If you’ve been tasked with evaluating AI models for your business, you’ve probably run into the same wall: every vendor claims their model is “state of the art,” every benchmark looks impressive, and none of it tells you what you actually need to know — which model should we run in our office?
This guide cuts through the noise. I’m going to compare four open-source model families dominating enterprise deployments in 2025 — Meta’s Llama 3, Mistral AI’s model family, Alibaba’s Qwen, and Google DeepMind’s Gemma 4 — and give you a framework for choosing the right one based on your actual use case, hardware, and budget.
The good news: all four are genuinely excellent. The choice isn’t “good vs. bad” — it’s about fit.
Why Open-Source Models Are Now a Serious Business Option
Three years ago, if you wanted capable AI, you had one realistic path: the OpenAI API. Today, open-source models running on local hardware can match or beat GPT-3.5 on most business tasks, and in some domains they’re competitive with GPT-4.
For South African businesses in particular, this shift matters enormously. Running your own models means:
- No data leaves your building — critical for compliance with POPIA and sector-specific regulations
- No dollar-denominated API bills — with the rand under persistent pressure, SaaS AI costs can balloon unpredictably
- No dependency on international internet connectivity — your AI works even when Telkom doesn’t
- One-time infrastructure cost rather than an open-ended subscription
The question isn’t whether to consider local models. It’s which one.
The Contenders
Llama 3 (Meta)
Meta’s Llama 3, released in 2024 and updated through 2025, is arguably the most widely deployed open-source model family in the world. Available in 8B, 70B, and 405B parameter sizes, it benefits from having the largest community, the most fine-tuned variants, and the broadest tooling support.
What makes Llama 3 stand out:
Llama 3 was trained on a significantly cleaned and curated dataset — Meta invested heavily in data quality rather than just data volume. The result is a model that handles nuanced instructions well, produces coherent long-form output, and rarely hallucinates in the dramatic ways that plagued earlier open-source models.
The 70B version in particular sits in a sweet spot: it’s genuinely capable for most business tasks, yet runnable on hardware that a serious SME can afford. The 405B version rivals frontier models but requires substantial infrastructure.
Llama 3 is best for:
- General-purpose business applications: drafting, summarising, Q&A
- Environments where community support and available fine-tunes matter
- Teams that want the widest selection of pre-built integrations
- English-primary workloads
Llama 3 is weaker at:
- Multilingual tasks outside the major European languages
- Heavily structured data extraction (compared to some competitors)
- Tasks requiring deep domain knowledge without fine-tuning
Hardware requirements (minimum for production use):
| Model Size | VRAM Required | Recommended Hardware |
|---|---|---|
| Llama 3 8B | 6–8 GB | Single mid-range GPU (RTX 4060/4070) |
| Llama 3 70B | 40–48 GB | 2× high-end GPUs or dedicated AI workstation |
| Llama 3 405B | 200+ GB | Multi-GPU server or DGX-class hardware |
Mistral (Mistral AI)
Mistral AI, the French startup that caused a sensation when it released its first model in 2023, has built a reputation for doing more with less. Their models consistently outperform others of similar size — a 7B Mistral model often beats competitors’ 13B models on benchmarks, and their larger models punch well above their weight class.
The Mistral family includes several flavours worth knowing:
- Mistral 7B — the original, still excellent for lightweight tasks
- Mixtral 8x7B — a “mixture of experts” architecture that gives you 45B-equivalent performance while only activating 12B parameters at any time. This is the clever trick that makes it fast and efficient
- Mistral Large — their flagship, competitive with GPT-4 class models
- Mistral Nemo — a compact 12B model developed with NVIDIA, optimised for edge deployment
What makes Mistral stand out:
Efficiency. If you’re cost-constrained on hardware — and most South African businesses should be thinking about this — Mistral gives you the best performance-per-rand of any model family. The Mixtral architecture in particular is remarkable: you get responses that feel like a 45B model but with the memory footprint of a 12B model.
Mistral also has a genuinely permissive licence structure, which matters for commercial deployment.
Mistral is best for:
- Organisations where hardware budget is a primary constraint
- High-throughput applications that need fast response times
- Coding assistance (Mistral performs exceptionally well on code tasks)
- Teams that want to run powerful AI on more modest hardware
Mistral is weaker at:
- Very long document processing (context window historically shorter than competitors)
- Tasks requiring extensive world knowledge (slightly less training data than Llama 3)
Hardware requirements:
| Model Size | VRAM Required | Recommended Hardware |
|---|---|---|
| Mistral 7B | 5–6 GB | Entry-level GPU (RTX 3060 or better) |
| Mixtral 8x7B | 24–28 GB | Single high-end GPU (RTX 4090) or 2× mid-range |
| Mistral Large | 80+ GB | AI workstation or dedicated server |
Qwen (Alibaba Cloud)
Qwen — short for Tongyi Qianwen — is Alibaba’s open-source model family, and it’s the one that most Western businesses overlook. That’s a mistake. The Qwen 2.5 series, released in late 2024, is genuinely world-class, and in several areas it outperforms both Llama and Mistral.
