AI tool comparison
Gemma 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
Gemma 4
Google's sharpest open models — multimodal, 256K context, runs on a Raspberry Pi
75%
Panel ship
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Community
Free
Entry
Gemma 4 is Google DeepMind's fourth-generation open model family, released April 2, 2026, under Apache 2.0. Four variants ship in the family: E2B and E4B edge models that run fully offline on phones, Raspberry Pi, and NVIDIA Jetson; a 26B Mixture-of-Experts model that activates only 3.8B parameters at inference; and a 31B Dense flagship. The 31B scores 1452 on the Arena AI text leaderboard (third among all open models), hits 89.2% on AIME 2026 math, and 85.2% on MMLU Pro — versus Gemma 3's 20.8% on AIME. All four model sizes accept text and image inputs. The edge models additionally handle native audio and video, making them the first on-device models with full multimodal coverage. Context windows reach 256K tokens on the large variants, enabling entire codebases or long documents in a single prompt. Native support for tool use, structured output, and agentic workflows is baked in from the start. For the open-source AI community, Gemma 4 is a watershed: a commercially permissive model that genuinely competes with closed-source alternatives on reasoning benchmarks. Gemma downloads crossed 400 million before this launch — Gemma 4's edge deployment story, combining on-device inference with frontier-class reasoning, looks set to make that number look small.
AI Models
Qwen3.6-35B-A3B
35B MoE model, only 3B active params, beats Claude Sonnet 4.5 on benchmarks
75%
Panel ship
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Community
Paid
Entry
Qwen3.6-35B-A3B is Alibaba's latest sparse Mixture-of-Experts model — 35 billion total parameters, but only 3 billion activate per forward pass. That efficiency makes it competitive with models three to four times larger at inference while fitting comfortably on consumer hardware. It's natively multimodal, handling image, video, document, and spatial reasoning inputs out of the box, with a 262K context window extensible to 1M tokens. The benchmark numbers have been drawing serious attention. SWE-bench Verified: 73.4% (vs Gemma 4-31B at 52%, and substantially above Claude Sonnet 4.5). MMMU: 81.7 (Claude Sonnet 4.5 scores 79.6). AIME 2026: 92.7. On local inference hardware, community reports show 79–187 tokens/second depending on GPU tier, making it genuinely usable for agentic workflows without API latency. Released under Apache 2.0. The timing matters. With Claude Opus 4.7 drawing community criticism over tokenizer-inflated pricing, Qwen3.6-35B-A3B is arriving as a credible local alternative for agentic coding. r/LocalLLaMA threads from the past week show active migration from Opus 4.7 to Qwen3.6 for cost-sensitive workloads. It's currently #1 trending on Replicate.
Reviewer scorecard
“Apache 2.0, runs on a Pi, 256K context, beats proprietary models on AIME — this is the open-source AI stack I've been waiting for. The agentic workflow support baked in natively means I'm not bolting on separate tooling. Shipping today.”
“73.4% SWE-bench with 3B active params is extraordinary efficiency. This runs on a single A100 at usable speed, which means you can deploy it self-hosted for agentic coding pipelines without paying frontier API rates. The Apache license seals it — this goes into our infra immediately.”
“The benchmark numbers are impressive on paper, but Gemma 3 was also hyped and underdelivered in production on complex multi-step tasks. The edge models are still unproven outside of Google's own hardware partnerships. Watch the community benchmarks before committing to a migration.”
“Alibaba benchmarks should be read with appropriate skepticism — SWE-bench scores are sensitive to eval harness choices and there have been reproducibility issues with some Qwen claims before. Also, the 262K context at 3B active params sounds too good; I'd want to see real-world retrieval accuracy at 200K+ before trusting it in production agentic pipelines.”
“On-device frontier-class intelligence with native audio and video is the inflection point for ambient AI. When a $35 Raspberry Pi can run a model that beats last year's GPT-4 on math, the entire economics of edge AI applications change overnight. This is the model that makes AI infrastructure costs asymptotically cheap.”
“MoE with sparse activation is clearly the dominant architecture for the next wave of open models. The fact that 3B active params can match 2024's frontier is a signal about where inference efficiency is heading. In 12 months, 'frontier-competitive' will mean running locally on a MacBook.”
“The document and PDF parsing, OCR, chart comprehension, and UI understanding built into every model size is huge for creative workflow automation. I can finally build tools that read design briefs, invoices, and mockups without needing a cloud API call. The offline capability means client data never leaves my machine.”
“Native multimodal handling of images, video, and documents at this efficiency is a game-changer for content pipelines. If the quality holds up on real-world design tasks, this replaces a stack of specialized models with one local deployment.”
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