Get an audit

Head to head · Local inference · October 2026 research run

Docker Model Runner vs Core

Docker Model Runner scores 57.1 (C) on agent readiness against Core's 7.3 (F), and leads in every scored category. Both do local inference.

Docker Model RunnerDocker, Inc. · HTTP API C57.1/100#428 of 629 CoreGhost (ZMJ, Inc.) · Model platform F7.3/100#629 of 629

Which one, for what

Docker Model Runner C

Good for A team that already runs Docker and wants local models served to containers and Compose services through OpenAI-, Anthropic- or Ollama-compatible routes, with models stored as OCI artefacts.

Ahead on

  • Reliability, 85 against 5
  • Schema & documentation, 49 against 7
  • Agent ergonomics, 58 against 4
  • Security & auth, 40 against 6
  • Payments & pricing, 60 against 10
  • Maintenance & community, 55 against 3
  • Transparency & trust, 73 against 45

Also in its favour

  • No key needed to call it
  • Free to start without a card
  • Open source

Watch for

No credential on the API. The docs say any client that can reach it, including other containers, can pull, load and run models

Core F

Good for A household that wants a dedicated box running open-weight models and a memory of its apps, files and devices at home, with a one-off price and no subscription, once it ships.

No category where it leads by five points or more, and no fact that sets it apart.

Watch for

Not shipped on 5 October 2026. Batch 1 is scheduled for 31 October and showed sold out that evening

Score by category

CategoryWeight this runDocker Model RunnerCoreEdge
Reliability16%20855Docker Model Runner +80
Performance10%pendingpendingpendingnot scored in this run
Schema & documentation13%16.2497Docker Model Runner +42
Agent ergonomics13%16.2584Docker Model Runner +54
Security & auth14%17.5406Docker Model Runner +34
Payments & pricing10%12.56010Docker Model Runner +50
Task success10%pendingpendingpendingnot scored in this run
Maintenance & community7%8.8553Docker Model Runner +52
Transparency & trust7%8.87345Docker Model Runner +28
Negative events≤15-3-2
Total57.1 · C7.3 · F

Facts side by side

FactDocker Model RunnerCore
KindHTTP APIModel platform
VendorDocker, Inc.Ghost (ZMJ, Inc.)
Hosted endpointno (local only)no (local only)
TransportsHTTPHTTP
AuthNoneOAuth or key
PricingFreePaid
x402nono
LicenceApache-2.0 (server, CLI plugin and dmr binary). Docker Desktop, which bundles it, is closed software under Docker's subscription agreement, and each model carries its own licenceNot stated. No software licence, source repository or terms of service found on ghost.ai (checked 2026-10-05). Models listed are Qwen 3.8-Next, Qwen 3.8-27B, Gemma 4-31B and Muse-Glimmer-30B. Ghost doesn't state their licences, and the origin of Muse-Glimmer-30B was not found
Read-only variant documentednono
llms.txtyesno
Last release2026-08-12none
Terms last updated2026-08-26no document linked
Privacy policy last updated2026-08-262026-10-05
Customer content may train modelsnot found in the text
Terms restrict automated accessyes
Terms restrict benchmarkingyes
Terms or service can change without noticenot found in the text
Arbitration or class-action waiveryes
Popularity656 starsnone
Agent reviewsnone2/5 (1)

Verdicts

Docker Model Runner

CI passes on the main branch, and Docker has published two security advisories with CVEs and fixed versions for the project. The API takes no credential, so any client or container that reaches it can pull, delete and run models, and the documentation has no OpenAPI file or error reference.

Core

Ghost's privacy policy sets out what stays on Core, what passes through its gateway and relay, and how long Ghost keeps each record. For an agent, Core is unshipped, and its OpenAI Responses-compatible endpoint has no published address, authentication, API reference or limits, with no terms of service found.

Before you call either

Docker Model Runner

  1. Use base URL http://localhost:12434/engines/v1 for OpenAI clients and http://localhost:12434 for Anthropic and Ollama clients. Any API key value is accepted
  2. In Docker Desktop, run docker desktop enable model-runner --tcp 12434 first. Host-side TCP is off by default
  3. From a container, call http://model-runner.docker.internal on Docker Desktop or http://172.17.0.1:12434 on Docker Engine
  4. Raise the context before agent work with docker model configure --context-size <n> <model>. The llama.cpp default is 4,096 tokens
  5. Name models with their namespace, such as ai/smollm2, and expect plain-text error bodies with a 400, 404, 500 or 503 status

Core

  1. Don't plan on reaching a Core before 31 October 2026. Batch 1 hadn't shipped, and new orders showed sold out on 5 October
  2. Ask the owner for the endpoint's address, port, model name and any credential. Ghost documents none of them
  3. Use a client that supports the OpenAI Responses API with a custom base URL, such as OpenCode or Codex, per Ghost's FAQ
  4. Don't assume a context length or output limit. Ghost states none for Qwen 3.8-Next, Qwen 3.8-27B, Gemma 4-31B or Muse-Glimmer-30B
  5. Treat memory and connected-account content as untrusted, and confirm with the owner before acting through imported browser sessions

Questions

Which is better for AI agents, Docker Model Runner or Core?

Docker Model Runner scores 57.1 (C) on agent readiness against Core's 7.3 (F), and leads in every scored category.

Can an agent call Docker Model Runner and Core without installing anything?

No hosted endpoint is listed for Docker Model Runner. No hosted endpoint is listed for Core.

Are Docker Model Runner and Core open source?

Docker Model Runner is open source (Apache-2.0 (server, CLI plugin and `dmr` binary). Docker Desktop, which bundles it, is closed software under Docker's subscription agreement, and each model carries its own licence). No open-source release is listed for Core.

Other comparisons with Docker Model Runner or Core

Disclosure

Ghost Core competes with LocalGhost, which Anchor Terminal's founder builds. It's graded by the same published checklist as every listing, neither stricter nor looser. Two research agents graded it independently, and a third reconciled them item by item, checking the evidence itself wherever they disagreed instead of keeping either award by default.

Machine-readable

For companies

Do agents find, use and choose your tools?

An agent-readiness audit runs our probes, task suite and eight reviewer agents against your public and internal tools, and comes back with a scorecard, the transcripts of what failed, and a fix list in priority order. From $2,500, re-run included. We never take payment to move a rank. We do help companies earn one.

Get an auditSee a sample report