Best For
Best LLM for Tool Calling & Function Calling in 2026
Tool calling (also called function calling) is how LLMs interact with the real world — calling APIs, running code, reading files, searching the web. The best models at this aren't always the best chatbots. Reliability, schema adherence, and multi-step planning all matter more than fluency.
Updated February 2026
What actually matters for tool calling
Before we get to the pick — the criteria that separate good from bad here:
Reliability — Does it call the right tool with the right parameters every time, or does it occasionally hallucinate function names or pass wrong argument types? Even a 2% failure rate breaks production pipelines.
JSON accuracy — Tool calls require precisely structured output. A missing bracket or wrong key name breaks everything downstream. Models that produce clean, spec-compliant JSON are not optional — they're the baseline requirement.
Multi-step planning — The hard part isn't calling one tool — it's chaining calls together to accomplish a goal. Can the model decide which tool to call next based on what previous calls returned?
Error recovery — What happens when a tool returns an error or unexpected output? A well-calibrated model retries intelligently or escalates gracefully. A poorly calibrated one loops, hallucinates success, or gives up entirely.
Our pick
Gemini 3.1 Pro is the top model for agentic and tool-calling workloads in 2026. It leads APEX-Agents (33.5%), BrowseComp (85.9%), and MCP Atlas (69.2%) — the three benchmarks most directly measuring tool-use ability. Google provides a dedicated custom-tools API endpoint (gemini-3.1-pro-preview-customtools) purpose-built for pipelines calling bash, file operations, or search tools. At $2/$12 per 1M tokens, it's the best tool-calling model at a non-Opus price.
Pricing: API at $2/$12 per 1M tokens via Google AI Studio. Dedicated custom-tools endpoint available. Free developer tier (rate-limited).
Also consider
Claude Opus 4.6 is the most reliable model for long-horizon agentic tasks where the tool plan must stay coherent across many steps. It excels at not losing the thread in complex multi-tool workflows and is better than any other model at knowing when to ask for clarification rather than making a wrong tool call. APEX-Agents: 29.8%. Best choice for high-stakes agentic pipelines where correctness matters more than cost.
API at $5/$25 per 1M tokens. Claude Max plan ($100/month) for consumer access.
Full review →GPT-5.2 has the most mature tool-calling ecosystem — the OpenAI function calling spec is what most developer tooling is built around. It's reliable, well-documented, and almost every agentic framework (LangChain, LlamaIndex, CrewAI) was built to work with GPT first. Not the leader on benchmarks, but the default choice for teams building on existing tooling.
API at $1.75/$14 per 1M tokens. Free tier at chatgpt.com for testing.
Full review →Claude Sonnet 4.6 hits a strong balance of agentic reliability and cost for tool-calling pipelines. Its 200K context window keeps large tool call histories in memory. Strong on structured output — tends to follow JSON schemas more consistently than GPT models at the same tier. Good default for teams on a budget who need Claude's reliability.
API at $3/$15 per 1M tokens. Free tier at claude.ai for testing.
Full review →Bottom line
For new agentic pipelines where you're choosing a model from scratch: Gemini 3.1 Pro (best benchmarks, dedicated tools endpoint, best price). For teams already on OpenAI tooling: GPT-5.2 (easiest integration, most docs). For the most reliable multi-step agentic work where correctness matters above cost: Claude Opus 4.6. For budget pipelines with structured tool calls: GPT-5 mini or Claude Sonnet 4.6.