DeepSeek V4 vs Qwen3.8
A source-verified deployment comparison of DeepSeek V4 vs Qwen3.8.
Values without a defensible common measurement basis are shown as not directly comparable.
Source-verified deployment comparison. The comparison separates architecture, resident weights, active parameters, context, license, checkpoint precision, runtime support and compatible hardware. It does not manufacture a universal winner.
| Field | DeepSeek V4 Pro 0813 | Qwen3.8 2.4T-A95B |
|---|---|---|
| Provider / family | DeepSeek | Qwen |
| Track | General | General |
| Generation | DeepSeek V4 | Qwen3.8 |
| Release date | 2026-08-13 | 2026-08-08 |
| Architecture | DeepseekV4ForCausalLM | Qwen3_5MoeForCausalLM |
| Dense / MoE | MoE | MoE |
| Total parameters | 1,650.5B | 2,400.0B |
| Active parameters | 49.0B | 95.0B |
| Modalities | Text | Text |
| Context window | 1,048,576 | 262,144 |
| License | MIT License | Qwen3.8-Max License |
| Commercial classification | Commercial Use Allowed: The official license permits commercial use; its obligations still apply. | Commercial Use Allowed With Conditions: Commercial use is conditional. Review the linked official terms before deployment. |
| Checkpoint formats | safetensors | safetensors |
| Public quantizations | Mixed FP4 / FP8 | BF16, FP8 |
| Self-hosting status | Deployable with certified configuration | Deployable with certified configuration |
Overview
Source-verified facts: DeepSeek V4 Pro 0813: DeepSeek / DeepSeek / General / DeepSeek V4 / 2026-08-13 · Qwen3.8 2.4T-A95B: Qwen / Qwen / General / Qwen3.8 / 2026-08-08
Architecture differences
Source-verified facts: DeepSeek V4 Pro 0813: DeepseekV4ForCausalLM · Qwen3.8 2.4T-A95B: Qwen3_5MoeForCausalLM
Parameters and active parameters
Source-verified facts: DeepSeek V4 Pro 0813: Total parameters: 1650.5B / Active parameters: 49B MoE · Qwen3.8 2.4T-A95B: Total parameters: 2400B / Active parameters: 95B MoE
Context window
Source-verified facts: DeepSeek V4 Pro 0813: 1,048,576 tokens · Qwen3.8 2.4T-A95B: 262,144 tokens
Modalities
Source-verified facts: DeepSeek V4 Pro 0813: Text · Qwen3.8 2.4T-A95B: Text
Runtime ecosystem
Source-verified facts: DeepSeek V4 Pro 0813: transformers · Qwen3.8 2.4T-A95B: transformers, vllm, sglang
Quantization options
Source-verified facts: DeepSeek V4 Pro 0813: Mixed FP4 / FP8 · Qwen3.8 2.4T-A95B: BF16, FP8
VRAM and memory requirements
Source-verified facts: DeepSeek V4 Pro 0813: Mixed FP4 / FP8 ~1,237.9 GB estimated weight memory · Qwen3.8 2.4T-A95B: BF16 ~4,800.0 GB estimated weight memory, FP8 ~2,400.0 GB estimated weight memory
Recommended GPU configurations
GPU sizing must use the exact checkpoint and precision. The release pages provide memory estimates and inventory-backed recommendations; technical compatibility is kept separate from rental availability.
Multi-GPU considerations
Large variants may require tensor parallelism or multiple nodes. Validate total resident weights, runtime support, interconnect, topology and headroom before choosing a GPU count.
License and commercial use
Source-verified facts: DeepSeek V4 Pro 0813: MIT License / Commercial Use Allowed / The official license permits commercial use; its obligations still apply. · Qwen3.8 2.4T-A95B: Qwen3.8-Max License / Commercial Use Allowed With Conditions / Commercial use is conditional. Review the linked official terms before deployment.
Coding, reasoning and relevant capabilities
Source-verified facts: DeepSeek V4 Pro 0813: Language and reasoning, Flagship · MoE, Text · Qwen3.8 2.4T-A95B: Language and reasoning, Flagship · MoE, Text
Deployment complexity
Source-verified facts: DeepSeek V4 Pro 0813: 1 checkpoints / 1 runtimes / 1 precisions · Qwen3.8 2.4T-A95B: 2 checkpoints / 6 runtimes / 2 precisions
Which workloads fit each model
Source-verified facts: DeepSeek V4 Pro 0813: Language and reasoning, General, Flagship · MoE · Qwen3.8 2.4T-A95B: Language and reasoning, General, Flagship · MoE
Key trade-offs
Select by workload, verified runtime, precision, memory, license and operational constraints. This comparison does not assign a universal winner.
Official sources
- https://huggingface.co/deepseek-ai/DeepSeek-V4-Pro-0813
- https://huggingface.co/deepseek-ai/DeepSeek-V4-Pro-0813/blob/72e1d3230f6c080a530b0a1d46f8eb4602340597/LICENSE
- https://huggingface.co/Qwen/Qwen3.8-2.4T-A95B
- https://huggingface.co/Qwen/Qwen3.8-2.4T-A95B/blob/207bd685a7e3696cfaff12ded7c6a7ea0f88c996/LICENSE
- https://huggingface.co/Qwen/Qwen3.8-2.4T-A95B-FP8
How should these models be compared?
Select by workload, license, runtime support, memory requirement and deployment constraints.
Last verified: 2026-08-25