Available in sizes from 0.5B to 72B parameters, Qwen covers a remarkable range. The Qwen 2.5 72B model has consistently ranked at or near the top of major benchmarks, including MMLU, HumanEval (coding), and GSM8K (mathematical reasoning).
What makes Qwen stand out:
Two things: multilingual capability and structured data handling. Qwen was trained with particular depth on Chinese and Asian language datasets, but this broader multilingual training also makes it stronger across a wider range of languages than most Western models — including better handling of South African languages in some cases. If your business operates across multiple languages or processes documents in languages other than English, this matters.
Qwen is also notably strong at structured tasks — extracting specific data from documents, following precise output formats, and handling tables and spreadsheet-like data. For business intelligence and document processing workflows, this is a meaningful advantage.
Qwen is best for:
- Multilingual business environments
- Structured data extraction and document processing
- Coding and mathematical reasoning tasks
- Organisations that want maximum raw capability within a given parameter count
Qwen is weaker at:
- Community support and ecosystem tooling (fewer third-party integrations than Llama)
- Some Western legal and regulatory domain knowledge
Hardware requirements:
| Model Size | VRAM Required | Recommended Hardware |
|---|---|---|
| Qwen 2.5 7B | 5–6 GB | Entry-level GPU |
| Qwen 2.5 32B | 20–24 GB | Single high-end GPU or 2× mid-range |
| Qwen 2.5 72B | 40–48 GB | 2× high-end GPUs or AI workstation |
Gemma 4 (Google DeepMind)
Google DeepMind’s Gemma 4 is the newest entrant in this comparison and, in several ways, the most surprising. Google has been a dominant force in AI research for years, but its open-source model offerings lagged behind Meta and Alibaba in the early days. Gemma 4 changes that calculus significantly.
Available in sizes ranging from compact edge-deployable variants up to 27B+ parameters, Gemma 4 builds on a lineage of models designed with a clear philosophy: do more with less, and make the results genuinely usable in production. The Apache 2.0 licence — one of the most permissive available — means there are no legal grey areas around commercial deployment.
What makes Gemma 4 stand out:
Two things set Gemma 4 apart from the rest of this field.
First, multimodal capability out of the box. While Llama, Mistral, and Qwen all have separate vision-capable variants, Gemma 4 integrates text and image understanding as a core feature rather than a bolt-on. For businesses dealing with invoices, forms, photographs, diagrams, or any visual content, this matters — your AI can look at an image and reason about it without needing a separate model or pipeline.
Second, instruction-following precision. Gemma 4 was specifically optimised for following complex, multi-step instructions accurately. In business contexts where you need the AI to consistently apply a specific process — extract these five fields in this format, always flag these risk terms, never include this type of information — Gemma’s training shows.
Google also optimised the Gemma family for efficiency at smaller sizes. The smaller Gemma 4 variants are genuinely impressive for their footprint, making them a strong candidate for businesses that want capable AI on limited hardware.
Gemma 4 is best for:
- Workflows that involve both text and images (invoices, forms, photos, scanned documents)
- Applications requiring precise, consistent instruction-following
- Organisations that need the certainty of a permissive commercial licence
- Deployments on modest hardware where a small-but-capable model is preferable to a large quantised one
- Teams already working within Google’s ecosystem (TensorFlow, JAX, Keras, Vertex AI)
Gemma 4 is weaker at:
- The ecosystem and third-party tooling isn’t yet as mature as Llama’s (though it’s growing fast)
- Very large parameter sizes for maximum raw capability (the largest Gemma models are smaller than Llama 3 405B or Qwen 72B)
- Multilingual breadth (strong on major languages, but not Qwen’s depth across Asian and African languages)
Hardware requirements:
| Model Size | VRAM Required | Recommended Hardware |
|---|---|---|
| Gemma 4 (small variant) | 4–6 GB | Entry-level GPU (RTX 3060 or better) |
| Gemma 4 (mid variant) | 12–16 GB | Mid-range GPU (RTX 4070 or better) |
| Gemma 4 27B | 20–24 GB | Single high-end GPU (RTX 4090) |
Head-to-Head: Task-by-Task
Rather than summarising benchmark numbers — which are often gamed and rarely reflect real-world performance — here’s how the four families compare on the tasks South African businesses actually need:
General Writing and Drafting
Winner: Llama 3 Llama 3’s output quality for English-language prose is consistently excellent. Reports, emails, policy documents, proposals — Llama 3 is the safe choice here. Gemma 4 is a close second, particularly when precise instruction-following is needed.
