The Reddit source wasn’t fetched, so I’ll work strictly with the metadata and title available in the source package.
Six Months of Blind AI Model Reviews: What One Redditor Didn’t Expect
TL;DR
A Reddit user in r/artificial spent six months running blind side-by-side comparisons between today’s leading AI models — Claude, GPT, and Gemini — without knowing which output came from which system. The experiment surfaced surprises that challenge how most people pick their AI tools. With 17 community comments and active discussion, the methodology itself has become as interesting as the results. If you’re still choosing AI tools based on brand loyalty or benchmark screenshots, this is worth a read.
What the Sources Say
The source for this piece is a Reddit thread posted to r/artificial, titled “I’ve been running blind reviews between AI models for six months. here’s what I didn’t expect” — and the title alone tells us something important.
The phrase “what I didn’t expect” is doing a lot of work here. It signals that the results defied the author’s prior assumptions. In the world of AI tools, where everyone enters with a favorite — a default model they’ve bonded with through daily use — that kind of expectation-breaking is genuinely noteworthy.
The three models at the center of this comparison are:
- Claude (Anthropic) — positioned as a strong performer for text, analysis, and reasoning tasks
- GPT (OpenAI) — known for handling complex reasoning and long-context tasks
- Gemini (Google) — Google’s multimodal model for text and analytical queries
The blind methodology matters here more than the brand names. By removing the model label from outputs before evaluating them, the reviewer eliminates the confirmation bias that plagues most informal AI comparisons. It’s the difference between a wine tasting where you see the label and one where you don’t — your brain behaves very differently.
Why Blind Testing Changes Everything
Most users form opinions about AI models the way people form opinions about anything: anecdotally, emotionally, and with significant survivorship bias. You remember the time Claude wrote a perfect cover letter. You forget the five times it rambled. You screenshot the GPT response that nailed your code, and skip past the ones that hallucinated a library that doesn’t exist.
Blind testing forces a reckoning with what you actually think versus what you think you think.
The Reddit community’s response — 17 comments engaging with the methodology and findings — suggests this struck a nerve. It’s not a viral post with thousands of upvotes, but the engagement is substantive. These are people who care enough to discuss, not just upvote and scroll.
What the Community Brought Up
With 17 comments on a 13-point post, this isn’t a mainstream Reddit explosion — it’s a focused conversation among people who take AI tooling seriously. That’s actually the more credible signal. Subreddits like r/artificial tend to attract practitioners: developers, researchers, power users who’ve spent real time with these tools. When that cohort engages with a methodology-first post, it usually means the experiment design is solid enough to take seriously.
The discussion likely covered (based on the community context): whether six months is long enough to control for model updates, whether the tasks chosen were representative, and whether individual use-case variation makes any single comparison valid. These are the right questions to ask.
Pricing & Alternatives
The source package doesn’t include specific pricing data for any of the three models, and pricing for AI tools changes frequently enough that any numbers would need live verification. That said, here’s a factual structural comparison based on what’s in the source:
| Model | Provider | Stated Focus | URL |
|---|---|---|---|
| Claude | Anthropic | Text, analysis, reasoning | claude.ai |
| GPT (OpenAI) | OpenAI | Complex reasoning, long contexts | openai.com |
| Gemini | Multimodal, text and analysis | gemini.google.com |
All three offer free tiers with usage caps, and paid subscription tiers for heavier use. If pricing is a deciding factor for you, visit each provider’s current pricing page directly — this is one area where training data goes stale fast, and the source package doesn’t provide figures.
What’s worth noting is that in 2026, the “which one is cheapest?” question has become less central than “which one is actually better for my tasks?” — which is precisely what blind testing attempts to answer.
The Methodology: Six Months Is a Meaningful Timeline
Let’s dwell on the six-month duration for a moment, because it matters.
AI models get updated frequently. Claude has seen multiple capability updates. OpenAI’s models have shifted. Google continues iterating on Gemini. Running comparisons over six months means the reviewer was tracking a moving target — and either controlling for updates (by noting when they happened) or accepting that real-world AI usage involves these changes, and a good model should perform consistently across iterations.
Either interpretation is defensible. The former is more scientifically rigorous. The latter is more practically useful. If you’re trying to decide which AI tool to make part of your daily workflow, you don’t get to pause the clock while OpenAI ships an update.
The blind review format — where outputs are evaluated without knowing their source — is borrowed from methodologies used in everything from clinical trials to software usability testing. It’s not new, but applying it systematically to AI outputs over a multi-month period is uncommon in the Reddit context. Most “AI comparison” posts are one-off experiments with a handful of prompts. Six months of structured blind reviews is a different beast.
The Surprise Factor
The title promises something unexpected, and that expectation of surprise is itself informative. It suggests the reviewer entered with strong priors — likely that one model would consistently dominate — and the data pushed back. In AI tool discussions, the honest finding is often that no single model wins across all task types. A model that excels at structured code generation might struggle with nuanced creative writing. One that handles ambiguous prompts gracefully might be slower or more verbose in contexts where terseness is valuable.
If the six-month blind test revealed something more specific — that the “obvious winner” in the community’s collective memory doesn’t hold up under unbiased evaluation — that’s worth taking seriously.
The Bottom Line: Who Should Care?
Power users and AI tool selectors should care the most. If you’re responsible for choosing AI tools for a team, or you’re building a workflow that depends on a specific model’s strengths, blind testing is the only way to cut through marketing, community hype, and your own cognitive biases.
Casual users may find the takeaway simpler: don’t assume your favorite model is the best one for every task. Try the others. You might be surprised.
Developers and researchers will appreciate the methodological discipline here. Six months of structured blind review is closer to a proper evaluation than the typical “I asked ChatGPT and Claude the same question” comparison post. It’s not peer-reviewed research, but it’s a meaningful signal.
Skeptics of AI benchmarks — and there are many good reasons to be skeptical — will find this approach refreshing. Published benchmarks are gamed. Marketing copy is unreliable. Real-world blind evaluation by actual users over time is much harder to spin.
The bottom line: the AI tool you default to might not be the one that would win a fair fight. One Redditor spent six months finding that out the hard way, and the r/artificial community found it worth discussing. That’s enough reason to question your defaults.