Model catalog
One endpoint, every model. Compare context windows, capabilities and real per-token pricing side by side, then swap the model string in your request — nothing else changes.
- 472
- Models
- 48
- Providers
- 4
- Free to try
- $0.0001
- Cheapest input / 1M
13 of 472 models
LanguageDeepSeek V4.1 Flash 1017
deepseek/deepseek-v4.1-flash-1017
DeepSeek V4.1 Flash 1017 served by Fireworks for multimodal reasoning, coding, tool calling, and structured output, with automatic prefix caching.
- Context
- 1.0M
- In / 1M
- $0.075
- Out / 1M
- $0.300
- Reasoning
- Tool use
- Vision
- +3
- Language
DeepSeek V4.1 Flash 1017 Fast
deepseek/deepseek-v4.1-flash-1017-fast
Higher-throughput variant of DeepSeek V4.1 Flash 1017 served by Baseten for multimodal reasoning, tool calling, and structured output, with automatic prefix caching.
- Context
- 1.0M
- In / 1M
- $0.600
- Out / 1M
- $2.40
- Reasoning
- Tool use
- Vision
- +3
- Language
DeepSeek V4.1 Flash Fast
deepseek/deepseek-v4.1-flash-fast
Higher-throughput variant of DeepSeek V4.1 Flash for responsive coding, reasoning, and agentic workloads.
- Context
- 1.0M
- In / 1M
- $0.600
- Out / 1M
- $2.40
- Reasoning
- Tool use
- Vision
- +3
- Language
DeepSeek V4.1 Flash
deepseek/deepseek-v4.1-flash
DeepSeek V4.1 Flash is an AI model from DeepSeek designed for fast, efficient multimodal interactions. It combines native multimodal capabilities with a new architecture focused on stronger performance, faster responses, and lower computational cost.
- Context
- 1.0M
- In / 1M
- $0.075
- Out / 1M
- $0.300
- Reasoning
- Tool use
- Vision
- +3
- Language
DeepSeek V4 Flash Vision Exp
deepseek/deepseek-v4-flash-vision-exp
DeepSeek-V4-Flash-Vision-Exp is an experimental multimodal model that combines the agentic reasoning, coding, and world knowledge capabilities of DeepSeek-V4-Flash with advanced visual understanding. It delivers major gains on vision-dependent agent workflows while performing strongly across leading coding, repository, data science, automation, and multimodal benchmarks.
- Context
- 1.0M
- In / 1M
- $0.216
- Out / 1M
- $0.647
- Reasoning
- Tool use
- Vision
- +3
- Language
DeepSeek V4 Pro 0813
deepseek/deepseek-v4-pro-0813
This is the 8/13 updated weights version of DeepSeek V4 Pro.
- Context
- 1M
- In / 1M
- $0.660
- Out / 1M
- $1.98
- Reasoning
- Tool use
- Implicit caching
- +2
- Language
DeepSeek V4 Flash
deepseek/deepseek-v4-flash
- Context
- 1M
- In / 1M
- $0.130
- Out / 1M
- $0.260
- Reasoning
- Tool use
- Implicit caching
- +2
- Language
DeepSeek V4 Flash 0731
deepseek/deepseek-v4-flash-0731
- Context
- 1M
- In / 1M
- $0.076
- Out / 1M
- $0.153
- Reasoning
- Tool use
- Implicit caching
- +2
- Language
DeepSeek V4 Pro
deepseek/deepseek-v4-pro
DeepSeek-V4 series incorporate several key upgrades in architecture and optimization: (1) a hybrid attention architecture that combines Compressed Sparse Attention (CSA) and Heavily Compressed Attention (HCA) to improve long-context efficiency; (2) ManifoldConstrained Hyper-Connections (mHC) that enhance conventional residual connections; (3) and the Muon optimizer for faster convergence and greater training stability
- Context
- 1M
- In / 1M
- $0.660
- Out / 1M
- $1.98
- Reasoning
- Tool use
- Implicit caching
- +2
- Language
DeepSeek V3.1
deepseek/deepseek-v3.1
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 to ensure compatibility with microscaling data formats.
- Context
- 164K
- In / 1M
- $0.250
- Out / 1M
- $0.950
- Reasoning
- Tool use
- Implicit caching
- +2
- Language
DeepSeek-R1
deepseek/deepseek-r1
DeepSeek-R1 provides customers a state-of-the-art reasoning model, optimized for general reasoning tasks, math, science, and code generation.
- Context
- 128K
- In / 1M
- $1.35
- Out / 1M
- $5.40
- Reasoning
- Tool use
- open weights
- +1
- Language
DeepSeek V3.2
deepseek/deepseek-v3.2
DeepSeek‑V3.2 from DeepSeek harmonizes high computational efficiency with superior reasoning and agent performance. It builds on three main techniques: DeepSeek Sparse Attention for long‑context efficiency, a scalable reinforcement learning framework, and a large‑scale agentic task synthesis pipeline. This model excels at long-context reasoning and agentic tasks, efficiently handling extended inputs while maintaining strong accuracy. Its sparse attention design enables it to process complex, multi-step workflows without excessive compute costs. Overall, DeepSeek‑V3.2 targets long‑context reasoning, tool‑using agents, and efficient deployment in production environments.
- Context
- 128K
- In / 1M
- $0.620
- Out / 1M
- $1.85
- Tool use
- open weights
- Implicit caching
- Language
DeepSeek V3.2 Thinking
deepseek/deepseek-v3.2-thinking
DeepSeek‑V3.2 from DeepSeek harmonizes high computational efficiency with superior reasoning and agent performance. It builds on three main techniques: DeepSeek Sparse Attention for long‑context efficiency, a scalable reinforcement learning framework, and a large‑scale agentic task synthesis pipeline. This model excels at long-context reasoning and agentic tasks, efficiently handling extended inputs while maintaining strong accuracy. Its sparse attention design enables it to process complex, multi-step workflows without excessive compute costs. Overall, DeepSeek‑V3.2 targets long‑context reasoning, tool‑using agents, and efficient deployment in production environments.
- Context
- 128K
- In / 1M
- $0.620
- Out / 1M
- $1.85
- Tool use
- open weights
- Implicit caching