May 2026 · AI Trends

Qwen 3.6 vs DeepSeek V4 vs Llama 4 Scout — Open-Weight LLM Comparison After Hands-On Testing

April 2026 was the month open-weight LLMs landed in waves. Alibaba shipped Qwen 3.6. DeepSeek dropped V4. Meta countered with Llama 4 Scout, leaning on multimodal. All three claim GPT-4o-class performance. Once you actually deploy them, though, the three head in completely different directions.

They look similar from the outside, but each one wins different jobs. There's no clear "best" model. The right answer depends on what you need. Want to run something light locally? Qwen. Building a coding agent? DeepSeek. Processing very long documents? Llama is the closest thing to an answer.

This post is a record of running all three side-by-side. Benchmarks, API prices, context length, Korean quality, local-deployment difficulty — all in one frame. It also lays out which model fits which situation. If you don't have time for the full read, the two comparison tables and the recommendation table cover most of the decision.

All three at a glance

Specs first, in one frame. Parameter count, context length, license, and multimodal support are the core differentiators. "Open-weight" hides a lot of variance.

Benchmark numbers come from the SWE-bench Verified and HumanEval+ official leaderboards. DeepSeek V4 has a clear edge on coding accuracy. On context length, Llama 4 Scout simply isn't in the same league as the others. For lightweight local serving, Qwen 3.6 is essentially the only viable choice.

Qwen 3.6 — the new standard for cheap local deployment

Qwen 3.6's main story is its MoE architecture. Of the 35B total parameters, only 3B are active per inference. That's why a single RTX 4090 can run a Q4-quantized version. You'll see 20-30 tokens per second — more than enough for everyday chat, summarization, and light coding.

The dual Thinking / Non-Thinking modes are also unusual. Prepend `/think` in the system prompt and reasoning steps activate, raising math and logic accuracy. Switch to `/no_think` and it answers like a regular fast chatbot. Two modes from one model is genuinely practical. Token cost can be tuned per situation.

Korean output quality is stable too. Alibaba clearly trained on a lot of non-Chinese Asian-language data. In direct comparison with GPT-4o, the Korean output difference is barely noticeable. For a local Korean chatbot, Qwen 3.6 is the first choice.

The weakness is coding. SWE-bench Verified 52.3% trails DeepSeek V4 (65.7%) by 13 points. Single-function generation is fine, but multi-file refactoring and agentic coding workflows show the gap. If coding is your main use case, pick a different model.

DeepSeek V4 — agentic coding SOTA

DeepSeek V4 is built for code. SWE-bench Verified 65.7% sits between GPT-4o (around 63%) and Claude Sonnet 4.6 (around 67%). It's effectively the top open-weight model on this metric. Add the price and it has the best coding workflow value, period.

The API is OpenAI SDK compatible. Swap one line of base_url and one model name, and existing code keeps working. Migration cost is near zero. Cline, Aider, Continue and other coding agents plug in immediately. For indie developers feeling Anthropic API costs, this is the most attractive option.

The downside is that 671B size. Local execution is essentially impossible. Full fine-tuning is out of reach too. You're tied to the API. In environments where data sovereignty matters or internet access is unreliable, this isn't an option. That's where Qwen 3.6 fits.

Llama 4 Scout — 10M context and multimodal

Llama 4 Scout's standout strength is context length. 10,000,000 tokens is roughly 80x the others. A 500K-line legacy codebase fits without RAG. Legal review, long-paper analysis, and book-length text processing all see real differences here.

Native multimodal is the other differentiator. Image, video, and text are processed in a single model. No separate vision encoder needed. It simplifies multimodal RAG pipelines. Tasks that combine PDF tables with surrounding text are a good fit.

Two weaknesses, though. First, fully filling the context spikes cost and latency. A 10M-token input plus output can take minutes to respond. Second, Korean is the weakest of the three — a consequence of English-heavy pretraining. For Korean services, Qwen or DeepSeek is better.

The benchmarks are average. SWE-bench 48.9% is the lowest of the three. On coding alone, the appeal is limited. But for tasks that require long context, no other model substitutes. The use case is well-defined.

