Gemma 4 vs Llama 4 vs DeepSeek V4: Best Free Open-Source AI Model in 2026 [Ranked]

A 31-billion parameter model is beating a 400-billion parameter model on math, coding, and agentic benchmarks. That sentence shouldn't make sense, but here we are in April 2026.

Google dropped Gemma 4 on April 2. Meta released Llama 4 on April 5. DeepSeek V4 is still "coming soon" after months of anticipation. Open-source AI reshuffled in one week. Here's where things stand.

Google released four Gemma 4 variants on April 2: E2B, E4B, 26B MoE, and 31B Dense. The 31B Dense is the one getting all the attention because of numbers like these:

A 31B model shouldn't beat something with 400B total parameters. But Gemma 4 was built from the ground up for intelligence-per-parameter. Google's approach was "make fewer parameters do more work" instead of "throw more parameters at the problem." It worked.

The licensing is the other big deal. Gemma 4 ships under Apache 2.0, which means completely unrestricted commercial use, modification, and redistribution. Previous Gemma versions had custom licenses with more restrictions. This is Google saying "take it and build whatever you want."

The edge models (E2B and E4B) run on consumer hardware including phones. They handle video, images, and audio natively. The E4B with a 128K context window is probably the most capable AI you can run on a laptop right now.

Meta released Llama 4 Scout and Maverick on April 5. Both use mixture-of-experts architecture and both process text, images, and video natively. That's not an add-on or a fine-tune. These models were trained from scratch as multimodal systems.

Scout has 17B active parameters across 16 experts (109B total) and a 10M token context window. That context window is absurd. You could feed it an entire book and still have room for questions.

Maverick has 17B active parameters across 128 experts (400B+ total). It's the more capable model and Meta claims it beats GPT-4o and Gemini 2.0 Flash on multiple benchmarks.

The gap between Llama 4 and Gemma 4 is narrow on text benchmarks. Where Llama 4 pulls ahead: multimodal tasks. If you need a model that understands images and video alongside text, Llama 4 is the best open option. If you only need text, Gemma 4 does it better with fewer resources.

The catch: Meta's license isn't truly open source. There are restrictions on large-scale commercial deployment. For most developers and startups this doesn't matter. For large enterprises deploying at scale, check the license terms carefully. Gemma 4's Apache 2.0 has no such restrictions.

DeepSeek V4 was supposed to launch in March. Then people said April. Now the expectation is Q2 or Q3 2026. The benchmarks that have been floating around (80%+ on SWE-bench, 1M context, native multimodal) are from leaked internal data and remain unverified.

DeepSeek V3.2 is what actually exists today. It's a solid model at an unbeatable API price ($0.14/M input tokens). Run the numbers in our AI cost calculator to see how it compares. For production workloads where cost matters more than bleeding-edge performance, V3.2 is hard to argue against.

But V4? Don't plan around it. We've seen months of "coming soon" and nothing to download. When it ships, we'll rank it. Until then, it's vaporware.

GLM-5

Zhipu AI's GLM-5 has 744B total parameters with 40B active. It ranks #1 among open models on LiveBench Agentic Coding. If you build AI agents and have the hardware, this is the coding model to watch.

Kimi K2.5

Moonshot AI's Kimi K2.5 has 1T total parameters with 32B active. The standout feature is Agent Swarm: it can spin up 100 parallel sub-agents for complex tasks. Scores 77.86 on LiveBench Coding. If you need a model that orchestrates multi-step workflows on its own, K2.5 does things other models can't.

Both require serious hardware. Neither runs on a laptop. They're enterprise-grade tools for teams with GPU clusters.

The takeaway: Gemma 4 is the only top-ranked model you can run on a single consumer GPU. Everything else requires enterprise hardware or cloud rental. This is why the 31B model beating 400B rivals matters so much. You don't need a data center to run the best model.

Open-source AI used to be "Llama or nothing." Not anymore. Gemma 4 proved smaller models can beat bigger ones with better architecture, and Llama 4 brought real multimodal capability to open weights. Meanwhile, GLM-5 and Kimi K2.5 signal genuine competition from Chinese labs.

For most developers, Gemma 4 31B is the answer. Best model you can run on affordable hardware, with an Apache 2.0 license that imposes zero restrictions. Start there. And if you're deciding between open-source and API-based tools, our task-by-task AI model guide covers both — or take our free AI Model Picker quiz for a personalized recommendation in 60 seconds.

What is the best open-source AI model in April 2026?

For efficiency and self-hosting: Gemma 4 31B Dense. Beats models 13x its size on math (89.2%), coding (80%), and agentic tasks (86.4%). Apache 2.0 license. Runs on a single RTX 4090. For multimodal: Llama 4 Maverick. For coding agents: GLM-5 or Kimi K2.5.

Can I run Gemma 4 on my own hardware?

The 31B model needs about 16-24GB VRAM (RTX 4090 or A100 with quantization). The E4B edge model runs on laptops and phones. All models are Apache 2.0 licensed for unrestricted commercial use.

Has DeepSeek V4 been released?

No. As of April 9, 2026, DeepSeek V4 has not shipped. Multiple rumored dates have passed. Current expectation is Q2 or Q3 2026. DeepSeek V3.2 is the latest available model.

Is Llama 4 truly open source?

Llama 4 Scout and Maverick are open-weight downloads on Hugging Face. But Meta's license restricts large-scale commercial deployment. Gemma 4 (Apache 2.0) and Kimi K2.5 (MIT) have no such restrictions.

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