Gemma 4 vs Qwen 3.5: Which Open-Weight Model Should You Use for Local AI Workflows?
Compare Gemma 4 and Qwen 3.5 on performance, size, context window, and local deployment to find the best open-weight model for your agentic workflows.
Two Strong Open-Weight Models, One Practical Question
The choice between Gemma 4 and Qwen 3.5 isn’t obvious. Both are capable open-weight models you can run locally, both come from major AI labs, and both have legitimate use cases for agentic workflows. But they make different tradeoffs — in size, architecture, reasoning style, and deployment flexibility.
If you’re building local AI workflows and need to decide which model deserves your VRAM, this comparison walks through what actually matters: hardware requirements, context window, multimodal capabilities, reasoning quality, multilingual support, and where each model shines in practice.
Let’s get into the details.
Background: Where Each Model Comes From
Gemma 4
Gemma 4 is Google DeepMind’s latest generation of open-weight models, released on April 2, 2026. It represents a significant leap from the Gemma 3 family — introducing mixture-of-experts architecture, trimodal support (text, image, and audio on smaller models), and a shift to the Apache 2.0 license for the first time. Google positions Gemma as a research-friendly, commercially-usable alternative to its proprietary Gemini models, sharing architectural DNA with Gemini but designed to run on hardware you actually own.
The models range from compact edge variants to a 31B dense model that ranks #3 on the Arena AI text leaderboard. All variants ship under Apache 2.0, removing the licensing restrictions that limited earlier Gemma releases.
Qwen 3.5
Qwen 3.5 comes from Alibaba’s Qwen research team, released in waves starting February 16, 2026. The Qwen team has consistently shipped some of the most competitive open-weight models in the world, and Qwen 3.5 is their most ambitious release yet — spanning eight open-weight model sizes from 0.8B to a 397B mixture-of-experts flagship (plus a Flash API-only variant).
A key feature carried forward from Qwen 3 is the hybrid thinking mode that lets you toggle between fast, direct responses and slow, chain-of-thought reasoning without switching models. Qwen 3.5 adds native multimodal support (vision + text baked into the core models), expands multilingual coverage to 201 languages and dialects, and offers a 262K native context window across every model size. All models are released under Apache 2.0.
Model Sizes and Hardware Requirements
One of the first things to figure out when picking a local model is whether your hardware can actually run it.
Gemma 4 Size Options
Gemma 4 ships in four variants — two compact “effective parameter” edge models and two larger models (one dense, one MoE):
| Model | Total Params | Active Params | Min VRAM (4-bit) | Typical Use Case |
|---|---|---|---|---|
| E2B | ~2.3B | 2.3B (dense) | 2–3 GB | Edge devices, embedded apps |
| E4B | ~4.5B | 4.5B (dense) | 4–6 GB | Consumer GPUs, fast inference |
| 26B (MoE) | 26B | 4B per token | 16–20 GB | Efficient high-quality inference |
| 31B (Dense) | 31B | 31B | 20–24 GB | Maximum quality, #3 on Arena AI |
The E4B is the sweet spot for most local workflows — capable enough for serious tasks, light enough to run on mainstream hardware like an RTX 3080 or 4080. The 26B MoE is interesting because it activates only 4B parameters per token (using 128 routed experts with 8 active plus a shared expert), delivering strong quality with relatively fast inference despite its total parameter count.
Qwen 3.5 Size Options
Qwen 3.5 has a broader size range across eight open-weight models, released in three waves — small, medium, and flagship:
| Model | Total Params | Active Params | Min VRAM (4-bit) | Notes |
|---|---|---|---|---|
| 0.8B | 0.8B | 0.8B (dense) | 1–2 GB | Very fast, mobile-viable |
| 2B | 2B | 2B (dense) | 2–3 GB | Light tasks, edge devices |
| 4B | 4B | 4B (dense) | 4–5 GB | Good general performance |
| 9B | 9B | 9B (dense) | 6–8 GB | Strong reasoning, solid coding |
| 27B | 27B | 27B (dense) | 16–20 GB | High-quality dense model |
| 35B (MoE) | 35B | 3B per token | 20–24 GB | Efficient mid-range |
| 122B (MoE) | 122B | 10B per token | 48+ GB | Near-frontier performance |
| 397B (MoE) | 397B | 17B per token | 80+ GB | Flagship, multi-GPU or server |
The MoE architecture in the larger variants means they activate only a fraction of parameters per token — so inference can be faster than a comparable dense model, but total memory requirements are still substantial.
