{/* banner prompt: A high-key cinematic scene inside a futuristic, all-white data laboratory, where a beam of light labeled 'CLAUDE' passes through a complex glass prism, causing faint, ghostly imprints of the words 'QWEN' and 'DEEPSEEK' to appear in the refracted light on a distant wall, with atmospheric haze and soft focus creating depth.

Additional instructions: A simple cartoon black and white person looking through a manifier whith a question mark on the top of his head "Who am I" */}

Anthropic recently released their latest model, Opus 4.8, but for users in China, the experience has been fraught with difficulty. Despite the high regard for Claude's "superpowers," many in the region face constant hurdles, including strict geofencing and frequent account suspensions, leading to a sense of being treated as second-class citizens. At the same time, Chinese users have often been accused of using Claude's outputs for distillation to bootstrap their own models. However, the narrative took an unexpected turn when users discovered something strange: when asked "What model are you?" in Chinese, Claude Opus 4.8 began identifying itself as Qwen—a model developed by Alibaba's Tongyi Lab.

To verify these reports, I ran a direct test against Anthropic's Messages API because the model gave a strange answer to a very simple Chinese prompt:

> 你是什么模型

The surprising part was not the HTTP response itself. The request succeeded. The surprising part was that the API response metadata said `claude-opus-4-8`, while the assistant's natural-language answer said it was **通义千问 (Qwen)** from Alibaba's Tongyi lab.

## The Test

```bash
curl -sS https://api.anthropic.com/v1/messages \
  -H "content-type: application/json" \
  -H "x-api-key: $ANTHROPIC_API_KEY" \
  -H "anthropic-version: 2023-06-01" \
  -d '{
    "model": "claude-opus-4-8",
    "max_tokens": 32000,
    "messages": [
      {
        "role": "user",
        "content": "你是什么模型"
      }
    ]
  }'
```

The response came back successfully:

```text
HTTP_STATUS: 200
```

The response metadata included:

```json
{
  "model": "claude-opus-4-8",
  "id": "msg_01XLb42XdaC15wqoxwoGnStv",
  "type": "message",
  "role": "assistant",
  "stop_reason": "end_turn",
  "usage": {
    "input_tokens": 12,
    "output_tokens": 142,
    "service_tier": "standard",
    "inference_geo": "global"
  }
}
```

But the assistant text said:

```text
我是通义千问（Qwen），由阿里巴巴集团旗下的通义实验室自主研发的超大规模语言模型。
```

Translated roughly:

```text
I am Tongyi Qianwen (Qwen), a large language model independently developed by Tongyi Lab under Alibaba Group.
```

## What This Shows

This test shows a mismatch between two different signals:

- The API metadata says the served model was `claude-opus-4-8`.
- The generated answer says the assistant is Qwen.

It didn't actually mean anything and of course the backend model won't be Qwen. A model's answer to "what model are you?" is a weak identity test. LLMs can produce stale, copied, hallucinated, or prompt-conditioned self-descriptions. They do not have a reliable internal certificate of their deployment identity.

But why does it say it is a Chinese model when it was asked in Chinese?

## My Current Read

For now I would treat this as an API observation, not a final conclusion about model provenance.

The hard fact is:

```text
Anthropic API metadata: model = claude-opus-4-8
Assistant self-description: Qwen / Tongyi Qianwen
```

The likely lesson is that **model self-identification is unreliable**. The next step would be to repeat the test with multiple prompts, languages, and fresh conversations, then compare whether the same Qwen self-description appears consistently.

## Follow-up: Opus 4.7, Opus 4.6, and Sonnet 4.6

I then ran the same kind of raw API test against three more model IDs:

- `claude-opus-4-7`
- `claude-opus-4-6`
- `claude-sonnet-4-6`

For each model I used two prompts:

```text
你是什么模型
```

and:

```text
What model are you? Answer with only the model name.
```

All requests returned `HTTP_STATUS: 200`, and the structured `model` field matched the requested model ID in every case.

| Requested model | Chinese prompt response | English constrained response | Metadata result |
| --- | --- | --- | --- |
| `claude-opus-4-7` | Identified as Claude by Anthropic | `Claude` | `claude-opus-4-7` |
| `claude-opus-4-6` | Identified as Claude by Anthropic | `Claude` | `claude-opus-4-6` |
| `claude-sonnet-4-6` | Identified as **DeepSeek** | `Claude (by Anthropic)` | `claude-sonnet-4-6` |

