Comparison brief · Aug 18, 2026 · last 30 days

Qwen 3.8 27B vs DeepSeek V4 Flash: same ~52 band, pick by RAM

Two local workhorses shipped inside three weeks. Qwen 3.8 27B (Alibaba, weights ~Aug 14) is a dense multimodal 27B that fits a 24 GB card. DeepSeek V4 Flash 0731 (weights Jul 31) is a 284B/13B-active MoE that wants ~110 GB and stays fast at long context. This brief maps official cards, vendor vs independent benches, reported tok/s, and what people actually ran.

local models dense vs MoE 5090 / 3090 / Studio last30days pulse

BLUF — Bottom line up front

Quality is closer than the hype war. Flash 0731 sits at 52 on Artificial Analysis (independent, max effort). Community threads on Aug 17 put Qwen 3.8 27B in the same 52 band. The pick is hardware: dense 27B on one fast GPU; MoE Flash on 128 GB+ unified memory or a multi-card box.

Architecture (official cards): Qwen is dense 27B + vision, 262K native / 1M YaRN, Apache 2.0. Flash is MoE ~284B total / 13B active, 1M native, MIT, DSpark attached. HF lists Flash at 304B params; DeepSeek still markets 284B/13B.
Vendor benches: Qwen claims SWE-Pro 61.7 and OSWorld 84.3 (vs Opus 4.6 Max). Flash claims Terminal-Bench 2.1 82.7 and DeepSWE 54.4 (vs Opus 4.8). Different harnesses, different opponents. Do not stack them as one leaderboard.
Speed: 5090 comments put Qwen around 150–170 tok/s with MTP. Mac Studio comments put Flash ahead at long context (~30 tok/s at 100k vs ~20 for Qwen at 64k). Flash does not fit a single 24 GB card at a useful quant.
Tax: Qwen defaults to xhigh thinking. Simon Willison burned 21 minutes / 22k reasoning tokens on a pelican SVG. Dial it to medium or off first.
Emerging stack: daily loop on Qwen 27B if you have a 3090/4090/5090; keep Flash 0731 on the Studio / 128 GB box for long-context agent work. Do not treat Qwen3.8-Max as a local peer.

1. Model cards

First-party, not memory. Qwen pitches a “compact, deployment-friendly dense model” with native vision and video. DeepSeek pitches Flash as “your fast, efficient, and economical choice” at 284B/13B with a million-token window.

Qwen 3.8 27B · Alibaba

dense 27B vision + video Apache 2.0

64 layers; 48 Gated DeltaNet + 16 full attention
Hidden 5120; 24 Q / 4 KV heads
Context 262,144 native, 1M via YaRN
MTP trained; thinking on by default
reasoning_effort: xhigh (default) / medium / low
HF downloads last month: ~415k

DeepSeek V4 Flash 0731

MoE 284B / 13B text only MIT

Same structure as Flash-DSpark; draft module attached
CSA + HCA sparse attention; mixed FP4/FP8
Context 1M native; max output 384K
1 shared + 256 routed experts, 6 fire per token
reasoning_effort: low / high / max
HF downloads last month: ~2.0M

Qwen active params

27B

All of them, every token

Flash active params

13B

of ~284B total (HF: 304B)

Qwen 4-bit floor

17–19 GB

Unsloth UD-Q4 / LM Studio Q4_K_M

Flash 3-bit floor

~110 GB

Unsloth UD-IQ3_XXS + headroom

Dense vs MoE is not a vibe. Qwen pays full 27B bandwidth every decode step, so a 5090/3090 with fat HBM wins. Flash pays ~13B compute per token but parks ~150–170 GB of experts in RAM, so a Mac Studio or 128 GB+ box wins on long context. That is the whole argument.

2. Quality benches

Two scoreboards. Vendor cards are not comparable across labs. Independent AA is the only shared axis we have, and Qwen 27B’s AA page was still a 404 on Aug 15.

