AI tool comparison
Meta Llama 4 vs Qwen3.6-35B-A3B
Which one should you ship with? Here is the side-by-side panel verdict, pricing read, reviewer split, and community vote comparison.
AI Models
Meta Llama 4
Open-weight multimodal MoE models with 10M context — free to run
100%
Panel ship
—
Community
Free
Entry
Meta released Llama 4 Scout and Llama 4 Maverick on April 5, 2026 — the first open-weight natively multimodal models built with a Mixture-of-Experts (MoE) architecture. Scout is a 17B active parameter model with 16 experts that fits on a single NVIDIA H100, with an industry-leading 10 million token context window. Maverick is also 17B active parameters but with 128 experts, delivering performance that benchmarks comparably to GPT-4o and DeepSeek v3 on reasoning and coding tasks. Both models process text, images, and video inputs, and are freely available for download on Hugging Face and llama.com. Llama 4 Scout was trained on 40 trillion tokens of data. The MoE architecture means the models punch well above their weight in active parameter count — Scout competes with models 5-10x its size on many benchmarks, while keeping inference costs low. This release closes the gap between open and proprietary models significantly. Organizations that previously needed to pay for GPT-4o or Claude for multimodal tasks can now run comparable capability locally or via any cloud provider. For the open-source AI ecosystem, Llama 4 is the biggest release of 2026 so far.
Open Source Models
Qwen3.6-35B-A3B
35B total, 3B active: Alibaba's lean MoE coding beast goes fully open source
75%
Panel ship
—
Community
Free
Entry
Alibaba's Qwen team open-sourced Qwen3.6-35B-A3B on April 16, 2026 — a sparse Mixture-of-Experts model with 35 billion total parameters but only ~3 billion active per forward pass. That architectural trick is the whole story: you get near-frontier performance while consuming compute comparable to a 3B dense model. It's available under Apache 2.0 on Hugging Face and ModelScope. The model supports a 262K token context window (extensible to 1M with YaRN), multimodal inputs including text, images, and video, and is purpose-built for agentic coding workflows. On SWE-bench and Terminal-Bench it outperforms the much larger dense Qwen3.5-27B, matching Gemma4-31B on several benchmarks. RefCOCO visual grounding score hits 92.0 — some multimodal metrics reach Claude Sonnet 4.5 territory. Community reaction has been immediate: r/LocalLLaMA lit up with benchmarks showing it solving coding tasks that models with 10x the active parameters couldn't handle. The FP8 quantized variant runs comfortably on a single 24GB consumer GPU, making this the most capable locally-runnable coding agent most developers have ever had access to.
Reviewer scorecard
“A multimodal MoE model that fits on a single H100 and handles 10M context is insane for the price of free. Scout is the model I'll be running for 80% of production workloads going forward — the economics versus GPT-4o or Claude don't even compare. Deploy it now.”
“3B active parameters with 35B parameter breadth is engineering magic. I'm getting near-frontier coding results in Cline and running it locally on a 3090 — the refusals are lower than Claude for security research too. Apache 2.0 means I can fine-tune it on my codebase. This is the best open-source coding model I've used.”
“I'll still reach for frontier proprietary models for the hardest reasoning tasks and production-critical applications where errors are costly. But I can't deny that Llama 4 Scout closes the gap more than I expected. The 10M context on Scout is genuinely unprecedented for open weights.”
“MoE models have notoriously bad batching throughput — if you're serving this at scale, the economics don't work out. And Alibaba's track record on long-term model support and safety filtering is shakier than Google or Anthropic. It's impressive in isolation, but enterprise teams should pressure-test it before replacing frontier APIs.”
“Llama 4 will commoditize multimodal AI the same way Llama 2 commoditized text generation. The 10M context window in an open-weight model is a civilizational-level unlock for researchers, non-profits, and countries that can't afford to depend on US cloud providers for advanced AI.”
“The gap between open and closed models is closing faster than anyone predicted. When a freely downloadable model matches Claude Sonnet on multimodal benchmarks, the frontier lab pricing power evaporates. Qwen3.6-35B-A3B is another milestone in the commoditization of intelligence — and commoditization always accelerates adoption.”
“An open-weight model that understands images and video means I can build custom creative pipelines without routing everything through proprietary APIs. For studios, agencies, and indie creators, Llama 4 fundamentally changes the cost structure of AI-assisted production.”
“I don't often care about coding models, but this one handles image + video understanding for design briefs surprisingly well. I used it to analyze a competitor's UI and generate a full redesign spec. The 262K context means I can feed entire brand guidelines without chunking.”
Weekly AI Tool Verdicts
Get the next comparison in your inbox
New AI tools ship daily. We compare them before you waste an afternoon.