Table of Contents
Quick Verdict
Both Mistral 7B and Gemma 2 9B deliver mediocre function-calling accuracy by production standards — 74.4% and 72.4% respectively. That means roughly 1 in 4 tool invocations will contain formatting errors or incorrect parameter mapping. On a dedicated GPU server, Mistral takes a narrow 2-point lead, but neither model is well-suited for reliability-critical agent workflows without significant prompt engineering.
The practical takeaway: if you need function calling from a sub-10B model, Mistral 7B is the slightly better option, but consider whether a larger model (like LLaMA 3 8B at 89.8% accuracy) would save more in retry costs than it adds in compute.
Full data below. More at the GPU comparisons hub.
Specs Comparison
Mistral’s 32K context window and Sliding Window Attention give it an edge for agent workflows that accumulate long tool-use histories. Gemma’s 8K limit constrains complex multi-step chains.
| Specification | Mistral 7B | Gemma 2 9B |
|---|---|---|
| Parameters | 7B | 9B |
| Architecture | Dense Transformer + SWA | Dense Transformer |
| Context Length | 32K | 8K |
| VRAM (FP16) | 14.5 GB | 18 GB |
| VRAM (INT4) | 5.5 GB | 7 GB |
| Licence | Apache 2.0 | Gemma Terms |
Guides: Mistral 7B VRAM requirements and Gemma 2 9B VRAM requirements.
Function Calling Benchmark
Tested on an NVIDIA RTX 3090 with vLLM, INT4 quantisation, and continuous batching. Function schemas included simple lookups, nested parameters, and multi-tool routing. See our tokens-per-second benchmark.
| Model (INT4) | Accuracy (%) | Calls/min | Avg Latency (ms) | VRAM Used |
|---|---|---|---|---|
| Mistral 7B | 74.4% | 41 | 200 | 5.5 GB |
| Gemma 2 9B | 72.4% | 49 | 167 | 7 GB |
Gemma processes calls faster (49/min versus 41/min at lower latency), but its slightly lower accuracy means more of those calls fail. When accounting for retries, effective throughput converges. See our best GPU for LLM inference guide.
See also: Mistral 7B vs Gemma 2 9B for Chatbot / Conversational AI for a related comparison.
See also: LLaMA 3 8B vs Mistral 7B for Function Calling for a related comparison.
Cost Analysis
Mistral’s 1.5 GB VRAM advantage at INT4 gives it more headroom for co-located services on the same GPU.
| Cost Factor | Mistral 7B | Gemma 2 9B |
|---|---|---|
| GPU Required (INT4) | RTX 3090 (24 GB) | RTX 3090 (24 GB) |
| VRAM Used | 5.5 GB | 7 GB |
| Est. Monthly Server Cost | £109 | £112 |
| Throughput Advantage | 15% faster | 4% cheaper/tok |
See our cost-per-million-tokens calculator.
Recommendation
Choose Mistral 7B if you need function calling from a lightweight model. Its slightly higher accuracy, wider context window, and lower VRAM footprint make it the marginally better option for agent workflows.
Choose Gemma 2 9B if your function schemas are simple and speed per call matters more than accuracy. Its 19% higher call throughput and 17% lower latency are useful for high-volume, error-tolerant workflows.
Both integrate with vLLM on dedicated GPU servers. For production-critical agents, consider upgrading to a model with higher baseline accuracy.
Deploy the Winner
Run Mistral 7B or Gemma 2 9B on bare-metal GPU servers with full root access, no shared resources, and no token limits.
Browse GPU Servers