Bottom line on hardware: If you have a single consumer GPU with 8–12 GB VRAM, both model families have strong options. Qwen 3.5 gives you more granular size choices, especially at the low end. If you want to push into the 27B+ range, Qwen 3.5’s 27B dense model and its MoE lineup (up to the 397B flagship) offer more flexibility than Gemma 4’s 26B MoE and 31B dense.
Performance: Reasoning, Coding, and Instruction Following
Raw benchmark numbers only tell part of the story. What matters more is how models actually perform on the tasks in your workflows.
Reasoning Quality
Both model families now support thinking modes — the ability to toggle between fast, direct responses and extended chain-of-thought reasoning.
Qwen 3.5’s hybrid thinking mode was a standout feature inherited from Qwen 3 and refined further. You can explicitly enable extended reasoning for complex problems — math, logic, multi-step planning — and disable it for simpler tasks where you want fast responses.
Gemma 4 also includes built-in thinking modes with multi-step reasoning support, a notable addition over Gemma 3. Combined with native function calling and structured JSON output, Gemma 4 is designed from the ground up for agentic workflows.
For workflows involving complex multi-step reasoning — like financial analysis, research synthesis, or planning tasks — both models are competitive. Qwen 3.5’s thinking mode is more battle-tested given its earlier release and the Qwen 3 lineage, but Gemma 4 closes the gap significantly.
Coding Assistance
Both models are competitive on coding tasks. Qwen 3.5 has generally ranked among the strongest open-weight models on HumanEval and related coding benchmarks, and this extends across multiple programming languages. The 9B and 27B variants are particularly strong for their size.
Gemma 4’s 31B dense model is also a solid coding assistant, with good performance on Python, JavaScript, and SQL. For most everyday coding automation tasks, you won’t notice a significant difference between the two families at comparable sizes.
Instruction Following and Output Formatting
Gemma 4 tends to be clean and consistent with structured outputs — JSON, markdown tables, formatted lists. This matters a lot for agentic workflows where downstream steps depend on predictable output format.
Qwen 3.5 is also strong here, though in thinking mode the model may include its reasoning process in outputs you didn’t ask for, which requires careful prompt engineering to suppress when you only want the final answer.
Context Window and Multimodal Capabilities
Context Window
Both model families support long context windows, which is important for document analysis, multi-turn conversations, and agentic tasks that accumulate a lot of context.
- Gemma 4: 128K tokens on E2B/E4B, 256K tokens on the 26B and 31B models
- Qwen 3.5: 262K tokens natively across all sizes (even the 0.8B model), extensible up to ~1M tokens
Qwen 3.5 has a clear advantage here. Every model in the family — including the smallest 0.8B — supports 262K tokens out of the box. Gemma 4’s larger models top out at 256K, which is close but slightly less, and the edge models are capped at 128K. If your workflows involve processing long documents, codebases, or extended conversation histories, Qwen 3.5’s consistent long context across all sizes is a practical advantage.
Multimodal Support
Both model families are natively multimodal — a significant shift from earlier generations where vision capabilities were often limited to separate model variants.
Gemma 4 supports text and image input across all sizes, with audio input additionally supported on the E2B and E4B edge models. It handles variable aspect ratios, document parsing, chart recognition, and handwriting OCR. Video is supported through frame extraction.
Qwen 3.5 is also natively multimodal with early text-vision fusion baked into the core models. You don’t need a separate Qwen-VL model — vision understanding is built into the same models you use for text. Qwen 3.5 outperforms the previous Qwen3-VL models on visual understanding benchmarks. For the full omnimodal experience (text, image, audio, video, and real-time interaction), Alibaba also offers Qwen3.5-Omni as a dedicated variant.
For local workflows requiring image + text, both are strong choices. Gemma 4 has the edge on audio input for its smaller models. Qwen 3.5’s vision capabilities are deeply integrated across the full lineup.
