Qwen3.5-4B vs Phi-4-mini: Choosing the Right 4B Model for Local Inference

Two 4B models. Both run in under 3.5 GB of VRAM at Q4 quantization. Both handle 100,000+ token contexts on a single consumer GPU. But they make very different bets about what a 4B model should be.

Qwen3.5-4B is a multimodal model with a 262K-token context window, native image and video input, on-demand extended thinking, and 201-language coverage — released by Alibaba’s Qwen team in February 2026 under Apache 2.0. Phi-4-mini is a text-only dense transformer purpose-built for math and structured reasoning — released by Microsoft in February 2025 under MIT license. At Q4 quantization they’re within 0.25 GB of each other on disk, yet they’re optimised for completely different workloads.

This post runs the two side by side on specs, benchmarks, and deployment so you can pick the one that fits your actual use case. Both models have dedicated posts on this blog already: Phi-4-mini local setup and benchmarks, and the Qwen3.5-0.8B architecture explainer covers the Gated DeltaNet hybrid attention design shared across the whole Qwen3.5 family.

Quick reference: Qwen3.5-4B vs Phi-4-mini

On hardware, both models are essentially tied. At Q4_K_M, Qwen3.5-4B requires roughly 3.5 GB total RAM (model weights plus runtime overhead) and Phi-4-mini requires approximately 3.2 GB. That 300 MB gap will not determine what hardware you can use.

How to run each model locally

Ollama handles both in a single command:

# Qwen3.5-4B — 3.4 GB download
ollama run qwen3.5:4b

# Phi-4-mini — 2.5 GB download
ollama run phi4-mini

Qwen3.5-4B starts with thinking mode on by default. To suppress the extended reasoning chain, prepend /no_think to your first message. For compute-sensitive workloads — streaming chat, autocomplete, anything where latency matters — turn thinking off or you’ll pay 1–3× in added generation time before you see output.

For GGUF / llama.cpp, the quantization files are at unsloth/Qwen3.5-4B-GGUF and unsloth/Phi-4-mini-instruct-GGUF on HuggingFace. Q4_K_M is the right starting point for both; Unsloth’s dynamic quantization skips quantizing the most sensitive attention layers, which preserves more accuracy than a flat 4-bit pass. For a full breakdown of when to pick GGUF over MLX or ONNX, see the format comparison guide.

Benchmarks

One caveat before the numbers: Qwen3.5-4B’s benchmark scores are almost certainly measured with thinking mode enabled, which means the model runs an extended internal reasoning chain before producing output. Phi-4-mini’s scores are measured with standard chain-of-thought prompting only. This caveat applies most to tasks involving deep reasoning — particularly GPQA Diamond.

Sources: Qwen/Qwen3.5-4B model card, microsoft/Phi-4-mini-instruct model card.

The GPQA Diamond figure (76.2 vs 25.2) illustrates why the protocol caveat matters. A GPQA Diamond score of 76.2 would put a 4B model above GPT-4o (~53%) and Claude 3.5 Sonnet (~65%) when those models were released — a result that is only coherent if extended thinking is running and contributing substantially to the answer. The more meaningful comparison is Phi-4-mini’s standard-CoT GSM8K and MATH scores against Qwen3.5-4B run with /no_think: that comparison exists in the community (the CloudNinjas benchmark post covers Qwen3.5 models under comparable conditions), and the Phi-4-mini advantage on pure arithmetic narrows but does not disappear.

For math-specific workloads without thinking: Phi-4-mini’s 88.6% GSM8K and 64% MATH are strong, well-documented numbers measured on a simple prompting baseline. Microsoft’s synthetic training approach specifically targets step-by-step mathematical derivation, and it shows.

Context window

Both context windows are large enough to load full codebases, long PDF documents, or extended conversation histories. The practical difference shows up at the architectural level.

Phi-4-mini uses a standard KV cache that grows proportionally with context length. At the full 128K, you’ll see several gigabytes of RAM usage on top of the base model weight — the RAM guide explains how to estimate this for your target context depth.

Qwen3.5-4B’s Gated DeltaNet layers — covering three quarters of the model — replace the growing KV cache with a fixed-size compressed state. The remaining quarter uses standard softmax attention for precise token retrieval. In practice this means Qwen3.5-4B’s memory overhead scales more predictably at long contexts than Phi-4-mini. If you’re regularly operating at 100K+ tokens, that structural difference matters.

Multimodal input

Qwen3.5-4B handles image and video input natively. Phi-4-mini is text-only.

If your workload involves processing screenshots, diagrams, scanned documents, or any visual input, Qwen3.5-4B is the only viable choice in this comparison. To use images via the Transformers library:

from transformers import AutoProcessor, AutoModelForImageTextToText

processor = AutoProcessor.from_pretrained("Qwen/Qwen3.5-4B")
model = AutoModelForImageTextToText.from_pretrained("Qwen/Qwen3.5-4B")

messages = [
    {
        "role": "user",
        "content": [
            {"type": "image", "url": "https://example.com/chart.png"},
            {"type": "text", "text": "What does this chart show?"}
        ]
    }
]

inputs = processor.apply_chat_template(
    messages, add_generation_prompt=True,
    tokenize=True, return_dict=True, return_tensors="pt"
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=512)
print(processor.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Ollama handles multimodal input as well, but at the time of writing the vision support works most reliably through the Transformers or vLLM paths for production use.

Which model is right for your use case?

For general development and coding

Qwen3.5-4B’s LiveCodeBench v6 score (55.8) and thinking mode combine well for iterative coding tasks — it reasons about the problem before writing code. It also has a context window large enough to load a full medium-sized codebase in a single call.

If you need a dedicated coding-specialist model at the 7–9B scale, Qwen3-Coder-Next is worth examining separately.

For math and step-by-step reasoning

Phi-4-mini’s 88.6% GSM8K and 64% MATH are the benchmark profile for a reasoning specialist trained specifically on high-quality mathematical derivation data. These scores were achieved without extended thinking. If you need reliable arithmetic and formal reasoning with low latency, Phi-4-mini is the narrower but more consistent tool.

For document and image analysis

Qwen3.5-4B. Phi-4-mini cannot process images.

For resource-constrained servers or edge deployments

Both models have nearly identical VRAM footprints at Q4. The tiebreaker is whether you need thinking mode at all: if yes, Qwen3.5-4B; if you need the lightest possible latency and math output, Phi-4-mini. For sub-1 GB deployments, neither applies — see Qwen3.5-0.8B.

What is the better 4B model in 2026?

Qwen3.5-4B has the broader capability profile — multimodal input, 262K context, stable thinking mode, 201 languages, and strong reasoning benchmarks when thinking is enabled. For most general-purpose local AI workloads, it does more with essentially the same hardware budget.

Phi-4-mini’s case rests on two specific advantages: cleaner math performance in fast-inference mode (no thinking latency), and a wider community of fine-tunes and quantization variants built up over the 15 months since its release. Both are genuine advantages depending on your use case.

Run both for your actual workload before committing. The best small language models comparison shows how these two fit into the broader 3–8B landscape.

Sources

• Qwen/Qwen3.5-4B — HuggingFace model card
• unsloth/Qwen3.5-4B-GGUF — quantization file sizes
• microsoft/Phi-4-mini-instruct — HuggingFace model card
• ollama.com/library/qwen3.5
• ollama.com/library/phi4-mini
• Qwen3.5 — official Qwen blog
• Local LLM Benchmark 2026 — CloudNinjas