Ollama Models for Coding and Roleplay: Candidates and Tests

Updated 24 October 2026 · Sources & corrections

Quick answer: Choose an Ollama coding model by running a small set of representative code tasks, and choose a roleplay model with a separate consistency and writing rubric. A model that completes code well is not automatically a better storyteller. Start with registry-listed tags your hardware can run, then compare recorded results.

This guide offers established candidates and an evaluation method. It does not rank every current model or claim independent speed, VRAM or accuracy measurements.

Coding candidates to evaluate

For an initial Qwen comparison, the registry lists these tags. Pulling downloads model data; choose one suitable for your available storage and runtime before requesting larger variants:

ollama pull qwen2.5-coder:7b
ollama run qwen2.5-coder:7b

The 14B and 32B tags are other candidates, not guaranteed upgrades for your workload. Registry file sizes are download artifacts, not total VRAM requirements. Avoid converting a size label into a promise that a model will fit comfortably with any context setting.

Use code tasks with checkable outcomes

1. Take a small bug with an existing failing test. Ask for a patch, then run the relevant tests.
2. Include an unfamiliar local API so you can check whether the model invents methods.
3. Ask for a narrow refactor and inspect unintended changes.
4. Evaluate completion separately from chat if editor autocomplete is your goal.

Keep prompts, context and scoring consistent across candidates. Record exact model tag, runner version, context settings, elapsed time and test outcomes if you measure them. Do not treat a passing example as proof of repository-wide correctness, or a generated explanation as proof that tests ran.

Before debugging model quality, confirm your application reaches the expected local server and tag. Our frontend connection guide covers that path. If a long prompt appears to lose information, use the client context-override checks.

Evaluate roleplay with a different rubric

A general chat candidate such as a registry-listed Llama 3.1 tag can provide a baseline before you investigate a fine-tune. Read the exact fine-tune card, base-model terms and intended use rather than assuming every merge has the same behavior or permissions.

Write a short character brief and score adherence, prose style, continuity across turns and unwanted repetition. Include a later question about an earlier fact. Keep prompts and generation settings fixed for the comparison, then tune one setting at a time. Reduced refusal behavior is not evidence of better reasoning or more reliable writing.

Choose generation settings according to observed output. There is no universal temperature or repetition setting that guarantees better prose. For Modelfile basics and service setup, see our Ollama guide.

Measure the running setup

Weights, context allocation, concurrency and runtime overhead all affect memory use. The Ollama context guide explains the memory tradeoff and using ollama ps to inspect the running model. Increase settings only after checking the configuration that actually serves your requests.

Local inference also does not establish that an editor extension or frontend keeps every request local. Check provider routing, telemetry and cloud fallbacks before supplying private code. Preserve a working configuration so you can reverse an experiment that causes failures.

Sources and verification scope

Reviewed against the linked official registry entries, publisher card and Ollama documentation on 24 October 2026. Pull commands are documentation-based examples and were not executed here. No original model benchmark, hardware-fit guarantee or cross-model winner is claimed. This update removes unsupported performance comparisons and corrects the Modelfile discovery link. The URL and original publication date are preserved. Read our sourcing and correction policy.