When the goal is to grind through a massive dataset at the lowest possible cost, parameter count stops mattering and tokens-per-pound takes over. Phi-3 Mini at 3.8B parameters might look outgunned next to Mistral’s 7B, but its dramatically lower cost per million tokens tells a different story. We tested both in full batch mode on a dedicated GPU server.
Key Specifications
| Specification | Mistral 7B | Phi-3 Mini |
|---|---|---|
| Parameters | 7B | 3.8B |
| Architecture | Dense Transformer + SWA | Dense Transformer |
| Context Length | 32K | 128K |
| VRAM (FP16) | 14.5 GB | 7.6 GB |
| VRAM (INT4) | 5.5 GB | 3.2 GB |
| Licence | Apache 2.0 | MIT |
Phi-3’s 3.2 GB INT4 footprint leaves 20+ GB free on an RTX 3090, which vLLM can use entirely for batch queues and KV-cache. That headroom directly boosts batch density. Memory planning: Mistral VRAM | Phi-3 VRAM.
Batch Results
RTX 3090, vLLM, INT4, max batch packing. Workload: 250K sentiment classification prompts. Speed reference: tokens-per-second benchmark.
| Model (INT4) | Batch tok/s | Cost/M Tokens | GPU Utilisation | VRAM Used |
|---|---|---|---|---|
| Mistral 7B | 475 | $0.17 | 92% | 5.5 GB |
| Phi-3 Mini | 354 | $0.06 | 92% | 3.2 GB |
Mistral pushes 34% more tokens per second (475 vs 354), but Phi-3 slashes the cost per million tokens to just $0.06 — nearly a third of Mistral’s $0.17. Both models hit 92% GPU utilisation, so the hardware is well saturated in each case. The cost gap is what matters for batch work: at 100M tokens per month, Phi-3 costs $6 versus Mistral’s $17.
Also see: Mistral vs Phi-3 for Chatbots | LLaMA 3 vs Mistral for Batch Processing
Monthly Spend
| Cost Factor | Mistral 7B | Phi-3 Mini |
|---|---|---|
| GPU Required (INT4) | RTX 3090 (24 GB) | RTX 3090 (24 GB) |
| VRAM Used | 5.5 GB | 3.2 GB |
| Est. Monthly Server Cost | £129 | £178 |
| Throughput Advantage | 10% faster | 6% cheaper/tok |
Run exact projections: cost-per-million-tokens calculator.
The Budget-Conscious Choice
Phi-3 Mini is the cost champion for batch processing. At $0.06 per million tokens, it is the most economical way to process large datasets on a single GPU. If your batch tasks are straightforward — classification, sentiment analysis, entity extraction — Phi-3’s quality at 3.8B parameters is more than sufficient, and the cost savings compound dramatically at scale.
Mistral 7B is worth the premium when batch tasks require nuanced reasoning: summarisation of complex documents, multi-step data extraction, or tasks where quality directly impacts downstream business decisions. Its 34% higher throughput also makes it faster for time-boxed batch windows.
Both models run efficiently on dedicated GPU servers. For engine selection: vLLM vs Ollama. Budget hardware: cheapest GPU for AI inference.
Batch Process for Less
Run Mistral 7B or Phi-3 Mini on bare-metal GPUs — flat monthly cost, no token caps, full root access.
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