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 generates tokens nearly twice as fast, making it the right pick when memory headroom or latency matters more than raw accuracy. Gemma 3 4B sits in the middle - not the fastest, not the most accurate at code - but the most capable when you need one model for coding, commit messages, documentation, and error explanations. Below are the actual benchmark numbers, the full test methodology, and how to configure 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 and 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 either owning a 3090 or accepting 10-15 tok/s throughput that made tab completions feel sluggish enough to turn off.

The latency argument is straightforward. 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, that difference is noticeable in practice - especially when completions appear mid-keystroke.

Privacy is the stronger case for many teams. Proprietary codebases, anything under an NDA, HIPAA-relevant application logic, or government-contract work often cannot legally be sent to an external API without creating audit risk or violating policy. A model running on your own hardware exfiltrates nothing.

Cost matters if you’re running an Ollama instance on hardware you already own. Cloud coding models range from $0.15 to $3.00 per million tokens. A developer using aggressive autocomplete burns through roughly 5-10 million tokens per month in completions alone. At $0.50/M (roughly what Gemini Flash and Claude Haiku charge), that’s $25-50 per month per developer, adding up to real money for a team. On existing hardware, local inference costs only the electricity - around $0.05-0.15 per hour for GPU compute at current rates.

SLMs are not a replacement for frontier models on complex tasks. But they handle the 80% of daily coding work that’s actually straightforward: single-function completions, docstring generation, 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 practitioners use for day-to-day work. It keeps most of the model’s quality while cutting VRAM usage significantly compared to F16 weights. Going lower (Q3, Q2) saves more memory but causes noticeable quality drops on code. Going higher (Q5_K_M, Q8) recovers a few points of accuracy at the cost of meaningfully more VRAM.

Three evaluation sets were used:

HumanEval+ is the extended version of OpenAI’s HumanEval, with 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 was chosen because it’s the second most common language in the average developer’s daily work after Python, and it tests the models’ ability to reason about types.

Custom real-world refactor set (50 problems) was built from 50 actual GitHub pull requests: refactoring a function to use a different data structure, adding error handling to an existing function, converting callback-style code to async/await, splitting a large function into smaller ones with clear boundaries, and similar tasks that represent what developers actually ask a model to do during a workday. This set correlates better with real-world usefulness than HumanEval+, which tests a narrow synthetic skill.

All three models ran with identical system prompts. VRAM was measured using 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 was generated.

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 - large enough to notice in daily use as more completions being accepted without manual correction.

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’re generating 10-30 tokens at a time, the wall-clock difference compresses considerably - both feel responsive. What does matter is TTFT: 18ms versus 38ms is the gap between a completion appearing mid-pause and appearing with a small but perceptible 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 require reading existing code, understanding its intent, and producing a transformed version - skills that lean on instruction-following capability. Gemma was trained on a broader instruction-following corpus, which shows up 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, then 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 configuration uses Phi-4 Mini for inline tab completions (speed matters here) and gives you two options in the chat sidebar: 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.

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. 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.

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.