GPT-5 for tool use
GPT-5 is ranked #4 on LLMDex's llm for tool use ranking out of 5 models we track for this use case. Below, the specific reasons it slots where it does, and when you should reach for an alternative.
Updated
At a glance
- Rank
- #4 of 5
- Context
- 400K tokens
- Output / 1M
- $10.00 / 1M tokens
- Released
- Aug 2025
Why GPT-5 fits this task
Three things about GPT-5 that map directly onto what this task rewards: Unified model, reasoning routed automatically per query; Excellent tool-use and JSON-mode discipline; Strong agent performance on SWE-bench Verified. Beyond the task-specific fit, GPT-5 also brings unified model, reasoning routed automatically per query and excellent tool-use and json-mode discipline, both of which compound when the workload broadens.
The criteria this task rewards
LLMDex ranks best llm for tool use on 5 criteria , these are the axes the ranking uses, in priority order:
- Berkeley Function Calling Leaderboard (BFCL) scores
- Parallel tool-call support
- Recovery from tool errors
- Schema obedience for nested arguments
- Cost overhead vs. plain chat
How GPT-5 scores on each axis
Where GPT-5 costs you: reasoning routing means latency is unpredictable per query. For most teams this is acceptable on this workload, the value of the strengths above outweighs the cost. For cost-bound workloads or teams with strict latency budgets, run an eval against the next two ranked models on real data before committing.
Strengths that pay off here
- Unified model, reasoning routed automatically per query
- Excellent tool-use and JSON-mode discipline
- Strong agent performance on SWE-bench Verified
- Robust safety post-training reduces hallucinations vs. GPT-4 line
Tracked weaknesses
- Reasoning routing means latency is unpredictable per query
- Output cost is high relative to mid-tier alternatives
When to pick something else
If you can pay slightly more or accept slightly different tradeoffs, Claude Sonnet 4.6 from Anthropic ranks one position higher and tends to win on the hardest cases. Anthropic's mid-tier 4.6 release, the workhorse model behind most production Anthropic deployments.
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Other models for tool use
- GPT-5.5 for tool use
OpenAI's mid-cycle GPT-5 refresh, improved reasoning, tool use, and multimodal grounding over the 2025 launch.
Read guide - Claude Opus 4.7 for tool use
Anthropic's mid-2026 flagship, ahead on SWE-bench, agent reliability, and writing quality.
Read guide - Claude Sonnet 4.6 for tool use
Anthropic's mid-tier 4.6 release, the workhorse model behind most production Anthropic deployments.
Read guide - Gemini 3 Pro for tool use
Google's late-2025 flagship, set new benchmarks on long-context, vision, and reasoning at competitive pricing.
Read guide
GPT-5 for other use cases
Direct comparisons
Frequently asked
Is GPT-5 good for tool use?
GPT-5 is ranked #4 on LLMDex's tool use list. OpenAI's unified flagship combining GPT-line breadth with built-in reasoning, replacing both GPT-4o and the o-series for most users.How much does GPT-5 cost for tool use?
GPT-5 costs $1.25 / 1M tokens for input tokens and $10.00 / 1M tokens for output tokens. For tool use workloads, output costs typically dominate; budget on the higher number.What's a cheaper alternative to GPT-5 for tool use?
The next ranked model on this task is Gemini 3 Pro. Compare both before committing.When should I NOT use GPT-5 for tool use?
Tracked weakness: Reasoning routing means latency is unpredictable per query. If that constraint is binding for your workload, the next-ranked model on this task is the safer pick.
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