Qwen 3.8 vs DeepSeek V4 Flash: 27B and Flash-Next Compared (2026)

“Qwen 3.8 vs DeepSeek V4 Flash” is really two comparisons, because Qwen 3.8 ships two open models that make sense next to Flash. Here’s both.

DeepSeek V4 Flash is the open model to beat in the cheap tier: 284B parameters, 13B active, MIT weights, a month of independent scoring, and a checkpoint that fits on two GPUs. Alibaba’s answer isn’t one model, it’s two. Qwen3.8-27B is a dense vision-language model small enough to run on a single card. Qwen 3.8-Flash-Next is a lean MoE that Alibaba calls a preview of the Qwen 4 architecture. People searching this comparison usually mean one of those two, and the answer is different for each.

So this post takes them in turn: the 27B against Flash for teams that want to self-host on modest hardware, and Flash-Next against Flash for teams choosing a Flash-class API model.

The three side by side

One number in that table is the story. Qwen3.8-27B, a dense model you can run on a single GPU, scores the same 52 on Artificial Analysis’s Intelligence Index as DeepSeek V4 Flash, a 284B MoE that needs two H200s. Ten times less memory for the same composite score. That’s the headline for anyone whose question is “what’s the best open model I can actually host.”

The benchmark you can compare, and the ones you can’t

As with every pairing in this release cycle, the vendors’ launch tables don’t overlap. Alibaba reported SWE-bench Pro and CoWorkBench for Flash-Next and its own suite for 27B; DeepSeek reported its own suite for V4 Flash. No vendor-published number puts these models on the same test.

The shared ruler is Artificial Analysis. As of early September: Flash-Next 56, Qwen3.8-27B 52, DeepSeek V4 Flash 52. Two readings of that. First, the Flash-class preview is ahead of DeepSeek’s Flash on the composite, by a smaller margin than GLM 5.3 Flash’s 57, and with an experimental label attached. Second, and more surprising, the dense 27B is level with DeepSeek’s much larger model.

Two caveats before you build a roadmap on that. Composite indexes hide task-level swings; a tie on the aggregate can be a rout in either direction on your specific workload. And DeepSeek V4 Flash has been out a month longer, with deeper independent coverage; the Qwen scores are newer and may move.

Speed

Here DeepSeek pulls away. On the vendors’ hosted APIs, Artificial Analysis measures V4 Flash at roughly 108 output tokens per second, Flash-Next at about 85, and Qwen3.8-27B at about 42. The DeepSeek number gets help from DSpark, the speculative-decoding module that ships with the checkpoint and drafts up to seven tokens per step in vLLM and SGLang. The 27B is dense, so every token runs all 27B parameters, and its hosted speed reflects that.

Those are hosted-API measurements and they reflect each provider’s serving stack as much as the model. Self-hosted numbers depend entirely on the engine and the card, and the 27B is the only one of the three where a single-GPU deployment is even an option. But if the comparison is API to API, DeepSeek is the fast one and the 27B is the slow one, and the ordering is not close.

Hardware: one GPU, two GPUs, or a cluster

This is where the three separate cleanly.

Qwen3.8-27B is the only model in this comparison that runs on a single card. Around 54 GB at BF16 fits an H100, H200, or RTX Pro 6000; around 27 GB at FP8 fits an L40S or RTX Pro 6000 comfortably and an RTX 5090 with short context; 4-bit quants land near 16 GB and run on a 24 GB consumer card. The 27B hardware breakdown has the full table, and the local guide covers Ollama and GGUF.

DeepSeek V4 Flash needs 2x H200 for the pre-compressed 166.9 GB checkpoint with moderate context, and an 8-GPU node for the full 1M window. The MoE rule applies: 13B active saves compute, not memory, so all 284B sit in VRAM.

Flash-Next holds roughly 176B parameters once the n-gram table is counted. It’s multi-GPU for certain, and tested configurations are still settling because the model is a week old and explicitly experimental. Treat it as an API model for now unless you enjoy being early.

If self-hosting cost is the deciding factor, the 27B wins by a wide margin, then DeepSeek, then Flash-Next.

Price

By API, Flash-Next is the cheapest of the three at $0.15 in and $0.47 out per million tokens, close to GLM 5.3 Flash’s launch rates. DeepSeek V4 Flash is $0.44 and $1.32 at peak after its August 17 increase, halved off-peak. Qwen3.8-27B is listed at $0.50 and $3.00, the most expensive per token here, which is odd for the smallest model until you remember that price is set by the provider’s economics, not the parameter count.

That 27B price is also why the self-hosting math flips so hard in its favor. A model that runs on one rented GPU, priced at $3.00 per million output tokens on the API, pays for its own hardware faster than anything else on this list. DeepSeek’s 2x H200 floor is the next-best self-host case. Flash-Next’s API price is low enough that hosting it yourself rarely pencils out.

