Mistral 7B vs Gemma 2 9B for Document Processing / RAG: GPU Benchmark

Quick Verdict

Mistral 7B processes 204 documents per minute with 89.5% retrieval accuracy and 93.0% context utilisation. Gemma 2 9B manages 141 docs/min at 89.4% accuracy and 86.9% utilisation. The throughput gap is massive — Mistral processes 45% more documents per minute — and the quality metrics are nearly identical. For RAG pipelines, this is unusually one-sided: Mistral 7B wins on speed without sacrificing accuracy. The only scenario where Gemma 2 9B makes sense is if your pipeline specifically benefits from its 8K-limited but safety-aligned output. On a dedicated GPU server, the numbers point firmly toward Mistral.

For broader model comparisons, see our GPU comparisons hub.

Specs Comparison

For RAG, Mistral 7B’s 32K context window is a structural advantage. It can ingest four times as many retrieved passages per query as Gemma 2 9B, or process entire long documents without chunking. Combined with its lighter VRAM footprint, Mistral 7B gives the retrieval pipeline significantly more room to work with on self-hosted infrastructure.

For detailed VRAM breakdowns, see our guides on Mistral 7B VRAM requirements and Gemma 2 9B VRAM requirements.

Document Processing Benchmark

We tested both models on an NVIDIA RTX 3090 (24 GB VRAM) using vLLM with INT4 quantisation and continuous batching. The benchmark measured document ingestion throughput, retrieval accuracy from a fixed corpus, and how efficiently each model used its available context window. For live speed data, check our tokens-per-second benchmark.

Mistral 7B’s 93.0% context utilisation — the percentage of retrieved context that the model effectively uses in its answer — is the standout number. Combined with its 32K window, this means Mistral can take more retrieved passages as input and waste less of that input when generating answers. For noisy document corpora where retrieval quality is imperfect, this combination of a larger window and better utilisation is a significant practical advantage. Visit our best GPU for LLM inference guide for hardware-level comparisons.

See also: Mistral 7B vs Gemma 2 9B for Chatbot / Conversational AI for a related comparison.

See also: LLaMA 3 8B vs Mistral 7B for Document Processing / RAG for a related comparison.

Cost Analysis

With a 45% throughput advantage, Mistral 7B processes nearly half again as many documents per hour on the same dedicated GPU server. For pipelines that ingest thousands of documents daily, this gap translates directly into fewer servers needed.

At 204 docs/min versus 141, Mistral 7B’s cost per processed document is roughly 30% lower. Use our cost-per-million-tokens calculator to run the exact numbers for your expected document volume.

Recommendation

Choose Mistral 7B for most RAG deployments. The 45% throughput advantage, higher context utilisation, 32K context window, and nearly identical retrieval accuracy make it the stronger choice for both ingestion-heavy and query-heavy pipelines. The Apache 2.0 licence simplifies commercial deployment.

Choose Gemma 2 9B only if your RAG application serves content in a domain where Google’s safety alignment adds material value — medical information retrieval, legal document processing, or any pipeline where you need the model to err on the side of caution when generating answers from retrieved content.

For large-scale RAG deployments, dedicated GPU hosting ensures consistent throughput without the variable performance of shared infrastructure.