AI tool comparison
Google Gemma 4 vs Tiny Aya
Which one should you ship with? Here is the side-by-side panel verdict, pricing read, reviewer split, and community vote comparison.
Open Source Models
Google Gemma 4
Google's first Apache 2.0 open model family with native multimodal
75%
Panel ship
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Community
Free
Entry
Gemma 4 is Google's newest open model family — E2B, E4B, 26B, and 31B sizes — built on Gemini 3 architecture. For the first time, Google has released Gemma under Apache 2.0, making the models fully commercial-friendly with no Google-specific use restrictions. Every model in the family is natively multimodal from training: text, image, video, and audio inputs are all first-class. Context windows run 128K–256K tokens depending on size, and the models include built-in function calling, structured JSON output, and agentic workflow support. The E2B and E4B variants target on-device mobile and laptop deployment, with native audio understanding designed for always-on assistant scenarios. NVIDIA has already published optimized Gemma 4 containers for RTX hardware. The Apache 2.0 license removes a major adoption barrier that held back Gemma 3 in commercial products. Gemma 4 landed at #1 on Hacker News with 1,400+ points — the open-source model community's reaction was immediate and enthusiastic.
Open Source Models
Tiny Aya
3B-parameter open model supporting 70+ languages — runs offline on a phone
75%
Panel ship
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Community
Paid
Entry
Tiny Aya is a family of open-weight small language models from Cohere Labs designed to bring multilingual AI to devices that can't access cloud inference. The 3.35B parameter models cover 70+ languages including many lower-resourced ones — African languages, South Asian languages, and Asia-Pacific languages that larger multilingual models either skip or handle poorly. The family includes five variants: a base pretrained model, a globally balanced instruction-tuned version (Global), and three region-specific models — Earth (Africa/West Asia), Fire (South Asia), and Water (Asia-Pacific/Europe). The region-specific models are tuned on data distributions that reflect the linguistic needs of each geography, rather than averaging across all languages and underserving everyone. On the leaderboard for Product Hunt's April 5th, Tiny Aya landed in the top three despite being a research release rather than a commercial product. The models run on Ollama, are available on HuggingFace and Kaggle, and were trained on 64 H100 GPUs — a comparatively modest run for this level of multilingual coverage.
Reviewer scorecard
“Apache 2.0 means I can embed it in commercial products without legal review overhead. Native audio + 256K context on a 26B model that runs on a single A100 is a killer combo for production agent work. This is the open model I've been waiting for.”
“Ollama support means this is running locally in ten minutes. The region-specific variants are a smart design choice — a model tuned for South Asian languages will outperform a globally averaged model on those languages even at smaller parameter counts. This is the right architecture for the problem.”
“Google has a history of releasing models and then quietly deprioritizing them once the PR cycle ends. Gemma 1 and 2 both got less maintenance than promised. The Apache license is great news, but trust has to be earned over time with consistent model updates.”
“3B parameters across 70+ languages means the average per-language capacity is thin. For high-resource languages like English, Spanish, or Mandarin, you're getting a model that's clearly behind purpose-built alternatives. The compelling use case is low-resource languages — but that's a narrow market compared to the general-purpose SLM space.”
“Native multimodal understanding — including audio — on models small enough for phones changes what ambient computing looks like. Gemma 4 on-device could be the model layer for a generation of always-on smart devices that don't need cloud inference.”
“The 5 billion people who don't speak English as a first language are the next wave of AI users — and they'll largely be on mobile, offline-capable devices. Tiny Aya is building the infrastructure for that wave. The region-specific model design suggests Cohere Labs is thinking seriously about this rather than treating multilingual support as a checkbox.”
“Image, video, and audio in one open model I can run locally? The creative tooling possibilities are enormous. I can build private multimodal workflows for client work without data leaving my machine. Apache 2.0 seals it — this is a Ship.”
“For content creators working in non-English markets, an offline model that actually handles your language well is transformational. Offline translation and transcription with no API costs or data privacy concerns is a real workflow unlock — especially for creators in regions with unreliable connectivity.”
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