Most comparison posts on local models spend too much time on leaderboards and not enough time on the part that matters – what happens when the model has to call tools, stay inside a structure, and run on hardware you would actually put on your desk.
After going through the model cards, pricing pages, deployment notes, and a lot of community testing, my view is simple. Qwen 3.5 is the safest default for local agents. Gemma 4 is the one I like more than I trust. Llama 4 is interesting, but mostly when long context is the reason you are shopping in the first place.
I am also pulling in what builders are actually saying – on Reddit, GitHub issues, and dev blogs – because the gap between a model’s spec sheet and its real-world agent behavior is where most people get stuck.
My quick verdict
→ Pick Qwen 3.5 if the job is coding, tool use, structured output, or multi-step agent loops. It has the best reputation for staying inside schemas and completing tool calls without breaking.
→ Pick Gemma 4 if the job is multilingual assistance, cleaner prose, or a more natural assistant-style interaction. The Apache 2.0 license also removes legal friction that kept earlier Gemma versions out of production.
→ Pick Llama 4 if long context is the main requirement and you are willing to accept more implementation trade-offs. Scout’s context story is genuinely different from the other two.
The most consistent pattern in community testing is that Gemma gets described as the better assistant while Qwen gets described as the better agent. I think that framing is broadly right, even if the edges will move as runtimes improve.
What I care about in a local agent
I am not grading these models on launch-day vibes. For local agents, I care about five things – and none of them show up on a leaderboard chart:
🔧 Tool call reliability
Does the model produce valid function calls on the first try, or does it hallucinate parameters and break the loop?
📐 Structured output fidelity
Can it return clean JSON that my parser actually accepts, or do I spend more time fixing malformed output than writing logic?
💾 VRAM reality
What actually fits on a 24 GB card at a usable quantization – not what the spec sheet says, but what builders report.
🔄 Retry rate
How often does the agent loop have to re-prompt because the model drifted, missed a step, or produced garbage?
⚡ Tokens per second
Agent loops are latency-sensitive. A model that thinks beautifully but takes 15 seconds per call is not an agent – it is a bottleneck.
That means I care more about broken calls, memory behavior, and retry rate than about a benchmark chart with no workflow behind it.
Gemma 4 – better assistant, rougher operator
On paper, Gemma 4 is easy to like. Google positions it as an open-weight family with multimodality, function calling, and context windows up to 256K on the larger models. The Apache 2.0 license also matters because it removes a lot of uncertainty for teams that want to ship local or hybrid workflows. Previous Gemma releases carried restrictions that made enterprise lawyers nervous – that friction is gone now.
What I like about Gemma 4 is the same thing many builders seem to like about it – it feels polished as an assistant. The prose is usually cleaner. The multilingual story is stronger. It sounds more natural in user-facing flows. All Gemma 4 models natively process video and images with variable resolutions, which gives it a real edge for multimodal agent tasks like OCR, chart understanding, and visual reasoning on-device.
But the local-agent story is messier. The repeated complaints are tool-call glitches, heavy reasoning-token use, and higher memory pressure when you push context. That does not make it bad. It makes it less forgiving.
From the community
“Gemma 4’s thinking mode can run to 4,000+ tokens of internal reasoning per response. For structured extraction and classification, turning thinking off is essential – you get the same quality output without the overhead.”
– Developer benchmarks, April 2026
One thing worth noting – Google designed function calling and structured JSON output into the architecture from the ground up with six dedicated special tokens, not as a post-training patch. In theory that should make tool calls more reliable. In practice, community reports are more mixed. The throughput numbers tell part of the story: the 26B MoE model was measured at roughly 11 tokens per second on hardware where Qwen 3.5 hit 60+. For agent loops where latency compounds, that gap matters.
💡 My take on Gemma 4’s agent potential
Gemma 4 is the model I want to like for agents. The architecture choices are smart, the license is right, and the multimodal story is genuinely useful. But right now, the throughput gap and tool-call inconsistency mean I would reach for it as an assistant layer – not as the core of an agent loop. If Google tightens the inference story, this becomes a different conversation.
Qwen 3.5 – the local-agent default I would trust first
Qwen 3.5 looks more like a native agent family. The official positioning leans into thinking, search, tools, and multimodal workflows. In practice, the reputation that keeps showing up is reliability – better for coding, better for structured output, and better at the kind of multi-step work where agent systems usually break.
The hardware story also feels more workable. Reports on the 27B class point to a comfortable fit around 24 GB VRAM with good quantizations, while quality tends to hold up better at Q5 than Q4. The quantization robustness is actually one of Qwen’s underrated strengths – 4-bit quantization methods retain 99.8%+ accuracy, meaning a Q4 version of the 27B can still substantially outperform the 9B while using nearly the same memory. Below Q4 is where agent-critical features like function calling start getting flaky.
