What is function calling in AI?

Function calling lets AI models interact with external tools and APIs by outputting structured calls with correct parameters. For example, extracting a city name from 'What's the weather in Tokyo?' and calling a weather API with the right parameters. Top models score above 90% on schema adherence.

Which AI is best for building agents?

Models scoring highest on multi-step orchestration benchmarks, where one tool's output feeds another's input. The gap between models is largest on complex chains — most models handle single-function calls well, but only the top 3-5 reliably orchestrate multi-step workflows.

Can AI call APIs automatically?

Yes. When given a function schema (describing available tools and their parameters), top models can select the right function, extract parameters from natural language, and output valid structured calls. This is the foundation for AI agents, chatbots with tools, and automated workflows.

An AI agent is a model that can plan and execute multi-step tasks by calling tools, reading results, and deciding what to do next. Tool calling accuracy is the core capability that determines agent reliability. The leaderboard above measures exactly this capability.

Do all LLMs support function calling?

Most frontier models support function calling, but quality varies significantly. Models fine-tuned specifically for tool use outperform general-purpose models, especially on complex orchestration. Check the scores above — single-function accuracy and multi-step orchestration are different skills.

Best AI for Tool Calling in 2026

Rankings of the best AI models for tool and function calling. Compare models by tool use accuracy and API integration capabilities.

208 models52 benchmarks

LLM Stats ResearchUpdated October 8, 2026208 models reviewedMethodology

At a glance

Model	Best for	Top strength	Watch out	Cost · Context
Claude Sonnet 5.5 Anthropic	The everyday default — quality close to Opus at a fraction of the cost	~5× cheaper than Opus while staying competitive on most non-frontier tasks	Trails Opus on the hardest reasoning + agent benchmarks	$2.00 / $10.00 1.0M ctx
Claude Opus 5.5 Anthropic	Frontier reasoning + nuanced long-form prose	Long-form coherence — voice and structure stay consistent over thousands of tokens	The highest output price of any frontier model — not the default for cost-sensitive workflows	$4.00 / $20.00 1.0M ctx
DeepSeek-V4.1-Flash DeepSeek	Best open-weight quality-to-price in the market	Frontier-adjacent quality at ~10× cheaper than US frontier	Routing through PRC providers may be a data-residency concern	$0.22 / $0.66 1.0M ctx
Llama 3.1 405B Instruct Meta	Mature open-weight family with massive ecosystem	Best-supported open-weight family for tooling	Surpassed by Llama 3.3 and Llama 4 on quality	—
GLM-5.3 Zhipu AI	Zhipu AI's flagship — competitive Chinese open-weight	Strong multilingual	Smaller third-party ecosystem in Western markets	$1.20 / $4.00 1.0M ctx

Claude Sonnet 5.5$2.00 / $10.00
The everyday default — quality close to Opus at a fraction of the cost
Strength
~5× cheaper than Opus while staying competitive on most non-frontier tasks
Watch out
Trails Opus on the hardest reasoning + agent benchmarks
Claude Opus 5.5$4.00 / $20.00
Frontier reasoning + nuanced long-form prose
Strength
Long-form coherence — voice and structure stay consistent over thousands of tokens
Watch out
The highest output price of any frontier model — not the default for cost-sensitive workflows
DeepSeek-V4.1-Flash$0.22 / $0.66
Best open-weight quality-to-price in the market
Strength
Frontier-adjacent quality at ~10× cheaper than US frontier
Watch out
Routing through PRC providers may be a data-residency concern
Llama 3.1 405B Instruct—
Mature open-weight family with massive ecosystem
Strength
Best-supported open-weight family for tooling
Watch out
Surpassed by Llama 3.3 and Llama 4 on quality
GLM-5.3$1.20 / $4.00
Zhipu AI's flagship — competitive Chinese open-weight
Strength
Strong multilingual
Watch out
Smaller third-party ecosystem in Western markets