Coding and Technical Tasks
Winner: Mistral / Qwen (tie) Both Mistral and Qwen have invested heavily in coding capability. Mixtral performs surprisingly well on complex code generation; Qwen 2.5 Coder (a specialised variant) is arguably the best open-source coding model available.
Document Processing and Data Extraction
Winner: Qwen / Gemma 4 (tie) Qwen’s precision with structured output gives it an edge for pure text extraction. But if your documents include scanned forms, photographs, or mixed image-and-text content, Gemma 4’s native multimodal capability makes it the stronger choice — it can see the document, not just read its text layer.
Multilingual Tasks
Winner: Qwen For businesses operating across South African languages, European languages, or any context beyond English-only, Qwen’s multilingual training depth gives it a clear edge. The others handle major world languages well but none match Qwen’s breadth.
Image and Visual Content Understanding
Winner: Gemma 4 This is Gemma 4’s clearest competitive advantage. None of the other base models in this comparison handle images natively the way Gemma 4 does. If your business works with scanned documents, product photos, site images, or any visual content, Gemma 4 is the only model in this list you can point directly at an image and ask questions about.
Running on Modest Hardware
Winner: Mistral / Gemma 4 (tie) Mistral’s efficiency architecture remains remarkable, but the smaller Gemma 4 variants give it genuine competition on the value-for-hardware metric. Either is an excellent starting point if your GPU budget is limited.
Integration and Ecosystem
Winner: Llama 3 The Llama ecosystem is simply more mature. More integrations, more fine-tuned variants for specific industries, more community knowledge, more tutorials. If your team is new to local AI deployment, Llama 3 will be easier to work with. Gemma 4 is catching up quickly, backed by Google’s resources.
Which Should You Choose?
Here’s the honest answer: your hardware determines your shortlist, and your primary use case makes the final call.
If you have a tight hardware budget (one GPU, 16–24 GB VRAM): Start with Mistral’s Mixtral 8x7B. You’ll get remarkable performance for the investment.
If you’re building a multi-department AI platform and want maximum versatility: Deploy Llama 3 70B. The ecosystem, community, and output quality make it the safest enterprise choice.
If document processing, multilingual capability, or structured data extraction is central to your use case: Seriously evaluate Qwen 2.5 72B. It’s underrated in this market.
If you’re a developer-heavy organisation prioritising coding assistance: Qwen 2.5 Coder or Mistral, depending on your hardware.
If your workflows involve images, scanned documents, or mixed visual and text content: Gemma 4 is the standout choice. Its native multimodal capability saves you from needing a separate vision model and pipeline.
If you need a commercially safe licence with no ambiguity: Gemma 4’s Apache 2.0 licence is the most permissive of the four. For businesses with cautious legal teams, this can be the deciding factor.
A Note on Quantisation
All four of these model families are available in quantised versions — compressed variants that run in less memory with modest quality trade-offs. A 70B model quantised to 4-bit precision runs in roughly 40GB of VRAM instead of 140GB+, which dramatically widens your hardware options.
For most business use cases, Q4 or Q5 quantisation delivers 90–95% of the full-precision model’s capability. This is the practical path to running genuinely powerful models on affordable hardware.
What About Running These in South Africa?
The models themselves are freely available and run locally — no downloads after initial setup, no API calls, no international bandwidth consumed per query. Once your model is running on your hardware in your office, every query stays in your building.
For South African businesses dealing with load shedding, this creates an important design consideration: your AI server needs its own power backup. A model that’s mid-inference when Stage 4 kicks in will lose that context. But a properly backed-up local AI server — with a UPS or solar — is infinitely more reliable than cloud AI during an outage. More on this in our upcoming guide to building a local AI server for your Johannesburg office.
Conclusion
The open-source LLM landscape in 2025 is genuinely competitive. Llama 3, Mistral, Qwen, and Gemma 4 are all production-ready, actively maintained, and capable of handling serious business workloads.
The choice comes down to fit:
- Llama 3 for versatility, ecosystem depth, and English-primary workloads
- Mistral for efficiency and coding on constrained hardware
- Qwen for multilingual capability and structured data tasks
- Gemma 4 for image-and-text workflows, precise instruction-following, and the cleanest commercial licence
And for South African businesses? All four offer something OpenAI cannot: complete data sovereignty, no foreign exchange exposure, and AI that works when the internet doesn’t.
Need help choosing and deploying the right model for your specific use case? Get in touch with us — we assess your workload, your infrastructure, and your budget, and recommend the right stack for your business.