Llama 3.3 70B vs Qwen3 32B — 70B versus 32B for local power users. Below is the full side-by-side: specifications, API pricing, context window, local hardware requirements, and a clear, data-driven recommendation on which to pick.
| Spezifikation | Llama 3.3 70B | Qwen3 32B |
|---|---|---|
| Entwickler | Meta | Alibaba |
| Typ | LLM (dicht) | LLM (dicht) |
| Parameter | 70B | 32 B |
| Kontextfenster | 128K | 128K |
| Modalität | Text → Text | Text → Text |
| Lizenz | Llama 3.3 Community (offen) | Apache 2.0 (offen) |
| Offene Gewichte | ✅ Ja | ✅ Ja |
| Input price ($/1M) | $0.10 | $0.08 |
| Output price ($/1M) | $0.32 | $0.28 |
| VRAM (4-Bit) | ca. 40 GB | ca. 20 GB |
| Min GPU (local) | 2× RTX 4090 / 1× 48 GB | RTX 4090 mit 24 GB (Q4) |
| Veröffentlicht | 2024 | 2025 |
Key differences
- Kosten: Qwen3 32B is 19% cheaper than Llama 3.3 70B on a blended-token basis.
- Offenheit: both are open-weight, so either can be self-hosted or fine-tuned. Compare their VRAM needs above to see what your GPU can run.
- Run Llama 3.3 70B locally: ~~40 GB at 4-bit (min 2× RTX 4090 / 1× 48GB).
- Run Qwen3 32B locally: ~~20 GB at 4-bit (min RTX 4090 24GB (Q4)).
Welches Modell sollten Sie wählen?
Choose Llama 3.3 70B if it fits your existing stack or you prefer Meta.
Choose Qwen3 32B if you want the lower per-token cost for high-volume workloads.
→ Estimate real costs in the API cost calculator · check local hardware in the VRAM-Rechner · browse all 30+ models.
All specs and prices are pulled live from our Datenbank für KI-Modelle and kept current. Compare either model against others, or estimate your own monthly spend with the free calculators above.