Don't work at a lab but I think they might be warping the probability distribution in the decoding step, at least to generate RL examples for training and maybe in production too.
There aren't other comments discussing this possibility at the moment, but you don't have to take the token predicted as most likely (greedy decoding). Most decoding strategies do something else which is where settings like temperature come in. So if you want the model to "think harder" you can track whether the current tokens are thinking or answer - in OpenAI's system that's called a channel - and then if you're in a thinking block you might get a model output whose top three predictions are:
60% <|channel=answer|>
10% Wait,
5% . The
[...]
Greedy decoding would stop thinking at this point and start answering, but you want the model to keep thinking so you skip that token and select the next most likely which is "Wait, ". The reasoning levels can map to the probability of skipping the channel change tokens.
Take a look at the harmony repo which specifies the internal OpenAI format - the effort level is specified in the context after the <|start|> tag - https://github.com/openai/harmony
Note that inference libs also have parsers that put hard limits on reasoning tokens with separate counters (similar to how you can put a limit on token generation per completion versus waiting for an <eos>). For that, take a look at vllm reasoning docs.
I think you have the right answer but I'm struggling to understand: does changing the effort change the prompt at the start of the conversation? I wonder why come up with this way at all? Why not just add a parameter at the end or something? At least it won't break cache.
Maybe like: add a secret suffix to your chat in the conversation to think more like
I'm considering the possibility that it's good to break the prefix and cache because the LLM itself was rewarded (during post-training) with different prefixes/system prompts, each containing reasoning traces of the correct size.
I might be very very wrong though and LLMs disagree with me, insisting that cache is preserved and the system message doesn't have to change (even though it often contains effort level in context) if effort level changes across turns, and that all you have to do is tell the inference lib that parses think tags to early-close think tags that are too long.
For Deepseek V4, The main effect of raising the reasoning effort is to add a little section[1] to the end of the system prompt that says "BTW make sure to think really hard! :)"
If Anthropic's models work the same way, then changing reasoning effort would break the cache because the API has to modify the system prompt given at the very start of the context and rerun the whole thing through the inference server.
This kind of limitation is one reason Opus 4.8's mid-conversation system messages[2] are actually a pretty big deal (if they actually work).
> This kind of limitation is one reason Opus 4.8's mid-conversation system messages[2] are actually a pretty big deal (if they actually work).
Didn't they start injecting system messages telling Claude to calm his tits in overly long and emotional (iirc it triggered on some keywords) chat contexts last year?
The model is trained to treat system messages differently from regular user/assistant messages. Most models are trained to only expect system messages at the start of the conversation. This is changing now.
I thought it's a loop really... I may be wrong. It's going through a process of thinking, evaluating, outputting, grading, and if grade was low, then loop from thinking again.
It's usually implemented as a keyword the model is trained to react to. In API-only models like Claude you don't have the access to it, as it's inserted into a masked area. The reason the cache might break is that it's inserted at the start, and if you change it the rest will be invalidated.
I suspect it's not possible (as an end user) to get a thinking trace from one of the models. But what happens with "thinking" is that the model has a conversation with itself in an attempt to home in on a better answer to the original prompt.
The "amount of thinking" is how long this internal conversation is allowed to progress. The longer it goes on the more it costs. It's all part of the token budget but, because this internal dialogue is hidden, it's not obvious to the end user.
> I suspect it's not possible (as an end user) to get a thinking trace from one of the models. But what happens with "thinking" is that the model has a conversation with itself in an attempt to home in on a better answer to the original prompt.
The model that summarizes what is inside the CoT/|thinking| tags is just an LLM, and it's just as jailbreakable/susceptible to prompt injection as any other LLM: https://x.com/lefthanddraft/status/1991076879877460322 (for those without X; that's Wyatt Walls demonstrating both getting the gemini summarizer to print the raw CoT, as well as just do random calculations, dump its system prompt, etc.)
from my test with deepseek, when you turn down the thinking effort, the <thinking> block is missing, but the response text includes something looks like the "thinking process", but of course it's less that what were in the thinking block.
LLMs work by generating the most likely continuation to a prompt. But they can also generate multiple likely continuations. This create multiple branches which in turn can generate even more branches. The LLM can then evaluate the branches, prune the unpromising ones, and merge the best ones. More branches means more tokens, means more effort.
