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
141

GPT-5.1-Codex-Mini

OpenAI
Text & chatVisionCoding
$0.25 / $2400K tokensAPI only2View
142

Granite 4.2 8B

IBM
Text & chatReasoningTool calling
$0.06 / $0.25131.1K tokensAPI only0.25View
143

Nemotron 3.5 Content Safety

NVIDIA
Text & chatVisionReasoning
$0.2 / $0.2131.1K tokensAPI only0.2View
144

Seed 2.1 Turbo

Bytedance Seed
Text & chatVisionVideo
$0.5 / $2.5262.1K tokensAPI only2.5View
145

GPT-5.1-Codex

OpenAI
Text & chatVisionCoding
$1.25 / $10400K tokensAPI only10View
146

MiniMax M2.1

MiniMax
Text & chatReasoningTool calling
$0.3 / $1.2204.8K tokensAPI only1.2View
147

MiniMax M2-her

MiniMax
Text & chatReasoningTool calling
$0.3 / $1.265.5K tokensAPI only1.2View
148

MiniMax M2

MiniMax
Text & chatReasoningTool calling
$0.255 / $1.02204.8K tokensAPI only1.02View
149

GPT-5.5 Pro

OpenAI
Text & chatVisionReasoning
$30 / $1801.05M tokensAPI only180View
150

Seed 1.6

Bytedance Seed
Text & chatVisionVideo
$0.25 / $2262.1K tokensAPI only2View
151

Sonar Pro Search

Perplexity
Text & chatVisionReasoning
$3 / $15200K tokensAPI only15View
152

GPT-5.1-Codex-Max

OpenAI
Text & chatVisionCoding
$1.25 / $10400K tokensAPI only10View
153

Qwen3 Max Thinking

Qwen
Text & chatReasoningAdjustable thinking
$0.78 / $3.9262.1K tokensAPI only3.9View
154

Seed-2.0-Code

Bytedance Seed
Text & chatVisionVideo
$0.5 / $3262.1K tokensAPI only3View
155

GPT-5.4 Pro

OpenAI
Text & chatVisionReasoning
$30 / $1801.05M tokensAPI only180View
156

Mistral Large 3

Mistral AI
Text & chatVisionReasoning
$0.5 / $1.5256K tokensAPI only1.5View
157

Qwen3.8 Omni Flash

Qwen
Text & chatVisionAudio
1M tokensAPI onlyView
158

MiMo-V2.6-Pro

Xiaomi
Text & chatVisionAudio
$0.435 / $0.871M tokensAPI only0.87View
159

MiMo-V2.6-Flash

Xiaomi
Text & chatVisionAudio
$0.14 / $0.281M tokensAPI only0.28View
160

MiMo-V2.6-Pro-UltraSpeed

Xiaomi
Text & chatVisionAudio
$4.35 / $8.71M tokensAPI only8.7View

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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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