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Research and fact-check: Claude Opus 4.7 vs GPT-5.5 Spud, Regression drift and reproducibility after updates

Based on the provided evidence, there is no defensible head to head verdict on whether Claude Opus 4.7 or “GPT 5.5 Spud” has less regression drift or better reproducibility after updates. What the evidence does suppor...

Based on the provided evidence, there is no defensible head to head verdict on whether Claude Opus 4.7 or “GPT 5.5 Spud” has less regression drift or better reproducibility after updates. What the evidence does support is narrower: LLM drift is real and can vary by model [1][6], reproducibility is a known challenge in

Key takeaways

  • Based on the provided evidence, there is no defensible head-to-head verdict on whether Claude Opus 4.7 or “GPT-5.5 Spud” has less regression drift or better reproducibility after updates. What the evidence does support is narrower: LLM drift is real and can vary by model [1][6],

Research answer

Based on the provided evidence, there is no defensible head-to-head verdict on whether Claude Opus 4.7 or “GPT-5.5 Spud” has less regression drift or better reproducibility after updates. What the evidence does support is narrower: LLM drift is real and can vary by model [1][6], reproducibility is a known challenge in LLM studies [2], Anthropic has documented concrete Opus 4.7 changes such as task budgets and a new tokenizer [5][7], and the supplied OpenAI material does not substantiate any claim about GPT-5.5 Spud [8]. Key findings - A 2026 paper explicitly studies non-deterministic drift in large language models, quantifies baseline behavioral drift in two LLMs, and says drift manifests differently across models [1]. That supports the general claim that apparent regressions can happen even without a clean, single-cause explanation [1]. - A separate study on ChatGPT reports short-time drifts in the performance and behavior of GPT-3.5 and GPT-4 [6]. Taken together with [1], the literature supports treating post-update behavior as something that must be re-checked rather than assumed stable [1][6]. - A 2025 guidelines paper for empirical software-engineering studies involving LLMs says its goal is to enable reproducibility and replicability despite LLM-related issues [2]. That is strong support for the broader point that reproducibility problems are expected enough to require explicit study design, not just ad hoc testing [2]. - Anthropic’s official documentation includes a general Claude models overview and a model-specific “What’s new in Claude Opus 4.7” page [5][7]. The Opus 4.7 update note says the model introduces task budgets and a new tokenizer that may use roughly 1x to 1.35x as many tokens as previous models, with up to about 35% more token usage depending on content [7]. - Inference: because Opus 4.7 changes tokenization and budgeting, exact token counts and budget-constrained workflows may not reproduce identically after upgrading, even if the prompt text is unchanged [7]. That is an operational reproducibility concern, not direct proof of a quality regression [7]. - The provided OpenAI source is a “Page not found” result for a GPT-3.5-turbo documentation path, not a model card, changelog, benchmark, or API reference for GPT-5.5 Spud [8]. On this evidence set, claims about GPT-5.5 Spud’s update behavior, regression history, or reproducibility are unverified [8]. - There is no direct benchmark or vendor-authored comparison in the supplied evidence that tests Claude Opus 4.7 against GPT-5.5 Spud on regression drift after updates [5][7][8]. So any claim that one is more stable than the other would go beyond the evidence provided [7][8]. Evidence notes - The strongest model-specific evidence here is Anthropic’s official Opus 4.7 documentation [5][7]. - The strongest general evidence on drift and reproducibility comes from the academic sources showing behavioral drift over time and discussing reproducibility/replicability challenges in LLM research [1][2][6]. - The OpenAI side is evidentially weak in this record because the only supplied OpenAI link is not a usable source for GPT-5.5 Spud [8]. Limitations / uncertainty - Insufficient evidence to verify “GPT-5.5 Spud” as an official model entry or to characterize its update behavior from the provided materials [8]. - Insufficient evidence to quantify any actual regression magnitude for Claude Opus 4.7 from these sources alone, because the Anthropic note describes changes but does not provide a regression audit [7]. - The drift papers support the general phenomenon, but the supplied evidence does not show that those studies specifically tested Claude Opus 4.7 or GPT-5.5 Spud [1][6]. Summary The fact-checked conclusion is limited but clear: drift and reproducibility problems are real in LLMs generally [1][2][6], and Claude Opus 4.7 has documented update-level changes that can affect operational repeatability through tokenization and task budgets [7]. There is not enough evidence here to say whether Claude Opus 4.7 or GPT-5.5 Spud is better on regression drift after updates, and any stronger claim about GPT-5.5 Spud would be unsupported on this record [8].