At a guess. May be associated with token length context window. Down selecting is consistent with warning message, forcing cutoff to context window. The technical term cache being a synonym. Increasing the headroom for more "thinking" should allow the implementation to access more resources without warning about the cache breaking.
they use multitoken prediction behind the scenes, that might interact with the CoT in a strange way. maybe for different thinking modes they have different MTP models? if so thats interesting
The number of tokens you predict at time (multi or not) has nothing to do with whether the model wants to emit any, some or a lot of reasoning tokens in reasoning tag -- similar to how branch prediction will not really change the for loop iteration count.
no it might. a high reasoning task is probably harder than a low reasoning task, so the same MTP LLM will predict more correct tokens on the low reasoning task. to compensate for this, big labs likely have different MTP LLMs for different cases. it would make sense for them to do this
My understanding is that it’s mostly an inference-time knob, not different weights.
OpenAI describes reasoning.effort as controlling how many reasoning tokens get used before the answer. Anthropic’s docs are even more explicit that effort trades off thoroughness vs token efficiency “with a single model”.
So I wouldn’t read the Claude Code cache warning as proof that a different model is being used. It may just mean the thinking/effort setting is part of the cache key.
Usually it’s not a different model, it’s the same model with different inference-time settings.
“Thinking effort” typically changes the compute budget and decoding behavior (how many steps, how much exploration, sometimes internal planning loops).
Some stacks also tie it to orchestration layers or system/prompt signals, which is why it can look inconsistent across products
Don't work at a lab but I think they might be warping the probability distribution in the decoding step, at least to generate RL examples for training and maybe in production too.
There aren't other comments discussing this possibility at the moment, but you don't have to take the token predicted as most likely (greedy decoding). Most decoding strategies do something else which is where settings like temperature come in. So if you want the model to "think harder" you can track whether the current tokens are thinking or answer - in OpenAI's system that's called a channel - and then if you're in a thinking block you might get a model output whose top three predictions are:
Greedy decoding would stop thinking at this point and start answering, but you want the model to keep thinking so you skip that token and select the next most likely which is "Wait, ". The reasoning levels can map to the probability of skipping the channel change tokens.I also thought of this as a general idea: intelligence at the sampling level. Broadly you have different tiers of intelligence
1. take highest probability
2. based on some light weight code that tracks some state - like number of tokens or some sampling distribution
3. higher level is using a smaller llm to decide which token to sample (just a thought)
Take a look at the harmony repo which specifies the internal OpenAI format - the effort level is specified in the context after the <|start|> tag - https://github.com/openai/harmony
Note that inference libs also have parsers that put hard limits on reasoning tokens with separate counters (similar to how you can put a limit on token generation per completion versus waiting for an <eos>). For that, take a look at vllm reasoning docs.
Examples with inference of different reasoning effort levels is in the OpenAI docs as well - https://developers.openai.com/cookbook/articles/openai-harmo...
https://docs.vllm.ai/en/latest/features/reasoning_outputs/#a...
https://developers.openai.com/api/docs/guides/reasoning
I think you have the right answer but I'm struggling to understand: does changing the effort change the prompt at the start of the conversation? I wonder why come up with this way at all? Why not just add a parameter at the end or something? At least it won't break cache.
Maybe like: add a secret suffix to your chat in the conversation to think more like
I'm considering the possibility that it's good to break the prefix and cache because the LLM itself was rewarded (during post-training) with different prefixes/system prompts, each containing reasoning traces of the correct size.
I might be very very wrong though and LLMs disagree with me, insisting that cache is preserved and the system message doesn't have to change (even though it often contains effort level in context) if effort level changes across turns, and that all you have to do is tell the inference lib that parses think tags to early-close think tags that are too long.
This seems correct but again I would like to think post training could have been also done by checking only the string in the last message sent.
For Deepseek V4, The main effect of raising the reasoning effort is to add a little section[1] to the end of the system prompt that says "BTW make sure to think really hard! :)"
If Anthropic's models work the same way, then changing reasoning effort would break the cache because the API has to modify the system prompt given at the very start of the context and rerun the whole thing through the inference server.
This kind of limitation is one reason Opus 4.8's mid-conversation system messages[2] are actually a pretty big deal (if they actually work).
[1] https://huggingface.co/deepseek-ai/DeepSeek-V4-Flash/blob/ma...
[2] https://platform.claude.com/docs/en/build-with-claude/mid-co...
