Qwen vs Llama: Open Models Compared

Qwen is the more active family right now: Qwen3.8-27B, released in August 2026, is Apache-2.0 and ships with vision, thinking controls and 262,144 tokens of native context. Meta's newest Llama weights on Hugging Face date from 2025 (Llama 4), carry a custom licence and sit behind an access request. For a new project, start with Qwen and use Llama when an ecosystem or tool needs it. Checked 31 October 2026.

The short version

What each family is

Qwen

Qwen is Alibaba's model family. On Hugging Face, the Qwen organisation's recent releases include Qwen3.8-27B (created 5 August 2026), Qwen3.8-Flash-Next (24 August 2026), Qwen3Guard safety models, image models and Qwen3-Coder-Next (January 2026). Our family page is Qwen models.

The Qwen3.8-27B model card describes a compact dense model built on the Qwen3.5 architecture, with gains across coding, professional work, research and long-horizon agent tasks. Details from the card:

• Type: causal language model with a vision encoder, so it takes images and video as well as text.
• Context length: 262,144 tokens natively, extensible up to 1,000,000.
• Thinking mode is on by default and can be switched off per request, with a reasoning_effort setting for depth.
• Parameters: about 27.8 billion by Hugging Face's count.
• Licence tag: Apache-2.0.
• Downloads: about 7.6 million.

The card also says a hosted version with more production features, such as 1M context by default and built-in tools, is available from Qwen.

Llama

One update before the Llama details: Meta's newest open weights are now Muse Spark Open (816B total, 42B active, released 15 October 2026), not a Llama model. It is gated, the licence requires the display line "Built with Muse Spark Open", and model-as-a-service above 20 million US dollars of group revenue needs a Meta agreement. The Llama notes below cover the older Llama releases.

Llama is Meta's family. Our page is Llama models. On the Hugging Face meta-llama organisation, the most downloaded model is Llama 3.1 8B Instruct (about 4.7 million downloads). The newest generations listed are Llama 4 Scout (about 108.6 billion total parameters) and Llama 4 Maverick (about 401.6 billion), both last modified in May 2025, plus Llama 3.3 70B Instruct from December 2024. We found no newer Meta weights under that organisation when we checked. Meta may publish newer models elsewhere, so check its own announcements.

All the Llama repositories we checked are gated: Hugging Face reports manual access approval, so you request access and wait.

Licences

Apache-2.0 is a standard permissive licence. The Llama licences are custom. Both the Llama 4 and Llama 3.3 community licences require a "Built with Llama" display and a notice file when you distribute, and ask you to request a licence from Meta if your products had more than 700 million monthly active users on the release date. The Qwen Flash model's custom licence also needs reading before commercial use. Licence tags can change, so check the model page at the time you adopt.

Sizes and what they imply

Total parameters are not the memory you need, because quantisation shrinks them, and a mixture-of-experts model only uses some experts per token. But weights must still be stored. A 27B dense model fits on one large GPU with quantisation. A 400B model needs several. For hardware guidance see Ollama vs vLLM, and for how to rent the GPUs see Runpod vs Lambda.

Running them

Locally. Ollama's repository lists Qwen and other families it targets, and its runtimes are covered in Ollama vs LM Studio and llama.cpp vs Ollama. A 27B model needs significant memory. Ollama's library has pages named qwen3.8, llama4 and llama3.3.

On a host. Groq lists Qwen3.8-27B as a preview model at $0.80 input and $4.00 output per million tokens, about 450 tokens a second, with a 131,072-token context. Cerebras lists Qwen 3.8 27B at about 1,850 tokens a second with 64K context on the free tier and 128K on paid tiers. Groq's production list includes Llama 3.3 70B and Llama 3.1 8B as enterprise models, priced through sales. Compare the vendors in Groq vs Cerebras.

In an agent. Both can sit behind an OpenAI-style endpoint for a coding agent. Open-source agents lists tools that accept a custom endpoint, and best open-source LLM for coding covers more models.

Capabilities

From the model cards, not from our tests.

We do not publish benchmark scores. Vendor-published scores compare their own models under their own settings, and they are hard to compare across families. Our guide to reading them is how to read AI benchmarks. For a fair comparison, use your own prompts.

What the weights cost in memory

Weights take parameters times bytes per parameter. This is simple arithmetic from the parameter counts above, and it covers weights only. The context cache and runtime overhead come on top.

So a 4-bit Qwen3.8-27B can fit a 24 GB card with room for context, while the same model at 16-bit wants an 80 GB card. The big Llama 4 Maverick needs several GPUs even at 4-bit. Quantising costs some quality, and how much depends on the method and the task. A mixture-of-experts model still needs all its experts in memory, even though each token uses only some of them, so total parameters, not active ones, set the memory bill.

To price the rental side, see the H100 rates in Runpod vs Vast.ai: at $2.69 to $3.99 an hour for 80 GB, a single card covers Qwen3.8-27B at 8-bit with room to spare.

A fair test between the two

The only comparison that counts is your own work. Here is a plan that takes an afternoon.

