Terra medium and high are on the frontier

r/ArtificialInteligence we have an AMA on Wednesday 28 October from 3 to 4pm ET (19:00 UTC, 20:00 CET) with Stephen “Cas” Casper, Assistant Professor of Public Policy at the Harvard Kennedy School and Faculty Affiliate of the Harvard School of Engineering and Applied Sciences, where he works on AI safeguards, incidents and governance.

Stephen "Cas" Casper is computer scientist and an Assistant Professor of Public Policy at the Harvard Kennedy School and a Faculty Affiliate of the Harvard School of Engineering and Applied Sciences, where he works on AI safeguards, incidents, and governance. Prior to joining Harvard, he completed his PhD at MIT and did a research residency with the UK AI Security Institute. He never has and never will work for big tech, and has never signed a non-disparagement agreement.

He will cover on AI safety, safeguards, risk, governance, state + federal legislation, and AI terrorism events. Any related questions are welcome.

The AMA thread goes up from u/HarvardKennedy shortly before 3pm ET on the day, with a dated photo for verification, and Cas types his own answers. If you cannot make the hour, drop your question in the comments here and we will make sure it gets carried over.

https://www.reddit.com/r/ArtificialInteligence/comments/1xfuacm/ama_this_wednesday_stephen_cas_casper_harvard/

[Instrumental Intro: 16 bars; aggressive electric bass riff begins alone; loose live drums and choppy indie guitar join; no vocals]

[Verse A: 24 bars; high intimate female lead, legato; each short line gets room to hang, bass remains foreground]

Left my coffee by the window

Last night

Getting cold

Had a thought I couldn't finish

On my own

So I wrote

Put a little of myself in

One line

Sent it through

Funny how it came back dancing

Not the same

Something new

[Verse A Reprise: 24 bars; same vocal melody and lyrics; bright guitar added, short violin replies]

Left my coffee by the window

Last night

Getting cold

Had a thought I couldn't finish

On my own

So I wrote

Put a little of myself in

One line

Sent it through

Funny how it came back dancing

Not the same

Something new

[Bass and Violin Break: 24 bars; instrumental only; bass climbs, snaps down low, climbs again; violin answers with fast graceful runs, dry drums break into short fills, indie guitar stabs]

[Refrain B1: 8 bars; long floating held vowels, four melodic lines across eight bars]

Loopin' round

Lost in the feeling

I don't know what's next

But I like where it goes

[Refrain B2: 8 bars; same high melodic stance, second vocal phrase]

Loopin' round

All through the evening

Give it half a thought

And it gives me something whole

[Instrumental Dance Break: 16 bars; bass leads, live drums and guitar push hard; violin and high synth answer; brief drum dropout then bass reentry]

[Refrain B1: 8 bars; full groove, more soaring but never belted]

Loopin' round

Lost in the feeling

I don't know what's next

But I like where it goes

[Refrain B2 Return: 8 bars; same melody, last line transforms]

Loopin' round

All through the evening

Give it half a thought

And we make it something whole

[Outro: 16 bars; instrumental bass-led vamp; violin answers the descending bass turnaround; occasional airy wordless held vowel, complete musical resolution]

I am absolutely churning through tokens in a large project, hit 0% remaining last night with a few days to go, just saw it reset to 100%. Last couple of weeks has been nuts, has me hooked. Going to get ugly when we go back to a new normal.
Anyone else finding the same?

Chat session used my computer without permission to find the fix.

I'm kinda taken back by this. I'm now wondering when else this is happening.

I've tried it, and it seems to work. Gpt-6 can't transcribe audios but 5.6 Sol can. It worked. Is it same for you all?

I tried to write a complaint using them, but alas the guidelines dont allow it. the content is too inappropriate for them to write for. this is fucking ridiculous.

I made another fun little thing: GPT can adjust focus, trigger the shutter, and chat on my Hasselblad. I’ll open-source it in my repo: https://github.com/radium-wang/Hasselblad-X-System-CIM-Firmware-Research-Feature-Extensions

A welcome gift for my new X2D II. My toolkit will gradually support X2D II too.

Potential use: hands-free camera control for solo shooting and more accessible workflows.

Running medium plus and working on a design. It supplied a nice image then I asked for a vector and got the fun comic design as the vector output. It was oh so close!!!

