The open-source AI model landscape has changed more in the past six months than in the previous two years combined. If you evaluated open models in mid-2025 and decided they weren’t ready for production, it’s time to look again. The gap between open and proprietary models has narrowed dramatically, and for certain use cases, it’s closed entirely.

Here’s where things actually stand in February 2026, with an honest assessment of what each major open model family does well, where it falls short, and when it makes sense to choose open over proprietary.

Meta’s Llama 3 Family

Llama 3 remains the most widely deployed open model family, and for good reason. The ecosystem around it is massive — fine-tuning tools, deployment frameworks, community-contributed improvements, and extensive documentation.

Llama 3.3 70B is the sweet spot for most enterprise applications. It handles general instruction following, summarisation, content generation, and analysis at a level that’s competitive with GPT-4o for the majority of tasks. The 70B parameter size means it’ll run on a single high-end GPU node, which keeps infrastructure costs manageable.

Llama 3.1 405B is Meta’s largest open model, and it’s genuinely impressive on complex reasoning tasks. But the hardware requirements are substantial — you’re looking at multi-GPU setups, which limits deployment options and increases costs significantly.

The Llama ecosystem’s biggest advantage is community support. There are hundreds of fine-tuned variants on Hugging Face optimised for specific tasks — medical analysis, code generation, legal document review, customer service. Some of these fine-tuned models outperform the base model by a significant margin in their specific domain.

Best for: General-purpose enterprise applications, organisations with in-house ML teams who want to fine-tune, deployments where data residency is critical.

Mistral

Mistral has positioned itself as the European alternative to both American proprietary models and Meta’s Llama. Their models tend to be efficient — delivering strong performance at smaller parameter counts.

Mistral Large (the latest version) competes directly with GPT-4o and Claude 3.5 Sonnet on most benchmarks. What sets it apart is its strength in multilingual tasks and European language support, which matters for APAC and European enterprise deployments.

Mixtral 8x22B uses a mixture-of-experts architecture that activates only a subset of parameters for each query, giving you large-model performance at reduced inference cost. For high-throughput applications where you’re processing thousands of requests, the cost efficiency is notable.

Mistral’s API pricing is also competitive with proprietary alternatives, which gives you a hybrid option — use their managed API when it’s convenient, self-host when you need control.

Best for: Multilingual applications, cost-sensitive high-throughput deployments, European data sovereignty requirements.

DeepSeek

DeepSeek is the wildcard that’s forced everyone else to up their game. The Chinese lab’s approach — building state-of-the-art models at dramatically lower training costs — has challenged assumptions about how much compute you need to build competitive AI.

DeepSeek-V3 hit GPT-4-class performance on standard benchmarks while reportedly being trained for a fraction of the cost of equivalent Western models. The mixture-of-experts architecture is efficient to run, and the model is genuinely good at coding, reasoning, and analysis.

DeepSeek R1 is their reasoning model, and it’s competitive with OpenAI’s o1 series on mathematical and logical reasoning tasks. For enterprises that need strong reasoning capabilities and are comfortable with the model’s origins, it’s a serious option.

The elephant in the room is geopolitics. Some enterprises — particularly those in government, defence, or critical infrastructure — won’t deploy a Chinese-origin model regardless of its technical merits. That’s a legitimate consideration, not xenophobia. Supply chain trust matters in AI just as it does in hardware.

Best for: Coding tasks, mathematical reasoning, organisations comfortable with Chinese-origin models and looking for strong performance at lower cost.

Qwen (Alibaba)

Alibaba’s Qwen family doesn’t get enough attention in Western markets, which is a mistake. Qwen 2.5 is exceptionally capable, with variants from 0.5B to 72B parameters. The smaller models (7B, 14B) punch well above their weight, which makes them interesting for edge deployment and resource-constrained environments.

Qwen’s coding capabilities are particularly strong — the Qwen 2.5-Coder variants are competitive with dedicated coding models at much larger parameter counts.

Best for: Edge deployment, coding assistance, applications where smaller model size is an advantage.

The Enterprise Decision Framework

Choosing between these models — and between open and proprietary — isn’t primarily a technical decision. It’s an organisational one. Here’s how I’d frame it:

Choose open models when:

• Data sovereignty and privacy are non-negotiable (you need on-premises or private cloud deployment)
• You have ML engineering capability to manage model deployment, monitoring, and updates
• Your use case benefits from fine-tuning on proprietary data
• You’re running high-volume inference where API costs would be prohibitive
• You want to avoid vendor lock-in to a single AI provider

Stick with proprietary models when:

• You need the absolute best performance on complex reasoning tasks (Claude Opus and GPT-4o still lead here, albeit by a shrinking margin)
• You lack the engineering resources to self-host and maintain models
• You need enterprise SLAs, support, and compliance certifications
• You’re deploying quickly and don’t want to manage infrastructure

The hybrid approach is what most sophisticated enterprises are landing on. Use proprietary APIs for complex, low-volume tasks where quality matters most. Deploy open models for high-volume, well-defined tasks where cost matters more. Fine-tune open models on your domain data for specialised applications.

What’s Changed Since Six Months Ago

The most significant shift is confidence. Six months ago, most CTOs I talked to viewed open models as experimental. Today, they’re running in production at major enterprises across finance, healthcare, and manufacturing. The Linux Foundation’s AI report noted that enterprise adoption of open-source AI models grew by over 300% in 2025.

The other change is tooling maturity. Deploying, monitoring, and managing open models in production used to require significant custom infrastructure. Projects like vLLM, TGI, and Ollama have made deployment substantially easier, and observability tools have caught up with what’s available for proprietary API monitoring.

Open-source AI isn’t the scrappy underdog anymore. It’s a legitimate first choice for an increasing number of enterprise applications. The models that are available today would have been considered state-of-the-art proprietary technology just eighteen months ago. That pace of progress isn’t slowing down.