PricingDeepSeek1mo ago
DeepSeek
DeepSeek-V3.2-Speciale is a high-compute variant of DeepSeek-V3.2 optimized for maximum reasoning and agentic performance. It builds on DeepSeek Sparse Attention (DSA) for efficient long-context processing, then scales post-training reinforcement learning...
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50.2
Quality Score
1334
Arena ELO
Unknown
Parameters
164K
Context
This measures the amount of verifiable public evidence we have, not how capable the model is. A missing field means it has not been verified yet, not that its value is zero.
14 of 22 public signals
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Dec 2025
Released
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Pricing
1
Benchmarks
20
Research
2
General
2
Recent launch, pricing, benchmark, and API signals linked to this model or its provider.
Quality: 14.5/100 | Price: $0/M tokens | Output: 0 tok/s | MMLU: 0.863% | HumanEval: 0.896%
Quality: 14.5/100 | Price: $0/M tokens | Output: 0 tok/s | MMLU: 0.863% | HumanEval: 0.896%
Quality: 14.5/100 | Price: $0/M tokens | Output: 0 tok/s | MMLU: 0.863% | HumanEval: 0.896%
View sourceQuality: 14.5/100 | Price: $0/M tokens | Output: 0 tok/s | MMLU: 0.863% | HumanEval: 0.896%
View sourceQuality: 14.5/100 | Price: $0/M tokens | Output: 0 tok/s | MMLU: 0.863% | HumanEval: 0.896%
View sourceQuality: 14.5/100 | Price: $0/M tokens | Output: 0 tok/s | MMLU: 0.863% | HumanEval: 0.896%
Quality: 14.5/100 | Price: $0/M tokens | Output: 0 tok/s | MMLU: 0.863% | HumanEval: 0.896%
Quality: 14.5/100 | Price: $0/M tokens | Output: 0 tok/s | MMLU: 0.863% | HumanEval: 0.896%
Quality: 14.5/100 | Price: $0/M tokens | Output: 0 tok/s | MMLU: 0.863% | HumanEval: 0.896%
Quality: 14.5/100 | Price: $0/M tokens | Output: 0 tok/s | MMLU: 0.863% | HumanEval: 0.896%
Quality: 14.5/100 | Price: $0/M tokens | Output: 0 tok/s | MMLU: 0.863% | HumanEval: 0.896%
Quality: 14.5/100 | Price: $0/M tokens | Output: 0 tok/s | MMLU: 0.863% | HumanEval: 0.896%
Quality: 14.5/100 | Price: $0/M tokens | Output: 0 tok/s | MMLU: 0.863% | HumanEval: 0.896%
Quality: 14.5/100 | Price: $0/M tokens | Output: 0 tok/s | MMLU: 0.863% | HumanEval: 0.896%
Quality: 14.5/100 | Price: $0/M tokens | Output: 0 tok/s | MMLU: 0.863% | HumanEval: 0.896%
Quality: 14.5/100 | Price: $0/M tokens | Output: 0 tok/s | MMLU: 0.863% | HumanEval: 0.896%
Quality: 14.5/100 | Price: $0/M tokens | Output: 0 tok/s | MMLU: 0.863% | HumanEval: 0.896%
Quality: 14.5/100 | Price: $0/M tokens | Output: 0 tok/s | MMLU: 0.863% | HumanEval: 0.896%
Quality: 14.5/100 | Price: $0/M tokens | Output: 0 tok/s | MMLU: 0.863% | HumanEval: 0.896%
Quality: 14.5/100 | Price: $0/M tokens | Output: 0 tok/s | MMLU: 0.863% | HumanEval: 0.896%
Quality: 14.5/100 | Price: $0/M tokens | Output: 0 tok/s | MMLU: 0.863% | HumanEval: 0.896%
Quality: 14.5/100 | Price: $0/M tokens | Output: 0 tok/s | MMLU: 0.863% | HumanEval: 0.896%
Quality: 14.5/100 | Price: $0/M tokens | Output: 0 tok/s | MMLU: 0.863% | HumanEval: 0.896%
Quality: 14.5/100 | Price: $0/M tokens | Output: 0 tok/s | MMLU: 0.863% | HumanEval: 0.896%
We introduce Nemotron-Cascade 2, an open 30B MoE model with 3B activated parameters that delivers best-in-class reasoning and strong agentic capabilities. Despite its compact size, its mathematical and coding reasoning performance approaches that of frontier open models. It is the second open-weight LLM, after DeepSeekV3.2-Speciale-671B-A37B, to achieve Gold Medal-level performance in the 2025 International Mathematical Olympiad (IMO), the International Olympiad in Informatics (IOI), and the ICPC World Finals, demonstrating remarkably high intelligence density with 20x fewer parameters. In contrast to Nemotron-Cascade 1, the key technical advancements are as follows. After SFT on a meticulously curated dataset, we substantially expand Cascade RL to cover a much broader spectrum of reasoning and agentic domains. Furthermore, we introduce multi-domain on-policy distillation from the strongest intermediate teacher models for each domain throughout the Cascade RL process, allowing us to efficiently recover benchmark regressions and sustain strong performance gains along the way. We release the collection of model checkpoint and training data.
We introduce Nemotron-Cascade 2, an open 30B MoE model with 3B activated parameters that delivers best-in-class reasoning and strong agentic capabilities. Despite its compact size, its mathematical and coding reasoning performance approaches that of frontier open models. It is the second open-weight LLM, after DeepSeekV3.2-Speciale-671B-A37B, to achieve Gold Medal-level performance in the 2025 International Mathematical Olympiad (IMO), the International Olympiad in Informatics (IOI), and the ICPC World Finals, demonstrating remarkably high intelligence density with 20x fewer parameters. In contrast to Nemotron-Cascade 1, the key technical advancements are as follows. After SFT on a meticulously curated dataset, we substantially expand Cascade RL to cover a much broader spectrum of reasoning and agentic domains. Furthermore, we introduce multi-domain on-policy distillation from the strongest intermediate teacher models for each domain throughout the Cascade RL process, allowing us to efficiently recover benchmark regressions and sustain strong performance gains along the way. We release the collection of model checkpoint and training data.