Table of Contents
Quick Verdict
At $0.07 per million tokens versus $0.11, Mixtral 8x7B processes batch workloads at 36% lower cost on identical hardware. If you are running a nightly classification job over 2 million support tickets or bulk-generating product descriptions, that gap compounds into hundreds of pounds of savings every month on a dedicated GPU server.
Mixtral achieves this through raw batch throughput: 225 tok/s versus LLaMA 3 70B’s 140 tok/s, courtesy of the MoE architecture’s lower per-token compute cost. LLaMA 3 70B fights back with marginally higher output quality, which matters for batch tasks where correctness is non-negotiable.
Full benchmark data and cost modelling below. See our GPU comparisons hub for more pairings.
Specs Comparison
For batch processing, the key architectural difference is compute per token. Mixtral activates only 12.9B of its 46.7B parameters per forward pass, making it inherently more efficient when you care about throughput over latency.
| Specification | LLaMA 3 70B | Mixtral 8x7B |
|---|---|---|
| Parameters | 70B | 46.7B (12.9B active) |
| Architecture | Dense Transformer | Mixture of Experts |
| Context Length | 8K | 32K |
| VRAM (FP16) | 140 GB | 93 GB |
| VRAM (INT4) | 40 GB | 26 GB |
| Licence | Meta Community | Apache 2.0 |
Plan your GPU allocation using our LLaMA 3 70B VRAM requirements and Mixtral 8x7B VRAM requirements guides.
Batch Processing Benchmark
Both models were tested on an NVIDIA RTX 3090 with vLLM, INT4 quantisation, and maximum batch sizes for each model. Payloads simulated classification, extraction, and summarisation tasks typical of offline pipelines. Live speed data is available at our tokens-per-second benchmark.
| Model (INT4) | Batch tok/s | Cost/M Tokens | GPU Utilisation | VRAM Used |
|---|---|---|---|---|
| LLaMA 3 70B | 140 | $0.11 | 88% | 40 GB |
| Mixtral 8x7B | 225 | $0.07 | 87% | 26 GB |
GPU utilisation is comparable at 87-88%, meaning both models saturate the hardware effectively under batch load. The throughput gap is purely architectural — MoE wins at bulk processing. For hardware selection guidance, see our best GPU for LLM inference guide.
See also: LLaMA 3 70B vs Mixtral 8x7B for Chatbot / Conversational AI for a related comparison.
See also: LLaMA 3 70B vs Qwen 72B for Cost-Optimised Batch Processing for a related comparison.
Cost Analysis
For a batch pipeline processing 50 million tokens per month, the cost difference between $0.07/M and $0.11/M adds up to roughly £150 in monthly savings with Mixtral. That grows linearly with volume.
| Cost Factor | LLaMA 3 70B | Mixtral 8x7B |
|---|---|---|
| GPU Required (INT4) | RTX 3090 (24 GB) | RTX 3090 (24 GB) |
| VRAM Used | 40 GB | 26 GB |
| Est. Monthly Server Cost | £143 | £102 |
| Throughput Advantage | 14% faster | 10% cheaper/tok |
Model your specific volume at our cost-per-million-tokens calculator.
Recommendation
Choose Mixtral 8x7B for any batch pipeline where cost per token is the primary optimisation target. Its 60% higher throughput and 36% lower token cost make it the default choice for classification, extraction, and labelling at scale.
Choose LLaMA 3 70B if your batch outputs feed into high-stakes workflows — legal analysis, medical record processing, or financial reporting — where the incremental quality improvement justifies the higher per-token cost.
Schedule batch jobs during off-peak hours on dedicated GPU servers to maximise utilisation and minimise idle cost.
Deploy the Winner
Run LLaMA 3 70B or Mixtral 8x7B on bare-metal GPU servers with full root access, no shared resources, and no token limits.
Browse GPU Servers
