DeepSeek V4 Family Overview: V4.1-Flash, V4-Pro and V3.2

DeepSeek is the most-downloaded open Chinese model family: V3.2 alone has over 7.3M downloads, V4-Flash-0731 is near 2.8M, and V4.1-Flash has passed one million. Since 2026 the V4 line has kept iterating — from V4-Pro and V4-Flash to V4.1-Flash — plus three speculative-decoding companion models: DSpark, DFlash, and Eagle3. This article maps the family and answers "with so many versions, how do I choose?"

Reading the download numbers

The download counts tell a useful story about community adoption. V3.2's 7.3M downloads and V4-Flash-0731's 2.8M indicate which versions the local-deployment community actually runs — these two have the most battle-tested GGUF conversions and the most operational knowledge circulating.

Newer snapshots like V4.1-Flash-1017 have fewer downloads simply because they are new. Their GGUF coverage may lag by days or weeks. If you depend on community quantizations, the pragmatic sequence is: deploy a battle-tested version now, watch the newest snapshot's conversion coverage mature, then upgrade.

This is a general pattern across all Chinese model families: the newest weights and the most deployable weights are not always the same, and bridging that gap is what community quantizers do over time.

Family map

V4 current: the Pro and Flash tracks

The V4 line splits into two tracks, with naming that directly signals positioning:

V4-Pro track (capability-first)

• DeepSeek-V4-Pro: released 2026-04, 333K downloads
• DeepSeek-V4-Pro-0813: August snapshot, 148K downloads
• DeepSeek-V4-Pro-Base: Base version for fine-tuning

V4-Flash track (speed-first)

• DeepSeek-V4-Flash: 1.1M downloads
• DeepSeek-V4-Flash-0731: July snapshot, 2.8M downloads — the most popular V4 version
• DeepSeek-V4.1-Flash: September version, 1.88M downloads
• DeepSeek-V4.1-Flash-1017: newest snapshot, 2026-10-17, 243K downloads

V3.2: the evergreen stock choice

DeepSeek-V3.2 was released in 2025-12 and has 7.3M downloads, the highest in the family. It stays evergreen for three reasons:

1. Mature ecosystem fit: after more than a year, llama.cpp, vLLM, and Ollama all have stable support
2. Complete community quantizations: GGUF at every tier, plus AWQ and GPTQ, all readily available
3. Predictable behavior: many production systems run it, so operational knowledge is abundant

If your scenario does not chase the newest capability, V3.2 remains a reliable stock choice.

Speculative decoding family: DSpark / DFlash / Eagle3

This is a distinctive part of the DeepSeek release: three speculative-decoding companion models. Speculative decoding works by having a small model quickly draft several tokens, then the large model verifies them in parallel; verified tokens are accepted directly, which speeds up generation.

Note that DSpark and DFlash have variants targeting different bases (Qwen3-4B/8B/14B, Gemma4-12B), meaning this acceleration scheme serves more than DeepSeek's own models. If you want faster local deployment, pairing a small draft model for speculative decoding is a mature approach.

Speculative decoding explained

Speculative decoding speeds up autoregressive generation by having a small draft model propose several tokens ahead, then the large model verifies them in parallel. Tokens the large model agrees with are accepted without recomputation, and disagreeing tokens are corrected.

This is why the companion models have three flavors:

• dspark: pairs with DeepSeek's own V4 models
• dflash: a block-level variant with a different schedule
• eagle3: pairs with third-party bases (Qwen3, Gemma4)

The benefit is purely speed. Output quality and the final text distribution are unchanged — the large model still decides every token. That means you can safely pair speculative decoding in production without worrying about quality regression.

A practical note: speculative decoding gain depends on the draft model matching the target model closely. The dspark variants for V4 are tuned against V4; the eagle3 variants for Qwen3-8B, for example, are tuned against Qwen3-8B. Pick the variant whose base matches the model you are accelerating.

Which version to lock

With so many V4 versions and snapshots, the practical rule is:

• For production, lock a date snapshot: V4.1-Flash-1017 for Flash, V4-Pro-0813 for Pro. Snapshots give reproducibility — behavior does not drift as upstream updates ship.
• For experiments, chase the main version: V4.1-Flash (without a date suffix) gets ongoing improvements.
• For stock deployments that never change: V3.2 is the most battle-tested option with the largest ecosystem.

A final caution: the DSpark/DFlash/Eagle3 draft models are version-sensitive. Pair a draft model with the target model's exact version, not just the same family. Check the companion model's README for its matched target.

Vision and OCR

• DeepSeek-V4-Flash-Vision-Exp: experimental vision variant (168K downloads) for multimodal trial
• DeepSeek-OCR-2: document recognition specialist (373K downloads), in the same lane as Baidu's Unlimited-OCR

How to deploy DeepSeek

DeepSeek is one of the best ecosystem-supported Chinese families, and its quantization coverage is unusually complete — most versions have GGUF at every tier, plus AWQ and GPTQ builds from multiple community quantizers, which means you rarely need to wait for a conversion to catch up.

Selection advice: for local personal runs, take V4.1-Flash or V3.2 GGUF at Q4_K_M; for servers, V4-Pro FP8; pair speculative decoding for maximum speed.

Selection quick reference

FAQ

Related reading

• Baidu ERNIE Family Overview — the other flagship-open Chinese family
• Hunyuan hy4 and A13B Local Deployment — Tencent Hunyuan's lightweight line
• Kimi K3 and GLM Integration Guide — two families callable on EXAIV
• Qwen GGUF Local Deployment Primer — local deployment basics
• Qwen Cost Optimization — general cost-optimization techniques