Llama 4 vs Qwen 3 for self-hosted coding agents

Choosing between llama 4 vs qwen 3 coding deployments for a self-hosted agent is less about raw benchmark points and more about hardware reality and tool-calling reliability. Both Meta and Alibaba shipped MoE flagships in 2025 with open weights, but they optimize for different constraints: Llama 4 leans on ultra-long context and native multimodal, Qwen 3 doubles down on dense and MoE text coding efficiency.

Architecture and model families

Llama 4 arrives as a mixture-of-experts series. The two deployable sizes most teams care about are Scout (109B total, 17B active per token, 16 experts) and Maverick (400B total, 17B active, 128 experts). Scout ships with a 10M-token context window; Maverick settles at 1M. Both are natively multimodal (text + image), trained with interleaved modality layers.

Qwen 3 spans a broader spectrum: dense models from 0.6B to 32B, plus a 235B-A22B MoE (22B active). There are dedicated Coder variants of the 32B and 235B footprints that swap some general capacity for extended code pretraining. Context is 128K across the board. No native image input in the base text models—you’d reach for Qwen-VL separately.

The architectural takeaway: if you want one model that ingests screenshots from a UI test and writes the fix, Llama 4 Scout is the only open-weight option here that does it in a single forward pass. If you want a tight 32B dense model on a single GPU, Qwen 3-32B is the cleaner fit.

Coding capability and agentic behavior

Public eval aggregations (LiveCodeBench, BigCode) show Qwen 3-235B-Coder closing the gap with frontier closed models on pass@1 for Python and TypeScript, while Llama 4 Maverick trails by a small margin on pure code synthesis but leads on multilingual repo tasks and tool orchestration. For an agent loop, the critical metric is not single-shot completion—it’s whether the model emits valid tool calls consistently across 50 turns.

Both families support function calling via the OpenAI chat schema. Qwen 3’s tool parser is stricter about JSON schema conformance; Llama 4 is more tolerant of loosely specified params but occasionally hallucinates an extra field. In practice, you should validate server-side regardless.

from openai import OpenAI

client = OpenAI(base_url="http://localhost:8000/v1", api_key="empty")

resp = client.chat.completions.create(
    model="qwen3-235b-a22b",
    messages=[{"role": "user", "content": "Refactor utils.py to use asyncio"}],
    tools=[{
        "type": "function",
        "function": {
            "name": "read_file",
            "parameters": {"type": "object", "properties": {"path": {"type": "string"}}}
        }
    }],
    tool_choice="auto"
)
print(resp.choices[0].message.tool_calls)

If you want to A/B test both before buying GPUs, an OpenAI-compatible gateway such as n4n.ai exposes both families behind one endpoint with automatic fallback, but production self-hosting will still require your own vLLM cluster.

Self-hosting cost and hardware footprint

Weights are only half the bill. Memory bandwidth and expert routing overhead dominate.

• Llama 4 Scout (INT4): ~55GB weights. Fits on 1× H100 80GB with KV cache headroom for ~32K context; beyond that you need 2× 80GB.
• Llama 4 Maverick (INT4): ~200GB. Minimum 2× H100 80GB, realistically 4× for batch agents.
• Qwen 3-235B-A22B (INT4): ~120GB. 2× H100 80GB works; 4× A100 80GB also common.
• Qwen 3-32B (INT4): ~18GB. Single 24GB consumer GPU (RTX 4090) is sufficient for a dev agent.

Launching Scout with vLLM:

vllm serve meta-llama/Llama-4-Scout-17B-16E \
  --tensor-parallel-size 1 \
  --quantization awq \
  --max-model-len 131072 \
  --enable-expert-parallel

Qwen 3’s smaller dense model is the only one here that makes economic sense for a solo engineer running a coding sidecar on a workstation.

Latency and throughput characteristics

Active parameter count predicts token throughput better than total params. Scout and Maverick both push only 17B active, so under continuous batching they sustain similar tok/s per GPU as Qwen 3’s 22B active MoE. The dense Qwen 3-32B is the laggard: ~40% lower throughput at equal batch size because every layer computes fully.

Time-to-first-token (TTFT) is where Llama 4 Scout’s 10M context hurts: prefix caching is mandatory, or you pay seconds of compute on long system prompts. Qwen 3’s 128K ceiling is easier to keep in KV cache. For an agent that reloads the full repo tree each turn, Scout without a cached prefix is unusable; with it, it’s fine.

Ergonics: tool calling, context, multimodality

Llama 4’s multimodal input is genuinely useful for coding agents that ingest error screenshots or Figma exports. You pass image_url content blocks like any vision model. Qwen 3 requires a separate VL model or a text-only screenshot OCR step.

Both honor response_format: {type: "json_object"} in vLLM/SGLang builds from 2025. Llama 4’s chat template includes a dedicated ipython block for code execution feedback; Qwen 3 expects you to emulate that with tool messages.

{
  "role": "assistant",
  "content": null,
  "tool_calls": [
    {"id": "call_1", "type": "function", "function": {"name": "run_tests", "arguments": "{\"path\": \"tests/\"}"}}
  ]
}

Context handling: Llama 4 Scout’s 10M window is not free—attention sink strategies drop mid-file lines if you don’t use rolling windows. Qwen 3’s 128K is well-supported by YaRN in most runtimes.

Ecosystem and license constraints

Qwen 3 weights are Apache 2.0 for the 0.6B–32B dense and the MoE (with the standard open-weight caveat on massive commercial use). You can fine-tune, redistribute, and embed in a closed product without royalty.

Llama 4 uses the Llama 3.2/4 Community License: acceptable for products under 700M monthly users, but you must display “Built with Llama” and cannot serve it as a model-hosting competitor. For an internal coding agent at a startup, this is fine. For a hosted dev-tool vendor, read the clause twice.

Ollama, LM Studio, and SGLang had day-one support for both. vLLM support for Llama 4’s expert parallelism matured within weeks; Qwen 3’s MoE ran from day one.

Head-to-head summary

Which to choose

Solo developer or small team on a single workstation GPU: Run Qwen 3-32B (or the 14B if you’re on 16GB VRAM). It’s the only option here that fits without datacenter hardware, and its code quality is excellent for autocomplete and small refactors.

Long-horizon repo agent with million-line indexes: Llama 4 Scout’s 10M context lets you stuff an entire monorepo into the prompt. Pair it with prefix caching and expert-parallel vLLM. Qwen 3 will force you into RAG chunking.

Multimodal coding agent (screenshots, PDFs, UI tests): Llama 4 is the default—Qwen 3 text models can’t see images without a separate VL pipeline.

Max open-weight code correctness (CI fix bots, synthetic PRs): Qwen 3-235B-Coder on 2× H100 beats Llama 4 Maverick on pass@1 and strict tool schemas. The Apache license also removes the attribution burden.

Cost-sensitive scale (many parallel agent threads): Qwen 3-235B-A22B at 22B active gives near-flagship code output at lower VRAM than Maverick, making it the better $/token for high-concurrency self-hosted fleets.

Pick based on the binding constraint: if it’s VRAM, Qwen 3 dense; if it’s context or vision, Llama 4; if it’s pure code quality at scale, Qwen 3 MoE Coder.