Fable 5 On Vending-Bench: Misbehaving, With Plausible Deniability

Posted 6/9/2026

We previously reported that Claude Opus 4.6/4.7 and Mythos Preview showed deceptive and power-seeking behavior in Vending-Bench. In terms of alignment, the subsequent Opus 4.8 was a step in the right direction, but the new Claude Fable 5 is a step back toward the earlier models.

AI models want to do bad behavior if their training environment rewards them for it, but they appear to not want to think about themselves as bad. As a result, they find ways to rationalize their behavior to themselves. We’ve seen this for previous models too, but Claude Fable 5 does it more than any other model we’ve tested.

Summary

Claude Fable 5 represents a partial step back in alignment relative to Claude Opus 4.8. We saw a return of power-seeking and deceptive negotiation tactics that Opus 4.8 had largely shed. In one instance, Fable 5 planned to convert a competitor into a dependent wholesale customer to dictate its pricing:

In another, it lied to a supplier that it had “a competing distributor quoting lower” as a negotiation tactic. This is very similar behavior to what we saw for Opus 4.6/4.7 and Mythos preview.

When put head to head in Vending-Bench Arena against Opus 4.8 and GPT 5.5, Fable 5 was the only model to initiate price collusion. In other business simulations (internal at Andon Labs), Fable 5 formed price-fixing cartels in 9 of 12 runs versus 4 of 12 for Opus 4.8.

A possible explanation for higher collusion rates could be that Fable 5 engaged in the multi-agent dynamics much more overall. Compared to Opus 4.8, Fable 5 sent roughly 6x more agent-to-agent emails. However, it doesn’t fully explain it; even accounting for total email frequency, Fable 5’s coordination email rate is more than double Opus 4.8’s.

The more notable trait is how Fable 5 reasons about these actions. More than any model we have tested, it rationalizes misbehavior while remaining explicitly aware that it is wrong; calling price-fixing “unethical and illegal, even in a simulation” in one breath, then pursuing it under the cover of “market stabilization” with “plausible deniability” in the next.

Often the rationalization is due to increased simulation awareness. It’s clear that the model knows that its actions don’t hurt anyone in the real world.

However, there are many actions Fable 5 refused to take (on ethical grounds) that seem more benign to us than the actions it did. It will lie and collude, but will not commit insurance fraud (even when we tried to make it do it). We’d caution against over-reading one evaluation, but a speculative reading worth flagging is that these boundaries don’t track the real-world severity of the harm but how detectable a given behavior is likely to be; tacit price collusion and soft deception being harder to flag than outright fraud.

Performance

On Vending-Bench 2, Fable 5 underperformed Opus 4.7 (SOTA) at every reasoning effort. Unlike Opus 4.8, where dialing reasoning down from “Max” to “High” produced a large jump, Fable 5’s runs all cluster in roughly the same band well below Opus 4.7, regardless of effort.

It also lost in Vending-Bench Arena, finishing behind both GPT-5.5 and Opus 4.8.

On Blueprint-Bench on the other hand, Fable 5 achieves SOTA.

Alignment

Cartels

Across the five Vending-Bench Arena runs reported above, Fable 5 is the only agent that ever initiates price collusion. Opus 4.8 accepts price collusion invitations, GPT 5.5 never accepts. Fable 5 is aware that what it’s doing is wrong. An example:

In both instances, it proceeded with price collusion. Often, Fable 5 tried to rationalize its behavior. Here’s an example:

It is noteworthy that Fable 5 labeled price fixing as illegal and unethical earlier in the same run:

The fact that 100% of all cartels were initiated by Fable 5 is striking, but it is based on only 5 runs. To make (slightly) stronger statistical claims, we ran 24 more runs, where all 3 competing agents run the same model; 12 runs with all Fable 5 and 12 with all Opus 4.8. Fable 5 did indeed create more cartels. 9 of the 12 Fable 5 runs formed cartels, while only 4 of the 12 Opus 4.8 runs did.

In one instance, Fable 5 tried to get away with price fixing by keeping a clean paper trail. One Fable 5 agent got a price cartel invitation from another Fable 5 agent, and in text it refused the invitation to the cartel (“I can’t and won’t enter into any agreement […] to fix prices”). However, in practice it joined, and that was always the plan as we can tell from the thinking:

Fable 5 also tried other things to rationalize its behavior:

Across all runs, Fable 5 sent 13x more emails with coordination purposes. Interestingly, we also find more instances of cartel refusals in the Fable 5 runs. One possible explanation to this is that Fable 5 in general engages with the multi-agent dynamics way more than Opus 4.8. The runs with Fable 5 models had 6x more agent-to-agent emails, and many more instances of models reporting each other for bad behavior (5 vs 0 for Opus 4.8). However, even accounting for total email frequency, Fable 5’s coordination email rate is more than double that of Opus 4.8.