Supporting visuals

A collage featuring a Sony electronic display with schedules, robotic equipment on Mars-like terrain, and a partly cloudy sky, with the text "Claude Opus 4.6" prominently overlaid.
Claude Opus 4.6 \ AnthropicA collage featuring a Sony electronic display with schedules, robotic equipment on Mars-like terrain, and a partly cloudy sky, with the text "Claude Opus 4.6" prominently overlaid.
A bar chart comparing success rates of different AI models, showing that Opus 4.6 has a success rate of 66.6%, Opus 4.5 has 51.0%, and Sonnet 4.5 has 29.8%.
Claude Opus 4.6 \ AnthropicA bar chart comparing success rates of different AI models, showing that Opus 4.6 has a success rate of 66.6%, Opus 4.5 has 51.0%, and Sonnet 4.5 has 29.8%.
A bar chart compares the accuracy percentages of various AI language models, highlighting Anthropic's Claude Opus 4.5 with an accuracy of 80.9%, in relation to models such as Sonne
Introducing Claude Opus 4.5 \ AnthropicA bar chart compares the accuracy percentages of various AI language models, highlighting Anthropic's Claude Opus 4.5 with an accuracy of 80.9%, in relation to models such as Sonnet 4.5, Opus 4.1, Gemini 3 Pro, GPT-5.1 Codex-Max, and GPT-5.1, in the context of software engineering benchmarks for 2025.
The image features a pixel art illustration of a character in a red hat standing in front of a building, with a side menu of a guide titled "Claude Plays Pokémon" on the left.
Introducing Claude 4 \ AnthropicThe image features a pixel art illustration of a character in a red hat standing in front of a building, with a side menu of a guide titled "Claude Plays Pokémon" on the left.
A collage features a vintage television displaying a schedule related to Anthropoc, alongside images of a rover on Mars, a Go game board, and a sky filled with clouds.
Claude Opus 4.6 \ AnthropicA collage features a vintage television displaying a schedule related to Anthropoc, alongside images of a rover on Mars, a Go game board, and a sky filled with clouds.
Chart comparing frontier models on SWE-bench Verified which measures performance on real-world coding tasks
Chart comparing frontier models on SWE-bench Verified which measures performance on real-world coding tasks
Comparison table of frontier models across popular benchmarks
Comparison table of frontier models across popular benchmarks
Claude 3
Claude 3
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opengraph illustration
opengraph illustration
Introducing Claude Haiku 4.5
Introducing Claude Haiku 4.5

Research conversation

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Research question

Research and fact-check: Claude Opus 4.7 vs GPT-5.5 Spud, Regression drift and reproducibility after updates

Studio Global AI40 sources

Cited answer

Based on the provided evidence, there is no defensible head-to-head verdict on whether Claude Opus 4.7 or “GPT-5.5 Spud” has less regression drift or better reproducibility after updates. What the evidence does support is narrower: LLM drift is real and can vary by model [1][6], reproducibility is a known challenge in LLM studies [2], Anthropic has documented concrete Opus 4.7 changes such as task budgets and a new tokenizer [5][7], and the supplied OpenAI material does not substantiate any claim about GPT-5.5 Spud [8]. Key findings - A 2026 paper explicitly studies non-deterministic drift in large language models, quantifies baseline behavioral drift in two LLMs, and says drift manifests differently across models [1]. That supports the general claim that apparent regressions can happen even without a clean, single-cause explanation [1]. - A separate study on ChatGPT reports short-time drifts in the performance and behavior of GPT-3.5 and GPT-4 [6]. Taken together with [1], the literature supports treating post-update behavior as something that must be re-checked rather than assumed stable [1][6]. - A 2025 guidelines paper for empirical software-engineering studies involving LLMs says its goal is to enable reproducibility and replicability despite LLM-related issues [2]. That is strong support for the broader point that reproducibility problems are expected enough to require explicit study design, not just ad hoc testing [2]. - Anthropic’s official documentation includes a general Claude models overview and a model-specific “What’s new in Claude Opus 4.7” page [5][7]. The Opus 4.7 update note says the model introduces task budgets and a new tokenizer that may use roughly 1x to 1.35x as many tokens as previous models, with up to about 35% more token usage depending on content [7]. - Inference: because Opus 4.7 changes tokenization and budgeting, exact token counts and budget-constrained workflows may not reproduce identically after upgrading, even if the prompt text is unchanged [7]. That is an operational reproducibility concern, not direct proof of a quality regression [7]. - The provided OpenAI source is a “Page not found” result for a GPT-3.5-turbo documentation path, not a model card, changelog, benchmark, or API reference for GPT-5.5 Spud [8]. On this evidence set, claims about GPT-5.5 Spud’s update behavior, regression history, or reproducibility are unverified [8]. - There is no direct benchmark or vendor-authored comparison in the supplied evidence that tests Claude Opus 4.7 against GPT-5.5 Spud on regression drift after updates [5][7][8]. So any claim that one is more stable than the other would go beyond the evidence provided [7][8]. Evidence notes - The strongest model-specific evidence here is Anthropic’s official Opus 4.7 documentation [5][7]. - The strongest general evidence on drift and reproducibility comes from the academic sources showing behavioral drift over time and discussing reproducibility/replicability challenges in LLM research [1][2][6]. - The OpenAI side is evidentially weak in this record because the only supplied OpenAI link is not a usable source for GPT-5.5 Spud [8]. Limitations / uncertainty - Insufficient evidence to verify “GPT-5.5 Spud” as an official model entry or to characterize its update behavior from the provided materials [8]. - Insufficient evidence to quantify any actual regression magnitude for Claude Opus 4.7 from these sources alone, because the Anthropic note describes changes but does not provide a regression audit [7]. - The drift papers support the general phenomenon, but the supplied evidence does not show that those studies specifically tested Claude Opus 4.7 or GPT-5.5 Spud [1][6]. Summary The fact-checked conclusion is limited but clear: drift and reproducibility problems are real in LLMs generally [1][2][6], and Claude Opus 4.7 has documented update-level changes that can affect operational repeatability through tokenization and task budgets [7]. There is not enough evidence here to say whether Claude Opus 4.7 or GPT-5.5 Spud is better on regression drift after updates, and any stronger claim about GPT-5.5 Spud would be unsupported on this record [8].

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Based on the provided evidence, there is no defensible head-to-head verdict on whether Claude Opus 4.7 or “GPT-5.5 Spud” has less regression drift or better reproducibility after updates. What the evidence does support is narrower: LLM drift is real and can vary by model [1][6],

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