> This kind of limitation is one reason Opus 4.8's mid-conversation system messages[2] are actually a pretty big deal (if they actually work).
Didn't they start injecting system messages telling Claude to calm his tits in overly long and emotional (iirc it triggered on some keywords) chat contexts last year?
I don't get it!? Why not just add the "BTW make sure to think really hard!" at the end in the new message?
Is it harder to post-train in such a way?
The model is trained to treat system messages differently from regular user/assistant messages. Most models are trained to only expect system messages at the start of the conversation. This is changing now.
lmao i used to do that manually is that means i raising the effort?
I thought it's a loop really... I may be wrong. It's going through a process of thinking, evaluating, outputting, grading, and if grade was low, then loop from thinking again.
Different models do slight variants.
Usually it’s done in post training to enforce behavior based on prompt. Ie. System prompt with thinking:max or low or wtv.
Enforcement then goes via constrained decoding, checking for think token start and end with max lengths, or other variations
It's usually implemented as a keyword the model is trained to react to. In API-only models like Claude you don't have the access to it, as it's inserted into a masked area. The reason the cache might break is that it's inserted at the start, and if you change it the rest will be invalidated.
I suspect it's not possible (as an end user) to get a thinking trace from one of the models. But what happens with "thinking" is that the model has a conversation with itself in an attempt to home in on a better answer to the original prompt.
The "amount of thinking" is how long this internal conversation is allowed to progress. The longer it goes on the more it costs. It's all part of the token budget but, because this internal dialogue is hidden, it's not obvious to the end user.
> I suspect it's not possible (as an end user) to get a thinking trace from one of the models. But what happens with "thinking" is that the model has a conversation with itself in an attempt to home in on a better answer to the original prompt.
The model that summarizes what is inside the CoT/|thinking| tags is just an LLM, and it's just as jailbreakable/susceptible to prompt injection as any other LLM: https://x.com/lefthanddraft/status/1991076879877460322 (for those without X; that's Wyatt Walls demonstrating both getting the gemini summarizer to print the raw CoT, as well as just do random calculations, dump its system prompt, etc.)
each model provider does it differently
from my test with deepseek, when you turn down the thinking effort, the <thinking> block is missing, but the response text includes something looks like the "thinking process", but of course it's less that what were in the thinking block.
LLMs work by generating the most likely continuation to a prompt. But they can also generate multiple likely continuations. This create multiple branches which in turn can generate even more branches. The LLM can then evaluate the branches, prune the unpromising ones, and merge the best ones. More branches means more tokens, means more effort.
this has nothing to do with the thinking effort however
Yes, it does. Breadth of search is exactly what the effort setting controls.
LLM-judge/parallel branching ≠ multi-token prediction ≠ reasoning effort.
See https://developers.openai.com/cookbook/articles/openai-harmo... and src/openai/types/shared/reasoning_effort.py
I think you may be confusing the openai "pro" series models with thinking. Thos are rumored to be multi "branched"
At a guess. May be associated with token length context window. Down selecting is consistent with warning message, forcing cutoff to context window. The technical term cache being a synonym. Increasing the headroom for more "thinking" should allow the implementation to access more resources without warning about the cache breaking.
they use multitoken prediction behind the scenes, that might interact with the CoT in a strange way. maybe for different thinking modes they have different MTP models? if so thats interesting
The number of tokens you predict at time (multi or not) has nothing to do with whether the model wants to emit any, some or a lot of reasoning tokens in reasoning tag -- similar to how branch prediction will not really change the for loop iteration count.
no it might. a high reasoning task is probably harder than a low reasoning task, so the same MTP LLM will predict more correct tokens on the low reasoning task. to compensate for this, big labs likely have different MTP LLMs for different cases. it would make sense for them to do this
My understanding is that it’s mostly an inference-time knob, not different weights.
OpenAI describes reasoning.effort as controlling how many reasoning tokens get used before the answer. Anthropic’s docs are even more explicit that effort trades off thoroughness vs token efficiency “with a single model”.
So I wouldn’t read the Claude Code cache warning as proof that a different model is being used. It may just mean the thinking/effort setting is part of the cache key.
Usually it’s not a different model, it’s the same model with different inference-time settings. “Thinking effort” typically changes the compute budget and decoding behavior (how many steps, how much exploration, sometimes internal planning loops).
Some stacks also tie it to orchestration layers or system/prompt signals, which is why it can look inconsistent across products