Gemma 4 vs Llama 3 vs Mistral: Choosing an Open-Source LLM for Australian Business

Three open-source model families dominate private LLM deployments in 2026: Google's Gemma 4, Meta's Llama 3, and Mistral AI's Mistral family. Each has strengths, and each is genuinely viable for Australian business deployments. The choice isn't about "which is best" — it's about which fits your specific hardware, use cases, and team size. This guide compares them directly across the dimensions that matter for enterprise deployment.

Why Model Choice Matters (and Doesn't)

For most enterprise document workflows — summarisation, search, drafting, Q&A — the differences between top-tier open-source models are smaller than the differences between any of them and a poorly configured deployment.

A well-tuned Gemma 4 9B with good RAG can outperform a poorly configured Llama 3 70B for many tasks. Model choice matters at the margins; deployment quality matters at the foundation.

That said, when comparing well-deployed systems, the model choice does affect:

• Inference speed (smaller models respond faster)
• Hardware cost (smaller models need less VRAM)
• Reasoning quality (larger models handle complex tasks better)
• Instruction following (varies by model family)
• Multilingual capability (some models are much stronger here)
• Licensing flexibility (Mistral is the most permissive)

This guide compares the three families across these dimensions.

The Three Families at a Glance

Gemma 4 (Google)

Released early-to-mid 2026, Gemma 4 is Google's open-weight model family. It builds on the Gemma 2 and Gemma 3 lineage with significant improvements in efficiency and capability.

Key sizes:

• Gemma 4 9B (most common)
• Gemma 4 27B
• Gemma 4 2B (for edge/efficient deployments)

Strengths: Excellent hardware efficiency, strong at structured tasks (summarisation, classification, document analysis), good safety properties out of the box.

Weaknesses: Slightly more conservative responses than competitors, can be less flexible on creative tasks.

Llama 3 (Meta)

Released through 2024-2026, the Llama 3 family is the most widely deployed open-source LLM family globally. Meta has consistently invested in the line.

Key sizes:

• Llama 3 8B (efficient deployments)
• Llama 3 13B / 70B (mid-range and large)
• Llama 3.1 405B (top-tier, datacentre only)

Strengths: Strong general reasoning, broad community support (tooling, fine-tunes, derivatives), well-documented.

Weaknesses: Larger models need significant VRAM, license has minor revenue thresholds (irrelevant for most), occasionally inconsistent on specific structured tasks.

Mistral (Mistral AI)

Founded by ex-Meta and Google DeepMind researchers, Mistral AI's models have particularly strong multilingual capabilities and characteristic instruction-following style.

Key sizes:

• Mistral 7B (small, efficient)
• Mistral Small / Medium
• Mistral Large (123B parameters)
• Mixtral 8x22B (mixture of experts)

Strengths: Multilingual (especially European languages), strong reasoning, fully permissive Apache 2.0 licence, efficient mixture-of-experts variants.

Weaknesses: Smaller ecosystem than Llama, some variants need careful prompt engineering.

Side-by-Side Comparison

Hardware Requirements (Approximate)

Practical takeaway: For small/mid-size business deployments, models in the 7B-27B range are the realistic targets. Beyond that, hardware costs scale quickly.

Capability Comparison (General Document Tasks)

Based on representative benchmarks (MMLU, HumanEval, GSM8K) and real-world enterprise document task evaluation:

Practical takeaway: Llama 3 70B is the capability leader if you can afford the hardware. Among smaller models, Gemma 4 9B and Llama 3 8B are very close — pick based on hardware fit.

Licensing Comparison

Practical takeaway: For Australian businesses, all three are functionally free to use commercially. Mistral has the most permissive licence. The Llama 3 revenue threshold is irrelevant unless you serve 700+ million monthly active users (you don't).

Use Case Matching

Use Case 1: Small Law Firm (12 Lawyers, 1 Office)

Hardware: Mac Mini M4 Pro 48GB or single-GPU workstation.

Recommended model: Gemma 4 9B or Llama 3 8B.

