Open Source LLM Releases in April 2026

April 2026 has been the busiest single month for open source LLMs in history. Meta, Alibaba, Google, and several smaller labs all shipped models in the same two-week window, giving developers more options than ever for running capable models without API dependencies. This post covers every open source LLM released in April 2026, with benchmarks, hardware requirements, and practical notes on which ones are actually worth downloading.

The April 2026 LLM Landscape at a Glance

| Model | Organization | Date | Parameters | License | Best For | |---|---|---|---|---|---| | Llama 4 Scout | Meta | Apr 5 | 109B (17B active) MoE | Llama 4 Community | Long context (10M tokens) | | Llama 4 Maverick | Meta | Apr 5 | 400B (17B active) MoE | Llama 4 Community | Multilingual, code | | Qwen 3 | Alibaba | Apr 8 | 0.6B / 4B / 8B / 14B / 32B / 72B | Apache 2.0 | Reasoning, tool use | | Qwen 3 MoE | Alibaba | Apr 8 | 235B (22B active) | Apache 2.0 | Cost-efficient inference | | Gemma 3n | Google | Apr 9 | 2B / 4B (effective) | Gemma | On-device, multimodal | | OLMo 2 32B | Ai2 | Apr 3 | 32B | Apache 2.0 | Research, full reproducibility | | Command A | Cohere | Apr 7 | 111B (11B active) MoE | CC-BY-NC | RAG, enterprise search |

Llama 4: Meta Ships Two Models at Once

Meta released Llama 4 Scout and Llama 4 Maverick on April 5, 2026. Both use a mixture-of-experts architecture, which means the total parameter count is high but only a fraction of those parameters activate for any given token.

Llama 4 Scout is the model to watch if you care about context length. It supports 10 million tokens of context natively, which is the longest context window in any open source model as of this writing. The 17B active parameters keep inference costs reasonable, and it runs on a single H100 GPU with quantization.

Llama 4 Maverick targets the performance end. With 128 experts and 17B active parameters per token, it competes with GPT-4o on coding benchmarks and multilingual tasks. The tradeoff: you need significantly more VRAM to host all 400B parameters, even if only 17B activate at inference time.

| Benchmark | Llama 4 Scout | Llama 4 Maverick | GPT-4o (reference) | |---|---|---|---| | MMLU Pro | 74.3 | 80.5 | 81.2 | | HumanEval | 72.0 | 81.7 | 84.1 | | MATH-500 | 78.4 | 85.1 | 86.0 | | Context Window | 10M | 1M | 128K | | Active Params | 17B | 17B | Unknown |

Both models ship under the Llama 4 Community License, which allows commercial use but includes Meta's standard acceptable-use restrictions. If you need a fully permissive license, look at Qwen 3 or OLMo 2.

Qwen 3: The Apache 2.0 Powerhouse

Alibaba's Qwen team released the full Qwen 3 family on April 8, spanning six dense model sizes (0.6B to 72B) plus a 235B MoE variant. The Apache 2.0 license makes the entire family usable in any commercial context without restrictions.

What sets Qwen 3 apart from previous Qwen releases is the "thinking mode" toggle. Each model supports both a chain-of-thought reasoning mode (slower, more accurate) and a direct response mode (faster, good enough for simple queries). You control which mode activates via the system prompt, giving you a single model that handles both use cases.

Qwen 3 32B is the sweet spot for most developers. It fits on a single RTX 4090 with 4-bit quantization, scores competitively with Llama 4 Scout on reasoning benchmarks, and the Apache 2.0 license means zero legal overhead. For local agent workflows, this is currently our default recommendation.

Qwen 3 MoE (235B, 22B active) offers near-72B-quality responses at roughly 32B inference costs. If you have multi-GPU hardware, this is the most cost-efficient high-quality option available.

Hardware requirements for the Qwen 3 dense models:

| Model | VRAM (FP16) | VRAM (Q4) | Consumer Hardware? | |---|---|---|---| | Qwen 3 0.6B | 1.5 GB | 0.5 GB | Yes, any modern GPU | | Qwen 3 4B | 9 GB | 3 GB | Yes, RTX 3060+ | | Qwen 3 8B | 17 GB | 6 GB | Yes, RTX 4070+ | | Qwen 3 14B | 30 GB | 10 GB | Yes, RTX 4090 | | Qwen 3 32B | 66 GB | 20 GB | Tight fit, RTX 4090 Q4 | | Qwen 3 72B | 148 GB | 42 GB | No, multi-GPU or cloud |

Gemma 3n: Google's On-Device Play

Google released Gemma 3n on April 9, designed specifically for phones and edge devices. The "n" stands for a new parameter-sharing technique that lets a 4B-effective-parameter model run in the memory footprint of a 2B model.

