Official model specifications

Read straight from each model's config.json — not second-hand. These numbers decide whether it runs, how much VRAM it needs, and how much context you get.

Worth noting. The V4 family's max_position_embeddings is 1,048,576 — that is where "one million token context" comes from, a config value rather than marketing. V4-Flash has 43 layers versus V4-Pro's 61, which is why it is cheaper.

Model	Layers	Hidden	Context	Vocab	Structure
DeepSeek-V4-Flash	43	4,096	1,048,576 (1M)	129,280	MoE
DeepSeek-V4-Flash-0731	43	4,096	1,048,576 (1M)	129,280	MoE
DeepSeek-V4-Flash-Base	43	4,096	1,048,576 (1M)	129,280	MoE
DeepSeek-V4-Flash-DSpark	43	4,096	1,048,576 (1M)	129,280	MoE
DeepSeek-V4-Flash-Vision-Exp	43	4,096	1,048,576 (1M)	129,280	MoE
DeepSeek-V4-Pro	61	7,168	1,048,576 (1M)	129,280	MoE
DeepSeek-V4-Pro-0813	61	7,168	1,048,576 (1M)	129,280	MoE
DeepSeek-V4-Pro-Base	61	7,168	1,048,576 (1M)	129,280	MoE
DeepSeek-V4-Pro-DSpark	61	7,168	1,048,576 (1M)	129,280	MoE
dspark_gemma4_12b_block7	5	3,840	262,144	262,144	Dense
DeepSeek-Coder-V2-Base	60	5,120	163,840	102,400	MoE
DeepSeek-Coder-V2-Instruct	60	5,120	163,840	102,400	MoE
DeepSeek-Coder-V2-Instruct-0724	60	5,120	163,840	102,400	MoE
DeepSeek-Coder-V2-Lite-Base	27	2,048	163,840	102,400	MoE
DeepSeek-Coder-V2-Lite-Instruct	27	2,048	163,840	102,400	MoE
DeepSeek-Math-V2	61	7,168	163,840	129,280	MoE
DeepSeek-Prover-V2-671B	61	7,168	163,840	129,280	MoE
DeepSeek-R1	61	7,168	163,840	129,280	MoE
DeepSeek-R1-0528	61	7,168	163,840	129,280	MoE
DeepSeek-R1-Zero	61	7,168	163,840	129,280	MoE
DeepSeek-V2	60	5,120	163,840	102,400	MoE
DeepSeek-V2-Chat	60	5,120	163,840	102,400	MoE
DeepSeek-V2-Chat-0628	60	5,120	163,840	102,400	MoE
DeepSeek-V2-Lite	27	2,048	163,840	102,400	MoE
DeepSeek-V2-Lite-Chat	27	2,048	163,840	102,400	MoE
DeepSeek-V2.5	60	5,120	163,840	102,400	MoE
DeepSeek-V2.5-1210	60	5,120	163,840	102,400	MoE
DeepSeek-V3	61	7,168	163,840	129,280	MoE
DeepSeek-V3-0324	61	7,168	163,840	129,280	MoE
DeepSeek-V3-Base	61	7,168	163,840	129,280	MoE
DeepSeek-V3.1	61	7,168	163,840	129,280	MoE
DeepSeek-V3.1-Base	61	7,168	163,840	129,280	MoE
DeepSeek-V3.1-Terminus	61	7,168	163,840	129,280	MoE
DeepSeek-V3.2	61	7,168	163,840	129,280	MoE
DeepSeek-V3.2-Exp	61	7,168	163,840	129,280	MoE
DeepSeek-V3.2-Exp-Base	61	7,168	163,840	129,280	MoE
DeepSeek-V3.2-Speciale	61	7,168	163,840	129,280	MoE
DeepSeek-R1-0528-Qwen3-8B	36	4,096	131,072	151,936	Dense
DeepSeek-R1-Distill-Llama-70B	80	8,192	131,072	128,256	Dense
DeepSeek-R1-Distill-Llama-8B	32	4,096	131,072	128,256	Dense
DeepSeek-R1-Distill-Qwen-1.5B	28	1,536	131,072	151,936	Dense
DeepSeek-R1-Distill-Qwen-14B	48	5,120	131,072	152,064	Dense
DeepSeek-R1-Distill-Qwen-32B	64	5,120	131,072	152,064	Dense
DeepSeek-R1-Distill-Qwen-7B	28	3,584	131,072	152,064	Dense