Why: Strong document analysis capability, comfortable hardware fit, no licence concerns. With RAG over the firm's precedent library and policy documents, either model serves the use case well.

Use Case 2: Mid-Tier Accounting Firm (60 Staff, Mixed Workflows)

Hardware: Single-GPU server (RTX A5000, 24GB VRAM).

Recommended model: Gemma 4 27B or Llama 3 13B.

Why: Stronger reasoning for complex tax and audit queries, room for many concurrent users, headroom for moderate RAG context windows. Gemma 4 27B has a slight edge for structured tax workflows; Llama 3 13B is more flexible.

Use Case 3: Healthcare Network (200 Staff, Sensitive Data)

Hardware: Multi-GPU server (RTX A6000 48GB or L40S).

Recommended model: Llama 3 70B (quantised) or Mistral Large.

Why: Clinical and policy documents require strong reasoning; multiple departments need concurrent access; medical terminology and complex query handling benefit from larger model capacity. Llama 3 70B is the most-deployed option in Australian healthcare AI; Mistral Large is preferred where multilingual patient communications matter.

Use Case 4: Wealth Management Firm (40 Advisors, APRA-Regulated)

Hardware: Single-GPU server (RTX A5000 or A6000).

Recommended model: Llama 3 13B-70B or Gemma 4 27B.

Why: Financial document analysis, regulatory compliance Q&A, client correspondence drafting. Llama 3's strong instruction-following helps with template-based outputs (statements of advice, fact-find summaries). For specific compliance contexts, see our APRA CPS 234 and AI analysis.

Use Case 5: Government Contractor (50 Staff, Sovereign Requirements)

Hardware: Air-gap-capable server (no internet connectivity).

Recommended model: Llama 3 70B or Gemma 4 27B.

Why: Sovereign deployment requirements favour widely-supported, well-documented models that can be validated offline. The model must run reliably without external dependencies — both these models are designed for this. For broader context, see our sovereign AI Australia guide.

Quantisation: How to Run Bigger Models on Smaller Hardware

Quantisation reduces a model's precision (e.g., from 16-bit to 4-bit per parameter), dramatically reducing memory requirements with modest quality impact.

For enterprise deployments, Q5 or Q4 quantisation is typically the sweet spot — meaningful memory savings with minimal capability loss. This is how a 70B model "fits" on a 48GB GPU.

Tools like Ollama handle quantisation transparently — you specify the model variant you want and the tool downloads the appropriate quantised version.

Model Selection Decision Tree

START: What's your team size?

├─ 5-15 users
│  └─ Mac Mini M4 Pro (24-48GB)
│     ├─ General document tasks → Gemma 4 9B or Llama 3 8B
│     └─ Multilingual or specific reasoning → Mistral 7B

├─ 20-50 users
│  └─ Single-GPU server (RTX A5000)
│     ├─ Document-heavy workflows → Gemma 4 27B
│     ├─ Strong reasoning needed → Llama 3 13B
│     └─ Multilingual or code-heavy → Mistral models

├─ 50-150 users
│  └─ Multi-GPU server (RTX A6000 or L40S)
│     ├─ Highest capability needed → Llama 3 70B (Q4 quantised)
│     ├─ Multilingual at scale → Mistral Large
│     └─ Hardware efficiency priority → Multiple Gemma 4 27B instances

└─ 150+ users
   └─ Enterprise GPU server (H100 class)
      ├─ Top-tier reasoning → Llama 3 70B unquantised
      ├─ Multilingual + reasoning → Mistral Large
      └─ Distributed multi-instance deployments

Other Considerations

Fine-Tuning Potential

If you plan to fine-tune the model on your domain-specific data:

• Llama 3 has the most extensive ecosystem for fine-tuning (LoRA, QLoRA, full fine-tuning tooling)
• Gemma 4 has good Google-supported tooling and works well with LoRA approaches
• Mistral supports standard fine-tuning workflows with Apache-licensed base models

For most enterprise deployments, RAG (retrieval augmented generation) over your documents is more practical than fine-tuning — see our RAG architecture guide for details.