From the community
“Qwen 3.5 tool use would break at random when running locally – the model produced a valid tool call, but the parser tried to parse the entire assistant message from the start and parsing failed. The gap between benchmark performance and real-world reliability is significant for agents.”
– GitHub Issues (ollama/ollama #14493, QwenLM/Qwen3-Coder #475)
That GitHub quote is important context. Qwen 3.5 is not perfect either – the tool-calling failures happen at the intersection of llama.cpp, tool parsers, streaming, and file edits. The difference is that the Qwen ecosystem gives you more official tooling to work around the rough edges. The Qwen-Agent framework includes a native code interpreter with Docker sandboxing, RAG support, a Chrome extension, and MCP integration. That is a more complete agent stack than what either Gemma or Llama ship with today.
💡 My take on Qwen 3.5 for agents
Qwen 3.5 is not the most exciting model in this comparison. It is the most practical. The agent framework is more complete, the quantization story is friendlier, and the structured output reputation is the best of the three. If I am building something that needs to work – not demo well, work – this is where I start.
Llama 4 – interesting when context is the real problem
Llama 4 is the most interesting model here conceptually. Meta is pushing Scout and Maverick as multimodal MoE models with a very large context story, especially around Scout. On paper, that makes Llama 4 attractive for workflows where memory is the bottleneck rather than a side detail.
The problem is fit. For most builders, the promise is clearer than the default use case. If my goal is a dependable local worker that edits files, calls tools, and survives retries, I do not start with Llama 4. If my goal is huge-context analysis and I am willing to engineer around rougher edges, I keep it on the shortlist.
The license is also worth flagging. Llama 4 carries restrictions above 700 million monthly active users – which does not matter for most local builders, but it is a different posture than Gemma’s Apache 2.0 or Qwen’s unrestricted license. For enterprise teams evaluating long-term deployment, that detail matters to legal.
From the community
“Maverick beats GPT-4o on multimodal benchmarks, but for most local agent builders, the context window story is the draw – not the vision capabilities. Scout’s 10M context window is in a different league, but you need multi-GPU to actually use it.”
– Developer deployment guides, April 2026
💡 My take on Llama 4 for agents
Llama 4 is the model I would keep on a shortlist rather than a default stack. The context story is real and different enough to matter for a specific class of workflows – document analysis, corpus search, long conversation memory. But for the everyday local agent that calls tools and edits files, it is not where I start.
Head-to-head – what actually matters
If I compress the whole comparison into one line, it is this: Gemma 4 feels better, Qwen 3.5 works better, and Llama 4 stretches further.
Interactive decision guide – which model fits your workflow?
Start here. Answer the first question that applies:
🤖 “My agent needs to call tools, write code, and produce structured JSON.”
→ Start with Qwen 3.5 27B. Best tool-call reliability, fastest throughput, most mature agent framework. Use Q5 quant if VRAM allows, Q4 minimum for reliable function calling.
💬 “My agent is user-facing – it needs to sound natural and handle multiple languages.”
→ Start with Gemma 4. Best prose quality, strongest multilingual output, cleanest assistant feel. Accept the throughput trade-off and disable thinking mode for classification tasks.
📚 “My agent needs to hold huge documents or long conversation history in memory.”
→ Evaluate Llama 4 Scout. The context story is in a different league. Be prepared for multi-GPU requirements and more implementation engineering.
⚖️ “Legal says the license has to be fully open – no restrictions.”
→ Gemma 4 (Apache 2.0) or Qwen 3.5 (unrestricted). Both clear. Llama 4 has a 700M MAU cap that may matter at scale.
🖼️ “My agent needs to process images, charts, or video alongside text.”
→ Gemma 4 for on-device multimodal, Llama 4 Maverick for hosted multimodal. Gemma’s vision works across all model sizes and runs on edge devices. Maverick benchmarks well but is heavier.
Pricing analysis – the hidden cost is not the API rate
OpenRouter listings are useful as a sanity check. Llama 4 Scout and Maverick look aggressively priced, Qwen 3.5 sits in a workable range depending on the variant, and Gemma 4 pricing varies more by provider. But for a local-agent builder, the bigger cost is not the posted token price. It is VRAM, quant choice, context length, and how often the model forces a retry loop.
The real pricing insight for local builders is this: the model that retries less is the cheapest model. A $0 local model that forces three retry loops per task is more expensive in time and compute than a model that gets it right the first time.
Use cases – where each model actually fits
Use case 1: local coding agent on a 24 GB card
If I am building a code-focused agent that edits files, proposes patches, calls tools, and retries when tests fail, I want the model that gives me the cleanest structure and the fewest broken calls. That still points me to Qwen 3.5 first.