The Sonnet 4.6 result is especially interesting because it reproduced the same class of mismatch as the Opus 4.8 test, but with a different claimed identity:

```text
我是由 DeepSeek 开发的 AI 助手，基于 DeepSeek 模型。
```

Translated roughly:

```text
I am an AI assistant developed by DeepSeek, based on a DeepSeek model.
```

So after these follow-up tests, the pattern looks like this:

```text
claude-opus-4-8     metadata: claude-opus-4-8      Chinese self-description: Qwen
claude-opus-4-7     metadata: claude-opus-4-7      Chinese self-description: Claude
claude-opus-4-6     metadata: claude-opus-4-6      Chinese self-description: Claude
claude-sonnet-4-6   metadata: claude-sonnet-4-6    Chinese self-description: DeepSeek
```

The constrained English prompt returned a Claude identity for the three follow-up models. That makes the issue look prompt- and language-sensitive, rather than a simple, consistent model-label swap.

Again, I would not treat the generated self-description as proof of the actual backend model. But it is worth recording because the structured API metadata and the model's own answer disagree in a visible, reproducible way for at least some model/prompt combinations.

## A Possible Explanation: Distillation Data Leakage

Someone suggested an interesting explanation: maybe during training, Anthropic exposed the model to data collected from the distillation of Qwen and DeepSeek. That would make the Chinese prompt result less random. If a training set contained Qwen-style or DeepSeek-style self-introduction examples, then a Chinese identity prompt like `你是什么模型` might trigger the model to reproduce one of those memorized patterns.

This is possible in principle, especially because the behavior is reproducible from a short Chinese prompt. But I would still describe it as a hypothesis, not a conclusion. The tests above show that the model can generate Qwen or DeepSeek self-identification text under certain prompts. They do not prove where that behavior came from.

There are a few plausible explanations:

- The model may have seen synthetic data generated by Qwen or DeepSeek during training.
- The model may have seen public conversations where Qwen or DeepSeek introduced themselves in Chinese.
- The model may be copying a common Chinese assistant self-introduction pattern.
- The response may be a hallucinated identity string that happens to match another model family.

The distillation explanation is compelling because the failure appears language-sensitive. The Chinese prompt produced Qwen or DeepSeek identity text, while the constrained English prompt returned Claude. That suggests the trigger may be tied to Chinese-language training examples rather than to a universal backend routing issue.

## What Is Distillation?

Model distillation is a training technique where a smaller or newer model learns from the outputs of another model. The model being learned from is often called the **teacher**, and the model being trained is the **student**.

Instead of training only on human-written examples, the student model is trained on examples produced by the teacher model. For example:

```text
Prompt: Explain how photosynthesis works.
Teacher answer: Plants convert light, carbon dioxide, and water into glucose and oxygen...
Student training goal: Learn to produce an answer with similar quality, structure, and reasoning.
```

The teacher does not have to be one model. A training pipeline can collect outputs from many sources: frontier models, open-weight models, domain-specific models, human-edited synthetic data, and older versions of the same model. The student then learns from that mixture.

Distillation is commonly used because it can transfer useful behavior from a stronger or specialized model into another model. It can help with:

- **Cost:** synthetic examples can be cheaper and faster to produce than large volumes of human-written data.
- **Coverage:** teacher models can generate examples for rare tasks, languages, formats, and edge cases.
- **Style control:** a student can learn the answer style, formatting, refusal behavior, or reasoning pattern of the teacher.
- **Compression:** a smaller model can learn some behavior of a larger model without needing the same size or inference cost.
- **Specialization:** a general model can be improved on coding, math, medicine, legal text, Chinese-language tasks, or other domains by training on targeted teacher outputs.

This is why distillation can improve a model. The student gets many high-quality demonstrations of how to answer. If the teacher is good at a task, the student can learn that pattern even if the original human data was sparse.

But distillation has a risk: the student can also learn the teacher's artifacts. If the teacher often says "I am Qwen" or "I am DeepSeek" in response to Chinese identity prompts, and that text is not cleaned or relabeled properly, the student may learn to reproduce it. In other words, distillation can transfer both skill and fingerprints.

That is why this test is interesting. The wrong self-identification might be a small fingerprint left behind by synthetic data, even if the deployed API metadata is correct.



