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# DeepSeek vs Qwen: Which Open Model Wins?
- URL: https://www.edgewisely.com/deepseek-vs-qwen/
- Published: 2026-10-27T21:57:58.000Z
- Updated: 2026-10-27T21:57:57.000Z
- Description: DeepSeek or Qwen? A head-to-head on verified benchmarks, real per-token pricing, MIT vs Apache licenses, and a clear verdict on which open-weight model to pick for coding, agents and self-hosting.
- Author: John Karpentar
- Tags: AI, Opinion

DeepSeek vs Qwen comes down to this: pick **DeepSeek's V4 line** for the strongest verified reasoning and coding scores, the lowest API price, and a clean MIT license; pick **Qwen** when you need a small, Apache-2.0 model that fits one GPU or a model that can see images and video. Both are open-weight and self-hostable. For most text and agent work in late 2026, DeepSeek edges ahead on cost-per-quality; Qwen wins on range.

## TL;DR

- **Verdict:** **DeepSeek-V4-Pro-0813** (reasoning) and the cheaper **V4.1-Flash** tier lead on verified coding benchmarks and undercut Qwen on price, both under a clean **MIT** license. **Qwen3.8** wins on breadth — a 2.4T flagship, a vision-capable **27B** that runs on one GPU, and multimodal input DeepSeek V4 no longer offers.
- **Flagship benchmarks (vendor-reported, Oct 2026):** On **Terminal Bench 2.1** the two flagships effectively tie — **DeepSeek V4-Pro 87.9** vs **Qwen3.8-2.4T 86.6**. DeepSeek leads **DeepSWE** (62.7 vs 56.6); Qwen leads **HLE** narrowly (43.6 vs 42.7) and publishes **GPQA Diamond 92.6** and **SWE-bench Pro 67.7**, which DeepSeek's card does not.
- **Pricing (per 1M tokens, Oct 2026):** DeepSeek V4-Pro is **$1.32 in / $3.96 out** at peak (about half off-peak), with a \~97% cache-hit discount. Qwen3.8-Max is **$2 in / $6 out**. Flagship-to-flagship, DeepSeek is roughly a third cheaper.
- **Licenses:** DeepSeek V4 weights are **MIT**. Qwen3.8-27B is **Apache 2.0**. Qwen's 2.4T flagship ships under a custom **"qwen3.8-max"** license — read the terms before you assume Apache.
- **Modality:** Qwen's **27B is a vision-language model** (image and video in); the **2.4T open weight is text-only**, and DeepSeek V4's experimental vision tier was retired. For images, reach for Qwen's 27B or the hosted Qwen3.8-Max service.

## What are DeepSeek and Qwen?

Both are Chinese open-weight model families you can call through a first-party API or download and self-host. The difference is shape: DeepSeek ships two text models under one license; Qwen ships a size ladder, some of it multimodal.

**DeepSeek** is built by DeepSeek-AI. Its current generation is the **V4** line, released under the **MIT license** per the [DeepSeek-V4-Pro-0813 model card](https://huggingface.co/deepseek-ai/DeepSeek-V4-Pro-0813?ref=edgewisely.com). Two models matter:

- **DeepSeek-V4-Pro-0813** — the flagship reasoning model. The model card lists **1.7T total parameters** (sparse mixture-of-experts; active count not disclosed on the card) and a **1M-token context** window with up to 384K output, per [DeepSeek V4's published specs](https://www.yottalabs.ai/post/deepseek-v4-release-date-specs-how-to-access-2026?ref=edgewisely.com).
- **DeepSeek-V4.1-Flash-1017** — the fast, cheap tier served as `deepseek-flash`. It replaced the older V4-Flash on the API in September, per [DeepSeek's model docs](https://api-docs.deepseek.com/quick%5Fstart/pricing?ref=edgewisely.com), and keeps the 1M-token context.

**Qwen** is Alibaba's family. Its current generation is **Qwen3.8**, and the lineup is wider:

- **Qwen3.8-2.4T-A95B** — the flagship open weight, also served as the **Qwen3.8-Max** API. The [model card](https://huggingface.co/Qwen/Qwen3.8-2.4T-A95B?ref=edgewisely.com) lists a **2.4T-parameter MoE with 95B active**, a **262K context extensible to \~1M**, a custom **"qwen3.8-max" license**, and — importantly — describes it as **"a text-only model."** Vision lives in the hosted Max service, not this open weight.
- **Qwen3.8-27B** — the efficient open weight, **Apache 2.0**, a **262K context**, and a native **vision-language** design that accepts image and video input, per the [Qwen3.8-27B model card](https://huggingface.co/Qwen/Qwen3.8-27B?ref=edgewisely.com).

