Best AI Models with Native Tool Use in 2026

Tool use is where frontier models split decisively. Two models with identical reasoning scores can differ by 30+ percentage points when you put them in a multi-turn agentic loop with real APIs, policy constraints, and sequential dependencies. The marketing copy says every top model now supports "function calling" - but the actual capability spread is wide.

TL;DR

Claude Sonnet 4.6 leads TAU-bench at 87.5% - the most reliable choice for multi-turn agentic tool use in production
GPT-5.4 supports 128 parallel tool calls with strict structured outputs, but structured outputs and parallel calls cannot be combined simultaneously
GLM-4.5 leads BFCL v3 at 76.7% for single-call precision; Qwen3.5-397B-A17B leads BFCL v4 at 72.9%
DeepSeek V4 supports 128 parallel tools as an open-weight model - the best production option if you need self-hostable agentic infrastructure
For local deployment, Qwen3.5 4B hits 97.5% pass rate on structured tool calling at just 3.4 GB

The split between BFCL and TAU-bench matters more than people realize. BFCL v3 measures JSON schema accuracy on individual function calls - it tells you whether the model gets the arguments right. TAU-bench measures whether the model can use tools correctly across a multi-turn conversation while following complex policies. A model can lead on BFCL and fall apart on TAU-bench. For production agents, TAU-bench scores matter more.

See the function calling benchmarks leaderboard for the full ranking table updated monthly.

Claude Sonnet 4.6 - Best for Multi-Turn Agentic Use

Claude Sonnet 4.6 leads the TAU-bench multi-turn evaluation at 87.5%, second only to the experimental Claude Mythos Preview at 89.2%. In the TAU-bench retail and airline domains - which simulate policy-constrained, multi-step customer service scenarios with real tool dependencies - Claude models occupy four of the top five positions.

The architecture decision that drives this is MCP-native integration. Anthropic's Tool Search feature reduces token overhead from large tool libraries dramatically. A five-server MCP setup normally consumes ~55K tokens in tool definitions before the conversation starts. With defer_loading: true, Claude loads only the tools it selects on demand, dropping that to roughly 14K tokens while maintaining access to the full library. On evaluations with large tool libraries, this approach improved task completion from 79.5% to 88.1%.

Claude Sonnet 4.6 supports up to 128 parallel tool calls. The practical constraint is context - a 1M-token window handles large tool result sets, but each tool call adds tokens on the return pass.

The tool choice behavior is granular: auto (model decides), any (model must call at least one tool), or specific tool name to force a particular call. This level of control matters when building agents where tool selection errors cause downstream failures. The extended thinking mode can be combined with tool use, which gives the model time to reason through which tools to invoke and in what order before committing.

Pricing: $3.00/$15.00 per 1M input/output tokens. Context window: 1M tokens.

TAU-bench: 87.5% (retail + airline combined)

GPT-5.4 - Best for Structured Output Integration

GPT-5.4 runs parallel tool calls by default - parallel_tool_calls is true unless you set it otherwise. The implementation supports up to 128 tools per request and handles the tool call lifecycle through a tool_calls array on the message object, with each call carrying a unique tool_call_id for result matching.

The structured output mode deserves attention. When you enable strict mode, GPT-5.4 guarantees that its tool call arguments will exactly match the JSON schema you define - not as a best effort, but as a hard constraint. This is different from how most models handle schema compliance, where hallucinated field names or wrong types are common failure modes.

Multiple computer screens showing code in a dark developer workspace Function calling accuracy depends on both argument precision (what BFCL measures) and multi-turn policy adherence (what TAU-bench measures) - two capabilities that don't always move together. Source: unsplash.com

The one constraint worth knowing: structured outputs and parallel tool calls can't be used simultaneously. If you need guaranteed schema compliance on tool arguments, set parallel_tool_calls to false. This is a real tradeoff in agent architectures that need both high-volume parallel tool dispatch and type-safe return values.

GPT-5.4 scores 78.3% on TAU-bench, which puts it behind Claude Sonnet 4.6 but competitive with the broader field. On BFCL single-call tests, it performs well on JSON precision but doesn't lead the field.

Pricing: $2.50/$10.00 per 1M input/output tokens. Context window: 400K tokens.

Gemini 2.5 Pro - Best for Combined Tool Strategies

Google's March 2026 tooling update changed the function calling calculus for Gemini. You can now combine built-in tools - Google Search, Google Maps grounding - with custom function definitions in a single API call. Context circulation preserves each tool call and its result in the model's context, so follow-up steps can reason over previous tool outputs without you manually threading state.

The practical example from Google's release: Gemini queries real-time weather via a built-in Search call, then passes that data to a custom venue-booking function. The model handles the sequencing. Previously, this required manual orchestration between tool calls.

Gemini 2.5 Pro uses the largest context window of any major API model at 2M tokens, which creates room for tool-heavy agents that build up large amounts of returned data across many steps. The Live API also supports tool use for real-time streaming scenarios.

The API format differs from OpenAI and Anthropic: function calls appear as Part objects with a function_call attribute, and tool choice is controlled via a mode parameter ("ANY", "NONE", or auto). Teams migrating between providers need to handle this translation, though most orchestration frameworks abstract it.

Pricing: $1.25/$10.00 per 1M input/output tokens. Context window: 2M tokens.

DeepSeek V4 - Best Open-Weight Option

DeepSeek V4 matches Claude Opus 4.6 on MCPAtlas Public (73.6 vs 73.8) while being fully open-weight. For teams that need self-hosted agentic infrastructure without sending data to a third-party API, this is the option worth assessing.