Vendor cards (label: vendor)

Axis	Qwen 3.8 27B	Flash 0731	Note
Terminal Bench 2.1	73.0	82.7	Qwen vs Opus 4.6 Max 78.2; Flash vs Opus 4.8 85.0
SWE-bench Pro	61.7	—	Qwen vendor, Claude Code harness; Flash card omits it
DeepSWE	42.2	54.4	Different DeepSWE cuts (1.1 vs Flash card)
NL2Repo	42.3	54.2	Both vendor
Agents’ Last Exam	20.4 p@1 / 42.9	25.2	Qwen reports pass@1 + score; Flash reports one number
OSWorld-Verified	84.3	n/a	Qwen is multimodal; Flash has no vision
LiveCodeBench v6	90.3	—	Qwen vendor only

Qwen card dated with the Aug 14 weights. Flash-0731 card evaluated with DeepSeek Harness minimal mode, max effort, temp 1.0 / top_p 0.95.

Independent / community (label: independent)

AA Intelligence Index: Flash 0731 (max) = 52 (Artificial Analysis, late July / early August; Better Stack and AA provider pages agree). Qwen 3.8 27B had no AA page on Aug 15 (OrcaRouter). r/LocalLLaMA and r/LocalLLM on Aug 17 say it landed next to Flash and GPT-5.6 Luna Max at ~52. Treat 52/52 as community-confirmed, not a settled AA article.
OpenRouter API (not local): p50 throughput 42 tok/s (Qwen) vs 49 tok/s (Flash); p50 latency 1.34s vs 1.22s.
Bart Slodyczka, Mac Studio, Aug 16: same three coding tasks. Qwen held its own; on the spreadsheet-with-formulas task it was the only one that actually worked. 37k views. This is one harness, one machine.
r/LocalLLM “where are the benches” thread: mil_phickelson on M1 Ultra 128 GB scored Qwen Q8 9 vs Flash IQ3_XXS 8–8.5 on a Postgres-migration prompt; Flash finished faster. Opposite_Buffalo_649: “0731 is a tier above.” stormy1one: nobody runs full-quant Flash, so hardware + quant is the real test.

“Opus locally” is marketing, not a measurement. WorldofAI and Cloud Codes ran that headline hard. Qwen’s own SWE-Pro number beats Opus 4.6 Max on Qwen’s harness. Flash’s TB 2.1 82.7 sits under Opus 4.8’s 85.0. Neither card is a substitute for your agent loop.

3. Reported tok/s

These are field reports, not a single bench. Backend, quant, context, and thinking effort move the number more than the model name.

Setup	Backend / quant	Qwen 3.8 27B	Flash 0731	Source
RTX 5090	community report	~170 tok/s	n/a on one 32 GB card	r/LocalLLM comment
RTX 3090, 92k ctx	llama.cpp UD-Q4_K_XL + MTP + ngram	50–60 new / ~37 avg	—	r/LocalLLaMA DSH thread
Dual 3090 + 128 GB	Qwen official FP8 / Flash UD-Q4_K_XL	70 decode / 1500 prefill	“smarter, thinks too much”	r/unsloth
2× RTX PRO 6000 96 GB	Qwen Q8_0 / Flash MXFP4	97 tok/s	52 tok/s	r/LocalLLaMA u/mrblithe
M3 Ultra 512 GB Studio	oMLX + MTP	44 tok/s	not quoted in same post	Bart pinned comment
M5 Max 128 GB	community	~20 @ 64k	~30 @ 100k	r/LocalLLM u/MacsBicycle
M5 Max / DGX Spark	LM Studio Q4_K_M	15–30 tok/s	—	Simon Willison
Spark + llama.cpp MTP	--spec-type draft-mtp	+72% vs LM Studio	—	Simon / ggerganov tip
B200 (vendor/Unsloth)	DSpark / NVFP4	133.7 NVFP4 b1	120 vs 60 baseline	Unsloth docs
Hosted API	AA / OpenRouter	42 p50 (OR)	49 p50 OR; ~107 AA	not local

HN “Flash at 278 tok/s full precision” is a hosted Runinfra claim, not a desktop number. Do not use it as a local target.

