Phi-4 Mini vs. Gemma 3 vs. Qwen 2.5: Best SLM for Coding Tasks in 2026

Qwen 2.5 Coder 7B is the most accurate of the three for Python and TypeScript completions. Phi-4 Mini (3.8B) uses the least VRAM and runs nearly twice as fast. Pick it when memory or latency counts more than raw accuracy. Gemma 3 4B sits in the middle. It is the best choice when you need one model for code, commit messages, docs, and error explanations. Below are the benchmark numbers, the test method, and how to set up each model in VS Code or Neovim.

Why Run a Coding Model Locally

The RTX 5060 Ti (8 GB VRAM) and RTX 5070 (12 GB VRAM) both launched in early 2026. They changed the math on local inference. A quantized 7B model now runs at interactive speeds on mid-range gaming hardware. Before that GPU generation, running anything useful locally meant owning a 3090. The other option was a slow 10-15 tok/s, sluggish enough to make you turn tab completion off.

The latency case is simple. A local model via Ollama returns the first token in under 40ms on modern consumer GPUs. A cloud API adds 200-500ms of network round-trip before you see anything. For inline code completion, you notice that gap, above all when completions appear mid-keystroke.

Privacy is the stronger case for many teams. Proprietary code, work under an NDA, HIPAA-relevant logic, or government-contract work often cannot legally go to an outside API. Sending it creates audit risk or breaks policy. A model on your own hardware sends nothing out.

Cost is a factor if you run Ollama on hardware you already own. Cloud coding models range from $0.15 to $3.00 per million tokens. A developer using heavy autocomplete burns roughly 5-10 million tokens per month in completions alone. At $0.50/M (about what Gemini Flash and Claude Haiku charge), that is $25-50 per month per developer. For a team, it adds up to real money. On hardware you own, local inference costs only the power: about $0.05-0.15 per hour for GPU compute.

SLMs do not replace frontier models on hard tasks. But they handle the 80% of daily coding work that is straightforward: single-function completions, docstrings, simple refactors, test stubs, boilerplate. For that workload, a well-chosen SLM running locally is genuinely competitive.

Test Setup and Methodology

All three models were tested at Q4_K_M quantization via Ollama 0.6.x on the following hardware:

Q4_K_M is the quantization level most people use for day-to-day work. It keeps most of the model’s quality and cuts VRAM use sharply against F16 weights. Going lower (Q3, Q2) saves more memory but drops code quality. Going higher (Q5_K_M, Q8) recovers a few points of accuracy at the cost of much more VRAM.

Three evaluation sets were used:

HumanEval+ is the extended version of OpenAI’s HumanEval. It has 161 Python function completion problems and stricter test cases than the original set. All runs used temperature 0 and pass@1 scoring.

MultiPL-E (TypeScript subset) is a multilingual code benchmark. TypeScript is the second most common language in daily work after Python, and it tests how well a model reasons about types.

Custom real-world refactor set (50 problems) was built from 50 actual GitHub pull requests. The tasks include swapping a function to a different data structure, adding error handling, converting callback code to async/await, and splitting a large function into smaller ones with clear boundaries. These are the tasks developers actually hand a model during a workday. This set tracks real-world usefulness better than HumanEval+, which tests a narrow synthetic skill.

All three models ran with identical system prompts. VRAM was measured with nvidia-smi at peak during generation. Token speed was averaged over 100 runs at batch size 1. Time to first token (TTFT) is the median latency before the first token appears.

Benchmark Results

Qwen 2.5 Coder 7B wins on every accuracy metric. The HumanEval+ gap between Qwen and Phi-4 Mini is 7.1 percentage points. That is large enough to feel in daily use: you accept more completions without fixing them by hand.

The speed numbers tell a different story. Phi-4 Mini at 95 tok/s is 83% faster than Qwen. For inline tab completion, where you generate 10-30 tokens at a time, the wall-clock gap shrinks a lot. Both feel responsive. TTFT is what counts: 18ms versus 38ms is the gap between a completion landing mid-pause and one landing with a small but real delay.

Gemma 3 4B’s most interesting result is the custom refactor benchmark, where it finishes second despite lower accuracy on both standard benchmarks. Refactor problems ask the model to read existing code, grasp its intent, and produce a changed version. Those skills lean on instruction-following. Gemma trained on a broader instruction-following corpus, and it shows here when tasks go beyond pure function synthesis.

Editor Integration

VS Code with Continue

Continue is the most practical way to use a local model for code in VS Code. Install the extension, open ~/.continue/config.json, and add:

{
  "models": [
    {
      "title": "Qwen 2.5 Coder (Chat)",
      "provider": "ollama",
      "model": "qwen2.5-coder:7b",
      "apiBase": "http://localhost:11434"
    },
    {
      "title": "Gemma 3 (General)",
      "provider": "ollama",
      "model": "gemma3:4b",
      "apiBase": "http://localhost:11434"
    }
  ],
  "tabAutocompleteModel": {
    "title": "Phi-4 Mini (Autocomplete)",
    "provider": "ollama",
    "model": "phi4-mini",
    "apiBase": "http://localhost:11434"
  }
}

This config uses Phi-4 Mini for inline tab completions , where speed is key. It also gives you two chat-sidebar options: Qwen 2.5 Coder for code-heavy questions and Gemma 3 for general reasoning. Switch between them with Cmd/Ctrl+L.

Neovim

Two plugins work well here. codecompanion.nvim is more minimal - sidebar chat plus inline completions with a clean Ollama backend configuration. avante.nvim is a closer approximation of the Cursor experience, with a persistent sidebar that shows model-suggested diffs inline.

