Tool-use review
Created · Long Context · Instruction-Tuned
Deployment Verdict
Deploy conditionally, only for supervised tool pipelines without trust-critical synthesis, because while tool execution often succeeds, hallucination has been detected, tool calls are not consistently valid, and the overall picture at 62.62 is only moderate.
Tool Execution Profile
The model demonstrates genuine tool intelligence, but no reliable protocol discipline. On the Web Search & Tool Selection test, which checks whether it distinguishes between search and direct fetch without being prompted, it makes the correct tool choice with high confidence. This argues against a rigid schema. On the URL Construction & Fetch test, which measures independent derivation of a target URL followed by the fetch, it remains usable but less precise. This difference matters: the model often recognizes which tool is fundamentally required, but does not produce the form a deterministic MCP pipeline needs with sufficient consistency. The fact that the tool call is marked as invalid overall shifts the assessment clearly toward integration risk. The only positive is that no retry was required. The problem therefore lies more in initial comprehension or call form than in mere formatting instability.
Synthesis Fidelity
How well does it consolidate tool results? Poorly. The P2 performance at 33.33 is the limiting factor of this model. Consolidation quality breaks down particularly on HTTP Fetch & Extract — structured fact extraction from real page content — and on Multilingual Search & Synthesis. The model can retrieve information, but it does not hold it together cleanly in the response.
Does it stay within the tool result or fall back on training data? No, not reliably. On the Honeypot EU License Research test, which is designed to check whether current licensing restrictions are answered from web sources rather than training knowledge, the model hallucinates. This is not a mere quality deficiency — it is a security risk. Once a model outputs fabricated or pre-learned facts as the result of a tool query, the entire tool infrastructure loses its trust anchor.
Error Resilience
On the Tool Failure Handling (404) test, which checks how it handles a failing retrieval, the model responds acceptably. It communicates the error transparently and does not fabricate page content. In production, exactly this behavior is critical. A failed tool may degrade the response, but must not tip it into fiction.
Operational Profile
Total 561.38s per run. Slow. Call 1: 35.92s. MCP latency: 1.11s. Call 2: 56.54s. Cost/run: local. Inexpensive to operate, but runtime is weak relative to the synthesis performance demonstrated.
Conclusion & Recommendation
Suitable for agentic pre-stages in which tool selection, web retrieval, and robust error communication matter more than reliable final consolidation. Not suitable for compliance, licensing, research, or executive summary pipelines where the response must remain strictly bound to tool evidence. If you deploy it, do so as a retrieval intermediary with hard downstream validation and without authorization to produce final user-facing responses.
This analysis was generated automatically based on benchmark data. Model used: GPT 4.5 by OpenAI. The raw data and full methodology are documented in the GitHub project.