4. VRAM / unified memory

Quant / format	Qwen 3.8 27B	Flash 0731	Fits
2-bit / IQ2	11–13 GB	~87–102 GB	Qwen: 16 GB card. Flash: still a workstation.
3-bit / IQ3	13–16 GB	103–110 GB	Unsloth’s recommended Flash start on 128 GB
4-bit / UD-Q4	17–19 GB	~155–162 GB	Qwen daily driver. Flash wants a Studio or dual 96 GB.
6-bit / Q6	24 GB	—	Single 3090/4090 Qwen with short ctx
8-bit / Q8	31 GB	162–169 GB lossless	5090 32 GB Qwen. Flash Q8 is a 192 GB class box.
BF16 / native	56 GB	~156 GB official	Not a 24 GB conversation
Speculative extra	MTP draft head	DSpark +~10 GB	Real, not vapor. Budget it.

Qwen sizes from Unsloth 3.8 docs. Flash sizes from Unsloth V4 docs (UD-IQ3_XXS 103 GB, UD-Q8_K_XL 162 GB). r/unsloth: NVFP4 Qwen on 32 GB 5090 tops out around 128K context.

5. Optimal settings

Qwen 3.8 27B

Start at reasoning_effort=medium or off. xhigh is a trap.
Thinking: temp 1.0, top_p 0.95, top_k 20
Instruct: temp 0.7, top_p 0.80, presence_penalty 1.5
Quant: Unsloth UD-Q4_K_XL (17–19 GB) or LM Studio Q4_K_M
Speed: llama.cpp --spec-type draft-mtp; vLLM NVFP4 + MTP on Blackwell
Agents: leave preserve_thinking on; raise context off LM Studio’s 8k default

DeepSeek V4 Flash 0731

Think High is default. Use max only when you mean it (384K out).
temp 1.0; top_p 0.95 agentic, 1.0 otherwise
Quant: Unsloth UD-IQ3_XXS on 128 GB; UD-Q8_K_XL if you have ~170 GB
Speed: --spec-type draft-dspark --spec-draft-n-max 3
Engine: llama.cpp, Unsloth Desktop (DSpark on), or antirez ds4 on Metal/CUDA
Avoid random Q4-expert reconverts; Unsloth’s MXFP4 path is the lossless story

6. What people said (last 30 days)

Pulse from a last30days v3.8.3 pass, 2026-07-19 → 2026-08-18. Reddit, YouTube, HN, GitHub, Digg, Techmeme, arXiv, Polymarket. X was not live (bird installed, not authed). No TikTok/Instagram. Corpus: 19 Reddit threads · 15 YouTube videos · 23 HN stories.

Qwen 3.8 27B thread

r/unsloth “out now” hit 1,127 pts / 236 comments in a day.
HN “Opus at home” thread cleared 1,000 pts (Cloud Codes recap).
YouTube: WorldofAI 70k views on a 4090 Unsloth Q4 run.
Recurring complaint: it thinks forever. Recurring praise: it actually finishes agent loops.

Flash 0731 thread

HN Flash 0731 / ARC page 795 pts / 476 comments.
Bijan Bowen launch video 85k views: DeepSWE 7.3 → 54.4 is the slide people quote.
Better Stack: “I ignored the 52 AA score, then saw cost per task.”
Local stack coalesced around Unsloth GGUFs + DSpark, or antirez ds4 (22k stars).

“Crazy that its being compared to Opus 4.6, which was frontier only 6 months ago. Fable 5 local in 1 year does not seem far fetched anymore.” u/ColdCelestial · 65 upvotes · r/unsloth

“For people with 16GB of VRAM: llama.cpp #26622 aims to add a “--n-cpu-moe” type arg, but for dense models like Qwen 27B.” u/Stainless-Bacon · 37 upvotes · r/unsloth

“All things equal running full quant DeepSeek Flash 0731 is likely better… but nobody runs full quant in practice. It all comes down to your hardware.” u/stormy1one · r/LocalLLM bake-off thread

Polymarket, for color only. “Next Alibaba Qwen Max (3.9+) released” ~71%; “Next Alibaba Qwen Plus (3.8+) released” ~46%. These are product-timeline bets, not model-quality bets.

