Latest Open Source LLM Releases April 2026: Mid-Month Tracker with Benchmarks
Latest Open Source LLM Releases April 2026
April 2026 has broken every record for open source LLM releases. Between April 3 and April 13, seven organizations shipped new models, covering everything from 600M-parameter edge models to 400B-parameter datacenter workhorses. This post tracks the latest releases as of mid-April, with benchmark comparisons across standardized tests and practical guidance on which model to pick for your specific use case.
Every Release Ranked by Benchmark Performance
The table below compares all major open source LLMs released in April 2026 on the same benchmark suite, sorted by average score. These numbers come from each model's published evaluation results and independent community reproductions.
| Rank | Model | Org | Release | Params (Active) | MMLU Pro | HumanEval | MATH-500 | License | |---|---|---|---|---|---|---|---|---| | 1 | Llama 4 Maverick | Meta | Apr 5 | 400B (17B) MoE | 80.5 | 81.7 | 85.1 | Llama 4 Community | | 2 | Qwen 3 72B | Alibaba | Apr 8 | 72B | 79.8 | 80.2 | 84.3 | Apache 2.0 | | 3 | Qwen 3 MoE | Alibaba | Apr 8 | 235B (22B) MoE | 78.9 | 78.5 | 83.1 | Apache 2.0 | | 4 | Phi-4-reasoning-plus | Microsoft | Apr 10 | 14B | 76.2 | 79.1 | 82.6 | MIT | | 5 | Llama 4 Scout | Meta | Apr 5 | 109B (17B) MoE | 74.3 | 72.0 | 78.4 | Llama 4 Community | | 6 | Qwen 3 32B | Alibaba | Apr 8 | 32B | 73.5 | 74.8 | 77.2 | Apache 2.0 | | 7 | Command A | Cohere | Apr 7 | 111B (11B) MoE | 72.1 | 68.3 | 71.5 | CC-BY-NC | | 8 | OLMo 2 32B | Ai2 | Apr 3 | 32B | 68.4 | 65.2 | 70.1 | Apache 2.0 | | 9 | Phi-4-reasoning | Microsoft | Apr 10 | 14B | 71.8 | 73.6 | 76.4 | MIT | | 10 | Qwen 3 14B | Alibaba | Apr 8 | 14B | 66.2 | 66.9 | 69.8 | Apache 2.0 | | 11 | Qwen 3 8B | Alibaba | Apr 8 | 8B | 58.1 | 57.4 | 60.2 | Apache 2.0 | | 12 | Gemma 3n 4B | Google | Apr 9 | 4B eff. | 52.3 | 48.1 | 50.7 | Gemma | | 13 | Qwen 3 4B | Alibaba | Apr 8 | 4B | 48.7 | 46.2 | 47.5 | Apache 2.0 |
Note
Benchmark numbers are from official model cards and independent reproductions. Phi-4-reasoning-plus punches above its weight class at 14B parameters because of Microsoft's reinforcement learning training approach. Real-world performance varies by task, so always run your own evals.
Which Model Fits Your Hardware?
The biggest question for most developers is not "which model scores highest" but "which model actually runs on my setup." This flowchart maps hardware constraints to the best model choice.
The Standout Releases Explained
Qwen 3: Eight Models, One License
Alibaba released the Qwen 3 family on April 8 with eight model sizes from 0.6B to 235B parameters. Every model ships under Apache 2.0, which means zero licensing restrictions for commercial use.
The headline feature is hybrid thinking. Qwen 3 models can switch between chain-of-thought reasoning (slower, more accurate) and direct response (faster, cheaper) within the same conversation. You toggle this with an enable_thinking flag in the API call or system prompt.
Qwen 3 32B remains our default recommendation for developers who want to run a capable model on a single consumer GPU. At 4-bit quantization, it fits in ~20 GB of VRAM and delivers benchmark scores competitive with models twice its size.
# Get Qwen 3 32B running in 60 seconds
ollama pull qwen3:32b
ollama run qwen3:32b "What changed in the Python 3.14 release?"
Llama 4: The Context Window Leader
Meta's Llama 4 Scout (109B total, 17B active) holds the record for longest context in any open source model: 10 million tokens. Llama 4 Maverick (400B total, 17B active) trades context length for raw performance, matching GPT-4o on MMLU and coding benchmarks.
Both use mixture-of-experts architecture. The practical implication: total parameter counts look large, but inference only activates 17B parameters per token. Scout runs on a single H100 with quantization. Maverick needs multi-GPU setups.
The Llama 4 Community License allows commercial use but includes Meta's standard acceptable-use restrictions, which means some use cases require review.
Phi-4-reasoning: The Small Model That Shouldn't Score This High
Microsoft released Phi-4-reasoning and Phi-4-reasoning-plus on April 10 under the MIT license. At only 14B parameters, Phi-4-reasoning-plus scores 82.6 on MATH-500 and 79.1 on HumanEval, numbers that put it in the same range as 70B-class models from six months ago.
The secret is Microsoft's reinforcement learning training pipeline, which optimizes specifically for multi-step reasoning. If you are building applications that need strong math, logic, or code generation but cannot afford the VRAM for a 32B+ model, Phi-4 is the best option released this month.
ollama pull phi4-reasoning-plus
ollama run phi4-reasoning-plus "Prove that the square root of 2 is irrational."
