When a RAG pipeline swallows a large corpus of documents before generating a short answer, the cost of ingesting those documents usually dominates the bill, so the input rate is the decisive factor. Alibaba’s Qwen3‑32B charges $0.08 per 1 M input tokens while Meta’s Llama 3.3‑70B instruct model asks $0.10, giving Qwen a clear edge on the side that consumes the bulk of the token budget. The output rates, however, are closer: $0.28 for Qwen3‑32B versus $0.32 for Llama 3.3‑70B, so the advantage narrows when the model produces the final answer.
### Token costs for RAG‑focused models
| Vendor | Model | Input | Cached Input | Output |
|--------|-------|-------|--------------|--------|
| Alibaba | qwen3‑32b | $0.08 | N/A | $0.28 |
| Alibaba | qwen3‑235b‑a22b‑2507 | $0.0875 | $0.0175 | $0.35 |
| Alibaba | qwen3‑7‑flash | $0.03 | $0.006 | $0.13 |
| Meta | llama‑3.3‑70b‑instruct | $0.10 | N/A | $0.32 |
| Meta | llama‑3.1‑8b‑instruct | $0.05 | $0.025 | $0.08 |
| Meta | llama‑4‑scout | $0.10 | N/A | $0.30 |
The table shows that the smallest Qwen flash variant is the cheapest overall, but its limited context window may not support the larger retrieval chunks that a 70‑billion‑token model can handle. The Qwen 3‑235B variant, while more expensive, offers a cached‑input discount that can be valuable for workloads that repeatedly use the same system prompt or few‑shot examples. Meta’s 8‑billion‑token Llama 3.1‑instruct is the most economical option for teams that can tolerate a smaller context window and need a lower upfront cost.
If a project demands a 70‑billion‑token model to maintain high‑fidelity reasoning over long retrieved passages, Llama 3.3‑70B is the direct competitor to Qwen 3‑32B, albeit at a modest premium on both input and output. Conversely, if the retrieval chunks are modest and the application can operate with a smaller model, the Qwen flash line delivers a dramatic reduction in ingestion costs, and the 3‑235B variant can be justified when cached‑input savings offset its higher per‑token rate. Start‑ups or production systems that need to scale to millions of prompt tokens per month will likely find the lower input price of Qwen models to translate into a lower total spend, provided the model’s performance meets their accuracy requirements.
The choice therefore hinges on whether the team prioritises the sheer scale of a 70‑billion‑token model or the cost‑efficiency of a smaller, lower‑priced variant. For high‑volume, cost‑conscious workloads, the Qwen 3‑flash or Qwen 3‑32B is the safer bet; for applications that need the breadth of a 70‑billion‑token context window, Llama 3.3‑70B remains the go‑to despite its slightly higher rates.
Pricing verified on 2026-09-12.