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
61	Solar Pro 3 Upstage	$0.15 / $0.6	131.1K tokens	API only	0.6	View
62	Ministral 3 14B 2512 Mistral AI	$0.2 / $0.2	262.1K tokens	API only	0.2	View
63	Ministral 3 8B 2512 Mistral AI	$0.15 / $0.15	262.1K tokens	API only	0.15	View
64	Ministral 3 3B 2512 Mistral AI	$0.1 / $0.1	131.1K tokens	API only	0.1	View
65	GPT-5.4 OpenAI	$2.5 / $15	1.05M tokens	API only	15	View
66	GPT-5.3-Codex OpenAI	$1.75 / $14	400K tokens	API only	14	View
67	Gemini 3.1 Flash-Lite Google	$0.25 / $1.5	1.05M tokens	API only	1.5	View
68	Grok 4.20 Reasoning xAI	$1.25 / $2.5	1M tokens	API only	2.5	View
69	Grok 4.20 Non-Reasoning xAI	$1.25 / $2.5	1M tokens	API only	2.5	View
70	DeepSeek V4 Pro DeepSeek		1M tokens	API only		View
71	DeepSeek V4 Flash DeepSeek		1M tokens	API only		View
72	Command A+ Cohere	$17.50–$32.50 / hr¹	128K tokens	API only		View
73	Command A Reasoning Cohere			API only		View
74	Command A Vision Cohere		128K tokens	API only		View
75	Command A Translate Cohere			API only		View
76	Jamba2 Mini AI21 Labs		256K tokens	API only		View
77	Jamba2 3B AI21 Labs		256K tokens	API only		View
78	Nova Premier Amazon			API only		View
79	Nova Pro Amazon		300K tokens	API only		View
80	Phi-4 Mini Instruct Microsoft	$0.075 / $0.30	131.1K tokens	API only	0.3	View

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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