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Gemma 4 vs Llama 4 vs Qwen 3.5: Open Source AI Models in 2026
The open source AI landscape in 2026 is nothing like it was even twelve months ago. Three major players have dropped landmark releases in the first quarter of this year, and they are genuinely competing at a level that makes closed models sweat. Google dropped Gemma 4 on April 2. Meta shipped Llama 4. Alibaba hit back with Qwen 3.5 back in February.
If you are a developer trying to figure out which one to bet your next project on, you are in the right place. We are going to break these models down across the stuff that actually matters: benchmarks, licensing, hardware demands, and where each one genuinely wins.
Before we get into the weeds, here is the quick picture.
Gemma 4 (Google, April 2, 2026) comes in four sizes ranging from 2.3B effective parameters up to a 31B dense model. This is the first time any Gemma release ships under Apache 2.0, which is a massive shift from the restrictive custom license that held back previous versions.
Qwen 3.5 (Alibaba, February 16, 2026) centers on a 27B dense flagship but spans from 0.8B all the way up to a 397B-A17B MoE model. It is natively multimodal, supports 201 languages, and has been making waves on coding benchmarks in particular.
Llama 4 (Meta, April 2025) introduced Scout and Maverick as the first natively multimodal open-weight Llama models. Maverick is a 400B MoE architecture with 17B active parameters per token. Scout pushes context windows up to 10M+ tokens, which is absurd in the best possible way.
Benchmarks are imperfect, but they are the best consistent window we have into model capability. Here is how the flagship models stack up on the evaluations developers actually cite.
| Benchmark | Gemma 4 31B | Qwen 3.5 27B | Llama 4 Maverick |
|---|---|---|---|
| MMLU Pro | 85.2% | 86.1% | 80.5% |
| GPQA Diamond | 84.3% | 85.5% | 69.8% |
| AIME 2026 | 89.2% | ~85% | N/A |
| LiveCodeBench v6 | 80.0% | 80.7% | 43.4% |
| SWE-bench Verified | N/A | 72.4% | N/A |
| MMMU Pro (Vision) | 76.9% | ~72% | ~65% |
What this tells you: Gemma 4 and Qwen 3.5 trade blows at the 27-31B scale on most reasoning benchmarks. Gemma 4 has a meaningful edge on math competition problems (AIME 2026 at 89.2% is outstanding), while Qwen 3.5 takes the lead on real-world code repair tasks via SWE-bench Verified at 72.4%.
The Llama 4 Maverick number on LiveCodeBench v6 (43.4%) is genuinely surprising given that it has 400B total parameters. MoE routing does not automatically translate to better code generation, and Maverick’s 17B active parameters per token appear to be stretched too thin across its expert pool for structured coding tasks.
On the LMArena leaderboard, which relies on crowdsourced human preference votes rather than automated metrics, Gemma 4 31B sits at #3 globally among open models. That leaderboard is the closest proxy we have for “how good does it actually feel to use,” and Gemma 4 consistently gets praise for natural, less robotic output.
This is where the conversation gets serious for anyone building commercial products.
Gemma 4 and Qwen 3.5 both ship under Apache 2.0. No usage restrictions, no monthly active user limits, no acceptable use policies to comply with. Commercial use, modification, redistribution — all fully permitted. This is the same license that powers Linux, Kubernetes, and TensorFlow.
For Gemma specifically, this is a dramatic shift. Every previous Gemma release (versions 1 through 3) used a custom Google license that scared off enterprise legal teams. The switch to Apache 2.0 on Gemma 4 is the clearest signal Google could send that they want to be a serious player in the open-weight ecosystem.
Llama 4 uses the Llama 4 Community License. It is free for companies under 700M monthly active users, but it comes with compliance requirements that legal teams will want to review carefully. If you are building a product that might scale, the Llama license adds a layer of overhead that Apache 2.0 simply does not have.
If licensing simplicity matters to you, Gemma 4 and Qwen 3.5 are tied on this front, and both beat Llama 4.
Context window matters more than most people think until they are working with long documents or large codebases.
Gemma 4 supports up to 256K tokens on its larger models, with 128K on the smaller edge variants. Qwen 3.5 maxes out at 128K context on the open-weight release, though the hosted Qwen3.5-Plus on Alibaba Cloud Model Studio offers a 1M context window with built-in tools.
Llama 4 Scout takes this to an almost comical extreme with a 10M+ token context window. If you genuinely need to process the equivalent of thousands of pages in a single context, Scout has no real competition among open models right now.
On modalities, Gemma 4 edge models (E2B and E4B) handle text, images, and audio. The 26B and 31B models handle text, images, and video but oddly lack audio input. Qwen 3.5 is the most versatile here — the flagship 397B model and the Omni variant support text, images, audio, and video input, plus real-time streaming speech output. No other model in this comparison can do that.
