The following comprehensive deep dive compares Nous Research Nomos 1 and DeepSeek Math V2, two state-of-the-art open-source mathematical reasoning models released in late 2025.
The choice between these two models comes down to a trade-off between absolute performance and efficiency:
• DeepSeek Math V2 is the heavyweight champion. With 685 billion parameters, it achieved a near-perfect 118/120 on the 2024 Putnam Competition, outperforming human experts and other AI models. It is designed for "self-verifiable reasoning" and requires massive compute resources.
• Nomos 1 is the efficiency marvel. It is a 30 billion parameter model (with only ~3B active) that scored 87/120 on the same Putnam exam—a score that would still rank 2nd globally among human participants. It is designed to run on consumer-grade hardware while delivering elite-level reasoning.
Head-to-Head Comparison
| Feature | DeepSeek Math V2 | Nous Research Nomos 1 |
|---|---|---|
| Developer | DeepSeek AI | Nous Research (with Hillclimb AI) |
| Release Date | November 27, 2025 | December 8, 2025 |
| Putnam 2024 Score | 118 / 120 (Near Perfect) | 87 / 120 (Rank #2 Human Equiv.) |
| Total Parameters | 685 Billion | 30 Billion |
| Active Parameters | ~37B (Estimated via MoE) | ~3 Billion |
| Base Architecture | Mixture-of-Experts (DeepSeek-V3.2 Base) | Mixture-of-Experts (Qwen-3 Base) |
| Core Innovation | Verifier-First Pipeline: Trains a verifier to check proof rigor before generating answers. | Two-Phase Reasoning: Workers solve problems, followed by a pairwise tournament to select the best answer. |
| Primary Goal | Solving the "Correct Answer ≠ Correct Reasoning" gap; Absolute SOTA. | Democratizing SOTA reasoning; running elite math models on local devices. |
| Hardware Reqs | Enterprise/Data Center (High-end GPUs) | Consumer/Prosumer (e.g., Single High-VRAM GPU) |
| License | Open Weights (Apache 2.0) | Open Weights (Apache 2.0) |
Deep Dive: DeepSeek Math V2
"The Absolute Performance King"
DeepSeek Math V2 represents the cutting edge of what is possible with massive scale and specialized training. Its performance on the Putnam exam (118/120) is historic, effectively "solving" a competition famously known for its brutal difficulty where the median human score is often close to zero.
1. Architecture: The Verifier-First Approach
Unlike traditional models trained to predict the next token, DeepSeek Math V2 was built to solve a specific flaw in AI math: models often get the right answer with the wrong working.
• Verifier-Generator Loop: DeepSeek first trained a "Verifier" model capable of reading a proof and scoring its logical rigor (not just the final answer).
• Meta-Verifier: To scale this, they built a "Meta-Verifier" to check the verifiers, creating an automated labeling pipeline. This allowed them to generate massive amounts of high-quality synthetic training data without human labeling.
• Outcome: The model doesn't just guess; it constructs proofs it "knows" are rigorous because it has been optimized against a harsh internal critic.
2. Performance Highlights
• Putnam 2024: 118/120 points.
• IMO 2025 (International Math Olympiad): Gold Medal level performance (Solved 5/6 problems).
• ProofBench: Outperforms Google's Gemini DeepThink on basic proof tasks and rivals it on advanced ones.
Deep Dive: Nous Research Nomos 1
"The Efficiency & Reasoning Specialist"
Nomos 1 is arguably the more impressive technical feat relative to its size. Achieving a score of 87/120 on the Putnam with a model 20x smaller than its competitors proves that specialized reasoning architectures can compete with massive scale.
1. Architecture: Structured Reasoning System
Nomos 1 does not rely on raw parameter count. Instead, it uses a structured inference process that mimics how a human team might solve a test:
• Phase 1: Solving & Self-Assessment: The model spawns multiple "workers" that attempt to solve the problem. Crucially, each worker assigns a confidence score (1-7) to its own solution.
• Phase 2: Tournament Selection: Instead of simple majority voting, the system runs a "pairwise elimination tournament" where solutions compete against each other. The system evaluates which solution is more rigorous and selects the winner as the final answer.
• Base Model: It is fine-tuned from Qwen-3 (30B), utilizing a Mixture-of-Experts (MoE) design that keeps active parameters low (~3B), allowing it to run efficiently.
2. Performance Context
• Putnam 2024: 87/120 points.
• Comparison: A base Qwen-3 model without this reasoning system scored only 24/120, highlighting that the "Nomos reasoning framework" is responsible for the massive performance jump.
• Efficiency: It can run on high-end consumer hardware (e.g., a MacBook Pro with significant unified memory or a consumer desktop with dual 3090s/4090s), whereas DeepSeek Math V2 requires a server cluster.
Critical Analysis: Which One Should You Use?
Choose DeepSeek Math V2 if:
• You need the absolute best accuracy available. If you are solving novel mathematical proofs, conducting research, or need a "ground truth" oracle, this is the superior model.
• You have enterprise-grade compute. Running a 685B parameter model is not feasible for individuals without access to H100/A100 clusters or massive API budgets.
• You care about "Show Your Work." The verifier-based training ensures that the step-by-step logic is as high-quality as the final answer.
Choose Nomos 1 if:
• You are a developer, researcher, or hobbyist with local hardware. You can download and run this model yourself.
• You want to integrate reasoning into an application. Nomos 1 offers a more practical balance of speed and intelligence for real-world applications where 100% accuracy on Olympiad-level math isn't strictly necessary, but high-level logic is.
• You are studying efficient AI. Nomos 1 proves that algorithmic improvements (inference-time search and selection) can yield results comparable to massive scaling.
Final Verdict
DeepSeek Math V2 is currently the smartest open-weight mathematician in the world, effectively solving the benchmarks used to test human geniuses. Nomos 1, however, is the most accessible genius, bringing near-SOTA reasoning capabilities into a form factor that the open-source community can actually use and build upon.