Pricing — API basis

The DeepSeek V4 API is the cheapest. Versus GPT-4o, input is roughly 1/18 and output 1/35. Llama 4 Scout pricing varies more across cloud providers — the row above averages Together AI and Fireworks AI. Qwen runs locally, so once traffic crosses a threshold, self-hosting becomes the cheapest path.

Recommendations by use case

Using all three together

You don't have to commit to one. Swapping models per workflow stage is the realistic move. A common pattern: Qwen for lightweight local classification and summarization, DeepSeek for code generation, Llama for the long-document inputs.

The big win here is cost optimization. Maybe 80% of your tokens go to simple classification and summarization — handle those locally on Qwen. Send the accuracy-critical 20% (code generation) to the DeepSeek API. Route occasional huge inputs to Llama. Average cost drops to roughly a tenth of GPT-4o-only.

Frequently asked questions

Q. Do all three models support Korean?

They do, but quality varies. Qwen 3.6 has rich Asian-language training data, so Korean output is stable. DeepSeek V4 also performs well in Korean. Llama 4 Scout is primarily English-pretrained, so Korean is relatively weaker. For Korean-language services, Qwen 3.6 or DeepSeek V4 is the better choice. From direct comparison, Llama 4 Scout occasionally produced unnatural Korean sentences.

Q. When should I use Qwen 3.6's Thinking mode?

Use it for tasks that require deep reasoning — math problems, logical inference, multi-step coding. For simple QA, summarization, or translation, Non-Thinking mode is faster and sufficient. Prepend `/think` or `/no_think` to the system prompt or first message to switch modes. Thinking mode consumes 2–3× more tokens, so factor in cost when using the API. For local deployment, Thinking mode can be used freely with no cost concern.

Q. Is the DeepSeek V4 API compatible with the OpenAI SDK?

Yes. The DeepSeek API provides an OpenAI-compatible endpoint. Just change `base_url` to `https://api.deepseek.com` and swap the `api_key` — existing openai Python SDK code works as-is. Change the model name to `deepseek-chat`. Parameters like `temperature`, `max_tokens`, `stream`, and `tools` follow the same format. In most projects, editing 2–3 lines is all it takes.

Q. Is Llama 4 Scout's 10M context actually useful in practice?

It makes a real difference for whole-codebase analysis, legal-document review, and long-paper summarization. For example, you can drop a 500K-line legacy codebase into context and ask questions. RAG-free ingestion of full-source documents becomes possible. That said, fully filling the 10M context dramatically increases cost and latency, so use it only when needed.

Q. Which of the three is easiest to fine-tune?

Qwen 3.6 is the most accessible. You can work with GGUF locally or fine-tune via LoRA using libraries like Unsloth. Llama 4 Scout also has a strong ecosystem and Meta's official fine-tuning guide. DeepSeek V4 at 671B is impractical for full fine-tuning — partial LoRA on selected layers, or substituting domain-specific system prompts, is the pragmatic path.

Q. Can local Qwen 3.6 ever be faster than the GPT-4o API?

On RTX 4090 with Q4 quantization, expect 20–30 tokens per second. GPT-4o API throughput (with network) typically runs 40–70 tok/s. APIs are often faster on raw speed, but removing network latency can flip the result for batch workloads. More importantly, local has zero per-token cost. Concurrent requests improve via parallel batching within GPU memory limits.

Wrap-up

All three are "open-weight LLMs," but they aimed in completely different directions. DeepSeek V4 for agentic coding. Llama 4 Scout for long-form multimodal. Qwen 3.6 for cheap local deployment. None of them fully replaces the others. Pick by purpose.

As of May 2026: DeepSeek V4 for agentic coding, Qwen 3.6 for lightweight self-hosting, Llama 4 Scout for large-document analysis. Mixing them is also a strategy. On API cost alone, this often beats subscribing to a single closed model. Open-weight has cleared the practical-option bar before, but this time the gap closed for real.

This article is based on publicly available information as of May 2026. Model updates and benchmark numbers should be verified against the latest official channels.