The headline difference: DeepSeek ships two text models under one clean license. Qwen ships a size ladder, with multimodality in the small model but a split license at the top.

## DeepSeek vs Qwen: the benchmark differences that matter

Flagship to flagship, the two are close, and each wins on different tests. Below are the numbers both vendors publish on their own cards — treat them as vendor self-reported.

| Model                        | Maker    | Params (total / active) | Context     | License              | API price (in / out, per 1M) | Modality             | Best for                       |
| ---------------------------- | -------- | ----------------------- | ----------- | -------------------- | ---------------------------- | -------------------- | ------------------------------ |
| **DeepSeek-V4-Pro-0813**     | DeepSeek | 1.7T / not disclosed    | 1M          | MIT                  | $1.32 / $3.96 (peak)         | Text                 | Hard reasoning, agents         |
| **DeepSeek-V4.1-Flash-1017** | DeepSeek | MoE (not disclosed)     | 1M          | MIT                  | Well below Pro               | Text                 | Cheap high-volume inference    |
| **Qwen3.8-2.4T-A95B (Max)**  | Alibaba  | 2.4T / 95B              | 262K → \~1M | qwen3.8-max (custom) | $2 / $6                      | Text                 | Breadth, frontier reasoning    |
| **Qwen3.8-27B**              | Alibaba  | 27B                     | 262K → \~1M | Apache 2.0           | Self-host                    | Text + image + video | Self-hosting, multimodal, edge |

All figures as of October 2026, from the vendors' model cards and published pricing.

![DeepSeek V4-Pro vs Qwen3.8 flagship benchmarks (Terminal Bench 2.1, DeepSWE, HLE)](https://storage.ghost.io/c/54/5a/545a66b3-60ef-480c-80ae-765bac52f6ec/content/images/2026/10/deepseek-vs-qwen-benchmarks.png)

Chart: Edgewisely, data from [DeepSeek and Qwen model cards](https://huggingface.co/deepseek-ai/DeepSeek-V4-Pro-0813?ref=edgewisely.com) (as of Oct 2026)

On the benchmarks both flagships report, it is a genuine split:

- **Terminal Bench 2.1** (agentic terminal tasks): DeepSeek V4-Pro **87.9** vs Qwen3.8-2.4T **86.6** — the one strictly identical comparison, and a near-tie.
- **DeepSWE** (coding agent): DeepSeek V4-Pro **62.7** vs Qwen's **56.6** (Qwen ran the 1.1 revision, so close but not identical).
- **HLE** (hard reasoning, no tools): DeepSeek V4-Pro **42.7** vs Qwen3.8-2.4T **43.6** — Qwen a hair ahead.
- **GPQA Diamond** and **SWE-bench Pro**: Qwen reports **92.6** and **67.7**; DeepSeek's V4-Pro card publishes neither, so there is no fair pairing.

The takeaway: there is no clear quality winner between the flagships. DeepSeek edges the coding-agent tests; Qwen looks stronger on graduate-level QA and the SWE-bench Pro number. Where DeepSeek pulls clearly ahead is price, which the next section covers.

## DeepSeek vs Qwen for coding: which is better?

For coding, **DeepSeek V4-Pro is the stronger text-only pick; Qwen's 27B is the one to use when your workflow needs to see images.**

On the coding-relevant benchmarks both families publish, DeepSeek V4-Pro's **DeepSWE 62.7** edges Qwen's **56.6**, and its **Terminal Bench 2.1 of 87.9** narrowly beats Qwen's 86.6\. DeepSeek's card also reports strong agent-coding numbers like **NL2Repo 61.5** and **Cybergym 83.3**. So on raw qwen vs deepseek coding scores, DeepSeek has a small, consistent lead among the pure-text models.

But the deepseek or qwen coding decision is not only about scores:

- **Pick DeepSeek** if your agent lives in a terminal and a repo, you want the lowest token cost, and you do not need image input. V4.1-Flash is the value tier for high-volume code generation; V4-Pro for the hard planning steps.
- **Pick Qwen3.8-27B** if your coding assistant needs to read a UI screenshot, a whiteboard photo, or a design mock, since the 27B takes image and video and posts a strong **LiveCodeBench v6 of 90.3** and **SWE-bench Pro 61.7** of its own.

A common cost-sensitive pattern is DeepSeek V4.1-Flash for the bulk of calls and V4-Pro for the few steps that need deep reasoning. If you route models across providers, an [LLM gateway like LiteLLM or OpenRouter](https://www.edgewisely.com/litellm-vs-openrouter/) makes that split a config change rather than a rewrite.