Gemma 3n supports text, images, audio, and video input natively. For mobile developers building AI features that need to run offline, this is the most practical option released in April. It runs on recent Android phones and on Apple Silicon Macs with Ollama or llama.cpp.

The Gemma license is permissive for most commercial use but includes Google's standard responsible-use terms. Read the license if your use case involves medical, legal, or financial applications.

OLMo 2 32B: Full Transparency for Research

Ai2's OLMo 2 32B, released April 3, is unique because the team published everything: training data (Dolma), training code, intermediate checkpoints, and evaluation results. If you are doing ML research and need to understand exactly what went into a model, OLMo 2 is the only option at this scale where that is possible.

Performance-wise, OLMo 2 32B matches Llama 3.3 70B on several benchmarks despite being less than half the size. The Apache 2.0 license and full reproducibility make it a strong choice for academic and research applications.

Command A: Cohere's RAG Specialist

Cohere released Command A on April 7, a 111B MoE model with only 11B active parameters. It is optimized for retrieval-augmented generation and agentic tool use. The model supports 256K context and 23 languages.

The non-commercial license (CC-BY-NC) limits commercial use without a separate agreement from Cohere, which makes this model best suited for research, prototyping, and evaluation rather than production deployment.

How to Pick the Right Model

The decision tree above covers the most common scenarios. Here is a quick summary:

• Need to run on a single consumer GPU: Qwen 3 32B at 4-bit quantization fits on an RTX 4090 and delivers strong all-around performance.
• Need the best quality per dollar: Qwen 3 MoE 235B activates only 22B parameters per token, keeping costs low while matching 72B-class performance.
• Need very long context: Llama 4 Scout with 10M tokens is unmatched. Nothing else comes close.
• Building for phones or edge: Gemma 3n runs in ~2GB of memory with multimodal support.
• Need full training transparency: OLMo 2 32B is the only model that publishes everything.

Common Pitfalls When Running April 2026 Models

• Quantization format mismatches. Llama 4's MoE architecture requires updated quantization tooling. If you are using llama.cpp, make sure you have a build from April 2026 or later. Earlier versions will either crash or produce garbage output with Llama 4 weights.

• Confusing total vs. active parameters. A 400B MoE model with 17B active parameters does not need 400B-model VRAM for inference. But it does need enough storage and memory to load all expert weights, even if most are idle. Budget ~2x the active parameter VRAM for comfortable MoE hosting.

• License assumptions. "Open source" does not always mean "do whatever you want." Llama 4 Community License restricts certain use cases. Command A is non-commercial. Only Apache 2.0 models (Qwen 3, OLMo 2) are truly unrestricted.

• Benchmark cherry-picking. Every model announcement highlights benchmarks where the model excels. Run your own evals on your actual tasks before committing. A model that scores 85 on MATH-500 might still hallucinate on your specific domain.

Running These Models Locally with Ollama

The fastest way to test any of these models is with Ollama. Here is how to get Qwen 3 32B running in under five minutes:

# Install or update Ollama
curl -fsSL https://ollama.com/install.sh | sh

# Pull Qwen 3 32B (Q4 quantization, ~20GB download)
ollama pull qwen3:32b

# Run interactively
ollama run qwen3:32b

# Or serve via API
ollama serve &
curl http://localhost:11434/api/generate \
  -d '{"model": "qwen3:32b", "prompt": "Explain MoE architecture in two sentences."}'

For Llama 4 Scout:

ollama pull llama4-scout
ollama run llama4-scout

For Gemma 3n (optimized for limited VRAM):

ollama pull gemma3n:4b
ollama run gemma3n:4b

What to Expect for the Rest of April

Mistral has hinted at a new open source release before the end of April 2026. The xAI team has been publishing research papers on Grok's architecture, which typically precedes an open weight release. And Anthropic's Haiku 4.5, while not open source, has set a new quality bar for small models that open source projects will be racing to match.

The pace of releases in April 2026 suggests that the "model moat" for proprietary providers continues to narrow. For developers building local AI workflows, the practical implication is simple: you now have multiple high-quality open source options at every scale, from 2B on-device to 400B datacenter-class.

Wrapping Up

April 2026 gave us more viable open source LLMs in a single month than all of 2024 combined. If you are building AI features that need to run without API dependencies, Qwen 3 32B is the safest all-around choice today. For specialized needs (ultra-long context, mobile deployment, research reproducibility), the other April releases each own their niche.

Fazm runs local LLMs as part of its desktop agent workflow. If you are building with these models, check out our open source agent on GitHub.