Modalities and licenses

Both Qwen models take image and video input natively. DeepSeek V4 Flash is text only. If vision is in the workload, DeepSeek is out and the comparison is 27B versus Flash-Next.

Licenses differ in a way that matters for products, not experiments. DeepSeek V4 Flash is MIT. Qwen3.8-27B is Apache 2.0, equally permissive. Flash-Next is qwen-community-1.0, a community license with conditions the other two don’t carry, so read it before committing weights to a commercial deployment. The 27B and DeepSeek are the two clean choices on this axis.

Which one to use

Choose Qwen3.8-27B if you want to self-host on one GPU, need image or video input, or want a permissively licensed model to fine-tune. It matches DeepSeek V4 Flash on the shared composite at a tenth of the memory, and it’s the only one of the three that fits a workstation-class deployment. Accept slower per-token speed as the trade.

Choose DeepSeek V4 Flash if throughput matters most, if you want the longest independent track record, or if your context needs reach past 262K. It’s the fast one, and its 2x H200 floor is still modest for a model this size.

Choose Flash-Next if you’re API-first, cost-sensitive, and want the highest shared score of the three, or if you’re evaluating where Qwen 4 is headed, which is the stated reason it exists. Don’t build a production dependency on it yet; Alibaba says it’s experimental and the license is stricter.

Most teams reading this will end up with two of the three: a self-hosted 27B for flat-cost volume and data control, and an API model for the tail. Since all three speak OpenAI-compatible interfaces, that split is routing configuration.

How to run each today

Qwen3.8-27B: self-host on a single GPU using the production guide, or call it through Yotta AI Gateway, where Qwen3.8-27B and Qwen3.8-Max are both live.

DeepSeek V4 Flash: self-host on a 2x H200 pod using our deployment tutorial, or call it through the Gateway, where V4 Flash and V4 Pro are live with flat pricing and no peak-hour windows.

Flash-Next: Alibaba’s API, or the weights on Hugging Face on multi-GPU capacity by the hour if you want to test the architecture yourself.

Since the 27B and DeepSeek V4 Flash sit behind the same Gateway key, an A/B between them on your own traffic is a model-string change.

Frequently asked questions

Is Qwen 3.8 better than DeepSeek V4 Flash? Depends which Qwen 3.8. On Artificial Analysis’s Intelligence Index, Flash-Next scores 56, and Qwen3.8-27B and DeepSeek V4 Flash both score 52. DeepSeek is the fastest of the three on hosted APIs by a wide margin. No vendor benchmark includes both sides.

Which is cheaper? By API, Flash-Next at $0.15 in / $0.47 out. DeepSeek V4 Flash is $0.44 / $1.32 at peak. Qwen3.8-27B is listed at $0.50 / $3.00. By self-hosting, the 27B wins outright: one GPU versus two H200s for DeepSeek.

Can I run any of them on a single GPU? Qwen3.8-27B, yes: an 80 GB card at BF16, a 48 GB card at FP8, or a 24 GB card at 4-bit. DeepSeek V4 Flash needs two H200s. Flash-Next is multi-GPU.

Which handles images and video? Both Qwen models. DeepSeek V4 Flash is text only.

Is Flash-Next production-ready? Alibaba labels it an experimental preview of the Qwen 4 architecture. Its numbers are strong but unreplicated, tested deployment configs are still settling, and the license is stricter than the other two. Evaluate it; don’t depend on it yet.

Which is faster? DeepSeek V4 Flash, around 108 output tokens per second on hosted APIs, then Flash-Next around 85, then Qwen3.8-27B around 42. Self-hosted numbers vary with the engine and hardware.

Are they on Yotta? Qwen3.8-27B, Qwen3.8-Max, DeepSeek V4 Flash, and DeepSeek V4 Pro are all live on Yotta AI Gateway with OpenAI-compatible endpoints. Flash-Next is not on the Gateway yet; this post will note it when it is.

Bottom line

DeepSeek V4 Flash is the fast, proven, text-only option with a two-GPU floor. Qwen3.8-27B matches it on the one shared score while fitting on a single card, with vision input and a permissive license, at the cost of speed. Flash-Next beats both on the composite and the API price but is an architecture preview with a community license, not a production commitment.

The full breakdowns: Qwen 3.8 27B hardware requirements, DeepSeek V4 Flash hardware requirements, the Flash-Next preview, and the three-way Flash-class comparison that adds GLM. To run the 27B against DeepSeek on your own traffic, both are live on Yotta AI Gateway, and single-GPU and multi-GPU pods by the hour cover the self-hosted side.