RAG quality hinges on a model’s ability to ground its answers in the retrieved context rather than hallucinating. Qwen 2.5 7B brings a 128K context window that can swallow entire document sections whole, while DeepSeek 7B counters with raw throughput that chews through document queues faster. We benchmarked both for a production-style self-hosted RAG pipeline to see which approach delivers better results per pound.
Model Specs
| Specification | DeepSeek 7B | Qwen 2.5 7B |
|---|---|---|
| Parameters | 7B | 7B |
| Architecture | Dense Transformer | Dense Transformer |
| Context Length | 32K | 128K |
| VRAM (FP16) | 14 GB | 15 GB |
| VRAM (INT4) | 5.8 GB | 5.8 GB |
| Licence | MIT | Apache 2.0 |
For RAG, context length is critical. Qwen’s 128K window lets you pass 10+ retrieved chunks without truncation, while DeepSeek’s 32K limits you to roughly 3-4 chunks per query at typical chunk sizes. Details: DeepSeek VRAM | Qwen VRAM.
Document Processing Performance
Test environment: RTX 3090, vLLM, INT4, continuous batching. Corpus: 25K technical documents, 512-token chunks, top-5 retrieval. Speed reference: tokens-per-second benchmark.
| Model (INT4) | Chunk Throughput (docs/min) | Retrieval Accuracy | Context Utilisation | VRAM Used |
|---|---|---|---|---|
| DeepSeek 7B | 257 | 84.6% | 88.2% | 5.8 GB |
| Qwen 2.5 7B | 199 | 91.5% | 94.4% | 5.8 GB |
Qwen dominates on quality: 91.5% retrieval accuracy versus 84.6%, and it utilises 94.4% of the provided context compared to DeepSeek’s 88.2%. That 6.9 percentage point accuracy gap means Qwen pulls the correct answer from retrieved chunks far more reliably — critical for compliance-sensitive applications like legal research or medical knowledge bases. DeepSeek compensates with 29% higher throughput (257 vs 199 docs/min), making it faster for bulk ingestion tasks.
Related reading: DeepSeek vs Qwen for Chatbots | LLaMA 3 vs DeepSeek for RAG
Cost Analysis
| Cost Factor | DeepSeek 7B | Qwen 2.5 7B |
|---|---|---|
| GPU Required (INT4) | RTX 3090 (24 GB) | RTX 3090 (24 GB) |
| VRAM Used | 5.8 GB | 5.8 GB |
| Est. Monthly Server Cost | £132 | £121 |
| Throughput Advantage | 5% faster | 7% cheaper/tok |
Run your document volume and accuracy requirements through our cost-per-million-tokens calculator to model total cost of ownership.
Which Model Fits Your RAG Pipeline?
Qwen 2.5 7B is the better RAG model. Its 128K context window, 91.5% retrieval accuracy, and 94.4% context utilisation make it the natural pick for any pipeline where answer correctness drives business value. If you are building a customer-facing knowledge base that handles 10K queries per day, Qwen’s accuracy advantage prevents the kind of wrong answers that erode user trust.
DeepSeek 7B earns its spot in throughput-first scenarios: nightly document indexing, bulk classification, or any pipeline where you need to process a backlog and accuracy above 84% is acceptable. Its MIT licence also avoids any commercial restrictions.
Both models deploy on a single dedicated GPU server. For pipeline architecture advice, see our self-host LLM guide.
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