Gemma 4·Llama 4·Mistral Small 4 — Tried All 3 Open-Source LLMs

April 2026 · AI Trends

In the first half of 2026, the open-source LLM landscape shifted. Google released Gemma 4, Meta released Llama 4, and Mistral released Small 4 in rapid succession. All three models are free to download and run. But their architectures and purposes are different.

The short answer: for long context, Llama 4 Scout. For commercial use with zero license headaches, Mistral Small 4. For on-device and edge deployments, Gemma 4 small variants.

I compared all three on parameter efficiency, context window, multimodal support, license, and edge deployment. If picking just one feels hard, I also laid out a way to combine all three.

MoE vs Dense — The Architecture Difference

MoE (Mixture of Experts) works like a bank with specialized tellers. Different staff handle different transactions. For each input, only the relevant expert parameters are activated. Llama 4 uses this structure.

Llama 4 Scout has 109B parameters in total, but only 17B are activated during inference. Compute cost is low. It has an advantage in inference speed over equivalent-size Dense models. Out of 16 experts, only the necessary subset is selected and computed per input.

Gemma 4 and Mistral Small 4 use Dense architectures. All parameters participate in every computation. The inference cost relative to model size is higher than MoE. However, memory requirements scale proportionally with parameter count, which makes capacity planning straightforward. Fewer variables when designing the operating environment.

Context Window — The Gap Between 10M and 128K

Llama 4 Scout's context window is 10M tokens. That's enough to fit dozens of books in a single prompt. It becomes possible to drop an entire codebase or document collection directly into the prompt without a RAG pipeline. RAG retrieves only the relevant pieces from an external database — but when the context is large enough, stuffing everything in at once can actually be faster.

Meta applied the iRoPE architecture to make this work. It's a technique that extends RoPE positional encoding in an interleaved fashion. Conventional RoPE suffers a sharp performance drop when sequences exceed the training length. iRoPE reduces that limitation and maintains accuracy even at the 10M token scale.

Gemma 4 and Mistral Small 4 sit at 128K. That's plenty for typical code reviews or summarizing long documents — a full novel, dozens of research papers, lengthy meeting transcripts. But for analyzing an entire large codebase in one shot or handling multi-million-token documents, there's no comparison to Llama 4 Scout.

Performance Benchmarks — What the Numbers Say

Benchmark results shift depending on which test you look at. Each company tends to pick conditions that favor their own model. It's worth checking independently maintained leaderboards like LMSYS Chatbot Arena or the HuggingFace Open LLM Leaderboard alongside official claims.

On coding, Llama 4 Maverick recorded top open-source performance on SWE-bench. Llama 4 Scout is smaller than Maverick, but its 10M context window gives it a practical edge when analyzing an entire codebase in one pass. Gemma 4 27B shows strong results on multimodal tasks compared to models of similar size. Mistral Small 4, at 22B, is widely regarded as competitive with 40B-class models.

Honestly, benchmark numbers and real-world experience can diverge. The actual difference in practical tasks — code review, document summarization, conversation quality — only shows up when you run the models yourself. All three are in the top tier. The right choice depends on the situation and the task type.

Multimodal Support — Models That See and Models That Don't

Multimodal means the ability to understand images alongside text. It's needed for tasks like analyzing UI screenshots, reading values from charts, or extracting text from photos. Gemma 4 and Llama 4 support multimodal input. Mistral Small 4 handles text only.

Gemma 4 27B accepts both images and text together. Sending a screen capture and asking for a code explanation is possible. Extracting information from product photos is possible. Llama 4 Scout and Maverick both support multimodal as well. Either model can handle practical vision tasks.

Mistral Small 4 being text-only is a drawback. But it's no disadvantage on text-only tasks. For pipelines where images aren't needed — fine-tuning or building a RAG system, for instance — license permissiveness becomes the more important deciding factor. If image processing is a hard requirement, Gemma 4 or Llama 4 is the pick.

License — Always Check Before Commercial Use

Mistral Small 4 is Apache 2.0. Commercial use is unrestricted. Attach it to an internal service as an API, embed it in a SaaS product, fine-tune and redistribute — no constraints. It's the most permissive license in the open-source ecosystem. For small teams without dedicated legal review capacity, this model is the safest choice.