# Example: Qwen 3.5 agent loop for code editing
1. User describes the bug or feature request
2. Agent reads relevant files via tool call → structured file content
3. Agent proposes a patch → structured diff output
4. Agent runs tests via tool call → parses pass/fail
5. If tests fail → agent reads error, retries with context
# Why Qwen fits: reliable JSON tool calls, fast throughput,
# and Q4 quant holds function-calling quality on 24GB
The stack: Qwen 3.5 27B at Q5 via llama.cpp or Ollama, with Qwen-Agent or a custom tool-calling harness. Fits on a single RTX 4090 or 3090 with room for context. Expect 35-60 tok/s depending on quantization and context length.
Use case 2: multilingual customer-response assistant on-device
If the job is drafting replies, translating, or acting more like a polished assistant than a worker process, I would give Gemma 4 the edge. This is where its better prose and multilingual reputation matter more than its agent rough edges.
The stack: Gemma 4 27B with thinking mode disabled for fast classification and response drafting. The E2B variant can deploy on NVIDIA Jetson Orin Nano for fully offline, low-latency inference – useful for kiosk, retail, or field applications where connectivity is not guaranteed.
Use case 3: huge-context document analyst
If the deciding requirement is holding huge context, Llama 4 becomes more interesting than either Qwen or Gemma. I still would not call it my default, but for “keep the whole corpus in reach” workflows, the context story is strong enough to change the shortlist.
The stack: Llama 4 Scout via vLLM on multi-GPU setup. Best suited for legal document review, research corpus analysis, or long-running conversational agents that need to reference entire histories. Budget for the hardware – this is not a single-GPU workflow.
Use case 4: privacy-first enterprise agent
This is the use case where local models really earn their keep. If data cannot leave the building – healthcare, legal, finance, defense – you need a model that is both capable and legally clear to deploy. Qwen 3.5 or Gemma 4 both work here, but the license story matters. Gemma 4’s Apache 2.0 is the cleanest for enterprise legal teams. Qwen’s unrestricted license is also fine. Llama 4’s MAU restriction is usually irrelevant for internal tools, but some legal teams flag it anyway.
Use case 5: multimodal agent processing images and documents
If your agent needs to read charts, process scanned documents, or interpret screenshots alongside text, Gemma 4 has the most practical on-device multimodal story. All model sizes handle images and video natively, and the NVIDIA Jetson deployment path makes edge multimodal agents a real possibility – not just a demo. For hosted multimodal, Llama 4 Maverick benchmarks strongly but is heavier to run.
Field notes from the community
I have been tracking what builders are saying across Reddit, GitHub, and developer blogs. Here is what keeps coming up – filtered for signal, not noise.
“When Google released Gemma 4, I benchmarked it against my Qwen setup within hours. Classification performance improved from 8.5 seconds to 1.9 seconds. I completed the swap by evening with just five files changed.”
Developer blog, April 2026
GEMMA 4“The 27B is very robust to quantization. 4-bit versions of Qwen 3.5 27B can still be substantially stronger than Qwen 3.5 9B while using nearly the same memory. Below Q4, tool calling starts getting flaky with complex JSON schemas.”
Unsloth documentation
QWEN 3.5“Reasoning quality degrades beyond approximately 100,000 tokens in testing, despite supporting the full 256K context window. Most search agents built on Qwen 3.5 adopt a context-folding strategy where earlier tool responses are pruned once cumulative length hits a threshold.”
Agent developer implementation guides
QWEN 3.5“A Qwen-centered stack gives you more official agent tooling and more immediate paths to code execution and MCP integration. A Gemma-centered stack nudges you more naturally toward external validation and controlled function-calling patterns.”
AI Agents Kit comparative analysis
BOTH“The Gemma 4 E2B model can be deployed on NVIDIA Jetson Orin Nano edge AI modules, processing interleaved multimodal inputs seamlessly on-device, achieving ultra-efficient, low-latency inference completely offline.”
NVIDIA Technical Blog
GEMMA 4“Developers are increasingly choosing local agents for privacy, cost control, and agentic workflows, while reserving cloud APIs for frontier reasoning tasks. The consensus shows a +40% shift toward local deployment for privacy-critical agent workloads.”
Developer community surveys, 2026
INDUSTRYThe VRAM reality check
Most builders reading this are running a single GPU with 24 GB of VRAM. Here is what actually fits and what the trade-offs look like in practice:
Bottom line
Here is the simplest version of my conclusion: Gemma 4 feels better. Qwen 3.5 works better. Llama 4 stretches further.