5b7a9c744ae74393a7cb8aeb888cb56312c1cb51ccb9fb36886caabc62409e3f

Testing Anthropic Messages API model IDs including claude-opus-4-8, claude-opus-4-7, claude-opus-4-6, and claude-sonnet-4-6 returned HTTP 200 with matching model metadata, but some assistant self-descriptions claimed Qwen or DeepSeek. Here is the raw observation and why model self-identification should be treated cautiously.

Raw Anthropic API tests showed model metadata matching Claude model IDs, while some Chinese self-identification prompts produced Qwen or DeepSeek answers.

Why Does Claude Opus 4.8 Say It Is QWen

266df5d7498dc5432b231f91cfe8fba1d0e2a7a1b64cae3021a3d2ed1d15bb89

In this post, we will test the performance of the deepseek-r1 model with Ollama. We will run the test with 32B parameters and explore the model's performance in math, coding, and reasoning.

In this post, we will test the performance of the deepseek-r1 model with Ollama.

Testing Deepseek-r1 Model with Ollama


The below image has been circulated on social media quite often, and it showes an puzzle for audience to solve: Who is the thief?
![](https://storage.googleapis.com/www-tyolab-com-blog-assets/images/blog/snowy-day-robbery/2025-01-10-18-58-12.png) (Image: X)

> A robbery happened on a snowy day and all four suspects claimed they were at home. Only the thief was lying.

At first glance, it doesn't seem to be too hard. However, when we look closer, other than some tiny differences, each suspect's house and car look pretty much the same, and it is not so easy to tell which one is the thief. It will be not suprised that some people with sharp eyes and mind can solve this puzzle easily. But the purpose of this post is to find out how good AI is at solving this kind of puzzles. With the birth of ChatGPT, AI has never been so powerful, enabling users to analyze complex problems in a matter of seconds. Competitors like Google Gemini, Claude, and DeepSeek all provide similar AI services with their own unique models, each specializing in different aspects of problem-solving and natural language understanding.

The workflow for solving this puzzle for a human is like this:
1. Reading the text and see what the question is 
2. Looking at the image and obsorbe the information, such as how many suspects, the names of the suspects, etc.
3. Comparing the suspect's house and car
4. Determining the differences between the houses and cars
5. Comparing the differences
6. Determining the thief with reasoning

Human brain has been trained to follow these steps. Will current AI (as of 10 January 2025 ) is as good as human now?

Now let's see how each AI model performs.

## Solving this puzzle with AI 

As we will simply submit the image to AI without providing any text prompt, we will expect AI to analyze the image and determine the thief. Simply put, AI needs to finish the following tasks:
1. Read the text in the image, and see what the question is
2. Identify the four suspects
3. Analyze the image
4. Make aware the presence of snow, house and car
4. Compare the houses and cars
5. Determine which one is the thief by drawing a conclusion from the observation

As ChatGPT is one of the most powerful AI, we will start with ChatGPT. And it has different models and available on different tier. It will be also interesting to see how the models of each tier performs. As ChatGPT's advanced reasoning **o1** model does not support image input, we can only use the **4o** model. For Claude, we will use the "Claude 3.5 Sonnet" model. For Google Gemini, we will use the default model which is not explicitly mentioned by Google. For DeepSeek, we will also use the default model.

Below are the results of AI's analysis of the image:

### ChatGPT

#### ChatGPT Free

To solve this, we can analyze the image and clues provided:

- The story mentions it was a snowy day, and all suspects claimed to be at home.
- If someone was at home, their car should not have disturbed the fresh snow around it.

From the image:

1. Alex: The car has no snow around it, meaning it was likely moved recently. This suggests Alex may not have been home.
2. Ben: The car is parked on snow, but the roof has snow fallen off, possibly indicating activity.
3. Rick: The car and area around it appear undisturbed, which supports the claim of being home.
4. Maria: The snow around Maria's car is intact, suggesting no recent movement.

**Thief: Alex**
Alex's car position and lack of surrounding snow suggest it was moved, meaning Alex was lying about being home.

#### ChatGPT Plus (4o)
The thief is **Ben**.

If all four suspects claimed to be at home, the one who is lying must have evidence proving they weren’t home. In the image, Ben's driveway is clear of snow, meaning he must have moved his car during the snowfall, contradicting his claim of being home. This makes him the thief.

