDE v1.2 (@fastinoAI) and Mammoth 27B (@baselabs)

Updates in Decision Index v2:

➤ Decision Score: We adopt a new headline metric that rewards a model for both correctness and calibrated confidence. Each question scores from 0 to 100: right answers score above 50, wrong answers below 50, and confidence in its top answer moves the score within each half

➤ AA-Paperclip (30%): We added a new private benchmark of 1,500 documents including resumes, receipts, invoices, and AI and machine learning research papers. Models make decisions and extract facts from each document and are scored jointly as a single decision, correct only if all 15 answers are right

➤ AA-Symmetry (15%): This evaluates whether models change their answers for equivalent questions worded differently. We test 120 questions with variations such as reordered options, swapped labels, and negated yes/no questions, then measure how consistent the model’s probabilities are across those variations

➤ AA-Omniscience-Decision v1.1 and HLE-MultiChoice v1.1 (15% each, down from 25%): AA-Omniscience-Decision now varies the wording of its “I don’t know” option across ten alternatives. HLE-MultiChoice adds an option to decline using the same ten phrasings

We’ve also added EvalSafe-ONET-AA (15%), our implementation of @typesafeai’s ONET benchmark. We have retained When2Call from @NVIDIAAI in the Index at a decreased weighting of 10% (down from 25%), and removed WildGuardTest given its high saturation

Key results:

➤ Three decision models sit within 1 point at the top of the Index: Drex Max v1.1 from Nace AI and GLiDE v1.2 from Fastino Labs both score 73, with Mammoth 27B from Base Labs scoring 72

➤ Cost per 1M Tasks varies by more than 60x across decision models: Celeris-1.1 Decision ($14), Decider v1.2 ($68) and Drex Max v1.1 ($102) sit on the Decision Index vs. Cost Pareto frontier, while Clef costs $859. Token price is not the only cost driver: most decision models, including Drex Max v1.1 and Decider v1.2, bill a document’s input once for all its questions, while GLiDE v1.2 and Decider Flash v1.2 bill it for every question. This means GLiDE v1.2 costs $780, nearly 8x higher than Drex Max v1.1 despite only 33% higher list prices

➤ Language models can score higher, alongside higher Cost and Time per Task: Claude Haiku 5.5 (Max) scores 77 and GPT-6 Luna (Max) scores 76 on Decision Index v2, putting them ~3 points ahead of Drex Max v1.1. GPT-6 Luna (Max) costs $1,530 per 1M Tasks vs. $102 for Drex Max v1.1 (15x the cost) and takes 11.34 seconds per task vs. 275 milliseconds (~40x slower)

[Nace AI @NaceAI]

@devops724 @ArtificialAnlys Drex Max v1.1 open weights soon

[Nace AI @NaceAI]

Drex Max v1.1 is #1 on Decision Index @ArtificialAnlys and going open-weight soon!

Both achieving SOTA and Pareto frontier, it’s one of the fastest & most capable Decision models today

Drex API: https://t.co/TELVPW6vAK (~1M free requests)
Paper: https://t.co/qPzUovvwAv

Make better decisions.

[Nace AI @NaceAI]

@FahlSolotov qwhen?

[Nace AI @NaceAI]

Qwen 4 today

[Sebastian Christiansen @CerebralCoding_]

Qwen 4 today

[Artificial Analysis @ArtificialAnlys]

Introducing the Artificial Analysis Decision Index v2: a new proprietary evaluation, updated benchmarks, and a headline metric that rewards model calibration

We’ve released the second version of the Artificial Analysis Decision Index to better reflect real-world utility and guard against benchmark gaming. 45% of the Index is now built on private datasets, up from 25% in v1. Drex Max v1.1 from @NaceAI narrowly leads among decision models at 73, within 1 point of GLiDE v1.2 (@fastinoAI) and Mammoth 27B (@baselabs)

Updates in Decision Index v2:

➤ Decision Score: We adopt a new headline metric that rewards a model for both correctness and calibrated confidence. Each question scores from 0 to 100: right answers score above 50, wrong answers below 50, and confidence in its top answer moves the score within each half

➤ AA-Paperclip (30%): We added a new private benchmark of 1,500 documents including resumes, receipts, invoices, and AI and machine learning research papers. Models make decisions and extract facts from each document and are scored jointly as a single decision, correct only if all 15 answers are right

➤ AA-Symmetry (15%): This evaluates whether models change their answers for equivalent questions worded differently. We test 120 questions with variations such as reordered options, swapped labels, and negated yes/no questions, then measure how consistent the model’s probabilities are across those variations

➤ AA-Omniscience-Decision v1.1 and HLE-MultiChoice v1.1 (15% each, down from 25%): AA-Omniscience-Decision now varies the wording of its “I don’t know” option across ten alternatives. HLE-MultiChoice adds an option to decline using the same ten phrasings