Multilingual Support
This is where Qwen 3.5 has a massive structural advantage.
Qwen 3.5 supports 201 languages and dialects — up from 82 in Qwen 3. This is one of the broadest multilingual coverage of any open-weight model family, with particular strength in Chinese, Japanese, Korean, Arabic, and other non-Latin scripts. The Qwen3.5-Omni variant extends this to 113 languages for speech recognition and 36 languages for speech generation. If you’re building workflows for global teams or non-English content, Qwen 3.5 is the clear default.
Gemma 4 supports multiple languages — English, Spanish, French, German, and others — but its training emphasis is more heavily English-centric. For multilingual document processing, customer support in multiple languages, or content generation in non-English markets, Qwen 3.5 is the stronger choice by a wide margin.
Licensing: What You Can Actually Do With These Models
For the first time in the Gemma family’s history, licensing is no longer a differentiator between these two model families.
Qwen 3.5 — Apache 2.0: One of the most permissive open-source licenses. You can use it commercially, modify it, redistribute it, and build products on top of it without restriction.
Gemma 4 — Apache 2.0: Google made a significant shift with Gemma 4, moving from its custom Gemma Terms of Use to the standard Apache 2.0 license. No custom clauses, no restrictions on redistribution or commercial deployment, no limitations on using the models to train other models. This is a major change from Gemma 3’s more restrictive licensing.
Both model families now offer identical licensing terms. For any business use case — running local inference, building internal tools, automating workflows, fine-tuning, or redistribution — you have full freedom with either choice.
Local Deployment: Tools and Ecosystem Support
Ollama
Both model families are available through Ollama, which is the easiest way to get up and running locally. A single ollama pull command gets you started. Gemma and Qwen models both have active community support in the Ollama library.
llama.cpp and GGUF Format
Both are available in GGUF format for use with llama.cpp, enabling fine-grained quantization control. This is the path for users who want to squeeze models into less VRAM with 3-bit or 4-bit quants.
LM Studio
LM Studio provides a GUI-friendly interface for both model families — good for teams that want local inference without command-line management.
vLLM (Server Deployment)
For teams running local inference at scale, vLLM supports both Gemma and Qwen architectures. Qwen’s MoE models have good vLLM support, making it feasible to serve the 397B flagship variant on a multi-GPU server setup.
Fine-Tuning
Both models support fine-tuning through standard tools. Qwen 3.5 has a particularly active fine-tuning community, with lots of community-maintained datasets and LoRA adapters available on Hugging Face. Gemma 4 also has good tooling support, including through Google’s own training infrastructure documentation.
Head-to-Head: Best For Each Use Case
| Use Case | Better Choice | Why |
|---|---|---|
| Complex multi-step reasoning | Slight edge: Qwen 3.5 | More mature thinking mode from Qwen 3 lineage |
| Long document processing | Qwen 3.5 | 262K native context on all sizes vs 256K max on Gemma 4 |
| Multilingual workflows | Qwen 3.5 | 201 languages vs English-centric Gemma 4 |
| Image + text tasks (local) | Tie | Both natively multimodal across the lineup |
| Audio input on edge devices | Gemma 4 | E2B/E4B support audio natively |
| Tight hardware budget (< 8GB VRAM) | Qwen 3.5 | More size options at low end (0.8B through 9B) |
| Coding assistance | Tie | Both strong at comparable sizes |
| Agentic tool use workflows | Tie | Both have function calling and structured output |
| Near-frontier local quality | Qwen 3.5 | 27B dense, 122B MoE, and 397B flagship options |
| Licensing flexibility | Tie | Both Apache 2.0 |
Running These Models in Automated Workflows With MindStudio
Picking a model is only half the problem. The other half is actually connecting it to your data, tools, and business processes.
MindStudio supports both Gemma and Qwen model families — along with 200+ other models — through a single visual workflow builder. You don’t need to manage API keys, configure inference servers, or write boilerplate code to connect your model to downstream tools.
Here’s where this matters practically: a lot of the comparisons above depend heavily on how you prompt, chain, and ground these models with real data. Swapping between Gemma 4 and Qwen 3.5 to test which performs better on your specific use case — your documents, your tasks, your output format requirements — takes minutes in MindStudio rather than days of infrastructure work.
