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

Prices from official vendor price lists · October 2026
166 models that call your functions and external APIs with structured arguments — the foundation of any integration. Output prices run from $0.059 to $180 per 1M tokens across the 142 models with a paid per-token price; 58 have a free tier and 76 ship open weights.
Tool calling
Services found: 166
#AI modelCapabilitiesAPI $/1M (in / out)Context windowPrice / moOutput $/1MOverviewVisit site
161

Hy4 preview

Tencent
Text & chatReasoningAdjustable thinking
$0.84 / $2.521M tokensAPI only2.52View
162

Fugu Max

Sakana
Text & chatReasoningAdjustable thinking
$2 / $6API only6View
163

Solar Mini 4

Upstage
Text & chatReasoningAdjustable thinking
$0.1 / $0.4512K tokensAPI only0.4View
164

Perceptron Mk1.5

Perceptron
Text & chatVisionAudio
$0.15 / $1.536.9K tokensAPI only1.5View
165

Ling 3.0 Flash VL

InclusionAI
Text & chatVisionVideo
$0.02 / $0.059256K tokensAPI only0.059View
166

Ternary Bonsai 2 27B

PrismML
Text & chatVisionReasoning
262.1K tokensAPI onlyView

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Reasoning models169 modelsCoding assistants17 modelsVision and images156 modelsLong context 1M+82 modelsFree tier available80 modelsOpen 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
#1
Anthropic
in $10out $501M tokens
GPT-6 Astra
#2
OpenAI
in $10out $501.05M tokens
GPT-6.1 Sol
#3
OpenAI
in $2out $101.05M tokens
GPT-6 Sol
#4
OpenAI
in $2out $101.05M tokens
GPT-6 Luna
#5
OpenAI
in $0.1out $0.51.05M tokens
Claude Opus 5.5
#6
Anthropic
in $4out $201M tokens
Claude Sonnet 5.5
#7
Anthropic
in $2out $101M tokens
Grok 4.7
#8
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.

Frequently searched

By capabilityReasoningCodingVisionLong context 1M+Free tierOpen weightsLow latencyRealtime voiceImage generation
By vendorAnthropic (13)OpenAI (30)Google DeepMind (17)xAI (8)Meta (6)DeepSeek (9)Mistral AI (11)Alibaba (Qwen) (34)Z.ai (12)Moonshot AI (5)
Popular modelsClaude Fable 5.1GPT-6 AstraClaude Opus 5Claude Fable 5GPT-5.6 SolGLM 5.3Grok 4.6Kimi K3GPT-5.6 Terra
By deliveryBatch APIPrompt cachingPriority tierFrontier tierProprietary APITool callingStructured JSONWeb searchMultilingual

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