DeepSeek R1 0528 Qwen3 8B
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探索 AI 模型 84991
DeepSeek-R1-Zero
DeepSeekWe introduce our first-generation reasoning models, DeepSeek-R1-Zero and DeepSeek-R1. DeepSeek-R1-Zero, a model trained via large-scale reinforcement learning (RL) without supervised fine-tuning (SFT) as a preliminary step, demonstrated remarkable performance on reasoning. With RL, DeepSeek-R1-Zero naturally emerged with numerous powerful and interesting reasoning behaviors. However, DeepSeek-R1-Zero encounters challenges such as endless repetition, poor readability, and language mixing. To address these issues and further enhance reasoning performance, we introduce DeepSeek-R1, which incorporates cold-start data before RL. DeepSeek-R1 achieves performance comparable to OpenAI-o1 across math, code, and reasoning tasks. To support the research community, we have open-sourced DeepSeek-R1-Zero, DeepSeek-R1, and six dense models distilled from DeepSeek-R1 based on Llama and Qwen. DeepSeek-R1-Distill-Qwen-32B outperforms OpenAI-o1-mini across various benchmarks, achieving new state-of-the-art results for dense models. **NOTE: Before running DeepSeek-R1 series models locally, we kindly recommend reviewing the Usage Recommendation section.** DeepSeek-R1-Zero & DeepSeek-R1 are trained based on DeepSeek-V3-Base. For more details regarding the model architecture, please refer to DeepSeek-V3 repository.
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查看详情 ↗deepseek-reasoner
DeepSeek最强 DeepSeek-R1 在数学、代码和推理任务中实现了与 OpenAI-o1 相当的性能；支持 32K 上下文
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查看详情 ↗DeepSeek-V2
DeepSeekToday, we’re introducing DeepSeek-V2, a strong Mixture-of-Experts (MoE) language model characterized by economical training and efficient inference. It comprises 236B total parameters, of which 21B are activated for each token. Compared with DeepSeek 67B, DeepSeek-V2 achieves stronger performance, and meanwhile saves 42.5% of training costs, reduces the KV cache by 93.3%, and boosts the maximum generation throughput to 5.76 times. We pretrained DeepSeek-V2 on a diverse and high-quality corpus comprising 8.1 trillion tokens. This comprehensive pretraining was followed by a process of Supervised Fine-Tuning (SFT) and Reinforcement Learning (RL) to fully unleash the model's capabilities. The evaluation results validate the effectiveness of our approach as DeepSeek-V2 achieves remarkable performance on both standard benchmarks and open-ended generation evaluation. Due to the constraints of HuggingFace, the open-source code currently experiences slower performance than our internal codebase when running on GPUs with Huggingface. To facilitate the efficient execution of our model, we offer a dedicated vllm solution that optimizes performance for running our model effectively.
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查看详情 ↗DeepSeek-V2.5
DeepSeekDeepSeek-V2.5 is an upgraded version that combines DeepSeek-V2-Chat and DeepSeek-Coder-V2-Instruct. The new model integrates the general and coding abilities of the two previous versions. For model details, please visit DeepSeek-V2 page for more information. DeepSeek-V2.5 better aligns with human preferences and has been optimized in various aspects, including writing and instruction following: **To utilize DeepSeek-V2.5 in BF16 format for inference, 80GB*8 GPUs are required.** You can directly employ Huggingface's Transformers for model inference.
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查看详情 ↗DeepSeek-V2.5-1210
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查看详情 ↗DeepSeek-V2.5
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查看详情 ↗DeepSeek-V2-Chat
DeepSeekModel Download | Evaluation Results | Model Architecture | API Platform | License | Citation Today, we’re introducing DeepSeek-V2, a strong Mixture-of-Experts (MoE) language model characterized by economical training and efficient inference. It comprises 236B total parameters, of which 21B are activated for each token. Compared with DeepSeek 67B, DeepSeek-V2 achieves stronger performance, and meanwhile saves 42.5% of training costs, reduces the KV cache by 93.3%, and boosts the maximum generation throughput to 5.76 times. We pretrained DeepSeek-V2 on a diverse and high-quality corpus comprising 8.1 trillion tokens. This comprehensive pretraining was followed by a process of Supervised Fine-Tuning (SFT) and Reinforcement Learning (RL) to fully unleash the model's capabilities. The evaluation results validate the effectiveness of our approach as DeepSeek-V2 achieves remarkable performance on both standard benchmarks and open-ended generation evaluation.
