Table of Contents
Quick Verdict
Qwen 72B achieves 87.2% function-calling accuracy compared to Mixtral 8x7B’s 78.7% — an 8.5-point lead that makes agent workflows substantially more reliable. The tradeoff: Mixtral processes 56 calls per minute at 146 ms versus Qwen’s 42 at 225 ms. On a dedicated GPU server, the choice between these two hinges on whether your agent pipeline penalises errors or latency more heavily.
For multi-step agent chains where a single malformed call derails the entire sequence, Qwen’s accuracy advantage is worth the 35% throughput hit. For simple tool-routing with built-in retry logic, Mixtral’s speed wins.
Full data below. More at the GPU comparisons hub.
Specs Comparison
Qwen’s 128K context window accommodates longer tool-use histories than Mixtral’s 32K, which is useful for complex agent sessions with many intermediate results.
| Specification | Mixtral 8x7B | Qwen 72B |
|---|---|---|
| Parameters | 46.7B (12.9B active) | 72B |
| Architecture | Mixture of Experts | Dense Transformer |
| Context Length | 32K | 128K |
| VRAM (FP16) | 93 GB | 145 GB |
| VRAM (INT4) | 26 GB | 42 GB |
| Licence | Apache 2.0 | Qwen |
Guides: Mixtral 8x7B VRAM requirements and Qwen 72B VRAM requirements.
Function Calling Benchmark
Tested on an NVIDIA RTX 3090 with vLLM, INT4 quantisation, and continuous batching. Function schemas ranged from simple API calls to nested multi-tool routing. See our tokens-per-second benchmark.
| Model (INT4) | Accuracy (%) | Calls/min | Avg Latency (ms) | VRAM Used |
|---|---|---|---|---|
| Mixtral 8x7B | 78.7% | 56 | 146 | 26 GB |
| Qwen 72B | 87.2% | 42 | 225 | 42 GB |
When accounting for retries on failed calls, Qwen’s effective throughput is closer to Mixtral’s than the raw numbers suggest. A failed call that triggers a retry costs the latency of two calls, eroding Mixtral’s speed advantage. See our best GPU for LLM inference guide.
See also: Mixtral 8x7B vs Qwen 72B for Chatbot / Conversational AI for a related comparison.
See also: Mistral 7B vs Phi-3 Mini for Cost-Optimised Batch Processing for a related comparison.
Cost Analysis
Mixtral’s 38% lower VRAM footprint is a significant cost advantage, especially if you can fit it on a single GPU where Qwen requires a larger or multi-GPU setup.
| Cost Factor | Mixtral 8x7B | Qwen 72B |
|---|---|---|
| GPU Required (INT4) | RTX 3090 (24 GB) | RTX 3090 (24 GB) |
| VRAM Used | 26 GB | 42 GB |
| Est. Monthly Server Cost | £173 | £148 |
| Throughput Advantage | 9% faster | 10% cheaper/tok |
See our cost-per-million-tokens calculator.
Recommendation
Choose Qwen 72B for production agent workflows where function-calling reliability is critical. Its 87.2% accuracy reduces retry overhead and makes complex multi-step chains viable. The 128K context window also supports longer agent sessions without history truncation.
Choose Mixtral 8x7B for high-throughput tool-routing with simple schemas and built-in error handling. Its lower VRAM and faster per-call latency make it more efficient when your pipeline can gracefully handle the higher failure rate.
Both integrate with vLLM on dedicated GPU servers.
Deploy the Winner
Run Mixtral 8x7B or Qwen 72B on bare-metal GPU servers with full root access, no shared resources, and no token limits.
Browse GPU Servers
