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AI model comparison

krea/Krea-2-Raw vs krea/Krea-2-Turbo

Compare pricing, context limits, supported inputs and deployment options to choose a model for your workflow.

Further training versus everyday image inference

Krea Raw is a base checkpoint intended for further training; the publisher does not recommend it for ordinary inference. Turbo is post-trained and distilled. This is a difference in purpose, rather than two equivalent presets for a speed contest.

Read the recorded specifications

The table below shows each profile's documented interface and recorded limits. Missing prices or measurements remain unknown. Open the source links on each profile to confirm the current checkpoint or API route.

Side-by-sideCapabilities and practical differences
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Specifications

Pricing, limits and capabilities

Specifications for krea/Krea-2-Raw and krea/Krea-2-Turbo. Higher limits and lower prices describe these figures only.
Specificationkrea/Krea-2-Rawkrea/Krea-2-Turbo
Providerkreakrea
Listed pricing routekreakrea
Model family——
Accepted inputstexttext
Produced outputsimageimage
Reasoning modeNot documentedNot documented
Tool callingNot documentedNot documented
Image inputNot documentedNot documented
Audio inputNot documentedNot documented
Structured outputNot documentedNot documented
LicenseKrea 2 Community LicenseKrea 2 Community License

Workload guidance

Which model should you choose?

A useful decision test

Use Turbo as the practical generation candidate. Use Raw when the project includes adaptation or post-training, then evaluate the resulting model on held-out prompts. Keep resolution, adapters and runtime settings with each result, and review the Krea 2 Community License for the intended use.

Inspect the first model profile and the second model profile. Keep your test inputs, runtime settings and acceptance criteria with the result so the comparison can be repeated.

Documentation and model profiles

Explore each model’s features, API requirements and licence terms in the sources below.

Questions people ask

It brings together documented inputs and outputs, token limits, deployment terms and available pricing. When both models have source-linked, verified results on a shared benchmark, those results are shown with their individual sources. The comparison is a starting point for evaluating your own workload.

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