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查看详情 ↗DeepSeek-V2-Chat-0628
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查看详情 ↗DeepSeek-V2-Lite
DeepSeekModel Download | Evaluation Results | Model Architecture | API Platform | License | Citation Last week, the release and buzz around DeepSeek-V2 have ignited widespread interest in MLA (Multi-head Latent Attention)! Many in the community suggested open-sourcing a smaller MoE model for in-depth research. And now DeepSeek-V2-Lite comes out: - 16B total params, 2.4B active params, scratch training with 5.7T tokens - Outperforms 7B dense and 16B MoE on many English & Chinese benchmarks - Deployable on single 40G GPU, fine-tunable on 8x80G GPUs
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查看详情 ↗DeepSeek-V2-Lite-Chat
DeepSeekModel Download | Evaluation Results | Model Architecture | API Platform | License | Citation Last week, the release and buzz around DeepSeek-V2 have ignited widespread interest in MLA (Multi-head Latent Attention)! Many in the community suggested open-sourcing a smaller MoE model for in-depth research. And now DeepSeek-V2-Lite comes out: - 16B total params, 2.4B active params, scratch training with 5.7T tokens - Outperforms 7B dense and 16B MoE on many English & Chinese benchmarks - Deployable on single 40G GPU, fine-tunable on 8x80G GPUs
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查看详情 ↗deepseek-v3
DeepSeekDeepSeek-V3的表现与OpenAI的GPT-4o和Claude-3.5-Sonnet相当，甚至在某些任务上超越了Llama 3.1和Qwen 2.5等开源模型
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查看详情 ↗DeepSeek-V3-0324
DeepSeekDeepSeek-V3-0324 demonstrates notable improvements over its predecessor, DeepSeek-V3, in several key aspects. - Significant improvements in benchmark performance: - MMLU-Pro: 75.9 → 81.2 (+5.3) - GPQA: 59.1 → 68.4 (+9.3) - AIME: 39.6 → 59.4 (+19.8) - LiveCodeBench: 39.2 → 49.2 (+10.0) - Improved the executability of the code - More aesthetically pleasing web pages and game front-ends
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查看详情 ↗deepseek-v3.1
DeepSeekDeepSeek-V3.1 is a hybrid model that supports both thinking mode and non-thinking mode. Compared to the previous version, this upgrade brings improvements in multiple aspects: - **Hybrid thinking mode**: One model supports both thinking mode and non-thinking mode by changing the chat template. - **Smarter tool calling**: Through post-training optimization, the model's performance in tool usage and agent tasks has significantly improved.
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查看详情 ↗DeepSeek-V3.1-Base
DeepSeekDeepSeek-V3.1 is a hybrid model that supports both thinking mode and non-thinking mode. Compared to the previous version, this upgrade brings improvements in multiple aspects: DeepSeek-V3.1 is post-trained on the top of DeepSeek-V3.1-Base, which is built upon the original V3 base checkpoint through a two-phase long context extension approach, following the methodology outlined in the original DeepSeek-V3 report. We have expanded our dataset by collecting additional long documents and substantially extending both training phases. The 32K extension phase has been increased 10-fold to 630B tokens, while the 128K extension phase has been extended by 3.3x to 209B tokens. Additionally, DeepSeek-V3.1 is trained using the **UE8M0 FP8 scale data format on both model weights and activations** to ensure compatibility with microscaling data formats. Please refer to DeepGEMM for more details.
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查看详情 ↗DeepSeek V3.1 (Reasoning)
DeepSeek基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。
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查看详情 ↗DeepSeek V3.1 Terminus
DeepSeekDeepSeek-V3.1 Terminus is an update to DeepSeek V3.1 that maintains the model's original capabilities while addressing issues reported by users, including language consistency and agent capabilities, further optimizing the model's...
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$0.27 / M 输入 TokenOpenRouter · deepseek/deepseek-v3.1-terminus
查看详情 ↗基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。
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查看详情 ↗DeepSeek V3.2
DeepSeekDeepSeek-V3.2 is a large language model designed to harmonize high computational efficiency with strong reasoning and agentic tool-use performance. It introduces DeepSeek Sparse Attention (DSA), a fine-grained sparse attention mechanism...
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$0.26 / M 输入 TokenOpenRouter · deepseek/deepseek-v3.2
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查看详情 ↗DeepSeek V3.2 Exp
DeepSeekDeepSeek-V3.2-Exp is an experimental large language model released by DeepSeek as an intermediate step between V3.1 and future architectures. It introduces DeepSeek Sparse Attention (DSA), a fine-grained sparse attention mechanism...
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$0.27 / M 输入 TokenOpenRouter · deepseek/deepseek-v3.2-exp
查看详情 ↗DeepSeek-V3.2-Exp-Base
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查看详情 ↗DeepSeek V3.2 Exp (Reasoning)
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可以调整厂商、分类或输入输出条件；新模型正持续收录。
COST EXPLORER
好模型，也要算好成本。
调整月度 Token 用量，直观比较不同模型的预估 API 开支。
1 M = 100 万 Token · 美元计价
仅比较同一渠道的基础文本 Token 报价
仅比较同一渠道的基础文本 Token 报价
使用已采集且单位可比的 USD / 百万 Token 渠道报价。报价为采集时快照；不计缓存优惠、阶梯价、推理额外输出与税费，请以来源的当前报价为准。
MODEL HANDBOOK
读懂模型，再做选择
API 价格应该怎么比较？+
先区分输入、输出和缓存价格，再使用同一任务和用量估算总费用。图像、音频和视频可能按张、时长或分辨率计费，不能直接与 Token 单价比较。
开放权重，就代表可以免费商用吗？+
不一定。必须逐一核对具体模型版本的许可证、商用限制及使用政策。自己部署还需要考虑显存、推理资源和运维费用。
排行榜第一，就是最适合我的模型吗？+
榜单取决于测试任务、版本与评测方法。建议用实际业务样本比较准确性、时延、稳定性和成本，再选择模型。