Model Update Cadence

• Llama 3 updates frequently (Meta releases multiple variants per year)
• Gemma 4 updates on Google's schedule (typically major version every 12-18 months)
• Mistral updates regularly with both major and incremental releases

For private deployments, frequent updates aren't necessarily an advantage — stability is often more valuable. The entity decides when to update; the model doesn't push updates uninvited.

Community Support and Documentation

• Llama 3 has by far the largest community: Hugging Face ecosystem, derivative fine-tunes, deployment tooling
• Mistral has strong community but smaller than Llama
• Gemma 4 has strong Google documentation and growing community

For most deployments served by Ollama, community size matters less — the deployment tooling abstracts most of the model-specific details. But for advanced configuration, Llama 3 has the deepest resources.

What AIRGAP LLM Typically Recommends

For most Australian business deployments AIRGAP LLM works with:

We don't lock clients into a model — the open-source nature means you can switch later if your needs change. Hardware investment carries over; model switching is largely a re-deployment exercise, not a re-build.

Practical Next Steps

For organisations evaluating these models for private deployment:

1. Define your use cases — document the actual workflows you want AI to support
2. Assess your hardware budget — this constrains realistic model choices
3. Run a pilot — deploy a small instance, validate against real documents
4. Measure capability against your needs — generic benchmarks matter less than your specific tasks
5. Plan for evolution — open-source models update; your deployment should be able to adopt newer models without re-architecting

For a tailored model recommendation based on your specific deployment, contact AIRGAP LLM for a free assessment.

Frequently Asked Questions

Which is the best open-source LLM for Australian business use?

For most Australian business document workflows, Llama 3 (8B for small teams, 70B for larger deployments) or Gemma 4 9B offer the best balance of capability, hardware efficiency, and licensing terms. Llama 3 has the strongest general reasoning. Gemma 4 is the most hardware-efficient. Mistral models are best when multilingual capability or specific reasoning patterns matter. The right choice depends on use case, hardware budget, and team size.

Can these models run on a Mac Mini?

Yes. A Mac Mini M4 Pro with 24GB unified memory can run Gemma 4 9B, Llama 3 8B, or Mistral 7B comfortably. A Mac Mini M4 Pro with 48GB can handle Gemma 4 27B or Llama 3 13B-quantised. For larger models (70B+), you need a dedicated GPU server with at least 48GB VRAM. The Mac Mini is the sweet spot for small office deployments serving 5-20 users.

What's the licensing situation for these models?

Gemma 4 uses the Gemma Terms of Use — a permissive licence allowing commercial use with some content-policy restrictions. Llama 3 uses the Llama 3 Community License — also permissive but with revenue thresholds for very large organisations (over 700M monthly users). Mistral models use Apache 2.0 — the most permissive open-source license. For Australian organisations under 700M monthly users (essentially all of them), all three are free for commercial deployment.

Do these models have current knowledge cut-offs that matter?

Yes, every LLM has a training data cut-off after which it has no knowledge. As of mid-2026, Gemma 4 cuts off around early 2025, Llama 3 around late 2024, and Mistral varies by version. For business use, this matters less than you might expect because most enterprise tasks involve retrieval augmented generation (RAG) over your current documents — the model's job is to reason over the documents you provide, not to remember the world. The model's parametric knowledge becomes a backup, not the primary information source.

How do I choose between these models for my specific deployment?

Start with your hardware and team size, not the model. For 5-15 users on Mac Mini hardware: Gemma 4 9B or Llama 3 8B. For 20-50 users on a single-GPU server: Llama 3 13B or Gemma 4 27B. For 50-150 users on multi-GPU servers: Llama 3 70B or Mistral Large. For multilingual work or specific reasoning needs, weight toward Mistral. For maximum general capability with strong instruction following, weight toward Llama 3. For hardware efficiency, weight toward Gemma 4.