For avante.nvim with Ollama as the backend:

require("avante").setup({
  provider = "ollama",
  ollama = {
    model = "qwen2.5-coder:7b",
    endpoint = "http://localhost:11434",
    timeout = 30000,
  },
})

If Ollama is running on a separate machine on your local network (useful for keeping GPU load off a laptop), replace localhost with the server’s LAN IP and set OLLAMA_HOST=0.0.0.0 in Ollama’s environment so it accepts remote connections.

Optimizing Context Size

Ollama’s default context window varies by model but is often larger than needed for tab completions. Explicitly configuring it keeps VRAM usage predictable. Create a Modelfile for completion use:

FROM phi4-mini
PARAMETER num_ctx 4096
PARAMETER temperature 0

ollama create phi4-mini-code -f phi4-mini-code.Modelfile

For chat-based work (reviewing a function, explaining an error, drafting a module), bump the context:

FROM qwen2.5-coder:7b
PARAMETER num_ctx 16384
PARAMETER temperature 0.1

At 16K context, Qwen 2.5 Coder 7B uses approximately 7.2 GB VRAM on the RTX 5070, still leaving room for the OS and other processes.

Running Two Models at Once

Phi-4 Mini at 4K context (2.8 GB) and Qwen 2.5 Coder at 4K context (5.1 GB) together use about 8 GB VRAM, which fits on the RTX 5070. To allow concurrent requests from both the completion engine and a chat window:

export OLLAMA_NUM_PARALLEL=2

Without this, one request blocks the other, which means a tab completion freezes if you have the chat sidebar open and vice versa.

Recommendations by Use Case

Dedicated coding, 12+ GB VRAM: Qwen 2.5 Coder 7B. Its lead on HumanEval+ and the refactor benchmark is consistent enough that you’ll notice fewer incorrect completions per day. The lower token speed relative to Phi-4 Mini doesn’t affect interactive use in a meaningful way.

Constrained hardware (8 GB VRAM, shared GPU): Phi-4 Mini 3.8B. At 2.8 GB VRAM for completions, it leaves 5+ GB for your browser, IDE, running Docker containers, or a game in the background. Its accuracy is meaningfully better than what was possible at this parameter count in 2024 or 2025. If you’d rather run something larger on the same 8 GB budget, our walkthrough of running a 26B model on an 8 GB budget shows three strategies that fit into that ceiling.

One model for everything: Gemma 3 4B. Code completions, commit message drafting, explaining stack traces, answering questions about unfamiliar libraries, writing docstrings - Gemma handles all of these more consistently than the coding-specialized models when the task isn’t pure code synthesis.

Best two-model setup: Phi-4 Mini for tab completions plus Qwen 2.5 Coder 7B for chat. This uses around 8 GB VRAM total at 4K context and routes each task to the model that handles it best.

RTX 5090 (24-32 GB VRAM): Skip the 7B tier and run Qwen 2.5 Coder 32B at Q4_K_M. The jump from 7B to 32B is significant - you’re near cloud-model quality for code. Alibaba’s newer Qwen3.6-35B-A3B sparse MoE is another option in this VRAM band: it activates only 3B parameters per token and scores 73.4 on SWE-bench Verified. At this card’s ceiling you can also weigh the 27-31B flagships head to head: our look at how Qwen 3.5 stacks up against Gemma 4 and Llama 4 breaks down which one fits your workload. The SLMs covered here exist for developers who don’t have that VRAM budget.

What These Models Still Can’t Do Well

Multi-file refactors are the most common failure case. When a task requires tracking dependencies across more than five or six files, understanding a codebase’s implicit conventions, or reasoning through a non-trivial architectural problem, all three models degrade noticeably. Treat them as single-file tools for now. For repo-scale agent work you need a far larger model, and MiniMax M2.7 shows what that costs: 230B parameters and a 96GB unified-memory floor .

HumanEval+ pass@1 measures a specific, narrow skill - completing a Python function from its docstring and signature. The custom refactor benchmark tracks closer to real daily use, which is why it’s worth considering separately. A model that scores four points higher on HumanEval+ but worse on refactors isn’t obviously better for the actual work.

All three models were tested at Q4_K_M. Testing at Q5_K_M shows roughly 1-2 percentage point improvements on HumanEval+ for each model, at the cost of 15-20% more VRAM. Q8 improves accuracy by another 1-2 points but uses VRAM closer to the full-precision footprint. For most setups Q4_K_M is the right choice, but if you have headroom and care about squeezing every bit of accuracy, Q5_K_M at the same context window is worth trying.

FIM (fill-in-the-middle) benchmarks - where the model fills in a gap in the middle of existing code rather than completing from the end - weren’t covered here. For tab completion specifically, FIM capability matters more than HumanEval+ pass@1. Qwen 2.5 Coder 7B has explicit FIM training, which is one reason to prefer it for tab completions even though Phi-4 Mini is faster. Continue supports FIM natively with Ollama-backed models; set "template": "fim" in the autocomplete model entry to enable it.

Power draw during sustained inference is worth knowing if you’re running these models on a laptop or a machine with a small power supply. The RTX 5070 pulls roughly 115-130W during continuous token generation at these quantization levels, compared to its 200W TDP under gaming load. All three models produce similar power draw since they’re all VRAM-bound at these sizes rather than compute-bound. Sustained sessions won’t push a desktop GPU near its thermal limit, but notebook GPUs with 80W power limits will see noticeably lower tok/s than the numbers here.