Qwen 3 vs Llama 4 Scout: throughput compared

Qwen 3 vs Llama 4 Scout throughput is the question most teams ask when picking a frontier-grade open-weight model for high-volume inference. Both target the same niche—cheap, fast, capable reasoning at scale—but they make different tradeoffs in architecture, context handling, and serving cost that directly affect tokens-per-second per GPU.

Architectures and capabilities

Qwen 3 ships in two flavors: dense models (0.6B–32B) and Mixture-of-Experts (MoE) models (30B-A3B up to 235B-A22B). The MoE variants activate 3–22B parameters per token, which keeps decode compute low while preserving capacity. Native function calling, multilingual coverage (including strong Chinese/English), and a built-in “thinking” mode are first-class.

Llama 4 Scout is a 17B-active / 109B-total MoE with 16 experts and a 10M-token context window. It is natively multimodal (text + image) via early fusion, but most high-throughput deployments use it as a text model. Its differentiator is extreme context length, not raw reasoning breadth.

For general instruction following, Qwen 3’s dense 32B and MoE 235B are competitive with Scout on MMLU-class benchmarks. Scout wins on tasks requiring retrieval across very long documents.

Price and cost model

Both are open-weight, so the dominant cost is GPU hours, not license fees. Scout’s 109B total params demand ~2× the VRAM of Qwen 3’s 235B-A22B at load time (same total), but active compute is similar (17B vs 22B). On a per-token basis, hosting cost tracks active parameter count and batch efficiency.

Commercial gateways price per output token. Without citing volatile numbers: Scout and Qwen 3 MoE typically land in the same bracket for equivalent active size, with Qwen 3 dense-32B often cheaper for low-QPS services because it fits on a single H100 without tensor parallelism.

Latency and throughput

This is where the Qwen 3 vs Llama 4 Scout throughput comparison gets nuanced. Throughput (tokens/sec) decomposes into prefill and decode.

• Prefill: Scout’s 10M context means attention cost scales with sequence length. At 128K tokens, prefill latency is markedly higher than Qwen 3’s 128K cap. For short prompts (<2K), both saturate the GPU similarly.
• Decode: With continuous batching, Qwen 3’s 22B-active MoE hits higher batch throughput on H100s because its expert count (8) is easier to shard than Scout’s 16. Scout’s smaller active size helps per-token latency but its larger expert count adds all-to-all communication overhead.

A minimal way to measure live throughput against an OpenAI-compatible endpoint:

from openai import OpenAI
import time

client = OpenAI(base_url="https://api.n4n.ai/v1", api_key="sk-xxx")

def measure(model, prompt, max_tokens=256):
    t0 = time.time()
    resp = client.chat.completions.create(
        model=model,
        messages=[{"role": "user", "content": prompt}],
        max_tokens=max_tokens,
        stream=True,
    )
    n = 0
    for chunk in resp:
        if chunk.choices[0].delta.content:
            n += 1
    return n / (time.time() - t0)

print("qwen3:", measure("qwen3-235b-a22b", "Summarize distributed MoE."))
print("scout:", measure("llama-4-scout", "Summarize distributed MoE."))

When serving through a gateway like n4n.ai, the OpenAI-compatible endpoint lets you swap models without code changes and automatically fall back if a provider is degraded, which matters more for Scout given its newer serving stacks.

Ergonomics

Qwen 3 uses a ChatML-style template with explicit /think and /no_think tokens. Tool calls are JSON-schema driven and stable across versions. Llama 4 Scout uses the Llama 4 prompt format with <|header_start|> markers; multimodal inputs require base64 image blocks.

For existing OpenAI SDK users, both are drop-in. Scout’s longer system prompt headroom is useful; Qwen 3’s thinking mode requires parsing a reasoning section before the final answer.

{
  "model": "qwen3-235b-a22b",
  "messages": [{"role": "user", "content": "Ping."}],
  "thinking": false
}

Ecosystem

vLLM added Qwen 3 support within days of release; SGLang and TensorRT-LLM follow closely. Llama 4 Scout is supported in vLLM nightly and Meta’s own Llama Stack, but production quantizations (AWQ, GPTQ) are less mature.

Qwen 3 benefits from a large Chinese-language toolchain and official ONNX exports. Scout’s advantage is the Meta marketing halo and first-party 10M-ctx serving examples.

Limits

• Qwen 3: 128K context max on most endpoints; MoE variants need 8×H100 for the 235B. Apache 2.0 license (permissive).
• Llama 4 Scout: 10M context but attention sink tricks needed beyond 1M; Llama Community License restricts some commercial use (>700M MAU threshold). Requires careful RoPE scaling config.

Comparison table

Which to choose

High-throughput API serving (short-to-medium context): Pick Qwen 3. Its MoE sharding and mature vLLM path give better tokens/sec per dollar at 4–32K context. Use the dense 32B if you’re GPU-constrained.

Long-document RAG or agent memory: Scout’s 10M context removes chunking complexity. Accept the prefill penalty; batch during off-peak.

Multimodal product: Scout is the only native choice today. Wrap image inputs early and cache prefill.

Regulated enterprise (license paranoid): Qwen 3’s Apache 2.0 is cleaner than Llama’s community terms.

Rapid prototyping with fallback: Route both through one endpoint that honors client directives and meters per token. That removes the “which model is up” problem and lets you A/B the Qwen 3 vs Llama 4 Scout throughput gap on your own traffic.