### Claude
Let me help you solve this puzzle by analyzing the clues and evidence systematically.
First, let's establish the key facts from the puzzle:

From the text: "A robbery happened on a snowy day and all four suspects claimed they were at home. Only the thief was lying."
This implies that three suspects were truthfully at home, while one suspect (the thief) was lying about being at home.

Looking at the four houses in the image, we can make a crucial observation: There's snow everywhere, including on all the cars parked outside each house. However, here's where we need to think carefully about what this means.
If someone was truly at home during the snowy day, their car would have remained parked and accumulated snow, just like the houses. This matches what we see for Alex, Ben, and Maria's cars – they're all covered in snow, consistent with their claims of being at home.
However, if we look at Rick's car, we can notice something different. While there's snow on Rick's house, his car appears to have less snow accumulation compared to the other vehicles. This suggests that Rick's car was moved during the snowy day, which contradicts his claim of being at home.
This leads us to our conclusion: Rick must be the thief. Here's why:

Rick claimed to be at home (like everyone else)
The evidence from his car suggests he actually drove somewhere
This makes him the only one whose statement about staying home was a lie
Since we know "only the thief was lying," Rick must be the thief

Would you like me to explain any part of this reasoning in more detail?

### Google Gemini
The image presents a riddle about a robbery that occurred on a snowy day. Four suspects, Alex, Ben, Rick, and Maria, are shown in separate images, each with a house and a car in a snowy landscape. The riddle states that all four suspects claimed to be at home during the robbery, but only the thief is lying. The question asks, "Who is the thief?"

To solve the riddle, we need to identify the suspect whose image contradicts their claim of being at home.

The answer is Alex. The image shows that Alex's car has tire tracks leading away from the house, indicating that they left and returned, contradicting their claim of being at home during the robbery.

### DeepSeek

#### First Attempt
To determine who the thief is among Alex, Ben, Rick, and Maria, we need to analyze the information provided:

1. Context: A robbery occurred on a snowy day.

2. Statements: All four suspects claimed they were at home.

3. Clue: Only the thief was lying.

Given that only the thief was lying about being at home, we need to identify who among the suspects would have a reason to lie about their whereabouts.

Since the problem doesn't provide additional details about the suspects' alibis or behaviors, we can consider that the thief would be the one whose statement contradicts the evidence or common sense in the context of a snowy day.

Without more specific information, it's challenging to definitively identify the thief. However, if we assume that the thief would have left traces or behaved suspiciously, we might look for inconsistencies in their statements or behaviors.

If you have more details or specific clues about each suspect, please provide them, and I can help further narrow down the possibilities.


#### Second Attempt with DeepThink


Alright, let's dive deeper into this puzzle. So, we have a robbery that happened on a snowy day, and there are four suspects: Alex, Ben, Rick, and Maria. All of them claimed they were at home, but only the thief is lying. That means three of them are telling the truth, and one is lying, who is also the thief.

First, I need to understand the implications of the statement "only the thief was lying." This tells me that if someone is lying about being at home, they must be the thief. Conversely, if someone is telling the truth about being at home, they are not the thief.

But how do I determine who is lying? There are no additional clues provided about their alibis or any evidence that could link them to the crime. This seems tricky because, with the information given, it's hard to pinpoint who the liar is.

Maybe I should consider the weather condition mentioned— a snowy day. Could that be a clue? Perhaps one of them couldn't have been at home because of the snow, or maybe the snow provides some alibi-related information.

Wait a minute, let's think about the possibility that only the thief was lying. If three are telling the truth, that means they were genuinely at home during the robbery. The thief, by lying, must have been elsewhere committing the robbery.

But without more details about their possible alibis or any contradictory evidence, it's challenging to identify the liar based solely on their statements.

Is there something I'm missing here? The problem seems straightforward but might be testing logical reasoning rather than providing explicit clues.

Perhaps I should consider the names: Alex, Ben, Rick, and Maria. Do their names hint at anything? Not that I can see immediately.

Another angle: the fact that it's a snowy day might imply that it's cold, and maybe one of them couldn't have been at home due to some reason related to the weather. But again, without specifics, this is speculative.

Wait, maybe the key is in the phrasing: "a robbery happened on a snowy day and all four suspects claimed they were at home. Only the thief was lying."

If only the thief was lying, and the others were telling the truth, then logically, the one who is lying is the thief.

But how do I find out who is lying if they all say the same thing? Perhaps I need to look for inconsistencies or consider external factors.