## How much does each cost?

DeepSeek is cheaper than Qwen at the flagship tier on first-party API.

| Model                    | Input / 1M     | Output / 1M    | Notes                                          |
| ------------------------ | -------------- | -------------- | ---------------------------------------------- |
| DeepSeek V4-Pro-0813     | **$1.32**      | **$3.96**      | Peak rate; \~half off-peak; cache hit \~$0.044 |
| DeepSeek V4.1-Flash-1017 | Well below Pro | Well below Pro | deepseek-flash, the high-volume tier           |
| Qwen3.8-Max (2.4T)       | **$2.00**      | **$6.00**      | Cached input $0.25                             |
| Qwen3.8-27B              | Self-host      | Self-host      | Open weight; no official per-token API rate    |

Prices as of October 2026\. DeepSeek V4-Pro figures are DeepSeek's published rates [as compiled in its V4 spec writeup](https://www.yottalabs.ai/post/deepseek-v4-release-date-specs-how-to-access-2026?ref=edgewisely.com); Qwen3.8-Max pricing is [Alibaba's metered API rate](https://www.yottalabs.ai/post/qwen-3-8-api-access-token-plan-pricing-2026.md?ref=edgewisely.com).

Flagship vs flagship, **DeepSeek V4-Pro is roughly a third cheaper on input and output than Qwen3.8-Max**. DeepSeek's cache-hit discount is aggressive — about 97% off repeated input — so workloads with heavy prompt reuse get cheaper still. And because both families are open-weight, **self-hosting resets the comparison**: on your own GPUs the 27B Qwen is far cheaper to serve than any trillion-parameter model from either side.

## Which should you self-host?

For self-hosting, **Qwen3.8-27B is the easiest to run and the cleanest to license; DeepSeek's V4 weights are the most permissive but far heavier.**

The license split matters more than most comparisons admit:

- **DeepSeek V4-Pro and V4.1-Flash are MIT** — about as permissive as it gets: commercial use, modification, redistribution, no copyleft.
- **Qwen3.8-27B is Apache 2.0** — also business-friendly, with an explicit patent grant.
- **Qwen3.8-2.4T (the flagship) is not Apache.** It ships under a custom **"qwen3.8-max" license**. If you standardize on Apache or MIT for legal reasons, the big Qwen model may not qualify — check the terms.

On hardware it is no contest at the small end: a **27B** model runs on a single high-memory GPU or a small node. DeepSeek's V4 models are trillion-parameter MoEs; even sparsely activated, they need serious memory to hold all experts, which puts them squarely in datacenter territory. If you are standing up your own inference, our [vLLM vs SGLang](https://www.edgewisely.com/vllm-vs-sglang/) comparison covers the serving layer, and [GPU cloud options](https://www.edgewisely.com/top-7-gpu-cloud-providers-for-ai-training-and-inference-2026/) cover the hardware.

Self-host DeepSeek V4 if you need frontier text quality on your own infrastructure and have the GPUs. Self-host Qwen3.8-27B if you want a capable, multimodal, Apache-licensed model that fits on hardware you already own.

## DeepSeek or Qwen: who should use which?

- **Building a text coding or terminal agent, cost matters:** DeepSeek. V4.1-Flash for volume, V4-Pro for hard steps. Lowest price-per-quality, MIT, 1M context.
- **Need image or video input:** Qwen3.8-27B (or the hosted Qwen3.8-Max). DeepSeek V4 is text-only now.
- **Self-hosting on modest hardware:** Qwen3.8-27B. It is the only model here that fits comfortably on a single GPU, and it is Apache 2.0.
- **Standardizing on a permissive license across your stack:** DeepSeek (MIT) or Qwen3.8-27B (Apache). Avoid depending on the custom-licensed Qwen 2.4T flagship if license purity is a hard requirement.
- **Want frontier reasoning and QA scores on paper:** either flagship — they trade wins. Decide on price and license, not a two-point benchmark gap.

## Our take

The DeepSeek-vs-Qwen choice is not really a quality gap. It is a difference in strategy, and the right pick depends on which strategy matches how you build.

DeepSeek is running a **narrow, deep, cheap** play. Two text models, one permissive MIT license, a 1M-token context, and prices that undercut nearly everyone. The V4 line is engineered to win on cost-per-task, and the verified vendor numbers back the quality claim: a near-tie with Qwen's 2.4T flagship on Terminal Bench 2.1 and a clear lead on DeepSWE, at roughly two-thirds of Qwen's API price. DeepSeek is not trying to be everything. It is trying to be the cheapest model you would actually trust with a reasoning or agent workload, and it largely succeeds — as long as that workload is text.

