Batch jobs care about one thing above all: how many tokens can you push through a GPU per pound spent. Latency does not matter when you are classifying 500K support tickets overnight or summarising a quarter’s worth of meeting transcripts. We ran DeepSeek 7B and Mistral 7B in full batch mode to find out which model gives you more output per hour of dedicated GPU time.
How the Models Compare on Paper
| Specification | DeepSeek 7B | Mistral 7B |
|---|---|---|
| Parameters | 7B | 7B |
| Architecture | Dense Transformer | Dense Transformer + SWA |
| Context Length | 32K | 32K |
| VRAM (FP16) | 14 GB | 14.5 GB |
| VRAM (INT4) | 5.8 GB | 5.5 GB |
| Licence | MIT | Apache 2.0 |
Both fit easily on an RTX 3090 at INT4, leaving enough headroom for large batch queues. Mistral’s lower VRAM footprint (5.5 GB) allows slightly larger batch sizes before the GPU starts swapping. See our DeepSeek VRAM and Mistral VRAM guides for quantisation planning.
Batch Throughput Results
Hardware: RTX 3090. Engine: vLLM with INT4 quantisation and max-batch packing. Workload: 100K classification prompts, average 64 input tokens, 32 output tokens. Live data: tokens-per-second benchmark.
| Model (INT4) | Batch tok/s | Cost/M Tokens | GPU Utilisation | VRAM Used |
|---|---|---|---|---|
| DeepSeek 7B | 255 | $0.10 | 97% | 5.8 GB |
| Mistral 7B | 285 | $0.12 | 86% | 5.5 GB |
Mistral edges out DeepSeek on raw tokens per second (285 vs 255), but DeepSeek achieves 97% GPU utilisation compared to Mistral’s 86%. That utilisation gap means DeepSeek squeezes more consistent performance out of the hardware — fewer idle cycles between batches. Mistral’s cost per million tokens is slightly higher at $0.12 vs $0.10.
Related: DeepSeek vs Mistral for Chatbots | LLaMA 3 vs DeepSeek for Batch Processing
Monthly Cost Comparison
| Cost Factor | DeepSeek 7B | Mistral 7B |
|---|---|---|
| GPU Required (INT4) | RTX 3090 (24 GB) | RTX 3090 (24 GB) |
| VRAM Used | 5.8 GB | 5.5 GB |
| Est. Monthly Server Cost | £164 | £95 |
| Throughput Advantage | 13% faster | 10% cheaper/tok |
At £95/month Mistral offers a lower sticker price, but DeepSeek’s higher GPU utilisation and lower cost-per-million-tokens ($0.10 vs $0.12) may make it cheaper at very high volumes. Plug your batch size into our cost calculator to see which crossover point applies to your workload.
Which Model for Your Batch Jobs?
Honestly, both models perform well here, and the choice depends on your secondary priorities.
DeepSeek 7B is the better pick for sustained overnight runs where you want the GPU pinned at near-100% utilisation. Its 97% utilisation means you waste almost no compute, and the $0.10/M token cost edges out Mistral. It also holds an MIT licence, simplifying commercial deployment.
Mistral 7B makes sense if your batch jobs are smaller and you prefer the lower server cost. It also leaves more VRAM free for co-running a secondary model — say a Gemma classifier alongside the main generation task.
Schedule your batch workloads during off-peak hours on dedicated GPU servers for maximum utilisation. For engine comparisons, see vLLM vs Ollama. For GPU selection, check cheapest GPU for AI inference.
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