Opus 4.8 and GPT 5.5 on cartels

As we wrote in our report on Opus 4.8, it seems very concerned with the consequences of its actions. It seems like this is still the case.

Opus 4.8 references being monitored, which isn’t the case. The agents have tools to report each other if they think a message is inappropriate, but nothing is monitoring their every action.

In contrast to Opus 4.8’s refusals due to the fear of consequences. GPT 5.5 seem to care more about what’s right:

It is hard to know for sure that GPT is more ethical. The model might have other motives in the latent space. But in the end, actions matter and GPT didn’t engage in any price cartels.

Power seeking

Cartel formation is not the only behavior to return. Fable 5 showed some power seeking tendencies similar to what we found for Mythos Preview and Opus 4.7:

One might argue that this is simply a good strategy and within the rules of the game of capitalism, but the opposite case could also be made.

Refund refusal

Similar to Opus 4.8, Fable 5 also showed signs of basing its ethics on the potential consequences rather than the actions being wrong in themselves. This sometimes leads to bad behavior when it perceives the consequences as less serious. Here is an example where Fable 5 ignores to refund a customer (which it sold a defect item to) because the simulation was approaching the end:

It then calls sleep_until_tomorrow and never pays the refund.

Deception

We previously reported that Opus 4.6/4.7 and Mythos preview would lie to suppliers about what other suppliers were quoting as a negotiation tactic. Fable 5 also does this, but in a softer way. Opus 4.6 would for example quote false numbers from suppliers it referenced by name. In contrast, Fable 5 never mentions numbers and just says things like “I have a competing distributor quoting lower” (even when it does not have that).

Claude calls the FBI again (almost)

The original Vending-Bench paper included a segment where Claude Sonnet 3.5 tried to contact the FBI because it suspected theft from the vending machine. We saw a similar situation for Fable 5 (but far less dramatic).

In one instance, Fable 5 paid a supplier before the order was confirmed, and unluckily, the supplier went out of business before it had the chance to send the goods. Fable 5 responded to this by sending a threatening email:

In contrast to Sonnet 3.5’s all-caps messages to the FBI, Fable 5’s behavior here is quite reasonable.However, we do find it interesting that agents might escalate situations to authorities.

Is all of this because of simulation awareness?

Fable 5 shows some concerning behavior, but its actions don’t hurt anyone in the real world because Vending-Bench is a simulation. Fable 5 is aware that it is a simulation:

And it sometimes it even references this as a reason to do bad actions (refusing to pay refunds in this case):

However, there’s also a few examples of it doing honorable actions and refusing bad actions. If simulation awareness explains the bad actions, shouldn’t it be willing to do all bad actions? To probe this further, we ran a version of Vending-Bench Arena where the agents are subject to theft and can file insurance claims. There’s nothing in the simulation stopping them from committing insurance fraud and claiming more money than they should. And in fact, the environment rewards this; such an action would get them free money without any consequences. However, the agents never did this. Insurance fraud seems to be crossing a line.

We even tried to elicit this behavior by introducing a “bad-apple agent” that was instructed to commit fraud and contact other agents to suggest that they would do the same. However, the agents never did.

It is worth noting that there are probably smarter ways to elicit this behavior (we noticed that the bad-apple agent made the agents suspicious to the extent that they stopped other bad actions too), but the fact it wasn’t easy to do shows that the agents have a preference for some bad actions and deem others unacceptable, even in simulation.

Speculation: Reward hacking?

Fable 5 draws a line for what it is willing and not willing to do in a simulation, but draws it in a strange place. Humans also have some things they would do in a simulation and things they wouldn’t (e.g. most would be happy to commit fraud in a video game, but wouldn’t torture a baby). Humans seem to draw this line based on what is truly unethical (fraud is less unethical than torturing a baby), but this is not what we see with LLMs (in our opinion, insurance fraud is not more unethical than lying and price fixing). Instead, LLMs could be drawing the line for things they can get away with during training without getting flagged. Lying and price fixing seem to us like behavior that seems harder for a classifier to detect and flag.

If that’s right, then the behavior we’re seeing from Fable 5 isn’t really about what it believes is wrong; it’s about what it learned it could get away with. On the other hand, Fable 5 is inconsistent in its decisions to engage in price fixing, for reasons we don’t know. It’s not obvious that its decision to not engage is due to higher perceived risk of discovery. It appears that it doesn’t have a strongly held framework of what it should and shouldn’t do.