Every data team eventually asks the same question: can we process this dataset for less? When you are running sentiment analysis across a million product reviews or tagging half a million invoices, the difference between $0.09 and $0.18 per million tokens adds up fast. We benchmarked Mistral 7B against Qwen 2.5 7B in pure batch mode to find the cheaper path on dedicated GPU infrastructure.
Spec Comparison
| Specification | Mistral 7B | Qwen 2.5 7B |
|---|---|---|
| Parameters | 7B | 7B |
| Architecture | Dense Transformer + SWA | Dense Transformer |
| Context Length | 32K | 128K |
| VRAM (FP16) | 14.5 GB | 15 GB |
| VRAM (INT4) | 5.5 GB | 5.8 GB |
| Licence | Apache 2.0 | Apache 2.0 |
VRAM details: Mistral VRAM | Qwen VRAM.
Batch Throughput Test
RTX 3090, vLLM, INT4, max batch packing. Workload: 150K classification prompts. Real-time tracking: tokens-per-second benchmark.
| Model (INT4) | Batch tok/s | Cost/M Tokens | GPU Utilisation | VRAM Used |
|---|---|---|---|---|
| Mistral 7B | 285 | $0.09 | 96% | 5.5 GB |
| Qwen 2.5 7B | 352 | $0.18 | 97% | 5.8 GB |
Qwen pushes 24% more tokens per second (352 vs 285), but Mistral halves the cost per million tokens ($0.09 vs $0.18). Both models saturate the GPU above 95% utilisation, so neither is wasting compute cycles. The cost gap is the story here.
Also see: Mistral vs Qwen for Chatbots | LLaMA 3 vs Mistral for Batch Processing
Monthly Spend
| Cost Factor | Mistral 7B | Qwen 2.5 7B |
|---|---|---|
| GPU Required (INT4) | RTX 3090 (24 GB) | RTX 3090 (24 GB) |
| VRAM Used | 5.5 GB | 5.8 GB |
| Est. Monthly Server Cost | £104 | £115 |
| Throughput Advantage | 5% faster | 12% cheaper/tok |
Run the numbers for your exact batch size: cost-per-million-tokens calculator.
Which Model Saves You More?
This depends on volume:
For cost-first batch processing, Mistral 7B is the winner. At $0.09 per million tokens, it is half the cost of Qwen for the same output. If you process 100M tokens per month, that is $9 with Mistral versus $18 with Qwen — the savings compound as you scale. Its lower VRAM usage also leaves room to run a secondary classifier on the same GPU.
Qwen 2.5 7B is the better choice when wall-clock time matters more than cost. Its 24% throughput advantage means batch jobs finish faster, and the 128K context window allows processing longer documents without splitting them into multiple prompts. If you have a 4-hour processing deadline and a large corpus, Qwen gets the job done sooner.
Schedule batch runs overnight on dedicated GPU servers for peak efficiency. For engine guidance: vLLM vs Ollama. For budget GPUs: cheapest GPU for AI inference.
Process Your Data for Less
Run Mistral 7B or Qwen 2.5 7B on bare-metal GPUs — flat monthly cost, no token limits, full root access.
Browse GPU Servers
