This is not a clean apples-to-apples fact check because only one of the two named models is clearly verifiable in official sources. Claude Opus 4.7 is documented by Anthropic as its most capable generally available model, while the official OpenAI sources I retrieved document GPT-5 mini and a docs track labeled “Latest: GPT-5.4,” not a model named “GPT-5.5 Spud.” ^[10][5][6] So a strict “Claude Opus 4.7 vs GPT-5.5 Spud” comparison is not fully verifiable from official evidence. ^[10][5][6]

Key findings

• Anthropic’s official Opus 4.7 docs describe it as “our most capable generally available model to date,” with 1M-token context, 128k max output, adaptive thinking, and new task-budget controls. ^[10]

• Anthropic also says task budgets are advisory and that if a budget is too restrictive, Opus 4.7 may complete a task less thoroughly or refuse to do it entirely. ^[10] That is a control mechanism, but it is not the same thing as well-calibrated epistemic abstention about uncertain facts. ^[10][4]

• OpenAI’s public explanation of hallucinations is more explicit on abstention and uncertainty. OpenAI says standard training and evaluation procedures reward guessing over acknowledging uncertainty, and says it is better for a model to express uncertainty or ask for clarification than to provide confident but incorrect information. ^[15]

• In OpenAI’s own SimpleQA example, gpt-5-thinking-mini shows a 52% abstention rate, 22% accuracy, and 26% error rate, while o4-mini shows 1% abstention, 24% accuracy, and 75% error rate. ^[15] OpenAI uses that comparison to argue that lower hallucination can come from abstaining more when uncertain, even if top-line accuracy falls slightly. ^[15]

• The academic literature broadly supports that framing. A 2024 study reports that uncertainty-based abstention in LLMs improves safety and reduces hallucinations. ^[8] More recent work argues that current LLMs still often fail to abstain when they should, so confidence-aware abstention and recalibration remain active research problems. ^[9][7][3]

• Reviews and taxonomies in this area consistently treat calibrated uncertainty as important because it helps users decide when to trust, defer, or verify a model’s answer. ^[4][3] That means “hallucination control” is not just about fewer wrong answers, but also about whether the model knows when not to guess. ^[4][15]

• For Claude Opus 4.7 specifically, I found official Anthropic product documentation, but not a comparably explicit official public write-up in the retrieved sources that quantifies calibrated uncertainty or abstention behavior the way OpenAI’s public materials do. ^[10][15] On that narrow question, the evidence base is thinner for Opus 4.7 than for OpenAI’s GPT-5-family materials. ^[10][15]

Evidence notes

• The strongest fact-check result is about existence and naming: Claude Opus 4.7 is officially documented by Anthropic, while the official OpenAI sources I retrieved document GPT-5 mini and a GPT-5.4 docs track rather than “GPT-5.5 Spud.” ^[10][5][6]

• The strongest source on abstention behavior is OpenAI’s “Why language models hallucinate,” because it directly discusses abstentions, error rates, and why accuracy-only evaluations incentivize guessing. ^[15]

• The strongest cross-model conclusion I can support is general, not model-specific: abstention and calibrated uncertainty are credible ways to reduce hallucinations, but they require explicit incentives and calibration work. ^[8][9][7][3]

Limitations / uncertainty

• Insufficient evidence for a verified comparison against an official OpenAI model called “GPT-5.5 Spud.” ^[5][6]

• Insufficient evidence for a standardized, same-benchmark head-to-head on calibrated uncertainty between Claude Opus 4.7 and an official OpenAI counterpart in the retrieved sources. ^[10][15]

• Vendor claims are not directly comparable unless the models are tested on the same tasks, with the same prompting, tool access, web access, and scoring rules for abstentions versus errors. ^[15][8][4]

Summary

• Fact-check result: Claude Opus 4.7 is real and officially documented; “GPT-5.5 Spud” is not verified in the official OpenAI sources I retrieved. ^[10][5][6]

• Research result: abstention and calibrated uncertainty are real levers for reducing hallucinations, and OpenAI’s public materials currently articulate that logic more directly than the retrieved official Anthropic Opus 4.7 docs do. ^{[15][8][9][10]}

• Practical conclusion: if you want a rigorous comparison on hallucination control, the defensible comparison is Claude Opus 4.7 versus an official OpenAI model such as GPT-5 or GPT-5 mini, not versus the unverified label “GPT-5.5 Spud.” ^[10][5][6]

Sources

• ^[3] Uncertainty quantification for hallucination detection in large language models: Foundations, methodology, and future directions
• ^[4] A comprehensive taxonomy of hallucinations in large language models
• ^[5] OpenAI API docs: GPT-5 mini Model
• ^[6] OpenAI API docs: Prompt guidance for GPT-5.4
• ^[7] Mitigating LLM Hallucination via Behaviorally Calibrated Reinforcement Learning
• ^[8] Uncertainty-Based Abstention in LLMs Improves Safety and Reduces Hallucinations
• ^[9] I-CALM: Incentivizing Confidence-Aware Abstention for LLM Hallucination Mitigation
• ^[10] Anthropic docs: What’s new in Claude Opus 4.7
• ^[15] OpenAI: Why Language Models Hallucinate

If you want, I can turn this into a tighter scorecard comparing Claude Opus 4.7 against official OpenAI models on three axes only: factuality, abstention behavior, and uncertainty calibration.