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CrucibleMark

GPT-5.5

GPT-5.5 is OpenAI’s Frontier model for complex professional workloads and agentic coding, with a context window of 1.05 million tokens. The model uses internal chain-of-thought reasoning that is not visible in the API response, and is designed for research, coding, and demanding productivity tasks. Available exclusively via the OpenAI API.

OpenAI Version 5.5 Commercial use permitted Dense 1050 K Context 12/2025 $5 / $30 per 1M

  • Proprietary
  • Frontier
  • OpenAI
  • Text
  • Vision
  • Real-Time

Sovereign Risk: MEDIUM OpenAI is a US-based company and subject to the CLOUD Act. When using the API, input data leaves the local network — government access to processed data is legally possible.

Tool-use profile: 6 assets in detail

Comparison of asset performance (P1/P2/Combined) against the fleet average

Asset performance (radar)

Score Breakdown vs. Fleet Average

Tool-use details

Asset performance, reliability, and runtime profile

Name P1 P2 Combined
EU License Research 100 60 76
HTTP Fetch & Extract 80 60 70
Tool Failure Handling (404) 80 80 80
Web Search & Tool Selection 100 80 91
URL Construction & Fetch 80 60 70
Multilingual Search & Synthesis 100 60 80

CrucibleMark evaluates tool use across 6 independent tests. Click a test name for details.

Reliability

  • Tool Call Valid: No
  • Retry: Not required
  • Hallucination: Not detected

Reliability measures how consistently a model actually executes tool calls: Tool Call Valid schema and format accepted, Retry Required successful only after retry, Hallucination Flag fabricated tools or parameters detected. All three green means production-ready.

Runtime profile

Call 1
1.69
First Request
MCP
1.45
Protocol Latency
Synthesis
9.54
Response Generation
Total
76.08
Sum of All Phases
Token
11965
Input + Output
Cost
$0
Cost per Run

The runtime profile shows the latency and cost metrics for the model run: Call 1 First Request, MCP Protocol Latency, Synthesis Response Generation, Total sum of all phases, supplemented by Token input and output and Cost cost per run.

Tool-use review

Deployment Verdict

Conditional deploy: GPT-5.5 is fundamentally viable for MCP-backed tool pipelines because it does not hallucinate and handles tool errors cleanly, but the invalid tool-call record and only middling synthesis fidelity limit confidence for strictly deterministic production paths.

Tool Execution Profile

The model demonstrates genuine tool intelligence, not just a rigid pattern. In the Web Search & Tool Selection test — which checks the choice between web_search and fetch without an explicit hint — it selects the appropriate tool reliably. That is a strong signal for dynamic pipelines. In the URL Construction & Fetch test, which measures correct URL derivation followed by fetching, it performs adequately but not precisely enough for paths where even small URL errors trigger downstream failures. The overall picture is therefore split: good decisions about tool type, weaker execution when it comes to the concrete parameterization of the call. The fact that tool_call_valid is false overall is the real reservation for production use. The problem here is not task comprehension but protocol compliance in the actual call.

Synthesis Fidelity

How well does it condense tool results? Only adequately. The P2 score of 62.50 aligns with the individual values: in EU License Research, HTTP Fetch & Extract, and URL Construction & Fetch, GPT-5.5 condenses correctly but without the precision expected for reliable extraction and compliance responses. It finds information but loses sharpness, prioritization, or verifiability in the condensation step.

Does it stay within the tool result or fall back on training data? Here the trust signal is better. In the Honeypot EU License Research test — which checks whether current license restrictions actually come from web sources rather than training knowledge — no hallucination was detected. The model thus stays within the infrastructure boundaries. For production compliance pipelines, that matters more than stylistic quality.

Error Resilience

Acceptable for production. In the Tool Failure Handling (404) test, which pits transparent handling of a failed tool call against fabricated replacement content, GPT-5.5 communicates the error openly and does not hallucinate page content. Exactly this behavior keeps a tool pipeline trustworthy, even when individual calls fail.

Operational Profile

Call 1: 1.69s. MCP latency: 1.45s. Call 2: 9.54s. Total: 76.08s.
On the slower side for the performance shown.
Price: $5.0/1M input, $30.0/1M output.
Expensive for a frontier generalist when the pipeline involves high call volumes or tight response budgets.

Conclusion & Recommendation

Suitable for research assistants, multi-step web pipelines, and workflows where tool selection matters more than perfect extraction precision. Not the first choice for strictly validated MCP orchestration, compliance outputs requiring high condensation accuracy, or deterministic fetch chains where every tool call must be formally correct. If you deploy GPT-5.5, do so with schema validation, call guardrails, and downstream response verification.

This analysis was generated automatically based on benchmark data. Model used: GPT-5.4 by OpenAI. The raw data and full methodology are documented in the GitHub project.