MindStudio also supports local model inference through Ollama and LM Studio, which means if you want the data privacy of fully local inference, you can connect your local Qwen 3.5 or Gemma 4 instance to the same workflow builder you’d use for cloud-hosted models. Your agents can call agent.searchGoogle(), agent.sendEmail(), or trigger any of 1,000+ business tool integrations without you writing the integration layer from scratch.
For teams evaluating open-weight models for agentic automation, this kind of flexibility — run any model, swap between them easily, connect to real tools — matters more than any single benchmark comparison.
You can try MindStudio free at mindstudio.ai and connect it to whichever model fits your use case.
FAQ
Is Gemma 4 or Qwen 3.5 better for local inference on a consumer GPU?
Both run well on consumer hardware, but Qwen 3.5 gives you more size options at the low end. If you have 8 GB of VRAM, both the Qwen 3.5 9B and Gemma 4 E4B are viable. If you have 6 GB or less, Qwen 3.5’s smaller variants (0.8B, 2B, 4B) give you more headroom. Both support native multimodal on consumer GPUs.
Which model has better reasoning capabilities?
Both now support thinking modes for explicit chain-of-thought reasoning. Qwen 3.5’s hybrid thinking mode is more mature, building on the Qwen 3 lineage. Gemma 4 added thinking modes as a new capability. For math, multi-step logic, and planning, Qwen 3.5 has a slight edge due to its longer track record with this feature, but both are strong reasoners.
What’s the context window for Gemma 4 vs Qwen 3.5?
Qwen 3.5 supports 262K tokens natively across all model sizes — even the 0.8B model — with extension up to ~1M tokens. Gemma 4 supports 128K tokens on its E2B/E4B edge models and 256K tokens on its 26B and 31B models. Qwen 3.5 has the advantage here, particularly on smaller models.
Can I use Gemma 4 or Qwen 3.5 for commercial applications?
Yes to both, with no caveats. Both model families are released under the Apache 2.0 license — the most permissive open-source license available. You can use them commercially, modify them, redistribute them, and build products on top of them without restriction. This is a significant change for Gemma 4, which previously used Google’s more restrictive custom Gemma Terms of Use.
How do Gemma 4 and Qwen 3.5 compare for multilingual tasks?
Qwen 3.5 is the stronger choice by a wide margin. It supports 201 languages and dialects — up from 82 in Qwen 3 — with particular depth in Chinese, Japanese, Korean, and Arabic. The Qwen3.5-Omni variant adds speech recognition in 113 languages. Gemma 4 supports multiple languages but is more strongly optimized for English. For global audiences or non-English content, Qwen 3.5 is the clear pick.
Which model should I use for agentic AI workflows?
Both are well-suited for agentic workflows, with native function calling, structured JSON output, and thinking modes. Gemma 4 was explicitly designed for on-device agents, with built-in system-instruction support and tool-use capabilities. Qwen 3.5 offers more flexibility in model sizing and reasoning depth. Testing both on your specific task is the most reliable way to decide — which you can do quickly using a platform like MindStudio that supports both.
Key Takeaways
- Gemma 4 is the better default for on-device agentic workflows (especially with audio input on E2B/E4B), teams that prefer Google’s model ecosystem, and use cases where the 31B dense model’s Arena AI #3 ranking matters.
- Qwen 3.5 is the stronger choice for multilingual use cases (201 languages), long-context workflows (262K native across all sizes), and teams that need the full range from 0.8B to 397B parameter models.
- Both families now share Apache 2.0 licensing and native multimodal support — previously key differentiators that no longer apply.
- Both support thinking modes, function calling, and structured output for agentic workflows. At equivalent parameter counts on coding and instruction-following tasks, the two families are closely matched.
- For deployment, both run on Ollama, LM Studio, and llama.cpp — the local deployment ecosystem supports both equally well.
- If you’re evaluating models for automated agentic workflows, using a platform that lets you swap between models without rebuilding infrastructure will save you significant time.
If you’re ready to put either model to work in a real workflow, MindStudio is the fastest way to connect open-weight models to the tools and data your team already uses — no server configuration required.