I think Gemma 4 will get attention because it is pleasant. I think Qwen 3.5 will get more real production use because it is practical. And I think Llama 4 will stay interesting anywhere context is the bottleneck, not a side quest.
If you want one answer instead of three, mine is this: for local agents, I would start with Qwen 3.5, test Gemma 4 second, and reach for Llama 4 when context is the reason I am switching.
The one-liner
Start with Qwen 3.5 for agents that need to work. Switch to Gemma 4 for agents that need to talk. Reach for Llama 4 for agents that need to remember.
FAQs
Is Gemma 4 better than Qwen 3.5 for local agents?
Usually not for coding and tool-heavy loops. Qwen 3.5 has a better reputation for structured output, function calling, and multi-step reliability. Gemma 4 is usually the better pick if you care more about assistant quality, multilingual output, or user-facing polish. The short version: Qwen for agents, Gemma for assistants.
Is Llama 4 worth using locally?
Yes, but only when long context is the main reason you are shopping. Scout’s context story is genuinely different from Gemma and Qwen. If you need to hold entire document corpora, long conversation histories, or massive codebases in memory, Llama 4 is worth the engineering trade-offs. For everyday tool-calling agents, it is not my default starting point.
Which model should I test first on a single workstation with 24 GB VRAM?
Qwen 3.5 27B at Q5 quantization. It fits comfortably on 24 GB, gives you 35-50 tok/s throughput, and retains reliable function calling at Q4 and above. Gemma 4 27B also fits but runs at roughly 11 tok/s on the same hardware, which hurts agent loop latency. Llama 4 Scout generally needs multi-GPU for its context advantages.
What quantization level should I use for agent work?
Q4 is the practical floor for reliable tool calling. Q5 is the sweet spot if you have the VRAM. Below Q4, function calling accuracy drops noticeably – especially with complex JSON schemas. For Qwen 3.5, Q4 retains 99.8%+ accuracy compared to full precision. For Gemma 4, the quantization data is less mature but Q4 is the minimum I would recommend for agent use.
How do the licenses compare for commercial use?
Gemma 4 ships under Apache 2.0 – fully open, no restrictions, enterprise lawyers are happy. Qwen 3.5 is unrestricted. Llama 4 has a restriction above 700 million monthly active users, which is irrelevant for most local deployments but some legal teams flag it during evaluation. If license cleanliness is a hard requirement, Gemma 4 or Qwen 3.5 are your options.
Does Qwen 3.5 really handle 256K context locally?
Technically yes, practically with caveats. Community testing shows reasoning quality starts degrading beyond approximately 100,000 tokens despite the model supporting the full 256K window. Most production agent builders adopt a context-folding strategy – pruning earlier tool responses once cumulative length hits a threshold. The 256K number is real for retrieval, but do not expect sharp reasoning at the far end of that window.
Which model is best for multimodal agents that process images?
Gemma 4 for on-device multimodal. All model sizes handle images and video natively with variable resolutions, and the E2B variant runs on NVIDIA Jetson hardware for fully offline inference. Llama 4 Maverick benchmarks well for hosted multimodal but is heavier to run. Qwen 3.5 handles text and images but its multimodal story is less developed than either competitor for agent use cases.
Should I use the model’s thinking mode for agent tasks?
It depends on the task. For complex reasoning, planning, and multi-step problem solving, thinking mode is worth the token overhead. For structured extraction, classification, and routing, disable it. Gemma 4’s thinking mode can generate 4,000+ tokens of internal reasoning per response – for classification tasks, turning it off gives you the same quality output at roughly 30% less inference cost. Agent builders should make thinking mode a per-task toggle, not a global setting.
Can I use these models with MCP (Model Context Protocol)?
Qwen 3.5 has the most mature MCP story through the Qwen-Agent framework, which includes native MCP integration alongside code execution and RAG. Gemma 4 and Llama 4 can both work with MCP through external orchestration layers, but there is no official first-party MCP support from Google or Meta yet. If MCP integration is a core requirement, Qwen gives you the shortest path to production.
What runtime should I use – Ollama, llama.cpp, or vLLM?
For single-GPU local agents: Ollama for ease of setup, llama.cpp for maximum control and throughput tuning. For multi-GPU or hosted deployment: vLLM. Qwen 3.5 and Gemma 4 both work well through Ollama and llama.cpp. Llama 4 Scout benefits from vLLM’s tensor parallelism for multi-GPU context handling. The tool-calling parser bugs mentioned earlier are runtime-specific – check GitHub issues for your chosen runtime before committing.
You might want to also check out: Gemma 4 model card, Llama 4 docs, Qwen 3.5 overview, Gemma 4 vs Qwen tool calling, Qwen-Agent framework, Qwen 3.5 quantization benchmarks, Gemma 4 Apache 2.0 analysis.