Capsule reviews of the top models

01
Anthropic
Claude Sonnet 5.5
The everyday default — quality close to Opus at a fraction of the cost
Strengths
- ~5× cheaper than Opus while staying competitive on most non-frontier tasks
- 200K context with consistent recall at depth
- Natural prose with few obvious AI tells
Watch-outs
- Trails Opus on the hardest reasoning + agent benchmarks
- No native multimodal image generation
When to useWhen you need Opus-class quality 80% of the time without paying Opus prices.
Input
$2.00/ M tokens
Output
$10.00/ M tokens
Context
1.0Mtokens
License
proprietary
See model page Compare side-by-side
02
Anthropic
Claude Opus 5.5
Frontier reasoning + nuanced long-form prose
Strengths
- Long-form coherence — voice and structure stay consistent over thousands of tokens
- Strong instruction following on tone, length, and format
- Reliable on multi-step tasks where errors compound (agents, refactors, synthesis)
Watch-outs
- The highest output price of any frontier model — not the default for cost-sensitive workflows
- Slower than mini/flash siblings; prefer Sonnet for interactive UX
When to useWhen output quality matters more than cost or latency.
Input
$4.00/ M tokens
Output
$20.00/ M tokens
Context
1.0Mtokens
License
proprietary
See model page Compare side-by-side
03
DeepSeek
DeepSeek-V4.1-Flash
Best open-weight quality-to-price in the market
Strengths
- Frontier-adjacent quality at ~10× cheaper than US frontier
- Open weights — can be self-hosted
- Strong coding and reasoning scores
Watch-outs
- Routing through PRC providers may be a data-residency concern
- Smaller third-party ecosystem than OpenAI
When to useCost-sensitive workloads at scale; on-prem requirements.
Input
$0.22/ M tokens
Output
$0.66/ M tokens
Context
1.0Mtokens
License
mit
See model page Compare side-by-side
04
Meta
Llama 3.1 405B Instruct
Mature open-weight family with massive ecosystem
Strengths
- Best-supported open-weight family for tooling
Watch-outs
- Surpassed by Llama 3.3 and Llama 4 on quality
When to useExisting fine-tunes; education and research.
See model page Compare side-by-side
05
Zhipu AI
GLM-5.3
Zhipu AI's flagship — competitive Chinese open-weight
Strengths
- Strong multilingual
- Open weights with permissive license on smaller variants
Watch-outs
- Smaller third-party ecosystem in Western markets
When to useBilingual workloads; open-weight experimentation.
Input
$1.20/ M tokens
Output
$4.00/ M tokens
Context
1.0Mtokens
License
glm_5_3
See model page Compare side-by-side

As of October 2026, GPT-6 Astra leads tool calling benchmarks with a score of 35.2, followed by Claude Sonnet 5.5 (34.3) and Claude Opus 5.5 (32.8). Rankings test function selection accuracy, parameter extraction, and multi-step tool chain orchestration from natural language instructions.

Ranked by 52 benchmarks including Berkeley Function Calling Leaderboard (BFCL) and real-world API integration tests, evaluating schema adherence, parameter accuracy, and error recovery.

Function calling lets AI models interact with external tools and APIs by outputting structured calls with correct parameters. For example, extracting a city name from 'What's the weather in Tokyo?' and calling a weather API with the right parameters. Top models score above 90% on schema adherence.
Models scoring highest on multi-step orchestration benchmarks, where one tool's output feeds another's input. The gap between models is largest on complex chains — most models handle single-function calls well, but only the top 3-5 reliably orchestrate multi-step workflows.
Yes. When given a function schema (describing available tools and their parameters), top models can select the right function, extract parameters from natural language, and output valid structured calls. This is the foundation for AI agents, chatbots with tools, and automated workflows.
An AI agent is a model that can plan and execute multi-step tasks by calling tools, reading results, and deciding what to do next. Tool calling accuracy is the core capability that determines agent reliability. The leaderboard above measures exactly this capability.
Most frontier models support function calling, but quality varies significantly. Models fine-tuned specifically for tool use outperform general-purpose models, especially on complex orchestration. Check the scores above — single-function accuracy and multi-step orchestration are different skills.

Coding Computer Use Reasoning Developer Tools