Guide · 2026-06-25

Qwen 3 235B-A22B vs Llama 3.1 405B — Flagship MoE vs Dense

Last updated 2026-06-25

Two flagship open-weight giants, two very different philosophies. We compare the 22B-active MoE against the 405B dense model on speed, cost, and quality.

By Mohamed Meguedmi · 8 min read

The core architectural divide: sparse MoE vs dense

This is not a like-for-like size comparison — it is a comparison of two design philosophies. Llama 3.1 405B is a dense transformer: every one of its 405 billion parameters fires on every token. Qwen3 235B-A22B is a Mixture-of-Experts (MoE) model: it holds 235B parameters in memory but routes each token through a small subset of experts, activating only ~22B parameters at a time (hence the "A22B" suffix).

The consequences ripple through everything that follows. The dense model is heavier to store, far heavier to compute, but conceptually simple and extremely predictable. The MoE model carries more total knowledge in weights yet runs like a 22B model at inference time — cheap to serve, fast to decode, but with the routing complexity and memory footprint of the full parameter count. If you want the deeper explanation of why active parameters dominate inference cost while total parameters dominate VRAM, see our methodology.

Benchmarks head to head

Numbers below are reported instruct/reasoning results aggregated from public leaderboards and the official model cards; treat them as directional, since prompt formatting and thinking budgets shift scores meaningfully. Qwen3 figures use its reasoning ("thinking") mode where applicable.

Benchmark	Qwen3 235B-A22B	Llama 3.1 405B	Edge
MMLU	~88–89	~88.6	Tie
GPQA Diamond	~71	~51	Qwen3
AIME 2024 (math)	~85	~26	Qwen3
LiveCodeBench	~70	~40	Qwen3
MATH	~92	~73	Qwen3
Broad multilingual knowledge	Strong (119 languages)	Strong (8 official)	Qwen3 (breadth)

The pattern is consistent: on tasks that reward step-by-step reasoning — competition math, graduate-level science, and code generation — Qwen3 235B-A22B opens a wide gap, largely because it was trained with an explicit reasoning mode that Llama 3.1 predates. On flat knowledge recall (MMLU) the two are effectively tied. Llama 3.1 405B remains a remarkably solid generalist and its outputs are well understood by years of community tooling, but it was released in mid-2024 and shows its age against a 2025-era reasoning model.

Hardware and VRAM requirements

Here the MoE design pays off twice — once in storage and again in compute. A dense 405B model is one of the most demanding open weights you can self-host. The figures below assume KV-cache headroom for moderate context; long-context workloads push these higher.

Model	FP16	Q8_0	Q4_K_M	Practical Q4 deployment
Llama 3.1 405B	~810 GB	~405 GB	~230 GB	4× A100 80GB (tight) to 8× for comfort
Qwen3 235B-A22B	~470 GB	~235 GB	~142 GB	2× A100 80GB, 4× RTX 6000 Ada 48GB, or a 192–512GB unified-memory Mac

The takeaway for teams sizing infrastructure: Qwen3 235B-A22B at Q4_K_M fits comfortably where Llama 3.1 405B simply will not. A single high-memory unified-memory machine can host the Qwen MoE; the Llama dense model generally demands a multi-GPU server with NVLink-class interconnect to avoid catastrophic tensor-parallel overhead. Browse exact quant builds for both in our model catalog.

Inference speed and throughput

Because Qwen3 235B-A22B only computes ~22B parameters per token, its decode speed behaves like a mid-size model, not a 235B one. In practice that means single-stream generation in the tens of tokens per second on a well-configured 2-GPU node, where Llama 3.1 405B often struggles into the low single digits to low teens on equivalent silicon. The dense model's every-token full-parameter pass is the bottleneck — there is no architectural escape hatch.

Rule of thumb: at the same quantization on the same GPUs, expect Qwen3 235B-A22B to deliver 5–10× the decode throughput of Llama 3.1 405B, while loading roughly 40% less weight memory.

The one caveat is prompt processing (prefill) and routing: MoE models can show more variable latency under heavy batching, and expert routing adds bookkeeping. But for the overwhelming majority of interactive and agentic workloads, the MoE wins decisively on tokens-per-second-per-dollar.

Cost to run

The economics follow directly from active-parameter compute. Hosted Qwen3 235B-A22B routes frequently price at well under $0.50 per million tokens for both input and output, while Llama 3.1 405B routes have historically sat at roughly $3–5/M input and $9–16/M output depending on provider. That is often a 10×+ swing in favor of the MoE for the same task.

For self-hosting, the cost story is the amortized hardware plus power: the Qwen model's smaller footprint and faster decode mean more requests served per GPU-hour. Plug your own token volumes and electricity rate into our cost calculator to compare the break-even point between renting a hosted route and buying the hardware to run either model locally. Every price and spec on this page is also available programmatically through the BestLLMfor public API (CC BY 4.0) and our open-source MCP server, so you can pull live comparison data straight into your own dashboards or agents.

Context, multilingual reach, and ecosystem

Both models target a 128K-token context window (Qwen3 variants extend toward ~131K–256K with the appropriate config), so neither is dramatically ahead for long-document work in their flagship releases. Where they diverge:

• Languages: Qwen3 advertises support across roughly 119 languages and dialects, with notably strong Chinese and broad Asian-language coverage. Llama 3.1 officially supports 8 languages, though community fine-tunes extend that.
• Reasoning toggle: Qwen3's hybrid thinking/non-thinking switch lets you trade latency for accuracy per request — a feature Llama 3.1 lacks natively.
• Maturity: Llama 3.1 405B has the deeper tooling moat: more quant formats, more fine-tunes, broader inference-engine support, and years of documented deployment patterns. For risk-averse, long-support deployments that is a real asset.
• License: Llama 3.1 ships under the Llama Community License (with its >700M MAU clause); Qwen3 is Apache 2.0, which is simpler for commercial redistribution. Confirm current terms on each model card before shipping.

Quick install (Ollama)

The fastest way to evaluate either model locally is via Ollama, assuming sufficient memory for your chosen quant.

# Qwen3 235B-A22B (MoE)
ollama run qwen3:235b

# Llama 3.1 405B (dense) — multi-GPU / large unified memory required
ollama run llama3.1:405b

See the official Qwen3 and Llama 3.1 library pages for available tags and exact digests. For lower-memory setups, pull a smaller quant tag rather than the default.

Which should you choose?

For the great majority of 2026 deployments — local or hosted, interactive or agentic — Qwen3 235B-A22B is the stronger default. It is cheaper to serve, faster to decode, lighter on VRAM, and measurably better at reasoning and coding. Llama 3.1 405B remains a credible choice when your constraints are organizational rather than technical: a frozen, well-understood dense baseline, specific license or procurement requirements, or an existing toolchain built around it.

Your priority	Pick	Why
Best reasoning / math / code	Qwen3 235B-A22B	Large lead on GPQA, AIME, LiveCodeBench
Lowest cost per token	Qwen3 235B-A22B	Only 22B active params to compute
Fits on fewer GPUs	Qwen3 235B-A22B	~142 GB vs ~230 GB at Q4_K_M
Fastest tokens/sec	Qwen3 235B-A22B	5–10× decode advantage
Apache 2.0 licensing	Qwen3 235B-A22B	Simpler commercial redistribution
Mature, frozen dense baseline	Llama 3.1 405B	Deepest tooling and deployment history
Broadest community fine-tunes	Llama 3.1 405B	Largest ecosystem of derivatives

Still weighing other contenders? Our catalog tracks current flagships side by side, and the about page explains how the editorial team scores them.