DeepSeek Models
Usage
306B
8.2M
22 of 35
Which models that traffic went to
- deepseek-v4-flash58.6%179B
- deepseek-v4-pro24.0%73.3B
- deepseek-v4-flash-073115.1%46.1B
- deepseek-v3.21.7%5.3B
- DeepSeek-V30.3%877M
- deepseek-v3.2-think0.1%453M
- deepseek-ocr0.1%208M
- DeepSeek-R1<0.1%150M
- 6 more models<0.1%53M
- deepseek-v4-flash58.4%4.8M
- deepseek-v3.213.9%1.1M
- deepseek-v4-pro9.9%809K
- DeepSeek-V3-Fast9.6%789K
- deepseek-v4-flash-07313.9%318K
- DeepSeek-V33.8%311K
- deepseek-ocr0.3%21.4K
- deepseek-v3.2-think0.1%9.9K
- 14 more models0.1%11.9K
All 35 DeepSeek Models
Open in model list| Modalities | ||||||||
|---|---|---|---|---|---|---|---|---|
| DeepSeek-V3 | Takes text, returns text. | 1.64M | — | $0.27$1.09/M | — | — | 67 tok/s | 1.23 s |
| DeepSeek-R1 | Takes text, returns text. | 1.64M | — | $0.40$2.00/M | — | — | 68 tok/s | 0.99 s |
| deepseek-v4-flash-0731 | Takes text, returns text. | 1.05M | 384K | $0.14$0.28/M | $0.03/M | — | 72 tok/s | 1.88 s |
| deepseek-v4-pro | Takes text, returns text. | 1.05M | 384K | $0.46$0.93/M | $0.0039/M | — | 53 tok/s | 1.36 s |
| deepseek-v4-flash | Takes text, returns text. | 1M | 384K | $0.15$0.31/M | $0.0031/M | — | 85 tok/s | 1.15 s |
| cc-deepseek-v3 | 164K | — | $0.30$0.30/M | — | — | 58 tok/s | 1.61 s | |
| cc-deepseek-v3.1 | Takes text, returns text. | 160K | — | $0.56$1.68/M | — | — | 59 tok/s | 0.69 s |
| DeepSeek-V3.1-Terminus | Takes text, returns text. | 160K | 32K | $0.56$1.68/M | — | — | 32 tok/s | 1.29 s |
| deepseek-r1-distill-llama-70b | Takes text, returns text. | 131K | — | $0.80$1.60/M | — | — | 101 tok/s | 1.21 s |
| deepseek-v3.2 | Takes text, returns text. | 128K | 64K | $0.30$0.45/M | $0.03/M | — | 23 tok/s | 2.50 s |
| deepseek-v3.2-think | Takes text, returns text. | 128K | 64K | $0.30$0.45/M | $0.03/M | — | 37 tok/s | 2.69 s |
| DeepSeek-V3.1-Think | Takes text, returns text. | 128K | 32K | $0.56$1.68/M | — | — | 32 tok/s | 1.29 s |
| DeepSeek-V3-Fast | Takes text, returns text. | 32K | — | $0.56$2.24/M | — | — | 150 tok/s | 1.46 s |
| DeepSeek-OCR | Takes text, vision, returns text. | 8K | — | $0.02$0.02/M | — | — | 105 tok/s | 1.74 s |
| deepseek-ocr | Takes text, vision, returns text. | 8K | — | $0.02$0.02/M | — | — | 105 tok/s | 1.74 s |
| deepseek-ai/DeepSeek-R1-Distill-Llama-8B | — | — | $0.01$0.01/M | — | — | — | — | |
| deepseek-ai/DeepSeek-R1-Distill-Qwen-1.5B | — | — | $0.01$0.01/M | — | — | — | — | |
| deepseek-ai/DeepSeek-R1-Distill-Qwen-7B | — | — | $0.01$0.01/M | — | — | — | — | |
| tngtech/DeepSeek-R1T-Chimera | — | — | $0.02$0.02/M | — | — | — | — | |
| deepseek-ai/DeepSeek-Prover-V2-671B | — | — | $0.10$0.10/M | — | — | — | — | |
| deepseek-ai/DeepSeek-R1-Distill-Qwen-14B | — | — | $0.10$0.10/M | — | — | — | — | |
| deepseek-ai/DeepSeek-Coder-V2-Instruct | — | — | $0.16$0.32/M | — | — | — | — | |
| deepseek-ai/deepseek-llm-67b-chat | — | — | $0.16$0.16/M | — | — | — | — | |
| deepseek-ai/DeepSeek-V2-Chat | — | — | $0.16$0.32/M | — | — | — | — | |
| deepseek-ai/DeepSeek-V2.5 | — | — | $0.16$0.32/M | — | — | — | — | |
| deepseek-ai/deepseek-vl2 | — | — | $0.16$0.16/M | — | — | — | — | |
| deepseek-ai/DeepSeek-R1-Distill-Qwen-32B | — | — | $0.20$0.20/M | — | — | — | — | |
| DeepSeek-v3 | — | — | $0.27$1.09/M | — | — | 67 tok/s | 1.23 s | |
| deepseek-v3 | — | — | $0.27$1.09/M | — | — | 67 tok/s | 1.23 s | |
| alicloud-deepseek-v3.2 | — | — | $0.27$0.41/M | $0.05/M | $0.34/M | — | — | |
| azure-deepseek-v3.2 | — | — | $0.58$1.68/M | — | — | — | — | |
| azure-deepseek-v3.2-speciale | — | — | $0.58$1.68/M | — | — | — | — | |
| deepseek-ai/DeepSeek-R1-Distill-Llama-70B | — | — | $0.60$0.60/M | — | — | — | — | |
| deepseek-ai/Janus-Pro-7B | — | — | $2.00$2.00/M | — | — | — | — | |
| deepseek-ai/DeepSeek-R1-Zero | — | — | $2.20$2.20/M | — | — | — | — |
DeepSeek on AIHubMix
Which DeepSeek model should I start with?
deepseek-v4-flash-0731 at $0.14/M input — the cheapest entry here that declares tool calling, and it carries a 1.05M context. Move up to deepseek-ai/DeepSeek-R1-Zero when answer quality matters more than cost, or to deepseek-v4-pro for long-form reasoning.
Which of these models reason before answering?
7 of the 35 models here declare a reasoning phase — they work through the problem before producing an answer, which helps on multi-step problems at the cost of extra output tokens. Use the Reasoning filter above the table to see them. The catalog does not record anything further about how they differ, so this page does not sort them into families.
Why are there several entries for the same model?
Because each row is a route you can call, not a model release. Some IDs name an upstream (azure-, alicloud-, cc-), some are the open-weight repository form (deepseek-ai/…), and some differ only in capitalisation, kept so older integrations keep working.
The catalog does not carry a field saying which of those a given row is, so this page does not sort them into buckets it would have to invent. Every row shows that route’s own price, context and speed — compare those directly, and open a model to see the upstreams that serve it.
How is cached input billed?
The Cache read column is the rate for input tokens served from the prompt cache — for example deepseek-v4-pro bills cache hits at 0.83% of the input rate and deepseek-v4-flash bills cache hits at 2% of the input rate. Cache write is the surcharge for putting a prompt into the cache in the first place, and only a few upstreams bill it separately. A dash in either column means the catalog carries no cache rate for that model, so plan on paying the full input rate.
Do I need a separate DeepSeek account?
No. One AIHubMix key covers every model on this page, and switching between them is a change to the model string — billing, rate limits, and logs stay in one place.
Start calling DeepSeek in one line
One key, one endpoint, 844 models across 35 providers.
AIHubMix