Try Muse Spark Open in OpenCode through Ollama Cloud

You can try Muse Spark Open as a coding model without buying the hardware needed to run its weights. Ollama offers a hosted model and a launch command that connects it to OpenCode.

We’ll use that connection for a tiny JavaScript change with a test. This keeps the first experiment small enough to inspect, including what the model reads and which commands the agent runs.

I haven’t compared the model’s coding performance on my projects. The setup below follows the official Ollama catalog. My Muse Spark Open overview covers the model release, hardware requirements, and license.

Connect to the hosted model

Install Ollama and OpenCode if you don’t have them. Open Ollama and follow its cloud sign-in prompts. Check Ollama’s current plans and your account’s access before starting a session.

Then, inside the project you want the agent to work on, run:

ollama launch opencode --model muse-spark-open:cloud

That exact integration is listed on the model’s catalog page. The :cloud suffix identifies the hosted model in the Ollama CLI. Its weights are not being downloaded to your laptop for this session.

The agent program still runs on your computer. It reads files and executes permitted tools there, while requests to the model go to Ollama’s cloud.

Use the launcher for this setup rather than combining manual provider snippets from different OpenCode releases. Ollama’s integration guide explains that the launcher supplies the selected model’s configuration while existing OpenCode settings still apply.

Check the selected model in OpenCode before sending the first task. If the launcher reports an unavailable model or account restriction, resolve that first instead of assuming it silently chose an equivalent model.

Make a disposable project

Create a small folder with a recent Node.js available:

mkdir muse-practice
cd muse-practice
git init

Save this as total.mjs. It adds a list of prices:

export function total(prices) {
  return prices.reduce((sum, price) => sum + price)
}

Save the initial test as total.test.mjs:

import test from 'node:test'
import assert from 'node:assert/strict'
import { total } from './total.mjs'

test('adds prices', () => {
  assert.equal(total([10, 20, 5]), 35)
})

Run the baseline test and record the starting code:

node --test
git add total.mjs total.test.mjs
git commit -m 'Add total practice fixture'

Now launch OpenCode from this folder using the Ollama command above.

Ask for one specific fix

The function has an edge case: reduce() without an initial value throws when the array is empty. Give the agent a request whose result you can verify:

Read total.mjs and its test.
Make total([]) return 0 while preserving the existing behavior.
Add a test for the empty array, then run node --test.
Change only those two files and show the diff.
Do not install packages or commit the change.

Inspect tool requests as they appear. This task needs file reads, small edits, and the local test command. It doesn’t need network access, deployment credentials, or files outside this directory.

The completed function can be this small:

export function total(prices) {
  return prices.reduce((sum, price) => sum + price, 0)
}

Read the added test and run node --test yourself. Also check git diff to confirm that the original test still exists and that the change stayed within the requested files.

A correct answer here verifies a basic tool workflow. It doesn’t establish how the model handles your framework, a large codebase, or a complex debugging session.

Check context and tool compatibility

Ollama’s model listing includes tool support and a large advertised context window. The usable request still depends on the provider’s endpoint and the client’s configuration.

Context includes more than source files. Instructions, conversation history, tool schemas, and command output also occupy it, with room needed for the response. A headline context limit doesn’t mean you should paste an entire repository into the first request.

Start with the files needed for the task and let the agent retrieve more when it can explain why. If requests fail as the session grows, inspect the actual limit or provider error before changing unrelated settings.

The hosted listing advertises text and image input. If you try a screenshot later, check whether the OpenCode connection accepts it and whether the model received it. The original model’s broader multimodal capabilities don’t establish that every hosting integration accepts the same media.

Know what this experiment sends and spends

A hosted session sends the selected project content to a provider. Ollama’s cloud documentation describes its handling of prompts and responses and links its privacy policy. Use a practice project before sending private code under an arrangement you haven’t reviewed.

Hosted access has account limits and billing terms. Downloadable model weights don’t make hosted inference unlimited or free. Check the account’s actual usage after the test, including any retries the agent made.

For a broader comparison of API billing and local hardware, see coding agents without another subscription. For this first trial, keep the question smaller: did the selected hosted model use the tools correctly, produce the expected fix, and leave a diff you can understand?

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