Function Calling at Scale: OpenAI vs Anthropic vs Bedrock

Function calling — LLMs invoking specific tools/functions based on user request — is substantial production capability in 2026. The three substantial providers (OpenAI, Anthropic, AWS Bedrock) have substantial diverged in subtle ways that matter substantially in production. Implementations that work substantially on one provider can fail substantially on another. This post walks through the substantial differences.

What function calling does#

The substantial pattern:

Tool definitions. Developer defines substantial functions with substantial input/output schemas.

LLM invocation. User input plus tool definitions sent to LLM.

Tool selection. LLM decides whether and which tool to call.

Tool arguments. LLM produces structured arguments for selected tool.

Tool execution. Application invokes the actual function with arguments.

Result return. Tool result sent back to LLM for substantial follow-up.

Final response. LLM produces substantial user-facing response incorporating tool results.

The substantial value: LLMs become substantially capable of orchestrating substantial real-world actions.

OpenAI function calling#

OpenAI introduced function calling in mid-2023; substantial maturity by 2026.

Substantial features:

• Substantial parallel tool calls. Multiple tools in one response.
• Substantial JSON mode — guarantees substantial JSON output.
• Substantial structured outputs — schema-validated outputs guaranteed.
• Substantial assistants API — substantial orchestration framework.

Substantial implementation:

• Tools defined as substantial JSON Schema with substantial descriptions.
• LLM responds with substantial tool_calls field containing substantial function name and substantial arguments.
• Application executes substantial functions, returns substantial results in substantial messages array.
• Conversation continues with substantial results.

Substantial considerations:

• Substantial token cost for substantial tool definitions in every call.
• Substantial cache discounts when prompt prefixes (including tools) are reused.
• Substantial occasional invalid arguments despite schema — substantial application-side validation matters.

Anthropic tool use#

Anthropic introduced substantial tool use in late 2023; substantial maturity by 2026.

Substantial features:

• Substantial parallel tool calls. Multiple tools in one response.
• Substantial prompt caching native — substantial cost reduction for substantial tool definitions.
• Substantial tool_choice parameter — substantial control over substantial tool selection.
• Substantial computer use capability — distinct from but related to tool use; Claude can substantially operate computer applications.

Substantial implementation:

• Tools defined as substantial JSON Schema with substantial descriptions.
• LLM responds with substantial tool_use content blocks.
• Application executes substantial functions, returns substantial tool_result content blocks.
• Conversation continues.

Substantial considerations:

• Substantial higher tool use reliability than substantial competitors in substantial benchmarks.
• Substantial prompt caching substantially reduces substantial cost for substantial tool-heavy applications.
• Substantial Constitutional AI training affects substantial tool use behavior — substantial different style than substantial OpenAI.

AWS Bedrock tool use#

Bedrock provides substantial tool use across substantial provided models (Anthropic Claude, Cohere, AI21, Meta Llama, plus the various).

Substantial features:

• Substantial provider abstraction — same API across substantial providers.
• Substantial Bedrock agents — substantial orchestration framework.
• Substantial Bedrock guardrails integration.
• Substantial knowledge bases integration.

Substantial implementation:

• Tools defined as substantial JSON Schema.
• Bedrock-specific API wraps substantial provider-specific API.
• Substantial agent framework adds substantial orchestration layer.

Substantial considerations:

• Substantial provider abstraction can substantially mask substantial provider-specific capabilities.
• Substantial Bedrock-specific features valuable when substantially using them; substantial lock-in.
• Substantial AWS-native integration for substantial AWS-anchored deployments.

The substantial differences that matter#

Several substantial differences:

Substantial tool selection accuracy. Substantial benchmarks show substantial differences in substantial tool selection quality across providers.

Substantial argument extraction accuracy. Substantial differences in substantial argument extraction quality.

Substantial parallel tool call patterns. Substantial providers handle substantial parallel calls substantially differently.

Substantial cost. Substantial token cost differences for substantial tool-heavy applications.

Substantial latency. Substantial differences in substantial latency, particularly for substantial parallel tool calls.

Substantial reliability under load. Substantial differences in substantial behavior under substantial concurrent load.

Substantial structured output guarantees. OpenAI’s substantial structured output guarantees; substantial Anthropic prompt-based; substantial Bedrock substantial provider-dependent.

The substantial agentic dimension#

Substantial 2024-2026 evolution: substantial focus on substantial agentic workloads.

Substantial multi-turn tool use. LLM substantially calling multiple tools across substantial turns to substantially accomplish substantial complex tasks.

Substantial tool result reasoning. LLM substantially reasoning about substantial tool results to substantially decide substantial next action.

Substantial planning and execution. LLM substantially decomposing substantial tasks into substantial subtasks, substantially executing substantial subtasks via tools.

Substantial error recovery. LLM substantially handling substantial tool failures, substantially retrying with substantial different approaches.

All three substantial providers substantially support substantial agentic patterns; substantial sophistication varies.

The decision framework#

For most teams in 2026:

Pick OpenAI for substantial parallel-call workloads and substantial structured output requirements where OpenAI’s substantial guarantees matter.

Pick Anthropic for substantial complex tool use where substantial reliability matters and substantial prompt caching produces substantial cost savings.

Pick Bedrock for substantial AWS-anchored deployments where substantial provider abstraction and substantial Bedrock features matter.

Pick combinations. Many substantial deployments use substantial multiple providers — different providers for different workloads. Substantial gateway tools (LiteLLM, plus the various) substantially help.

What we typically see at clients#

Common patterns:

Single-provider implementations. Substantial enterprises pick one provider, integrate substantial deeply.

Multi-provider with abstraction. Substantial larger enterprises use substantial abstraction layer to substantially avoid lock-in.

Provider-specific optimization. Substantial sophisticated deployments use substantial provider-specific features when they substantially matter.

Substantial migration projects as new providers improve — substantial common pattern in 2025-2026.

Where pdpspectra fits#

Our AI integration practice builds production AI systems with substantial function-calling capabilities and substantial multi-provider patterns.

Related reading: the Bedrock vs OpenAI vs Anthropic post, the LLM routing post, and the sub-100ms inference post.

---

Function calling implementations substantially differ. Talk to our team about your AI architecture.