Qwen3.8-Flash-Next vs DeepSeek V4 Flash 0731 284B
Side-by-side VRAM requirements, benchmark scores, and GPU compatibility for local AI inference.
Quick verdict
DeepSeek V4 Flash 0731 284B is more hardware-efficient: it needs 108.5 GB at its UD-Q2_K_XL build vs 123.0 GB for Qwen3.8-Flash-Next's Q4_K_M, fitting on 16 GPUs natively.
Analysis
Qwen3.8-Flash-Next and DeepSeek V4 Flash 0731 shipped four weeks apart, both MoE checkpoints built around a lightweight indexer that scores context in blocks to cut long-context cost, but they land in very different states: DeepSeek's is a finished, agentic-tuned flagship with a real quant ladder, Qwen's is an explicit preview of a future architecture with no confirmed community build yet.
The two indexer schemes work differently underneath the similar pitch. Qwen Sparse Attention (QSA) narrows what each query attends to for compute, but per Qwen's own technical report it still selects from the same full per-token cache a standard full-attention layer needs, so its KV cache grows exactly like an unmodified hybrid stack. DeepSeek's Compressed and Heavily Compressed Attention (CSA/HCA) genuinely folds multiple token positions into one stored entry, a real 10x reduction DeepSeek publishes directly: at the shared 262,144-token context, DeepSeek V4 Flash 0731's KV cache is 2.3 GB against Qwen3.8-Flash-Next's 6.4 GB, despite DeepSeek's context ceiling running four times further, to 1,048,576 tokens, where its cache is still only 9.2 GB. Weights run the opposite direction: DeepSeek is the much bigger checkpoint, 284B total parameters against 180B, activating more than twice as much per token, 13B against 6B, and its real download (Unsloth's natively-repacked quants, since the routed experts ship in MXFP4 rather than a conventional format) recommends UD-Q2_K_XL at 96.8 GB, close to Qwen3.8-Flash-Next's real 180B-parameter total at Q2_K, 68.6 GB. On the one benchmark field both report, the two land almost exactly together despite that 13B-vs-6B compute gap: GPQA Diamond 91.7 for Qwen against 91 for DeepSeek (Artificial Analysis's independently-run figure). Licensing differs in a way that matters practically: DeepSeek ships MIT, no conditions at all, while Qwen's Community 1.0 terms require a separate license for any Model-as-a-Service or 'AI Work Assistant' business. Modality splits too: Qwen accepts text, image, and video, DeepSeek V4 Flash 0731 is text only.
Bottom line: DeepSeek V4 Flash 0731 is the one to actually deploy: it matches Qwen3.8-Flash-Next's GPQA score while activating more than twice the parameters, ships a real quant ladder from Unsloth, and carries the least restrictive license of the two. Qwen3.8-Flash-Next is worth tracking for QSA and the eventual production 'Qwen3.8-Flash', and its own compute-per-token is genuinely lower, but as shipped today it has no confirmed community quant and needs a bigger real download than its 125B/6B headline suggests, because of the 51B n-gram table neither DeepSeek model has an equivalent of.
Total vs. active parameters: two different sparsity ratios
Both models are MoE, so total parameters (the VRAM bill) and active parameters (the compute bill) move independently. DeepSeek routes through a larger slice of a larger model; Qwen routes through a smaller slice of a smaller one.
Two different 'sparse attention' tricks, one real cache saving
Both models market an indexer that scores context in blocks, but only one of them actually shrinks what gets stored. Restricted to the 262,144-token window both can reach (a quarter of DeepSeek's own ceiling), the difference in what each trick actually buys is direct.
KV cache only, at FP16. Weights and activation overhead sit on top of these figures.
At 262,144 tokens, DeepSeek V4 Flash 0731's Compressed/Heavily Compressed Attention keeps its KV cache at 2.3 GB. Qwen3.8-Flash-Next's Qwen Sparse Attention, which only narrows compute rather than storage per Qwen's own technical report, needs 6.4 GB for the same context, nearly three times as much, even though DeepSeek's real context ceiling continues four times further before it needs the trick at all.
VRAM at each real build (8k context)
DeepSeek V4 Flash 0731 284B ships as a handful of fixed prebuilt files rather than a standard quantization ladder, so it has no real Q4_K_M/FP16/etc. equivalent to Qwen3.8-Flash-Next's. Ranked smallest to largest instead.
| Rank | Qwen3.8-Flash-Next | GB | DeepSeek V4 Flash 0731 284B | GB |
|---|---|---|---|---|
| 1 | Q2_K | 77.0 GB | UD-IQ1_S | 92.5 GB |
| 2 | Q3_K_M | 97.2 GB | UD-Q2_K_XL | 108.5 GB |
| 3 | NVFP4 | 101.0 GB | UD-IQ3_XXS | 116.6 GB |
| 4 | Q4_K_M | 123.0 GB | UD-Q4_K_XL | 173.8 GB |
| 5 | Q5_K_M | 143.8 GB | UD-Q8_K_XL | 181.4 GB |
| 6 | Q6_K | 165.7 GB | — | — |
| 7 | Q8_0 | 214.5 GB | — | — |
| 8 | BF16 | 403.4 GB | — | — |
| 9 | FP16 | 403.4 GB | — | — |
| 10 | FP32 | 806.6 GB | — | — |
Each column is that model's own real builds, smallest first; rank pairs them by position, not by matching precision.
