The Open Source AI Model Landscape in 2025

Why I Ended Up Writing This

Someone on the team said "let's try open source models." But nobody actually knew the current landscape. Myself included. How far had Llama come? What was Mistral? And what about Qwen? So I spent a week researching and organizing. (The notes turned into this post.)

Meta's Llama: Still the Reference Point

Llama 3.1 dropped in July 2024, followed by 3.2 and 3.3. Three sizes: 405B, 70B, 8B. The most practical ones for real work are 8B and 70B. 405B inference costs are too high for most teams.

The 8B model is surprisingly decent. For simple classification, summarization, and translation tasks, it roughly matches GPT-3.5 level. We tried it for customer inquiry classification and hit 79% accuracy. (No fine-tuning.)

But Korean language performance is honestly underwhelming. It feels about 20-30% worse than English. Makes sense given Korean's small share of training data, but "makes sense" doesn't make it less frustrating.

Mistral: The Unexpected European Contender

Mistral really grew this year. Mistral Large 2 is being rated at GPT-4 level, and the open source Mistral 7B and Mixtral 8x7B perform well too. Mixtral's MoE (Mixture of Experts) architecture is impressive -- near-large-model performance with significantly fewer compute requirements.

I used Mixtral 8x7B as a code review assistant for two weeks. Its code smell detection was pretty solid. But the 32K context window struggles with long files. We have a legacy file that's 2,400 lines and it just couldn't handle it. (Refactoring that file should come first, admittedly.)

Chinese Models: Qwen, DeepSeek, Yi

Honestly, a year ago I wouldn't have seriously considered Chinese open source models. But things changed a lot this year.

Qwen 2.5 has strong multilingual performance. Often better than Llama for Korean. The 72B model beats Llama 70B on quite a few benchmarks. DeepSeek V3 has produced GPT-4 level results on coding benchmarks.

But there are practical concerns. Data privacy questions. Our security team asked whether feeding internal data into these models is acceptable. Since they're open source and running locally, there's no data leak risk per se, but management gets uneasy just because they're "Chinese models."

Specialized Models Are Worth Watching Too

Coding-focused CodeLlama and StarCoder 2. Text-to-image Stable Diffusion XL. Speech recognition Whisper. These purpose-built models significantly outperform general LLMs in their domains.

Our team uses Whisper for automated meeting transcription, and Korean recognition accuracy is 92.7%. Converting a 1-hour meeting recording to text takes 4 minutes 38 seconds. This is genuinely production-ready.

But Open Source Isn't Always the Answer

Don't overlook infrastructure costs. Self-hosting a 70B model requires at least 2 A100s. Monthly rental minimum around $1,400. With APIs, you pay per usage, so if traffic is low, APIs can actually be cheaper.

For our team, below 1,200 API calls per day, OpenAI's API was more economical. Past 1,200, self-hosting starts winning. This breakeven point varies with model size and infrastructure setup, so you have to run the numbers yourself.

In the end, neither "always open source" nor "always API" is the answer. Our team went hybrid: open source 8B for classification, API for complex generation tasks.