1. Pick the model sizes you can actually run: Qwen3.8-27B against Llama 3.3 70B is a realistic pairing for one 80 GB card, with the 70B at 4-bit.
2. Collect ten prompts from your real work. Include a bug fix, a refactor, a code review, a long-context question and one prompt in each language you use.
3. Run both through the same runtime with the same quantisation level and sampling settings, so you compare models and not setups.
4. For Qwen, run each prompt twice, once with thinking on and once off, and note the token counts.
5. Score the answers blind: have a colleague rank them without knowing which model wrote which.
6. Record speed and memory use.
7. Repeat after any model update, since both families move.

If the models tie, pick on licence and ecosystem. If one wins clearly on your tasks, pick that one and ignore the leaderboard.

Ecosystem and support

Llama's strength has been its ecosystem: years of tutorials, fine-tunes, tools and hosts built around it. Many tools list Llama first. That head start does not vanish, but Meta's newest weights we could find are from 2025, and Qwen has shipped repeatedly since. Hugging Face shows Qwen3.8-27B with about 7.6 million downloads in under three months, against about 4.7 million for Llama 3.1 8B Instruct over its much longer life. Downloads are a rough signal of use, not quality.

If you depend on a specific fine-tune, a guardrail model or a tutorial built on Llama, check that a Qwen equivalent exists before you switch. Qwen publishes its own guard models (Qwen3Guard), image models and coding models, so the family is broad.

Privacy and data handling

Downloaded weights run on your hardware, so prompts stay with you. Hosted versions send prompts to the host. Qwen also offers a hosted version of Qwen3.8-27B. We did not read the privacy terms of Qwen's hosted API; for Groq, see Groq. Alibaba is a Chinese company. If your policy limits models by origin, note that open weights run locally do not call home, though you should confirm that on your setup. For private deployment guidance, see private AI.

Limits and gotchas

• Gated Llama access. You must request and be approved on Hugging Face before you can download.
• Licence differences. Apache-2.0 for Qwen3.8-27B, custom for Llama and Qwen Flash.
• Thinking costs tokens. Qwen3.8's thinking mode is on by default, so output tokens add up. Turn it off for simple tasks.
• Size. Llama 4 Maverick is far larger than most single machines can host.
• Preview hosting. Groq lists Qwen3.8-27B as preview, which can be discontinued at short notice.
• Stale comparisons. Posts that call Llama the default open model predate these releases.

Who should pick which

A developer wanting a single-GPU coding model

Qwen3.8-27B or Qwen3-Coder-Next, subject to the memory you have.

A company needing clear commercial terms

Qwen3.8-27B's Apache-2.0 licence is the simplest to read. Have counsel review any custom licence before you use it.

A team with Llama-dependent tooling

Stay on Llama 3.3 or 4 until you test a replacement.

A researcher

Both are fine. Qwen's recent releases and open access make it easy to start.

Which card fits which model

Using the 4-bit weight sizes from the memory table, and leaving headroom for the context cache, here is a first guess at hardware. Treat these as starting points to test, since the context cache grows with prompt length.

The practical point is that one 80 GB card covers the pairing we recommend in the test section, Qwen3.8-27B against Llama 3.3 70B at 4-bit, with room left over. Anything beyond that, such as Llama 4 Maverick at about 201 GB at 4-bit, needs several cards.

The hosted route, priced

If you do not want to run a GPU, the one hosted price we found is Groq's preview listing for Qwen3.8-27B at $0.80 per million input tokens and $4.00 per million output. For a job of 150M input and 15M output tokens a month, that is 150 x $0.80 plus 15 x $4.00, so $120 plus $60, or $180 a month. Compare that with an H100 at $2.69 an hour, which is about $1,964 a month if it runs the whole month. The hosted route is cheaper at that volume by a wide margin, and the rented card only wins at much higher, steady volume.

Groq lists the model as a preview, so it can be withdrawn on short notice. That makes it a good place to test and a risky place to build production on. We did not find a listed hosted price for the Llama models in the sources we read.

Choosing by what you already have

Questions people ask

Why is Llama gated? Hugging Face reports manual access approval on the Llama repositories we checked, so you request access and wait. Qwen3.8-27B downloads without that step.

Does thinking mode make Qwen slower? It is on by default and adds output tokens, so it costs time and money. Turn it off for simple tasks.

Can both run in Ollama? Ollama lists Qwen among the families it targets. Check its library for the exact model names before you plan around it.

Our take

Right now, Qwen is the better default for a new open-model project: it is more recent, permissively licensed on its flagship compact model, and ungated. Llama remains important for its ecosystem, and its licence and access process are heavier.

We did not benchmark either, and we make no quality claims. The facts come from Hugging Face and the vendors' docs on 31 October 2026. One more point in Qwen's favour is practical: no approval queue stands between you and the download, so a trial costs minutes, not days. One point for Llama is the long tail of fine-tunes and guides built over several years, which can save time if your task is common. Run your own tasks through both on a hosted route first, which costs cents, then decide whether the hardware is worth buying.

Before you adopt either

Write down four things on the day you choose: the exact model name and revision, the licence tag you read, the quantisation you run, and the date. Open-weights families ship new versions every few months, and a note like that turns a vague memory into something your team can check. Re-run your own test prompts after any update, and keep the previous weights until the new ones pass.

Related pages

• Qwen models and Llama models
• Open vs closed models
• Compare models
• DeepSeek vs Claude