Llama 4 uses the Meta Commercial License. Platforms with over 700 million MAU need a separate agreement with Meta. At the startup or indie developer scale, commercial use is effectively free. But if the service could grow quickly, the license terms are worth reading in full before shipping.

Gemma 4 follows the Google Gemma Terms of Use. There are specific restrictions. Using Gemma model outputs to train other LLMs is limited. Using it to develop competing AI services is also restricted. General application development is fine, but AI-adjacent services need a careful read of the specific clauses.

Edge Deployment Reality

Edge deployment means running a model directly on a device without a server. It's like putting AI on a smartphone or local PC. Works without an internet connection. Advantages include privacy protection and minimizing response latency.

Gemma 4 2B and 9B have explicit on-device deployment support from Google. The 2B variant can run on a typical smartphone. The 9B needs a high-end mobile device or something like an Apple Silicon MacBook. Gemma 4 27B is closer to local server territory than edge.

Mistral Small 4 is 22B. With 4-bit quantization applied, it drops to roughly 12–14GB VRAM, making it runnable on an M2 Pro MacBook or a single RTX 3090. Llama 4 Scout, due to the MoE architecture requiring the full 109B in memory, is not realistic for typical edge environments. A multi-GPU server is what it actually needs.

Deployment Cost — Local vs Hosted API

The biggest advantage of open-source models is zero inference cost when running locally. There's an upfront hardware investment, but no per-token charges after that. In fp16, Gemma 4 27B needs roughly 54GB VRAM, Mistral Small 4 needs roughly 44GB, and Llama 4 Scout needs roughly 218GB.

Applying 4-bit quantization cuts memory requirements to about one-quarter. Gemma 4 27B drops to around 14GB, Mistral Small 4 to around 12GB. A single RTX 3090 becomes viable. There's some performance degradation from quantization, but for general use it's hard to notice.

If local isn't an option, hosted APIs work fine. Platforms like Together AI, Groq, and Fireworks offer all three models via API. Per-token costs apply, but they're cheaper than GPT-4-tier pricing. For commercial projects, pairing the license-friendly Mistral Small 4 with a hosted API is a realistic starting point.

Side-by-Side Comparison

Recommendations by Use Case

FAQ

Q. How does Llama 4 Scout achieve a 10M context window?

Meta applied the iRoPE architecture. It extends conventional RoPE positional embedding in an interleaved fashion to handle long sequences. Standard RoPE suffers a sharp performance drop beyond its training length. iRoPE reduces that limitation and maintains accuracy at the 10M scale.

Q. Which model has the most permissive license for commercial use?

Mistral Small 4. It's Apache 2.0, so there are no MAU limits. Fine-tuning and redistributing is also free. For small teams without legal staff to review license terms, Mistral Small 4 is the safest pick.

Q. Is MoE always better than Dense?

No. MoE has lower inference compute, but all weights still need to be loaded into memory. In edge environments, a small Dense model is more practical. If multi-GPU servers are available, the inference speed advantage of MoE is meaningful.

Q. Which model is strongest at coding?

Llama 4 Maverick recorded top open-source performance on SWE-bench. For basic coding assistance or code review, Mistral Small 4 is sufficient. For analyzing an entire repository in one shot, Llama 4 Scout's 10M context is more practical.

Q. Is there any point in using all three at the same time?

Yes. Long document analysis with Llama 4 Scout, image processing with Gemma 4, commercial API work with Mistral Small 4 — routing by task type is a realistic architecture. When using cloud APIs, costs get optimized at the same time.

Wrapping Up

Reducing the three to one doesn't make sense. They're built for different jobs. For processing long documents without RAG, Llama 4 Scout. For commercial projects with no license constraints, Mistral Small 4. For on-device, Gemma 4 small variants.

Combining all three based on the situation is also realistic. Routing requests to the right model captures both cost efficiency and performance. Open-source LLMs are no longer just "alternatives to proprietary models." For certain tasks, they're already on equal footing.

※ All figures and information in this article are based on each model's official announcements. Model performance and license terms may change with updates.
※ Always read each model's official license documentation directly before any commercial use. This article is not legal advice.