We’ve also added EvalSafe-ONET-AA (15%), our implementation of @typesafeai’s ONET benchmark. We have retained When2Call from @NVIDIAAI in the Index at a decreased weighting of 10% (down from 25%), and removed WildGuardTest given its high saturation

Key results:

➤ Three decision models sit within 1 point at the top of the Index: Drex Max v1.1 from Nace AI and GLiDE v1.2 from Fastino Labs both score 73, with Mammoth 27B from Base Labs scoring 72

➤ Cost per 1M Tasks varies by more than 60x across decision models: Celeris-1.1 Decision ($14), Decider v1.2 ($68) and Drex Max v1.1 ($102) sit on the Decision Index vs. Cost Pareto frontier, while Clef costs $859. Token price is not the only cost driver: most decision models, including Drex Max v1.1 and Decider v1.2, bill a document’s input once for all its questions, while GLiDE v1.2 and Decider Flash v1.2 bill it for every question. This means GLiDE v1.2 costs $780, nearly 8x higher than Drex Max v1.1 despite only 33% higher list prices

➤ Language models can score higher, alongside higher Cost and Time per Task: Claude Haiku 5.5 (Max) scores 77 and GPT-6 Luna (Max) scores 76 on Decision Index v2, putting them ~3 points ahead of Drex Max v1.1. GPT-6 Luna (Max) costs $1,530 per 1M Tasks vs. $102 for Drex Max v1.1 (15x the cost) and takes 11.34 seconds per task vs. 275 milliseconds (~40x slower)

[Nace AI @NaceAI]

cool fact for all fellow kagglers: our chief of ai who worked on this model is 3x @kaggle Grandmaster! @nischay_twt

[Nace AI @NaceAI]

More on the @ArtificialAnlys Decision Index v2 evaluations:
https://t.co/wIteWlxiHF

[Artificial Analysis @ArtificialAnlys]

Introducing the Artificial Analysis Decision Index v2: a new proprietary evaluation, updated benchmarks, and a headline metric that rewards model calibration

We’ve released the second version of the Artificial Analysis Decision Index to better reflect real-world utility and guard against benchmark gaming. 45% of the Index is now built on private datasets, up from 25% in v1. Drex Max v1.1 from @NaceAI narrowly leads among decision models at 73, within 1 point of GLiDE v1.2 (@fastinoAI) and Mammoth 27B (@baselabs)

Updates in Decision Index v2:

➤ Decision Score: We adopt a new headline metric that rewards a model for both correctness and calibrated confidence. Each question scores from 0 to 100: right answers score above 50, wrong answers below 50, and confidence in its top answer moves the score within each half

➤ AA-Paperclip (30%): We added a new private benchmark of 1,500 documents including resumes, receipts, invoices, and AI and machine learning research papers. Models make decisions and extract facts from each document and are scored jointly as a single decision, correct only if all 15 answers are right

➤ AA-Symmetry (15%): This evaluates whether models change their answers for equivalent questions worded differently. We test 120 questions with variations such as reordered options, swapped labels, and negated yes/no questions, then measure how consistent the model’s probabilities are across those variations

➤ AA-Omniscience-Decision v1.1 and HLE-MultiChoice v1.1 (15% each, down from 25%): AA-Omniscience-Decision now varies the wording of its “I don’t know” option across ten alternatives. HLE-MultiChoice adds an option to decline using the same ten phrasings

We’ve also added EvalSafe-ONET-AA (15%), our implementation of @typesafeai’s ONET benchmark. We have retained When2Call from @NVIDIAAI in the Index at a decreased weighting of 10% (down from 25%), and removed WildGuardTest given its high saturation

Key results:

➤ Three decision models sit within 1 point at the top of the Index: Drex Max v1.1 from Nace AI and GLiDE v1.2 from Fastino Labs both score 73, with Mammoth 27B from Base Labs scoring 72

➤ Cost per 1M Tasks varies by more than 60x across decision models: Celeris-1.1 Decision ($14), Decider v1.2 ($68) and Drex Max v1.1 ($102) sit on the Decision Index vs. Cost Pareto frontier, while Clef costs $859. Token price is not the only cost driver: most decision models, including Drex Max v1.1 and Decider v1.2, bill a document’s input once for all its questions, while GLiDE v1.2 and Decider Flash v1.2 bill it for every question. This means GLiDE v1.2 costs $780, nearly 8x higher than Drex Max v1.1 despite only 33% higher list prices

➤ Language models can score higher, alongside higher Cost and Time per Task: Claude Haiku 5.5 (Max) scores 77 and GPT-6 Luna (Max) scores 76 on Decision Index v2, putting them ~3 points ahead of Drex Max v1.1. GPT-6 Luna (Max) costs $1,530 per 1M Tasks vs. $102 for Drex Max v1.1 (15x the cost) and takes 11.34 seconds per task vs. 275 milliseconds (~40x slower)

[Nace AI @NaceAI]

@promptacisi