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

Tool calling
Services found: 166
#AI modelCapabilitiesAPI $/1M (in / out)Context windowPrice / moOutput $/1MOverviewVisit site
101

Claude Opus 4.6

Anthropic
Text & chatVisionReasoning
$5 / $251M tokensAPI only25View
102

GPT-5.6 Sol Pro

OpenAI
Text & chatVisionReasoning
$2 / $101.05M tokensAPI only10View
103

GPT-6 Astra Pro

OpenAI
Text & chatVisionReasoning
$10 / $501.05M tokensAPI only50View
104

Claude Opus 4.7

Anthropic
Text & chatVisionReasoning
$5 / $251M tokensAPI only25View
105

Qwen3.5-Flash

Qwen
Text & chatVisionVideo
$0.065 / $0.261M tokensAPI only0.26View
106

Kimi K2.5

Moonshot AI
Text & chatVisionReasoning
$0.45 / $2.25262.1K tokensAPI only2.25View
107

Qwen3.6 35B A3B

Qwen
Text & chatVisionVideo
$0.1 / $0.9262.1K tokensAPI only0.9View
108

GPT-5.2

OpenAI
Text & chatVisionReasoning
$1.75 / $14400K tokensAPI only14View
109

Qwen3.5-35B-A3B

Qwen
Text & chatVisionVideo
$0.3125 / $1.25262.1K tokensAPI only1.25View
110

GLM 5

Z.ai
Text & chatReasoningTool calling
$0.6 / $1.92204.8K tokensAPI only1.92View
111

Nemotron 3 Nano Omni

NVIDIA
Text & chatVisionAudio
$0 / $0256K tokensAPI only0View
112

Qwen3.5-9B

Qwen
Text & chatVisionVideo
$0.1 / $0.15262.1K tokensAPI only0.15View
113

Grok 4.20

xAI
Text & chatVisionReasoning
$1.25 / $2.52M tokensAPI only2.5View
114

Qwen3 VL 32B Instruct

Qwen
Text & chatVisionReasoning
$0.104 / $0.416131.1K tokensAPI only0.416View
115

MiniMax M2.5

MiniMax
Text & chatReasoningTool calling
$0.27 / $1.08204.8K tokensAPI only1.08View
116

Qwen3 VL 235B A22B Instruct

Qwen
Text & chatVisionReasoning
$0.21 / $1.9262.1K tokensAPI only1.9View
117

GPT-5.6 Terra Pro

OpenAI
Text & chatVisionReasoning
$2 / $121.05M tokensAPI only12View
118

GPT-5.1

OpenAI
Text & chatVisionReasoning
$1.25 / $10400K tokensAPI only10View
119

Qwen3.6 Plus

Qwen
Text & chatVisionVideo
$0.325 / $1.951M tokensAPI only1.95View
120

Claude Opus 4.5

Anthropic
Text & chatVisionReasoning
$5 / $25200K tokensAPI only25View

Browse by need

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

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.

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.

Frequently searched

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