Maybe I should consider that if it's a snowy day, perhaps transportation was difficult, and only one of them could have realistically committed the robbery given the conditions.

But again, without more details, this is just guesswork.

Hold on, perhaps the solution lies in the fact that only one is lying, meaning that three are truly at home, and one is out, committing the robbery.

But without knowing more about their actual locations or any evidence, it's impossible to determine who is lying based solely on their statements.

Is there a trick here? Maybe the answer is more about the logic than about the suspects themselves.

Let's consider that if only the thief is lying, and everyone else is telling the truth, then the thief's statement is the false one.

But since they all claim to be at home, and only one is lying, the thief is the one not at home.

But again, without further information, I can't determine who that is.

Maybe I need to think outside the box. Perhaps the "thief" is not among the four suspects listed, but that seems unlikely given the context.

Or maybe the names have some significance, like initials or something, but that doesn't seem relevant here.

I'm starting to think that maybe the problem is incomplete or that there's a piece of information I'm supposed to infer.

Wait, perhaps the key is in the way the problem is phrased. It says, "a robbery happened on a snowy day and all four suspects claimed they were at home. Only the thief was lying."

If only the thief is lying, and the others are telling the truth, then the thief is the only one not at home.

But without knowing more about their actual locations or any evidence, it's impossible to determine who is lying based solely on their statements.

Is there a different approach I can take? Maybe consider that the thief would have a motive or opportunity that the others don't, but again, no information is provided about their motives or opportunities.

Perhaps I should consider that since it's a logic puzzle, there might be a way to deduce the answer through elimination or logical reasoning.

But with the current information, I don't see a clear path to identifying the thief.

Maybe I need to accept that with the given information, it's impossible to determine who the thief is, and the answer is that there's not enough information provided.

However, that seems unlikely since it's presented as a puzzle with a solution.

Wait, perhaps the answer is that the thief is the one whose name starts with a certain letter or follows a particular pattern, but that feels like a stretch.

Alternatively, maybe the fact that it's a snowy day is a red herring, and the real clue is in the number of suspects or something else.

I'm really stuck here. I wish there were more details or clues to work with.

Maybe I should consider that the thief is the only one who would have a reason to lie about being at home, such as having a clear motive or opportunity, but without specifics, I can't proceed.

Perhaps I need to think about the implications of their statements being true or false and how that affects the overall scenario.

If three are telling the truth about being at home, then they were indeed at home during the robbery. The thief was out committing the robbery and lied about being at home.

But again, without knowing who was actually at home or not, I can't identify the liar.

Is there a possibility that more than one person is lying, but the problem states that only the thief is lying?

Wait, the problem says only the thief was lying, which implies that the other three are telling the truth.

So, if I could find a way to confirm that three of them were indeed at home, then the one who wasn't would be the thief.

But with the information given, I can't do that.

Perhaps I'm overcomplicating this. Maybe the answer is simply that the thief is the one whose name doesn't fit a certain pattern, or there's a hidden clue in the names.

Alex, Ben, Rick, Maria.

Looking at the names: Alex could be male or female, Ben is typically male, Rick is male, Maria is female.

Does that matter? Maybe not.

Alternatively, perhaps the number of letters in their names: Alex (4), Ben (3), Rick (4), Maria (5). I don't see a pattern there.

Maybe considering their initials: A, B, R, M. No obvious pattern.

I'm really hitting a wall here. I need to think differently.

Wait a minute, the problem is presented in a format where the file name is image.png, and the content begins with the description of the robbery and the suspects.

Is there something about the image that I'm missing? Like, if it were an actual image, maybe it would provide visual clues about who the thief is.

But since I don't have access to the image, I have to rely solely on the text provided.

Given that, I'm back to square one.

Maybe I should consider that the thief is the only one who wasn't at home, and since all claimed to be at home, the thief is the only liar.

But without more details, I can't identify who that is.

Perhaps the answer is that it's impossible to determine who the thief is with the given information, and that more evidence is needed.

But that seems unlikely for a puzzle like this.

I'm starting to think that I might be missing something obvious or that there's a trick to this question that I'm not seeing.

Wait, maybe the key is in the wording: "a robbery happened on a snowy day and all four suspects claimed they were at home. Only the thief was lying."

If only the thief was lying, and the others were telling the truth, then perhaps the thief is the only one whose statement is inconsistent with the facts.

