GPT-5.4 vs Grok 4 for Tool Calling

Tool Calling requires: correct function selection, precise argument formatting, correct sequencing of multi-step calls, and predictable, schema-compliant outputs (our 'tool calling' benchmark: function selection, argument accuracy, sequencing). Key LLM capabilities for this task are structured output (JSON/schema adherence), agentic planning (decomposing goals and sequencing calls), safety calibration (avoiding unsafe or unauthorized tool usage), long context (holding large state across many calls), and observability features (logprobs/top_logprobs) for debugging. In our testing both GPT-5.4 and Grok 4 score 4/5 on the tool calling test, so the raw task score is a tie. We then look to supporting proxy metrics: GPT-5.4 scores higher on structured output (5 vs 4), agentic planning (5 vs 3), and safety calibration (5 vs 2), while Grok 4 provides practical tool-calling engineering features (description notes parallel tool calling and its API exposes logprob/top_logprob parameters). Those differences explain why GPT-5.4 is better for complex, sequenced, or safety-sensitive tool workflows, while Grok 4 can shine for parallel or highly instrumented tool pipelines.

Where GPT-5.4 shines (based on our scores and specs):

• Long multi-step orchestration: sequencing 50+ steps that need full-session context (GPT-5.4 context_window 1,050,000; long context 5/5).
• Safety-sensitive tool calls: systems that must refuse unsafe requests or validate permissions (safety calibration 5 vs Grok 4's 2 in our tests).
• Strict schema adherence for downstream execution: APIs that require exact JSON arguments (structured output 5 vs 4).
• Cost-sensitive heavy prompting: lower input cost (2.5 vs 3 per mTok) reduces bill for large prompt contexts. Where Grok 4 shines (based on our data):
• Parallel tool invocations: Grok 4's description explicitly supports parallel tool calling, useful when you need concurrent API calls.
• Debuggable tool-choice tuning: Grok 4 exposes logprobs/top_logprobs and temperature controls (supported parameters) for inspecting alternative tool choices.
• Classification-driven routing: Grok 4 scores higher on classification in our testing (classification 4 vs GPT-5.4's 3), which helps when choosing which tool to call based on intent.

For Tool Calling, choose GPT-5.4 if you need robust safety, strict schema compliance, extensive multi-step planning, very large context handling, or lower input cost. Choose Grok 4 if your workflow benefits from native parallel tool calling, richer sampling/logprob controls for debugging, or you prioritize classification-driven routing.