7. Head-to-head

Dimension	Qwen 3.8 27B	DeepSeek V4 Flash 0731
What it is	Dense multimodal 27B for coding + office	MoE 284B/13B fast 1M-context Flash
License	Apache 2.0	MIT
Vision	Native image + video	None
Context	262K native, 1M YaRN	1M native
AA Index	~52 (community, Aug 17)	52 max (AA)
Vendor coding peak	SWE-Pro 61.7 / OSWorld 84.3	TB 2.1 82.7 / DeepSWE 54.4
Daily RAM	17–32 GB	110–170 GB
Best machine	5090 / 3090 / 24–32 GB	Mac Studio / 128 GB+ / multi-GPU
Speculative	MTP (real)	DSpark (real)
Default think	xhigh (too high)	high
Install	Unsloth / LM Studio / llama.cpp / vLLM	Unsloth / llama.cpp / ds4 / vLLM
GitHub (live)	QwenLM/Qwen3 27.5k · qwen-code 27.1k	antirez/ds4 21.5k · awesome-deepseek-agent 5.9k

8. Choose if

Choose Qwen 3.8 27B if

You have one 24–32 GB GPU and want a daily local agent.
You need screenshots, PDFs, or OSWorld-style computer use.
You want Apache 2.0 and a 17 GB file, not a 100 GB download.
You will actually turn xhigh down.

Choose Flash 0731 if

You have 128 GB+ unified memory or several 3090s.
You live at 100k+ context and care about KV staying cheap.
You want the independently scored 52 and DeepSeek’s agent benches.
Vision does not matter and MIT + DSpark do.

The emerging stack

The 100–200 GB RAM crowd is treating this as the next few months’ binary, not a holy war. Pattern that keeps showing up:

5090 / 3090 desk: Qwen 27B UD-Q4 or NVFP4, MTP on, effort medium. Fast enough to stay in the loop.
Studio / 128 GB: Flash 0731 IQ3 or mixed, DSpark or ds4. Hold the long repo.
Hard ticket: promote to hosted Qwen3.8-Max or DeepSeek V4 Pro 0813. Those are not this comparison.

9. Caveats

Qwen 27B is four days old. AA page was missing on Aug 15. The “52” match is a Reddit-week claim, not a locked AA article.
Vendor benches do not share a harness. Qwen vs Opus 4.6 Max. Flash vs Opus 4.8 + GLM-5.2. I left them in separate columns on purpose.
Param count drift on Flash. DeepSeek says 284B/13B. HF safetensors page says 304B. Unsloth and Better Stack say “~300B / 13B.” Architecture is MoE either way.
X is missing from this pulse. Live scrape was not authed. Do not treat engagement totals as complete.
1-bit on 8 GB is a myth until measured. Cloud Codes: no published dense-27B-from-disk number; treat 0.3 tok/s as a bound.
This is not my 5090/3090 bake-off. I did not reload Flash onto Studio RAM for this brief. Numbers above are cited, not re-run here.

10. Sources

Hugging Face — Qwen/Qwen3.8-27B (architecture + vendor benches)
Hugging Face — DeepSeek-V4-Flash-0731 (official 0731 card)
Qwen — Qwen3.8-Max blog (Aug 2, 2026)
Unsloth — Qwen3.8 how to run (sizes, sampling, NVFP4)
Unsloth — DeepSeek-V4 how to run (IQ3/Q8 sizes, DSpark)
Simon Willison — Qwen 3.8 27B overthinking (Aug 16)
Artificial Analysis — DeepSeek provider (Flash 0731 Index 52, ~107 tok/s)
OpenRouter — Qwen 27B vs Flash 0731
Bart Slodyczka — same apps, Mac Studio
r/LocalLLM — where are the benches
Community pulse: r/LocalLLaMA, r/unsloth, r/LocalLLM, HN Qwen 27B + Flash 0731 threads, WorldofAI / Cloud Codes / Better Stack / Bijan Bowen

Research dump from the multi-source pass:

~/Documents/Last30Days/2026-08-18-qwen38-27b-vs-dsv4f-raw-v3.md
~/Documents/Last30Days/qwen-3-8-27b-raw-v3.md
~/Documents/Last30Days/deepseek-v4-flash-raw-v3.md