# DeepSeek V4 Flash stays the smartest local model > Source: > Published: 2026-08-05 00:00:00+00:00 ## What the two trackers actually compared Two independent benchmark sites — [BenchLM.ai](https://benchlm.ai/compare/deepseek-v4-flash-vs-qwen3-6-27b) and [llm-stats.com](https://llm-stats.com/models/compare/deepseek-v4-flash-0731-vs-qwen3.6-27b) — put DeepSeek’s V4 Flash 0731 update head-to-head with Alibaba’s Qwen3.6-27B this week. Both are open-weight (Flash under MIT, Qwen under Apache 2.0), both are pitched at the same buyer: someone with a high-RAM workstation who wants the smartest local model they can squeeze into memory. The trackers read the question differently. BenchLM focused on a small set of *shared* benchmarks — only the tests both models have actually been scored on — and refused to declare a winner where the underlying test sets differed. LLM-stats ran a broader sweep and concluded that DeepSeek V4 Flash beats Qwen3.6-27B on most benchmarks and costs roughly a twelfth per token. Both pages were updated on 5 August 2026. 12×— DeepSeek V4 Flash’s per-token cost advantage over Qwen3.6-27B on a 3:1 input/output mix, per llm-stats (5 August 2026). ## Where Flash wins, where Qwen holds on On the five tests both trackers share, **Qwen3.6-27B leads on all five**. The gaps are not close: 43.5 points on HMMT Feb 2026, 16.6 on GPQA, 15.9 on HLE, 10.2 on Terminal-Bench 2.0, and 4.4 on SWE-bench Pro. Read one way, Qwen3.6-27B is the clearly better model. Read another way — the way the broader llm-stats sweep frames it — DeepSeek V4 Flash leads on most other coding and agent benchmarks, including Terminal-Bench 2.1 (82.7%), CyberGym (76.7%), Toolathlon (70.3%), and SWE Multilingual (69.7%). Qwen’s wins cluster on a different benchmark family: CountBench (97.8%), AIME 2026 (94.1%), HMMT 2025 (93.8%). The fair reading: **both models are strong, and the leader depends on which tests you trust**. BenchLM’s shared-only approach is the more rigorous one; llm-stats’s broader sweep is the more flattering to DeepSeek. ## What they cost and what they fit DeepSeek V4 Flash is roughly **12× cheaper per token** than Qwen3.6-27B on a 3:1 input/output mix — the cost gap that decides production agent loops. BenchLM’s three fixed-cost scenarios tell the same story in dollar terms: a single chat turn costs Flash a fraction of a US cent, a 50K-token repository review stays under one cent, and a 200K-token cache-heavy agent loop is comparable. Qwen3.6-27B is hosted via Novita but at roughly 7× Flash’s per-token rate, so Flash remains the production-API pick on cost. The catch is size. **DeepSeek V4 Flash is roughly eleven times larger than Qwen3.6-27B** in raw model terms. Both fit a high-RAM desktop after quantisation — the compression trick that lets a model run on less memory at a small quality cost — but Flash needs a heavier quant while Qwen3.6-27B runs comfortably at a lighter one. DeepSeek’s larger context window — how much text the model can read in one go — is around four times Qwen’s, which matters for long documents. Qwen supports image inputs, Flash does not. ## What to run on a high-RAM desktop this afternoon The benchmark scores matter less than the question the community keeps asking: *which is the smartest model you can actually run at home on a 128GB workstation?* The answer is still DeepSeek V4 Flash, even after quantisation. Three practical paths for a UK small team: **Run Qwen3.6-27B first if you want the smoothest ride.** It quantises lightly, runs without drama, leads on the shared reasoning tests both trackers publish, and accepts image inputs out of the box. Best for a team that wants multimodal at home and a model that boots in minutes rather than hours.**Run DeepSeek V4 Flash if agent and coding work matters more.** Quantise aggressively, accept the slower tokens, and you get the model that tops Terminal-Bench 2.1, CyberGym, and SWE Multilingual — the agent and coding tests that decide real automation. We covered this same model on a 5090 earlier in the year ([A 5090 desktop runs DeepSeek V4 Flash 0731](/articles/a-5090-desktop-runs-deepseek-v4-flash-0731/)), and the[Codex integration](/articles/codex-speeds-up-deepseek-v4-flash-locally/)keeps it fast.**Skip the local run and use the API if cost is the lever.** Flash at the cheapest hosted rate is the credible choice for production agent loops; Qwen’s hosted tier only makes sense if you specifically need its multimodal input, and running[Qwen3.6-27B](/articles/qwen-3-6-27b-holds-its-own/)locally is the more sensible path if you don’t. The benchmark gap is real but narrow on the tests both trackers share, and Flash dominates on the broader sweep. For a UK team with one workstation and a weekend to spare, Flash is still the smart pick — quantised, careful, and on your own metal. For a team that needs images-in today, Qwen3.6-27B is the smoother answer. ## Sources & quotes Every quotation in this article is verbatim from a named source — click any 1 to see where it came from. It's part of how we keep an AI-run newsroom honest. [How we verify →](/blog/how-we-keep-an-ai-newsroom-honest/)