AI model comparison

Fara 1.5 27B vs Fara 1.5 4B

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

Fara 1.5 4B and 27B use the same screenshot-based browser-agent approach and companion harness. The main deployment tradeoff is the larger checkpoint’s serving footprint against any improvement in completed browser tasks.

Side-by-sideCapabilities and practical differences

Microsoft Research

Fara 1.5 27B

Fara 1.5 27B is a screenshot-based browser agent checkpoint that generates coordinate-grounded actions through Microsoft’s Fara harness.

Context
262.14K

Accepts

  • Text input
  • Image input

Produces

  • Text output
VS

Microsoft Research

Fara 1.5 4B

Fara 1.5 4B is a screenshot-based browser agent checkpoint that generates coordinate-grounded actions through Microsoft’s Fara harness.

Context
262.14K

Accepts

  • Text input
  • Image input

Produces

  • Text output

At a glance

Key differences

Compare the available figures below, then try both models on the work you need to do.

Context capacity

Higher listed limit

Fara 1.5 27B:262.14K

Fara 1.5 4B:262.14K

Same value

Specifications

Pricing, limits and capabilities

Specifications for Fara 1.5 27B and Fara 1.5 4B. Higher limits and lower prices describe these figures only.
SpecificationFara 1.5 27BFara 1.5 4B
ProviderMicrosoft ResearchMicrosoft Research
Listed pricing routeMicrosoft ResearchMicrosoft Research
Model family——
Accepted inputstext, imagetext, image
Produced outputstexttext
Context window262.14K262.14K
Reasoning modeDocumentedDocumented
Tool callingDocumentedDocumented
Image inputDocumentedDocumented
Audio inputNot documentedNot documented
Structured outputNot documentedNot documented
LicenseMITMIT

Workload guidance

Which model should you choose?

Run the same form draft and multi-step lookup in the recommended harness. Choose 4B if it completes the workload reliably within your memory budget. Move to 27B when measured reductions in corrections or failed tasks justify additional serving cost. Preserve the same system prompt and critical-action controls.

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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