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There is no single best Amazon Bedrock model for every AI agent. Choose by first defining what the agent must do, then rule out models that lack the required capabilities, API support, or deployment-Region availability. Finally, compare the remaining candidates on representative tasks, tool use, cost, and throughput. AWS identifies these as key model-selection considerations, and its model catalog and compatibility details can change; check the current documentation before implementation.
Start with the agent’s job
Write down the work the agent must complete before comparing model names. A support agent that answers from a knowledge base, for example, has different requirements from an agent that interprets images or calls several business tools. Your use case determines which capabilities matter and what a successful result looks like.
Turn the job into a small set of representative tasks and define acceptance criteria for each. Criteria might include whether the agent gives a correct answer, chooses the right tool, supplies the required arguments, and follows the expected workflow. AWS recommends evaluating models by comparing their outputs for a use case; this task set is a practical way to apply that guidance, not a claim that any model has been tested here. AWS: Using models with Bedrock
Filter candidates by capability and agent feature
Before comparing answer quality, check whether a candidate can meet the application’s hard requirements. AWS’s model-availability guidance identifies capabilities such as modality, context window, and tool use as selection dimensions. Check the exact model’s current details rather than inferring support from its family or from another Bedrock feature.
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- Input and output modalities: Confirm the model accepts the application’s inputs and produces the outputs it needs.
- Context window: Check that it can handle the amount of conversation history, retrieved material, and instructions your agent must provide.
- Tool use: Verify that it can participate in the required tool-calling pattern and that the specific Bedrock agent feature you plan to use accepts it.
Feature-specific support lists should be treated narrowly. For example, AWS’s multi-agent collaboration page lists supported models for that feature; it does not establish universal support—or universal lack of support—for other Bedrock agent patterns. AWS: Model availability & compatibility · AWS: Supported Regions, models, and Amazon Bedrock Agents features for multi-agent collaboration
Multi-agent collaboration is a specific case
For the multi-agent collaboration feature described on AWS’s page, the documented collaborator models include Anthropic Claude 3 Haiku, Claude 3 Opus, Claude 3 Sonnet, Claude 3.5 Haiku, Claude 3.5 Sonnet, Claude 3.5 Sonnet V2, Amazon Nova Pro, Nova Lite, and Nova Micro. The page excludes supervisor and collaborator agents customized with custom orchestration. Treat this as a feature-specific list, not a complete model list for every kind of Bedrock agent, and re-check the current page for your architecture.
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Confirm API, endpoint, and Region fit
A model that looks suitable on paper can still be the wrong choice if it is incompatible with the API or endpoint your application uses, or cannot run in the required AWS Region. Verify compatibility for the exact model and integration path. AWS recommends bedrock-runtime for new applications in its overview, while model compatibility remains model-specific. AWS: Overview – Amazon Bedrock · AWS: Model availability & compatibility
Check the current regional listing for the model and any relevant inference profile. If cross-Region inference is an option, assess it against the application’s latency and governance requirements rather than assuming it is interchangeable with running in a single Region. Availability and supported paths can change, so use the current AWS documentation for the intended deployment Region.
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Once capability and integration requirements have narrowed the list, run the same representative tasks through each candidate. Use consistent prompts, tool definitions, and success criteria so the comparison reflects the agent’s actual job. Review both the final answers and the steps that matter to the workflow, such as tool selection and whether the returned result is usable.
Then assess operational fit alongside task quality. AWS lists cost and throughput among model-selection considerations. Consider your expected input and output usage, request pattern, and service target, and consult current pricing and capacity options. A headline model price alone does not establish the total cost or whether the model can meet the workload’s throughput needs. AWS: Model availability & compatibility
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.A practical selection workflow
- Define success: Describe the agent’s tasks and the criteria each result must satisfy.
- Shortlist by capability: Remove models that do not support required modalities, context needs, tool use, or the exact Bedrock agent feature.
- Verify integration: Confirm model compatibility with the API and endpoint planned for the application.
- Check deployment constraints: Confirm current model and inference-profile availability for the required Region; consider latency and governance if evaluating cross-Region inference.
- Evaluate remaining candidates: Compare outputs on the same representative tasks, then assess current cost and throughput fit for the expected workload.
This sequence is a practical way to apply AWS’s evaluation and selection criteria, not an AWS-mandated procedure. Start with the candidates that meet the non-negotiable requirements, then let evidence from your own task set decide among them.
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