DeepSeek-Prover-V2-7B	30	4,096	65,536	102,400	Dense
dflash_qwen3_4b_block7	5	2,560	40,960	151,936	Dense
dflash_qwen3_8b_block7	5	4,096	40,960	151,936	Dense
dspark_qwen3_14b_block7	5	5,120	40,960	151,936	Dense
dspark_qwen3_4b_block7	5	2,560	40,960	151,936	Dense
dspark_qwen3_8b_block7	5	4,096	40,960	151,936	Dense
deepseek-coder-1.3b-base	24	2,048	16,384	32,256	Dense
deepseek-coder-1.3b-instruct	24	2,048	16,384	32,256	Dense
deepseek-coder-33b-base	62	7,168	16,384	32,256	Dense
deepseek-coder-33b-instruct	62	7,168	16,384	32,256	Dense
deepseek-coder-5.7bmqa-base	32	4,096	16,384	32,256	Dense
deepseek-coder-6.7b-base	32	4,096	16,384	32,256	Dense
deepseek-coder-6.7b-instruct	32	4,096	16,384	32,256	Dense
DeepSeek-OCR	12	1,280	8,192	129,280	MoE
DeepSeek-OCR-2	12	1,280	8,192	129,280	MoE
DeepSeek-Prover-V1	30	4,096	4,096	102,400	Dense
DeepSeek-Prover-V1.5-Base	30	4,096	4,096	102,400	Dense
DeepSeek-Prover-V1.5-RL	30	4,096	4,096	102,400	Dense
DeepSeek-Prover-V1.5-SFT	30	4,096	4,096	102,400	Dense
ESFT-gate-code-lite	27	2,048	4,096	102,400	MoE
ESFT-gate-intent-lite	27	2,048	4,096	102,400	MoE
ESFT-gate-law-lite	27	2,048	4,096	102,400	MoE
ESFT-gate-math-lite	27	2,048	4,096	102,400	MoE
ESFT-gate-summary-lite	27	2,048	4,096	102,400	MoE
ESFT-gate-translation-lite	27	2,048	4,096	102,400	MoE
ESFT-token-code-lite	27	2,048	4,096	102,400	MoE
ESFT-token-intent-lite	27	2,048	4,096	102,400	MoE
ESFT-token-law-lite	27	2,048	4,096	102,400	MoE
ESFT-token-math-lite	27	2,048	4,096	102,400	MoE
ESFT-token-summary-lite	27	2,048	4,096	102,400	MoE
ESFT-token-translation-lite	27	2,048	4,096	102,400	MoE
ESFT-vanilla-lite	27	2,048	4,096	102,400	MoE
deepseek-coder-7b-base-v1.5	30	4,096	4,096	102,400	Dense
deepseek-coder-7b-instruct-v1.5	30	4,096	4,096	102,400	Dense
deepseek-llm-67b-base	95	8,192	4,096	102,400	Dense
deepseek-llm-67b-chat	95	8,192	4,096	102,400	Dense
deepseek-llm-7b-base	30	4,096	4,096	102,400	Dense
deepseek-llm-7b-chat	30	4,096	4,096	102,400	Dense
deepseek-math-7b-base	30	4,096	4,096	102,400	Dense
deepseek-math-7b-instruct	30	4,096	4,096	102,400	Dense
deepseek-math-7b-rl	30	4,096	4,096	102,400	Dense
deepseek-moe-16b-base	28	2,048	4,096	102,400	MoE
deepseek-moe-16b-chat	28	2,048	4,096	102,400	MoE

How to use this table

You want to know	Look at
Will it fit in VRAM	Layers × hidden (bigger means more memory)
Can it hold a long document	Context
Is it MoE (sparse activation)	Structure
Embedding table size	Vocab

Note

Data comes from config.json inside the Hugging Face model cards, refreshed weekly. Official parameter counts (total vs active) are quoted differently from config values; we only show the config values.

Release timeline · Derived models · Self-hosting

Official model specifications

How to use this table

Note

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