PricingDeepSeek1mo ago
DeepSeek
DeepSeek-V2, a strong Mixture-of-Experts (MoE) language model characterized by economical training and efficient inference. DeepSeek-V2 adopts innovative architectures including Multi-head Latent Attention (MLA) and DeepSeekMoE.
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37.1
Quality Score
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Arena ELO
16B
Parameters
16K
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.
16 of 22 public signals
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145.4K
Downloads
148
Likes
May 2024
Released
5/5 signals
3/4 signals
2/5 signals
3/4 signals
3/4 signals
Parameters
16B
Training compute
Not reported
Dataset scale
Not reported
Base model
Not reported
Gaps we are still tracking
Metadata sources
Pricing
1
Benchmarks
19
Research
2
General
2
Recent launch, pricing, benchmark, and API signals linked to this model or its provider.
language: 0.0 | coding: 1.0 | Overall: 0.5
language: 0.0 | coding: 1.0 | Overall: 0.5
language: 0.0 | coding: 1.0 | Overall: 0.5
View sourcelanguage: 0.0 | coding: 1.0 | Overall: 0.5
View sourcelanguage: 0.0 | coding: 1.0 | Overall: 0.5
View sourceWe 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.
language: 0.0 | coding: 1.0 | Overall: 0.5
language: 0.0 | coding: 1.0 | Overall: 0.5
language: 0.0 | coding: 1.0 | Overall: 0.5
language: 0.0 | coding: 1.0 | Overall: 0.5
language: 0.0 | coding: 1.0 | Overall: 0.5
language: 0.0 | coding: 1.0 | Overall: 0.5
language: 0.0 | coding: 1.0 | Overall: 0.5
language: 0.0 | coding: 1.0 | Overall: 0.5
language: 0.0 | coding: 1.0 | Overall: 0.5
language: 0.0 | coding: 1.0 | Overall: 0.5
language: 0.0 | coding: 1.0 | Overall: 0.5
language: 0.0 | coding: 1.0 | Overall: 0.5
language: 0.0 | coding: 1.0 | Overall: 0.5
language: 0.0 | coding: 1.0 | Overall: 0.5
language: 0.0 | coding: 1.0 | Overall: 0.5
language: 0.0 | coding: 1.0 | Overall: 0.5
DeepSeek published benchmark or leaderboard evidence for DeepSeek-V2-Lite-Chat.