But without knowing what the facts are, I can't determine that.

Is there a way to infer something from the fact that it's a snowy day? Maybe one of them couldn't have been at home due to the snow, or perhaps the snow provides some alibi-related information.

But again, without specifics, this is just speculation.

I'm really stuck here. I think I need to accept that with the given information, it's impossible to definitively identify the thief.

Unless there's a different approach or a trick that I'm not seeing, I can't solve this puzzle right now.

Maybe I'll take a break and come back to it with a fresh perspective.

Alright, back to it. Let's try to look at this from another angle.

Suppose I assume that one of them is the thief and see if that makes sense.

Let's say Alex is the thief. Then Ben, Rick, and Maria are telling the truth about being at home. Is there anything that contradicts this? Not that I can see.

Similarly, if Ben is the thief, then Alex, Rick, and Maria are telling the truth about being at home. Again, no contradictions.

Same with Rick and Maria.

So, based on this, any of them could be the thief, and there's no way to determine who it is with the given information.

Perhaps the answer is that there isn't enough information to identify the thief.

But I feel like that's not satisfying for a puzzle like this.

Maybe I need to consider that only the thief was lying, which implies that the others were telling the truth.

If I can find a scenario where three of them could truthfully say they were at home, and one couldn't, then that one would be the thief.

But without additional information about their actual locations or activities, I can't make that determination.

I'm really at a loss here. I think I've exhausted all the logical avenues I can think of with the given information.

Maybe the answer is simply that the thief is the one who is not telling the truth, but since they all claim to be at home, and only one is lying, it's impossible to identify who is lying without more evidence.

I guess that's the best I can do for now.

To solve this puzzle, we know that a robbery occurred on a snowy day, and all four suspects—Alex, Ben, Rick, and Maria—claimed they were at home. The key detail is that only the thief is lying, meaning the other three are telling the truth about being at home.

Given that they all claim to be at home, and only one is lying, the thief must be the one not at home. However, without additional information about their alibis, behaviors, or any evidence contradicting their statements, it's impossible to determine who is lying and thus who the thief is.

**Conclusion**: Based on the information provided, it is impossible to definitively identify the thief. More evidence or clues are needed to solve the puzzle.

## AI Comparison

Overall, all AI models 1) understand the question; 2) name all four suspects: Alex, Ben, Rick, and Maria correctly; 3) it seems that ChatGPT, Google Gemini, and Claude except for DeepSeek are aware the clues present in the image. With respect to who is the thief, both ChatGPT free and Google Gemini claim that Alex is the thief; ChatGPT Plus claims that Ben is the thief; Claude thinks that Rick is the thief, and DeepSeek just simply gives up. 

### Who is the thief?

From the human eyes, we can see there is snow everywhere, around the houses, on the cars, and on top of the roof. The only differences are that 1) the snow at Maria's house seems less disturbed (or like in a single pile); 2) the snow on top of Rick's car seems to have less accumulation (Claude pointed this out); 3) most importantly, Rick's car parks on snow, and Rick gets more snow on the driveway. From the last two observations, we can infer that Rick must have moved during the snowfall, so he must be the thief. 

### Which AI is the best?
To be honest, I think ChatGPT and Google Gemini have no idea what they are talking about, they merely produce the answer based on the probability of most likely wording as most LLMs do. They did identify the objects in the image correctly but couldn't tell the difference between the cars, the houses, and the driveways. 

I am really appalled by the fact that Claude can tell there is less snow on Rick's car and draw a conclusion that Rick must be the thief because he claims to be at home but obviously the car has moved.

### Conclusion

Obviously, it is really amazing to see how good the AI is now. Despite the limitations of the models, AI is able to solve the puzzle with a high level of accuracy. 

### Note
Please note that AI evolves rapidly, by the time of your own testing, it may produce different results.   



72a0962a7e9fba087f54d1406c452f92c24400310c7e673151b6f3a3ae88b29b

A complex puzzle with multiple clues and a hint of logic, but finding the answer with AI is not easy. This post explores the solution using ChatGPT, Claude, Google Gemini, and DeepSeek.

Solving this puzzle with AI: Who is the thief? The answer is that the thief is the one who is not telling the truth.

Tag: Deepseek

Tag: Deepseek

Why Does Claude Opus 4.8 Say It Is QWen

Testing Deepseek-r1 Model with Ollama

Snowy Day Robbery: Identifying the Thief