AI models with tool calling: 166 that can call your functions

Tool calling

Services found: 166

#	AI model	API $/1M (in / out)	Context window	Price / mo	Output $/1M	Overview
81	Phi-4 Reasoning Microsoft		32.8K tokens	API only		View
82	Phi-4 Mini Reasoning Microsoft		128K tokens	API only		View
83	Qwen3.8 Max Alibaba	$2 / $6	1M tokens	API only	6	View
84	Qwen3 VL Plus Alibaba	$0.2 / $1.6	256K tokens	API only	1.6	View
85	Qwen3 VL Flash Alibaba	$0.05 / $0.4	256K tokens	API only	0.4	View
86	ERNIE 5.1 Baidu	$0.56 / $2.53		API only	2.53	View
87	ERNIE 5.0 Baidu	$1.4 / $5.6	119K tokens	API only	5.6	View
88	ERNIE 4.5 Turbo VL Baidu		128K tokens	API only		View
89	Doubao Seed Evolving ByteDance			API only		View
90	Doubao Seed 2.1 Turbo ByteDance			API only		View
91	Doubao Seed 2.0 Code ByteDance			API only		View
92	Doubao Seed 2.0 Lite ByteDance			API only		View
93	Doubao Seed 2.0 Mini ByteDance			API only		View
94	Doubao Seed Vision ByteDance			API only		View
95	GLM 5.2 Z.ai	$0.966 / $3.036	1.05M tokens	API only	3.036	View
96	Solar Pro 4 Upstage	$0.30 / $1.20	524.3K tokens	API only	1.2	View
97	GPT-5.6 Luna Pro OpenAI	$0.2 / $1.2	1.05M tokens	API only	1.2	View
98	Qwen3.7 Flash Qwen	$0.03 / $0.13	1M tokens	API only	0.13	View
99	Qwen3.8 Flash Qwen	$0.15 / $0.47	1M tokens	API only	0.47	View
100	Kimi K2.6 Moonshot AI	$0.95 / $4	262.1K tokens	API only	4	View

Browse by need

Reasoning models169 models

Coding assistants17 models

Vision and images156 models

Long context 1M+82 models

Free tier available80 models

Open weights88 models

Tool calling is what turns a chatbot into software. The model returns a structured call - function name plus typed arguments - and your code executes it, feeds back the result and lets the model continue. Every integration worth building rests on it.

All 166 models here support it, from Claude Fable 5.1 at $50 per million output tokens down to budget options at $0.25. What varies is reliability under pressure: how well the model picks the right tool from a long list, and how many calls it can chain before losing the thread. Frontier models are noticeably better at long chains.

Structured, not guessedThe model returns a typed call, so your code never parses prose to decide what to run.

Chains break at scaleReliability drops as the tool catalogue grows; frontier models hold longer chains together.

Pairs with JSON outputTool calling picks the action, structured output guarantees the shape of the final answer.

Models with this capability

The 166 models below all support this capability. Prices are input and output per 1M tokens from the vendor official price list; for dirhams multiply by 3.6725. Tap a card to open the model page.

Claude Fable 5.1

Anthropic

in $10out $501M tokens

GPT-6 Astra

OpenAI

in $10out $501.05M tokens

GPT-6.1 Sol

OpenAI

in $2out $101.05M tokens

GPT-6 Sol

OpenAI

in $2out $101.05M tokens

GPT-6 Luna

OpenAI

in $0.1out $0.51.05M tokens

Claude Opus 5.5

Anthropic

in $4out $201M tokens

Claude Sonnet 5.5

Anthropic

in $2out $101M tokens

Grok 4.7

xAI

in $2out $6500K tokens

How to choose

Keep tool descriptions short and unambiguous — most failures are description problems, not model problems.
Limit the catalogue per request; twenty tools is manageable, two hundred is not.
Handle partial failure explicitly: models recover better when the error message says what to do next.
Cache the tool definitions — they are the same on every call and caching cuts that input cost.

Common mistakes

One giant toolSplitting into several narrow tools raises accuracy far more than prompt tuning.

No timeout on chainsWithout an iteration cap an agentic loop can call tools until the budget is gone.

Who this suits

Integration teamsConnecting an assistant to internal APIs, CRMs and databases.

AutomationWorkflows where the model decides the next step rather than following a script.

Support productsLooking up orders, issuing refunds, checking availability inside the conversation.

Related filters

Agentic workflows5 models built for autonomous multi-step work

Structured JSON151 models with schema-guaranteed output

Code execution47 models that run code in a sandbox

Reasoning169 models that think before answering

All 239 modelsThe full rating with filters and prices

Frequently asked questions

What is the difference from structured output?

Tool calling decides which function to run and with what arguments. Structured output constrains the shape of the final answer. Most production systems use both.

Do cheap models call tools reliably?

For a handful of well-described tools, yes. Accuracy drops as the catalogue grows and as chains get longer, which is where frontier models pull ahead.

Can the model run the tool itself?

Not by default — your code executes the call. For models that run code in a vendor sandbox, see code execution.

Can the model call several tools at once?

Most current models support parallel calls, which cuts latency on independent lookups.

What happens if the model invents a tool?

A well-formed implementation rejects unknown names and returns an error the model can act on.

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AI models with tool calling: 166 that can call your functions

Browse by need

Models with this capability

How to choose

Common mistakes

Who this suits

Related filters

Frequently asked questions

What is the difference from structured output?

Do cheap models call tools reliably?

Can the model run the tool itself?

Can the model call several tools at once?

What happens if the model invents a tool?

Frequently searched