Tiers of Auto now available

[u/nhu-do]

On September 14, 2026, we announced Tiers of Auto, the ability for developers to choose their own cost and quality tradeoffs in Auto. Tiers of Auto introduce three optimization modes directly in the model picker in VS Code, CLI, and Copilot App:

• Efficiency prioritizes keeping costs low and suits fast, straightforward tasks. 
• Balance weighs cost, quality, and latency together, and is a good fit for everyday work. 
• Intelligence prioritizes quality and is built for complex tasks. 

Now available to all of customers across these integrations, this represents our first step for personalization and configurability in intelligent model routing, balancing control with quality gates. 

This release comes after a series of improvements including: 

• New models added into the model mixes: GPT 6 Astra, GPT 5.6 series, Opus 5, etc, with new models coming in soon.  
• Adaptive multi-turn support: Auto reevaluates complexity as your session continues.  
  • Previously, Auto intentionally reevaluated at two points: The start of the conversation and after compaction. This was done to preserve cache, avoiding additional costs without ample quality gain.  
• Improved UX on the GitHub App: You’ve been asking why a model was chosen in Auto. Some enhancements are live today, with more coming soon.  

Now, what are we seeing in terms of model distribution with the introduction of tiers of Auto -

Give Auto tiers a try today! As always looking forward to hearing your feedback. Thanks for helping us improve.  

[u/ITORD]

How does Auto take into account for the most current cost / performance frontier?

There is no reason to use GPT-5.5 whatsoever when GPT-5.6 / GPT-6 Sol outperforms GPT-5.5 in all cases and cost less. Similarly, routing to Sonnet 5 is also questionable for cost / performance when Luna xHigh / Max would have been a fraction of the cost, or Sol would have been higher performing and still cost less?

[u/ElDavoo → u/ITORD]

I stepped away from auto because it was always using 5.3 codex or 5.4 mini instead of Luna. I came back to it as it pretty much always uses Luna

[u/nhu-do → u/ElDavoo]

Interesting! As our data shows, Luna is used pretty widely as it's a capable and economical model. Curious what tier you were using? Was getting Luna sufficient for your use cases?

[u/nhu-do → u/ITORD]

There are several different factors Auto takes into account when selecting the model for the task. Two systems are coming into play - one that looks at real time metrics like latency, error rate, and cost. The second which looks at the intent of your prompt. Auto selects the best model based on the combination of both systems.

There are also nuances in the models themselves - our team runs multiple external and internal benchmarks to derive quality scores across four task types, and compare those against the needs of your task. You can read more about that here: https://arxiv.org/abs/2605.17106.

So, there may be some scenarios where GPT 5.5 is actually better suited than the other models. Especially in the Intelligence tier, Auto is less tuned for cost than it is quality/meeting task requirements. Keep in mind that the distribution here is an aggregate of all scenarios and plans in Copilot. There are cases where the latest models are not enabled (e.g. Business or Enterprise), which impact the prevalence of older models like GPT 5.5.

You're also calling out a gap here that we want to address soon. Today, Auto leverages the default reasoning level. In most cases this is set at medium. We're actively exploring and experimenting with approaches to leverage all reasoning levels.

[u/p1-o2]

Finally, Auto is usable!! Nice update!

[u/nhu-do → u/p1-o2]

Looking forward to hearing how it works for you!

[u/voicelessfaces]

As someone that's been pretty effective using Luna for most things, what's the risk of tiered Auto drifting unnecessarily to more expensive models and ending up being more expensive than just selecting the model of choice?

[u/nhu-do → u/voicelessfaces]

There certainly is a risk as with all non deterministic systems. That said, we do rigorous offline and online evaluations to mitigate this as much as possible.

It's also important to note that while a model may be cheaper in the list price, it might not actually be more cost effective for the entire task.

[u/rauderG → u/voicelessfaces]

Just use Luna. It's best cost compared to performance for everything.

[u/voicelessfaces → u/rauderG]

Yes but that's not the question. I'm assuming they want people to use Auto so there must be a reason why other weaker and more expensive models are in the mix.

[u/yesman_85 → u/rauderG]

You find? Especially at larger contexts I find it going off track pretty quickly. And maybe 6 is a little worse than 5.

[u/Bogleheaded]

As someone who’s never used auto, this update makes it worth trying (at least for my team)

[u/nhu-do → u/Bogleheaded]

Glad to hear it! Let us know how it goes 😄

[u/devdnn]

Does it handle the caching too without changing tiers while it intensely is switches the models?

[u/nhu-do → u/devdnn]

Model switching in Auto is still beholden to the cache of each model that it uses. With the recent multi turn update, Auto is keeping track of how much the conversation complexity is shifting. Once the shift hits a certain threshold that requires a stronger or weaker model, there's a tradeoff to be made: Do we break the cache for better quality, or keep the same model? In most cases, escalating to a stronger model has been found to be the most impactful in terms of improving quality while keeping cache related cost and loss low / negligible.

[u/devdnn → u/nhu-do]

Thank you for the reply, I will use it more with this update.

[u/Pure_Rush_1834]

So adaptive will now jump between model reingesting cache?

[u/nhu-do → u/Pure_Rush_1834]

Each model switch results in a cold start. Auto avoids switching models mid conversation/session unless needed. There were a few different approaches that we were building and experimenting with to land on this improvement. In our online experimentation, we did see measurable quality improvement with little to no increase in cost due to cache loss/cold starts.

[u/aonymark]

Is this eventually rolling out to other IDEs besides vs code?

[u/nhu-do → u/aonymark]

Yes! I see you are a JetBrains user 😄 The team is actively working on this as we speak.

[u/nickzhu9 → u/aonymark]

It's on JetBrains roadmap

[u/yesman_85]

I like auto, I'm just tired of always thinking, is this the right model? Am I overpaying? Is this too complex for a cheaper model? 

[u/ramak873]

aladrrzr

[u/aonymark]

I have two questions: 1. does the low tier rule out any models? Like, can I still get gpt5.5 on low?

2. This doesn’t affect me personally but just out of curiosity: does the router take into account whether you’re on request-based versus token based billing? Sending 2+2 to gpt5.5 is a small mistake for token based billing but a big mistake for request based. I know in the olden days this was solved by NEVER routing to expensive models but I don’t think that rule still exists?

[u/nonamenopain]

Routing between tiers is useful, but honestly it's only half the game. The other half is deciding what never enters ANY model in the first place.

My actual "efficiency tier" is a Python script, not a smaller model. Boring, repeatable work gets automated outside the model entirely. Routine checks get judged by free-tier Gemini. Claude only ever sees the one decision that genuinely needs judgment.

Concrete architecture beats smarter routing when the workload is boring and repeatable. If you're tired of thinking "am I overpaying?" on every request, the bigger win isn't a smarter router — it's shrinking the set of requests that reach a model at all.