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Docker Model Runner (DMR) documents OpenAI-compatible, Anthropic-compatible, and Ollama-compatible API formats. To check whether your application works with each, keep the model and prompt intent fixed, call each format through its own endpoint, and assert the response contract your application actually uses. This is a compatibility test—not proof that the APIs have identical behavior or produce identical text.
Define what your application must get right
Before sending requests, write down the behavior under test. For a basic non-streaming chat call, useful assertions include an HTTP success status, a parseable response in the expected API format, and non-empty assistant output. Add any fields or constraints your application depends on, such as a tool-call structure or a particular error-handling path.
Do not compare generated prose for exact equality across the three interfaces. Docker documents API-format compatibility, not identical semantics or output text; sampling and runtime details can also affect generated responses. Compare contract behavior and application requirements instead.
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Keep the model and runtime variables fixed
Use the same DMR model identifier in each request. Docker’s API reference includes namespaced identifiers such as ai/smollm2 and tagged identifiers such as ai/smollm2:360M-Q4_K_M. Record the exact identifier and tag rather than relying on a vague model name.
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For results that another developer can interpret or reproduce, capture the Docker Desktop or Docker Engine version, host operating system, inference engine, model identifier and tag, context configuration, sampling settings, and hardware backend. The runtime is part of the test conditions: Docker documents llama.cpp as the default engine, while vLLM and Diffusers have narrower platform and GPU support. See Docker’s Model Runner overview and requirements and setup documentation for the applicable environment details.
Send each request using its own API contract
DMR’s API reference documents distinct endpoints, base URLs, and request formats. Preserve those differences in your tests instead of pretending the three APIs share one schema. Docker’s documented host TCP setup requires TCP access to be enabled where applicable.
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| API format | Documented base URL | Chat endpoint | Request contract to exercise |
|---|---|---|---|
| OpenAI-compatible | http://localhost:12434/engines/v1 |
/chat/completions relative to that base URL (full route: /engines/v1/chat/completions) |
Use the chat-completions schema and only the parameters your application needs, such as model, messages, and max_tokens. |
| Anthropic-compatible | http://localhost:12434 |
/v1/messages |
Exercise the Messages schema with fields such as model, messages, and max_tokens; include system prompts or other options only if your app uses them. |
| Ollama-compatible | http://localhost:12434 for the documented host TCP setup |
/api/chat |
Use the Ollama chat payload. Use /api/generate instead if the application relies on prompt completion rather than chat. |
The documented OpenAI route is therefore http://localhost:12434/engines/v1/chat/completions. Docker’s DMR REST API reference provides the format-specific request and response details. The exact fields you assert should follow the contract your application consumes.
Build a repeatable contract test
- Choose one app behavior. For example, test a non-streaming chat request with a user message and a maximum-token bound. Define expected status, response fields, and output constraints before calling the APIs.
- Fix shared inputs. Use the same model identifier and keep the prompt’s intent and relevant sampling settings stable. Record the runtime configuration with the test output.
- Make one request per API format. Use each format’s documented base URL, endpoint, payload schema, and client configuration. Do not pass a field merely because another API supports it.
- Assert both format and application behavior. Check the HTTP status, parse the response, validate the expected format-specific fields, and confirm any app-level requirements such as usable assistant output.
- Separate optional capabilities. If the application streams, calls tools, or depends on structured output, test that behavior explicitly for each API rather than treating a successful basic chat as proof of compatibility.
- Save the conditions with the result. Record the operating system, Docker version, engine, model identifier and tag, and relevant model and hardware configuration so a later run can be compared meaningfully.
Docker says Testcontainers for Java and Go, as well as Docker Compose, support Model Runner. They can help provision repeatable environments, but Docker’s documentation does not describe a first-party contract-testing suite. Treat the assertions above as a test design you implement for your application, not a built-in DMR test feature.
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Test the differences that can break an integration
Parameters and response behavior
Docker lists OpenAI-compatible chat parameters including model, messages, max_tokens, temperature, top_p, streaming, stop, and penalty parameters. Anthropic Messages and Ollama chat have their own schemas. Test only the options your app sends, and validate the response format expected by the relevant client.
Streaming
If the application consumes streamed output, test event framing, incremental parsing, and how the stream signals completion separately for each interface. Docker provides streaming examples, but the formats should not be assumed interchangeable. A passing non-streaming test does not establish streaming compatibility.
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Tool and function calling
Docker documents function calling support for the OpenAI-compatible implementation with llama.cpp and compatible models. If tool calling is a requirement, pin those conditions and assert the function-call behavior your application needs. Do not infer support for every model or engine from a successful plain chat request.
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Docker states that the OpenAI-compatible implementation ignores the Authorization header, and its overview says the Model Runner API is not authenticated. An Authorization header should not be treated as protection for a DMR endpoint. Keep the API off untrusted networks during testing and account for this unauthenticated access in the test environment.
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Token counting
DMR uses the model’s native token encoder for token counting, which may differ from OpenAI’s. Do not assert that counts match another provider or use cross-provider token parity as evidence that the application contract works.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Interpret a pass without overclaiming
A passing test means the tested application behavior worked under the recorded model, API format, and runtime conditions. It does not establish identical output, equivalent support for every parameter, or compatibility for untested features. When comparing results, separate what Docker documents—routes, schemas, and stated limitations—from behaviors your own assertions actually exercised.
Docker’s documented minimum Docker Desktop versions are Windows 4.41+ and macOS 4.40+; requirements and backend support depend on platform and engine. Check the current Docker Model Runner requirements before setting up a new test environment, and include the installed version in any reported result.
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