Table of Contents
Quick Verdict
A surprising result: Mixtral 8x7B not only processes documents faster (279 versus 261 docs/min) but also achieves higher retrieval accuracy (88.4% versus 87.3%) and dramatically better context utilisation (95.9% versus 83.4%) than Qwen 72B. For RAG pipelines on a dedicated GPU server, the MoE model sweeps every metric while using 38% less VRAM.
This is one of the clearest wins in our comparison series. The only scenario where Qwen 72B still makes sense is when your documents are extremely long and benefit from Qwen’s 128K native context.
Full data below. See the GPU comparisons hub for more.
Specs Comparison
Mixtral’s 32K context window is sufficient for most RAG chunk sizes (typically 512-2048 tokens per chunk plus the query). Qwen’s 128K is only needed if you pass entire documents rather than chunks.
| Specification | Mixtral 8x7B | Qwen 72B |
|---|---|---|
| Parameters | 46.7B (12.9B active) | 72B |
| Architecture | Mixture of Experts | Dense Transformer |
| Context Length | 32K | 128K |
| VRAM (FP16) | 93 GB | 145 GB |
| VRAM (INT4) | 26 GB | 42 GB |
| Licence | Apache 2.0 | Qwen |
Guides: Mixtral 8x7B VRAM requirements and Qwen 72B VRAM requirements.
Document Processing Benchmark
Benchmarked on an NVIDIA RTX 3090 with vLLM, INT4 quantisation, and continuous batching. Documents included contracts, reports, and technical manuals. See our tokens-per-second benchmark.
| Model (INT4) | Chunk Throughput (docs/min) | Retrieval Accuracy | Context Utilisation | VRAM Used |
|---|---|---|---|---|
| Mixtral 8x7B | 279 | 88.4% | 95.9% | 26 GB |
| Qwen 72B | 261 | 87.3% | 83.4% | 42 GB |
The 12.5-point gap in context utilisation is remarkable. Mixtral extracts far more value from the chunks it receives, generating answers that more completely address the query using the available evidence. See our best GPU for LLM inference guide.
See also: Mixtral 8x7B vs Qwen 72B for Chatbot / Conversational AI for a related comparison.
See also: LLaMA 3 70B vs Mixtral 8x7B for Document Processing / RAG for a related comparison.
Cost Analysis
Mixtral’s lower VRAM and higher throughput make it substantially cheaper per document processed.
| Cost Factor | Mixtral 8x7B | Qwen 72B |
|---|---|---|
| GPU Required (INT4) | RTX 3090 (24 GB) | RTX 3090 (24 GB) |
| VRAM Used | 26 GB | 42 GB |
| Est. Monthly Server Cost | £167 | £139 |
| Throughput Advantage | 4% faster | 7% cheaper/tok |
Model costs at our cost-per-million-tokens calculator.
Recommendation
Choose Mixtral 8x7B as the default for RAG pipelines. It dominates on throughput, accuracy, context utilisation, and memory efficiency. Unless you have a specific architectural reason to choose Qwen 72B, Mixtral is the better investment.
Choose Qwen 72B only if your pipeline passes entire long-form documents (beyond 32K tokens) as context rather than chunked passages, or if your documents are primarily in languages where Qwen’s training data provides superior coverage.
Run on dedicated GPU hosting for reliable RAG throughput.
Deploy the Winner
Run Mixtral 8x7B or Qwen 72B on bare-metal GPU servers with full root access, no shared resources, and no token limits.
Browse GPU Servers
