Rankings of the best AI models for tool and function calling. Compare models by tool use accuracy and API integration capabilities.
The best AI for tool calling right now is Gemini 3.5 Flash by Google, followed by Claude Opus 4.8 — ranked by function-calling accuracy, schema adherence, and multi-tool orchestration benchmarks.
| Model | Best for | Top strength | Watch out | Cost · Context |
|---|---|---|---|---|
| Gemini 3.5 Flash Google | Newest Google generation — strong frontier challenger | Massive native context window | Newer release; provider coverage still expanding | $1.50 / $9.00 1.0M ctx |
| Claude Opus 4.8 Anthropic | Frontier reasoning + nuanced long-form prose | Long-form coherence — voice and structure stay consistent over thousands of tokens | The highest output price of any frontier model — not the default for cost-sensitive workflows | $5.00 / $25.00 1.0M ctx |
| Claude Mythos Preview Anthropic | Anthropic preview model — early-access benchmark only | Strong early signal on research + retrieval tasks | Preview-only; pricing and availability subject to change | — |
| Llama 3.1 405B Instruct Meta | Mature open-weight family with massive ecosystem | Best-supported open-weight family for tooling | Surpassed by Llama 3.3 and Llama 4 on quality | — |
| GPT-5.5 OpenAI | OpenAI's frontier — strongest all-around model on most benchmarks | Frontier scores across reasoning, math, coding, and research | Premium pricing — match the variant (Pro / Instant) to the task | $5.00 / $30.00 1.1M ctx |
| GPT-5.3 Codex OpenAI | Stable mid-tier OpenAI — broadest deployment footprint | Highly compatible across providers + libraries | Older than 5.4/5.5 — outscored on newer benchmarks | $1.75 / $14.00 400K ctx |
| Qwen3.7 Max Alibaba Cloud / Qwen Team | Alibaba's newest — strongest open-weight Asian frontier | Excellent multilingual coverage (50+ languages) | Western provider coverage lags | $1.25 / $3.75 1.0M ctx |
| Claude Opus 4.7 Anthropic | Frontier reasoning + nuanced long-form prose | Long-form coherence — voice and structure stay consistent over thousands of tokens | The highest output price of any frontier model — not the default for cost-sensitive workflows | $5.00 / $25.00 1.0M ctx |
Newest Google generation — strong frontier challenger
Frontier reasoning + nuanced long-form prose
Anthropic preview model — early-access benchmark only
Mature open-weight family with massive ecosystem
OpenAI's frontier — strongest all-around model on most benchmarks
Stable mid-tier OpenAI — broadest deployment footprint
Alibaba's newest — strongest open-weight Asian frontier
Frontier reasoning + nuanced long-form prose
Newest Google generation — strong frontier challenger
When to useCross-modal workflows; tasks where a 1M+ context actually helps.
Frontier reasoning + nuanced long-form prose
When to useWhen output quality matters more than cost or latency.
Anthropic preview model — early-access benchmark only
When to useEvaluation and benchmark comparison only — not for production.
Mature open-weight family with massive ecosystem
When to useExisting fine-tunes; education and research.
OpenAI's frontier — strongest all-around model on most benchmarks
When to useWhen you want the single highest-scoring model and budget isn't the constraint.
Stable mid-tier OpenAI — broadest deployment footprint
When to useExisting pipelines pinned to a specific version; codex-flavored coding work.
As of June 2026, Gemini 3.5 Flash leads tool calling benchmarks with a score of 43.2, followed by Claude Opus 4.8 (42.7) and Claude Mythos Preview (41.4). Rankings test function selection accuracy, parameter extraction, and multi-step tool chain orchestration from natural language instructions.
Ranked by 34 benchmarks including Berkeley Function Calling Leaderboard (BFCL) and real-world API integration tests, evaluating schema adherence, parameter accuracy, and error recovery.
Function calling lets AI models interact with external tools and APIs by outputting structured calls with correct parameters. For example, extracting a city name from 'What's the weather in Tokyo?' and calling a weather API with the right parameters. Top models score above 90% on schema adherence.
Models scoring highest on multi-step orchestration benchmarks, where one tool's output feeds another's input. The gap between models is largest on complex chains — most models handle single-function calls well, but only the top 3-5 reliably orchestrate multi-step workflows.
Yes. When given a function schema (describing available tools and their parameters), top models can select the right function, extract parameters from natural language, and output valid structured calls. This is the foundation for AI agents, chatbots with tools, and automated workflows.
An AI agent is a model that can plan and execute multi-step tasks by calling tools, reading results, and deciding what to do next. Tool calling accuracy is the core capability that determines agent reliability. The leaderboard above measures exactly this capability.
Most frontier models support function calling, but quality varies significantly. Models fine-tuned specifically for tool use outperform general-purpose models, especially on complex orchestration. Check the scores above — single-function accuracy and multi-step orchestration are different skills.