Model specifications
| Spec | Qwen3.8-Flash-Next | DeepSeek V4 Flash 0731 284B |
|---|---|---|
| Org | Alibaba | DeepSeek |
| Parameters | 180B | 284B |
| Architecture | MoE (6B active) | MoE (13B active) |
| Context | 256k tokens | 1024k tokens |
| Modalities | text, vision, video | text |
| License | Qwen Community 1.0 | MIT |
| Commercial | Yes | Yes |
| Released | 2026-08-26 | 2026-07-31 |
| GPUs (native) | 22 / 119 | 16 / 119 |
Benchmark scores
| Benchmark | Qwen3.8-Flash-Next | DeepSeek V4 Flash 0731 284B |
|---|---|---|
| GPQA Diamond | 91.7 | 91.0 |
| LiveCodeBench | 91.9 | N/A |
| SWE-bench Pro | 62.5 | N/A |
Green = higher score (better). N/A = not yet available. ~ = inherited from a base model, not independently reported for that release itself.
GPUs that run only Qwen3.8-Flash-Next(6)
- NVIDIA RTX Pro 600096 GB
- AMD Strix Halo (96GB)96 GB
- Apple M5 Ultra (96GB)96 GB
- Apple M3 Ultra (96GB)96 GB
- Apple M3 Max (96GB)96 GB
- Apple M2 Max (96GB)96 GB
GPUs that run only DeepSeek V4 Flash 0731 284B(0)
Every GPU that runs DeepSeek V4 Flash 0731 284B also runs Qwen3.8-Flash-Next.
GPUs that run both natively(16)
- NVIDIA B300 288GB288 GB
- NVIDIA B200 180GB180 GB
- NVIDIA H200 141GB141 GB
- NVIDIA DGX Spark (128GB)128 GB
- AMD Instinct MI300X192 GB
- AMD Strix Halo (128GB)128 GB
- Apple M5 Ultra (512GB)512 GB
- Apple M5 Ultra (256GB)256 GB
- Apple M5 Max (128GB)128 GB
- Apple M4 Max (128GB)128 GB
- Apple M3 Ultra (512GB)512 GB
- Apple M3 Ultra (256GB)256 GB
- +4 more GPUs run both
Which should you use?
- • You have limited VRAM: it's a smaller model needing 123.0 GB vs 108.5 GB
- • You need vision/image understanding
- • It's the newer release (2026-08-26 vs 2026-07-31); check the benchmark table above for what actually improved
- • You want maximum capability and have a 109 GB+ GPU
- • Long context matters: it supports 1024k tokens vs 256k
Frequently asked questions
- Which is better, Qwen3.8-Flash-Next or DeepSeek V4 Flash 0731 284B?
- Qwen3.8-Flash-Next has 180B parameters vs 284B for DeepSeek V4 Flash 0731 284B, so DeepSeek V4 Flash 0731 284B is the larger model. DeepSeek V4 Flash 0731 284B is more hardware-efficient, needing 108.5 GB at its UD-Q2_K_XL build vs 123.0 GB for Qwen3.8-Flash-Next's Q4_K_M. Qwen3.8-Flash-Next runs on more GPUs natively (22 vs 16).
- How much VRAM does Qwen3.8-Flash-Next need vs DeepSeek V4 Flash 0731 284B?
- At 8k context, Qwen3.8-Flash-Next needs approximately 123.0 GB of VRAM at its Q4_K_M build, while DeepSeek V4 Flash 0731 284B needs 108.5 GB at its UD-Q2_K_XL build. At the largest build each ships, Qwen3.8-Flash-Next requires 403.4 GB (FP16) vs 181.4 GB (UD-Q8_K_XL) for DeepSeek V4 Flash 0731 284B.
- Can you run Qwen3.8-Flash-Next on the same GPUs as DeepSeek V4 Flash 0731 284B?
- Yes, 16 GPUs can run both natively in VRAM, including NVIDIA B300 288GB, NVIDIA B200 180GB, NVIDIA H200 141GB. However, 6 GPUs can run Qwen3.8-Flash-Next but not DeepSeek V4 Flash 0731 284B, and no GPU can run DeepSeek V4 Flash 0731 284B without also fitting Qwen3.8-Flash-Next.
- What is the difference between Qwen3.8-Flash-Next and DeepSeek V4 Flash 0731 284B?
- Qwen3.8-Flash-Next has 180B parameters (6B active, MoE) with a 256k context window. DeepSeek V4 Flash 0731 284B has 284B parameters (13B active, MoE) with a 1024k context window. Licensing differs: Qwen3.8-Flash-Next is Qwen Community 1.0 while DeepSeek V4 Flash 0731 284B is MIT.
- Which model fits in 24 GB of VRAM, Qwen3.8-Flash-Next or DeepSeek V4 Flash 0731 284B?
- Neither fits in 24 GB: Qwen3.8-Flash-Next needs 123.0 GB at Q4_K_M and DeepSeek V4 Flash 0731 284B needs 108.5 GB at UD-Q2_K_XL. Both require a multi-GPU server with 123 GB+ of combined VRAM.
- Which handles long context better, Qwen3.8-Flash-Next or DeepSeek V4 Flash 0731 284B?
- At 262,144 tokens, DeepSeek V4 Flash 0731's Compressed/Heavily Compressed Attention keeps its KV cache at 2.3 GB. Qwen3.8-Flash-Next's Qwen Sparse Attention, which only narrows compute rather than storage per Qwen's own technical report, needs 6.4 GB for the same context, nearly three times as much, even though DeepSeek's real context ceiling continues four times further before it needs the trick at all.