The function calling implementation supports up to 128 parallel function calls with JSON mode for structured output and fill-in-the-middle code completion with tool use. Five of the six current DeepSeek models support function calling, and the V4 series shares a consistent feature set: 1M token context, 384K maximum output, and parallel tool execution.

The TAU-bench score for DeepSeek V4 sits in the mid-70s range, below the Claude cluster but competitive with the broader field. Where it wins is the combination of open weights and competitive agentic performance - there's no other open-weight model with comparable MCPAtlas scores and 128-parallel-tool support as of May 2026.

Pricing (API): $0.28/$0.42 per 1M input/output tokens for DeepSeek V3.2 (the current hosted variant). Self-hosting costs are hardware-dependent.

GLM-4.5 - Best Single-Call Precision

GLM-5 from Z.AI leads BFCL v3 at 76.7%, with GLM-4.5 holding the top position on single-call function accuracy. On TAU-bench, GLM-5 (Reasoning) reaches 83.4% and GLM-5 at 82.1% - placing it solidly in the upper tier for multi-turn scenarios.

GLM-4.5 and GLM-4.7-Flash are the most useful for teams that want strong tool calling without the full inference cost of the largest frontier models. GLM-4.7-Flash also performs well in the local LLM tool-calling evaluations, hitting 95% pass rate in structured tool scenarios.

The BFCL v3 lead on single-call precision is genuine - GLM handles parameter extraction accurately across parallel and nested function call patterns. The limitation relative to Claude is the multi-turn policy adherence drop-off in complex agentic scenarios.

TAU-bench: GLM-5 Reasoning at 83.4%; GLM-5 at 82.1%

BFCL v3: GLM-4.5 at 76.7% (top ranked)

Qwen3.5 - Best for Local Deployment

Laptop computer showing code on screen in an office environment Local LLM tool calling tests reveal a clear divide: Qwen and GLM models dominate the structured output category, while reasoning-specialized models like DeepSeek-R1-Distill struggle with the format discipline required for function calling. Source: unsplash.com

For self-hosted and local inference scenarios, the Qwen3.5 4B is the current benchmark leader at 97.5% pass rate in the 13-model local LLM evaluation - at just 3.4 GB. The Qwen3.5 35B-A3B adds context length and multi-step reasoning for more complex agent chains.

The evaluation covered tool selection accuracy, argument precision, parallel tool call handling, edge cases, and format compliance. Qwen3.5 4B scored 8/8 on selection and arguments, with only one failure in multi-tool scenarios across 40 test cases.

One caveat: Qwen models that excel at parallel tool calling occasionally over-parallelize sequential workflows. The architecture is trained to dispatch tools in parallel, which becomes a failure mode when tasks have strict sequential dependencies. For workflows that require waiting for one tool result before invoking the next, Nemotron Nano 4B (95% pass rate at 4.2 GB) handles sequencing more conservatively.

On BFCL v4, the larger Qwen3.5-397B-A17B leads the leaderboard at 72.9%, with Qwen3.5-122B-A10B at 72.2% - showing that the architecture scales well on function calling accuracy across model sizes.

Benchmark and Pricing Comparison

Model	BFCL v3	TAU-bench	Max Tools	Context	Input $/1M	Output $/1M
Claude Sonnet 4.6	-	87.5%	128	1M	$3.00	$15.00
Claude Opus 4.6	-	84.8%	128	1M	$5.00	$25.00
GLM-5 Reasoning	-	83.4%	-	-	-	-
GPT-5.4	-	78.3%	128	400K	$2.50	$10.00
DeepSeek V4	-	~73-75%	128	1M	$0.28	$0.42
GLM-4.5	76.7%	79.7%	-	-	-	-
Qwen3.5-397B	72.9% (BFCL v4)	77.5%	-	-	API varies	-
Gemini 2.5 Pro	-	-	128	2M	$1.25	$10.00

BFCL v3 scores reflect single-call function accuracy. TAU-bench scores reflect multi-turn agentic task completion. Dashes suggest not publicly benchmarked on that evaluation.

Which Model Fits Which Use Case

Production agents with complex multi-turn tool chains: Claude Sonnet 4.6 is the current leader by TAU-bench margin. The MCP-native integration, Tool Search feature, and 87.5% multi-turn score make it the most reliable option for agents that need to follow policies across many steps. The $3.00/$15.00 per million pricing is mid-range for what you get.

Agents requiring strict JSON schema compliance: GPT-5.4 with structured outputs in strict mode gives a hard guarantee on argument format. This matters when downstream systems have zero tolerance for type mismatches or unexpected field names. Disable parallel calls when using strict mode.

High-volume tool-heavy agents on a budget: Gemini 2.5 Pro at $1.25/$10.00 per million with a 2M context window handles large tool result accumulation at lower cost than Claude or GPT-5.4. The built-in tool combination (Search + Maps + custom functions) reduces external API dependencies.

Self-hosted agentic infrastructure: DeepSeek V4 is the only open-weight model with 128-parallel-tool support and MCPAtlas scores competitive with frontier models. If your data can't leave your infrastructure, this is the path.

Local deployment on consumer hardware: Qwen3.5 4B at 3.4 GB is the current accuracy leader for local tool calling. For workflows with strict sequential tool dependencies, substitute Nemotron Nano 4B, which handles wait-for-result patterns more conservatively.

For teams building AI agent frameworks, the model choice for tool use is often the highest-leverage infrastructure decision. A 10-point gap in TAU-bench translates directly to reduced error recovery overhead in production agent loops. The Claude cluster's lead on multi-turn accuracy is reproducible enough to treat as a real capability difference rather than benchmark variance.