Llama 4 Scout and Maverick are text and image only, with no audio or video support at launch.
This is where deployment realities hit.
Gemma 4 E2B (2.3B effective) and E4B (4.5B effective) can run on consumer hardware with a decent GPU. The 26B A4B MoE model activates just 3.8B parameters per token, making it nearly as fast as a 4B model despite having 25.2B total parameters. The 31B dense model needs something like an RTX 4090 or an A100 to run comfortably at full speed.
Qwen 3.5 27B dense is similarly demanding — you need at least 56GB of VRAM for efficient inference, which means a single high-end GPU or quantized serving. The 397B-A17B MoE flagship activates only 17B parameters per token, which sounds manageable until you remember you still need to load all 397B parameters into memory, which requires a serious server setup.
Llama 4 Maverick (400B MoE) is the heaviest hitter here. Datacenter hardware is essentially mandatory unless you are willing to run it heavily quantized, which degrades performance.
For most developers, Gemma 4 offers the best quality-to-hardware-ratio. You can run the 26B A4B model on hardware that costs under $10,000 total, and it still ranks 6th on the Arena AI leaderboard with a score of 1441 — within striking distance of the flagship 31B model.
If your primary use case is writing and fixing code, the picture is clear.
Qwen 3.5 leads on SWE-bench Verified at 72.4%, which evaluates real GitHub issue resolution rather than synthetic coding puzzles. If you are building tools that need to understand existing codebases, write patches, and fix bugs in production, Qwen 3.5 has a measurable advantage.
Gemma 4 is no slouch — it scores 80% on LiveCodeBench v6 and has a Codeforces ELO of 2150, which puts it in competitive programming territory. It just does not match Qwen 3.5 specifically on the SWE-bench metric.
Qwen 3.5 also has a significant advantage in agentic workflows. Its post-training process uses reinforcement learning across millions of agent environments, training it to plan, use tools, and correct its own errors. The Terminal-Bench 2.0 score of 52.5 is a massive jump from Qwen3-Max-Thinking’s 22.5.
For function calling and tool use, both Gemma 4 and Qwen 3.5 support native function calling out of the box. Llama 4 has made improvements here, but the community tooling ecosystem for Llama function calling is still catching up.
All three models have hosted API options if you do not want to run them yourself.
Gemma 4 pricing via Google AI Studio starts around $0.1300 per million tokens for the 31B model, with significantly lower costs for the smaller variants.
Qwen 3.5 via Alibaba Cloud Model Studio is priced to compete aggressively. The headline claim from Alibaba is that Qwen 3.5 is 60% cheaper than GPT-5.2 while being 8x faster at equivalent context lengths. The hosted Plus variant includes tools like search and code interpreter bundled in.
Llama 4 via Meta’s own hosting or through providers like Replicate and Anyscale offers competitive per-token pricing, but the license overhead for larger deployments can add hidden costs in compliance review time.
For cost efficiency at scale, Qwen 3.5 and Gemma 4 are the strongest contenders.
Here is the honest, practical breakdown.
Use Gemma 4 when: You want the best Apache 2.0 open model with strong all-around reasoning, better output quality than the benchmarks suggest, and hardware requirements that do not require a server farm. The 26B A4B MoE model in particular is the efficiency champion of this generation — you get top-10 open model performance at 4B-model speeds.
Use Qwen 3.5 when: Your primary workload involves coding, agentic tasks, document understanding, or multilingual support. The SWE-bench numbers do not lie, and the Omni variant’s real-time speech capabilities are genuinely unique in the open-weight space.
Use Llama 4 when: You need the absolute largest context window available in an open model (Scout at 10M+ tokens), or you have specific infrastructure that already has optimized Llama serving in place. For pure performance-per-dollar at the datacenter scale, Maverick holds its own — just do not expect it to outperform Qwen 3.5 on coding tasks.
The open source AI world in 2026 is genuinely competitive in a way it was not even a year ago. All three of these models represent real, production-ready options that can replace closed model dependencies for most workflows. The right choice depends on your specific constraints around hardware, licensing, and workload type.
If you are building structured data or schema markup for your AI-enhanced site, our JSON-LD Schema Generator can help you mark up your content in ways that make it easier for models like these to understand your pages. And if you are working on the visual side of your project, our Color Palette Tool makes it simple to build consistent, accessible color systems that play well with AI-driven design tools.
The open model era is here. Pick the one that fits your stack and ship something great.
Benchmark data sourced from official model release documentation, Hugging Face model cards, and the LMArena leaderboard as of April 2026.