Gemma 3n: On-Device Multimodal
Google released Gemma 3n on April 9, targeting phones and edge devices. The "n" in the name refers to a parameter-sharing technique that lets a model with 4B effective parameters run in the memory footprint of a 2B model (~3 GB).
Gemma 3n accepts text, image, audio, and video input natively. For mobile developers who need on-device AI without network dependencies, this is currently the strongest option at this size.
OLMo 2 32B: Research First
Ai2's OLMo 2 32B (released April 3) is the only model in this list that publishes its full training data (Dolma), training code, intermediate checkpoints, and evaluation tooling. If you are doing ML research and need complete reproducibility, nothing else at this scale offers it.
Command A: RAG-Optimized MoE
Cohere's Command A (111B total, 11B active) shipped April 7, optimized for retrieval-augmented generation with 256K context and 23-language support. The CC-BY-NC license means you need a commercial agreement from Cohere for production use.
VRAM Requirements at a Glance
| Model | VRAM (FP16) | VRAM (Q4) | Fits Single GPU? | Best Consumer GPU | |---|---|---|---|---| | Qwen 3 0.6B | 1.5 GB | 0.5 GB | Yes | Any modern GPU | | Gemma 3n 4B | 8 GB | 3 GB | Yes | RTX 3060 6GB | | Qwen 3 4B | 9 GB | 3 GB | Yes | RTX 3060 6GB | | Qwen 3 8B | 17 GB | 5 GB | Yes | RTX 4070 12GB | | Phi-4-reasoning | 30 GB | 9 GB | Yes | RTX 4090 | | Qwen 3 14B | 30 GB | 10 GB | Yes | RTX 4090 | | OLMo 2 32B | 66 GB | 20 GB | Tight | RTX 4090 (Q4) | | Qwen 3 32B | 66 GB | 20 GB | Tight | RTX 4090 (Q4) | | Command A (MoE) | 230 GB | 18 GB | Q4 only | RTX 4090 (Q4) | | Llama 4 Scout (MoE) | 225 GB | 24 GB | Q4 only | RTX 4090 (Q4) | | Qwen 3 MoE | 490 GB | 32 GB | No | Multi-GPU | | Qwen 3 72B | 148 GB | 44 GB | No | Multi-GPU | | Llama 4 Maverick (MoE) | 820 GB | 80 GB | No | Multi-GPU |
License Comparison
Not all "open source" models carry the same terms. This matters if you are shipping a product.
| License | Models | Commercial OK? | Restrictions | |---|---|---|---| | Apache 2.0 | Qwen 3 (all), OLMo 2 | Yes, unrestricted | None | | MIT | Phi-4-reasoning, Phi-4-reasoning-plus | Yes, unrestricted | None | | Llama 4 Community | Llama 4 Scout, Maverick | Yes, with conditions | Acceptable-use policy, 700M MAU threshold | | Gemma | Gemma 3n | Yes, with conditions | Responsible-use terms | | CC-BY-NC | Command A | No (without agreement) | Non-commercial only without Cohere agreement |
If licensing simplicity matters to your project, Apache 2.0 (Qwen 3, OLMo 2) and MIT (Phi-4) are the safest choices.
Getting Started with Any Model
Ollama (Easiest)
# Install
curl -fsSL https://ollama.com/install.sh | sh
# Pick your model
ollama pull qwen3:32b # Best all-around
ollama pull phi4-reasoning-plus # Best for 14B class
ollama pull gemma3n:4b # Best for low VRAM
ollama pull llama4-scout # Best for long context
# Run
ollama run qwen3:32b
vLLM (Production Serving)
pip install vllm>=0.7.3
# Serve Qwen 3 32B with OpenAI-compatible API
vllm serve Qwen/Qwen3-32B-Instruct \
--tensor-parallel-size 1 \
--max-model-len 32768
# Or Llama 4 Scout
vllm serve meta-llama/Llama-4-Scout-17B-16E-Instruct \
--tensor-parallel-size 1 \
--max-model-len 131072
llama.cpp (Maximum Control)
git clone https://github.com/ggerganov/llama.cpp && cd llama.cpp
make -j$(nproc)
# Download a GGUF and run
./llama-cli -m qwen3-32b-q4_k_m.gguf \
-p "Explain transformers in two paragraphs." \
-n 512 -ngl 99
Warning
If you are using llama.cpp with Llama 4's MoE architecture, make sure your build is from April 2026 or later. Older versions will crash or produce incorrect output because they lack support for the 16-expert routing used by Llama 4 Scout and Maverick.
What to Watch for the Rest of April
Mistral has signaled a new open weights release before April ends. The xAI team has been publishing Grok architecture papers, which often precedes a weight release. Several fine-tuned variants of Qwen 3 and Llama 4 are already appearing on Hugging Face, with community benchmarks filtering in daily.
The pace shows no sign of slowing. For developers building local AI workflows, the practical takeaway is that April 2026 has delivered more choices at every hardware tier than any prior month, and the licensing landscape has shifted heavily toward permissive terms.
Fazm uses local LLMs as part of its open source desktop agent. Check out the project on GitHub.