Qwen is running a **broad, full-ladder** play. A 2.4T flagship, a 27B workhorse, image and video input in the small model, and sizes for everything from edge to datacenter. That breadth is genuinely valuable if your product is not just text-in-text-out. But it comes with a messier story: a split license, and a flagship that is text-only despite the "multimodal family" framing. This is the single most misreported thing about Qwen3.8, so it is worth stating plainly — the open 2.4T weight does not take images; the vision lives in the 27B and in the hosted Max service. If you picked the flagship expecting to feed it screenshots, you picked wrong.

Here is what the benchmarks capture and what they miss. Terminal Bench 2.1 is the fairest number because both cards report the same version, and it is a dead heat. The rest of the comparison is partial: the two vendors publish mostly different benchmark suites, so DeepSeek's Cybergym and NL2Repo have no Qwen pairing, and Qwen's GPQA Diamond and SWE-bench Pro have no DeepSeek pairing. Anyone who tells you one flagship is decisively smarter than the other is reading past the gaps in the data. What none of these text benchmarks capture is modality — and that single axis, not a reasoning score, is what actually decides the choice for a large class of products.

For people building agents and products, the practical read is this. If you are shipping a text-based coding or research agent, DeepSeek is the sharper tool: cheaper, MIT-licensed, and strong on the agent benchmarks. The V4.1-Flash tier is the quiet standout — a cheap, high-volume model that lets an agent make its thousands of routine calls between the few expensive planning calls you route to V4-Pro. If you are building anything that ingests images, documents-as-pictures, or video, Qwen is the only real option here, and Qwen3.8-27B is the one to reach for first: Apache 2.0, single-GPU-friendly, and multimodal. The 2.4T flagship is impressive, but between the custom license and the higher API price, it is a harder sell than DeepSeek's V4-Pro for most teams.

The real story under the benchmark noise is the open-weight economics. Both families have made frontier-grade quality something you can run yourself, and the MIT and Apache licenses mean you are not locked into either vendor's API. Start on the hosted endpoint for speed, move to self-hosting when volume justifies it, with no rewrite. That optionality is worth more than a couple of points on any single benchmark.

**My verdict: default to DeepSeek V4 for text and agent work, and reach for Qwen3.8 when you need multimodality or a single-GPU footprint.** Best for coding and cost: DeepSeek. Best for breadth and vision: Qwen's 27B. Avoid the Qwen 2.4T flagship if you need a permissive license, and avoid DeepSeek V4 if your use case depends on image or video input — it does not do it. One honest caveat: this space moves monthly, so re-check the licenses and prices before you commit production spend.

## Frequently Asked Questions

### Is DeepSeek or Qwen better for coding?

DeepSeek is better for most text coding. On vendor-reported benchmarks, DeepSeek V4-Pro beats Qwen's flagship on DeepSWE (62.7 vs 56.6) and Terminal Bench 2.1 (87.9 vs 86.6), and it is cheaper per token. Choose Qwen's 27B only if your coding workflow needs image or video input, which DeepSeek V4 does not support.

### Is Qwen better than DeepSeek overall?

It is a trade, not a knockout. The flagships tie on Terminal Bench 2.1; DeepSeek leads DeepSWE while Qwen leads HLE and posts higher GPQA Diamond and SWE-bench Pro numbers. DeepSeek is cheaper and MIT-licensed; Qwen offers a vision-capable 27B and a wider size range. Pick on price, license, and modality.

### Is DeepSeek cheaper than Qwen?

Yes, at the flagship tier. As of October 2026, DeepSeek V4-Pro is $1.32 input / $3.96 output per 1M tokens at peak versus Qwen3.8-Max at $2 / $6, and DeepSeek's cache-hit discount is roughly 97%. DeepSeek's V4.1-Flash tier is cheaper still. Self-hosting changes the math, since the 27B Qwen is cheapest to run.

### Are DeepSeek and Qwen open source?

Both are open-weight. DeepSeek's V4-Pro and V4.1-Flash are MIT-licensed. Qwen3.8-27B is Apache 2.0\. The exception is Qwen's 2.4T flagship, which uses a custom "qwen3.8-max" license rather than Apache, so confirm the terms before commercial use. All publish downloadable weights you can self-host.

### Can you self-host DeepSeek and Qwen?

Yes, both publish weights on Hugging Face. Qwen3.8-27B is the easiest, fitting on a single high-memory GPU under Apache 2.0, and it is multimodal. DeepSeek's V4 models are trillion-parameter mixture-of-experts systems, so they need datacenter-scale memory despite sparse activation. Start on the hosted API and move to self-hosting as volume grows.