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 E2B or E4B.

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 — Different Architectures

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 Scout, Mistral Small 4, and Gemma 4 26B all use 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.

Mistral Small 4 distributes 119B parameters across 128 experts with ~22B active per token. Raw compute looks like a 22B Dense model, but the full 119B still needs to live in memory. Gemma 4 26B takes the MoE-small path: 26B total with about 3.8B activated per token, targeting "4B-like speed with the intelligence of something much larger."

Gemma 4's small variants (E2B, E4B) and the 31B use Dense architectures. All parameters participate in every computation. 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 — 10M, 256K, 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.

Mistral Small 4 sits at 256K. Gemma 4 depends on the variant: E2B and E4B (small) are 128K, while 31B and 26B (medium) are 256K. For code reviews, long-document summaries, entire novels, dozens of research papers, or lengthy meeting transcripts, 128K is already enough. Once the workload crosses into whole-repository analysis or multi-million-token documents, Llama 4 Scout is the only real option.

Benchmarks — The Actual Numbers

Performance comparisons depend heavily on which benchmark you look at. Every lab picks the conditions that favor its own model. Independent leaderboards like LMSYS Chatbot Arena and SWE-bench Verified have to be cross-checked.

On coding, Llama 4 Maverick recorded between 76.8 and 80.8 on SWE-bench (the range depends on the evaluation variant). That puts it in the open-source top tier — but not "absolute #1." On SWE-bench Verified, open-weight competitors like GLM-5 (77.8) and GLM-4.7 land right alongside it. 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 31B shows strong results on multimodal tasks compared to models of similar size. Mistral Small 4, per Mistral's own evaluations with ~22B active parameters, matches or surpasses GPT-OSS 120B and Qwen-class models on several key benchmarks.

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 — Images, Video, Audio

Multimodal means understanding inputs beyond text: analyzing UI screenshots, reading values from charts, extracting text from photos, handling voice commands. As of 2026, none of these three models is text-only.

Gemma 4 is natively multimodal across every variant. Text, image, video, and OCR inputs are all supported. The E2B and E4B small variants additionally accept native audio for speech recognition, which puts voice assistants and on-device transcription summaries directly in reach. Llama 4 Scout and Maverick use an early-fusion approach — text and vision tokens are unified inside the foundation model itself.

Mistral Small 4 is the first model in the Mistral Small series to support native vision input. Images are passed in the standard API message array alongside text and processed within the same 256K context window. If your pipeline doesn't need vision, license permissiveness (Apache 2.0) becomes the bigger reason to pick it.

Licenses — Check Before Shipping Commercially

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 Llama 4 Community License. Services with more than 700 million monthly active users must request a separate license from Meta, which Meta can grant or deny at its sole discretion. At the startup or indie developer scale, commercial use is effectively free. There's one more thing to track, though: you're required to display a "Built with Llama" badge on a related website or in-app page — check your branding policy 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 the model on the device itself, with no server in between. Embedding AI into a phone or a local PC. It works without an internet connection. Advantages show up in privacy protection and latency.

Gemma 4 E2B and E4B have explicit on-device deployment support from Google. Both also accept native audio input, so voice assistants and local transcription workflows are immediate use cases. E2B runs on a typical smartphone; E4B needs a high-end mobile device or something like an Apple Silicon MacBook. Gemma 4 31B and 26B MoE are closer to local-server territory than edge.

Mistral Small 4 has 119B total weights. Full-weight fp16 loading is a non-starter for edge environments. With 4-bit quantization and a runtime optimized for the ~22B active path, a high-end multi-GPU workstation or an M2 Ultra tier device can run it. 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 31B needs roughly 62GB VRAM, Mistral Small 4 needs roughly 238GB (119B full weights), and Llama 4 Scout needs roughly 218GB.

Applying 4-bit quantization cuts memory requirements to about one-quarter. Gemma 4 31B drops to around 16GB, Mistral Small 4 to around 60GB, Llama 4 Scout to around 55GB. A single RTX 4090 or M2 Max becomes viable for Gemma 4 31B. Mistral Small 4 and Llama 4 Scout usually still need multi-GPU even after quantization. Gemma 4 E4B at 4-bit sits at about 3GB, small enough to run comfortably on a laptop.

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.

At a Glance

Picks by Use Case

FAQ

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

Meta applied iRoPE — an interleaved extension of RoPE position encoding. Standard RoPE degrades sharply beyond the training sequence length. iRoPE eases that limitation and keeps accuracy at the 10M scale.

Q. Which model is the most commercial-friendly?

Mistral Small 4. Apache 2.0 means no MAU cap, no branding requirement, no special contracts. Fine-tuning and redistribution are unrestricted. If you don't have legal review capacity, this is the safest choice.

Q. Is MoE always better than Dense?

No. MoE reduces inference compute but still requires all weights in memory. For edge deployment, a Dense small model or a compact MoE like Gemma 4 26B (3.8B active) is more realistic. MoE's inference speed advantage only pays off when you already have a multi-GPU server.

Q. Which is strongest at coding?

Llama 4 Maverick scores 76.8–80.8 on SWE-bench — open-source top tier, but not "absolute #1." Open-weight models like GLM-5 (77.8) show up in the same neighborhood on SWE-bench Verified. For basic coding support or code review, Mistral Small 4 is sufficient. For whole-repo analysis, Llama 4 Scout's 10M context is the more practical pick.

Q. Is it worth using all three at once?

Yes. Route long-document analysis to Llama 4 Scout, multimodal requests (including audio) to Gemma 4, and commercial API traffic to Mistral Small 4. With hosted APIs, you also get cost optimization on top of capability matching.

Wrap-up

Reducing the three down to one doesn't make sense — they're meant for different jobs. For RAG-free processing of huge documents, Llama 4 Scout. For commercial projects that can't afford license ambiguity, Mistral Small 4. For on-device deployment, Gemma 4 E2B or E4B.

Combining all three by use case is the realistic pattern. Routing by request type lets you capture both cost and quality. Open-source LLMs are no longer "alternatives to commercial models." For specific tasks, they've already become equivalent choices.

※ Specs and numbers in this article are accurate as of April 13, 2026. Model performance and license terms may change with updates.
※ Always review each model's official license documentation before commercial use. This article is not legal advice.