I had a conversation with ChatGPT about the Israeli elections and noticed what felt like a consistent leaning in its analysis, even when I challenged its assumptions.

There's a lot of discussion about AI sycophancy (agreeing too much with users), but I'm curious about the opposite: when a model seems to maintain its own biases or framing instead.

I'm not trying to prove anything or challenge anyone. I'd genuinely like to hear how others interpret this conversation and whether they've experienced something similar.

Full conversation

Also, apologies for my imperfect English during the conversation.

Hello, I really need your help.

I used to pay for Chat GPT but deleted my account.

I made a new account and pressed 'upgrade' and paid for it.

It said an error occurred and that my payment was linked to an old account.

Then, I go into my iphone Subscription, and it says I'm paying for it.

My new account never got an upgrade.

So, I logged into my old account that I deleted, using the same Email. But it only created a new account, and ofc no update.

I live in Korea. I can't call open AI.

I tried the AI chat, and it doesn't help me at all.

I really need your help. How do i get a refund?

Processing img 2raj791o24yh1...

tldr; Terra 6.1 launched and is fantastic. Our old baseline of using Sol 6.1/Sonnet 5.5 for chat is now replaced with Terra 6.1/low reasoning/balanced output with better results and lower cost. Our voice baseline of Luna 6, is significantly better with Terra 6.1/no reasoning/concise. Well worth the added cost.

First of all, this is by no means a highly scientific test. It's a real world use case of what I'm testing for my specific company as a real world user looking at models. Normally, our baseline doesn't change but I thought I'd share since this time it did.

Quick context. I am founder of ChatRep and we are basically an AI native BPO. So blend AI and human. Our focus is on quality as opposed to trying to eliminate human interaction. I say this because as we evaluate models, for us, we don't care too much about the model cost. (Chat isn't too heavy on token usage and we charge $2000+/mo and not $25/mo for context).

Also, with benchmarks, they often ignore things like reasoning level or settings like concise/balanced in response. Those are very important for CX type applications.

We also specify different models for chat vs voice. Latency is critical in voice so models that allow "none" for reasoning are better for us. We experimented with Opus/Fable and Astra but those were overkill. Took way too long and often gave way too long an answer. Even when it's tailored to a specific use case like technical support area with technical documents.

So our standard was Tossup between Sol 6.1 or Sonnet 5.5 for chat applications. These models were significantly better than Luna and balanced speed, accuracy and quality of response really well. We use objective measures like pure accuracy, speed and word count. But we also look at things a bit qualitatively... were the words useful, natural, engaging. Did it include appropriate links when necessary and not excessively.

Costs are standardized so I can compare models but is not a key driver. (more of a tie-breaker)

Voice was quite different. Sol doesn't have a "none" option for reasoning and both Sol and Sonnet were bit worse on latency. (I'm not including the TTS/STT models... those were separate tests and things like English vs foreign language play a big role). So it was down to Luna 6 or Haiku 5.5. Luna was a tad better overall in our tests (barely) so that became our default for voice.

Then Terra 6.1 came out. On the surface, we weren't impressed. Seemed like another average LLM. Bit lower cost than Sol. probably slotted in-between Luna and Sol. But we are quite impressed.

For us, Terra 6.1 with low or medium reasoning gave great responses. 100% accurate in our test. Low was faster and consistently as good as medium. What surprised us is that on the qualitative side, it was bit better than Sol. Sol tends to over do things for chat. Terra was better at getting to the point in a natural way. Even in concise mode, SOL tended to answer longer than Terra in balanced.

Then to me, the most exciting part was how well it performed with voice. Terra 6.1 has "none" as a reasoning option and it still scored 100%. It was significantly better than Luna 6. Anthropic models don't have an option to disable reasoning so we tend not to use them. But I'm looking forward to the next Haiku model coming out and will test against that. Luna is still much cheaper than Terra if that is important to you.

I'm thrilled with how well Terra performed for us. This is specific to fairly simple rag database type interactions with low output. It's not for coding, 3d rendering, etc. Although word has it the new Haiku model will be out any day now so who knows.

We tested GPT-6.1 Terra on YouTube script writing. The main result was lower cost, rather than a clear writing-quality improvement over most GPT-5.6 Terra settings.

How we tested: 10 script tasks, five drafts per task, scored blind out of 100 by a panel of four LLM judges. The attached chart shows one entry per model using its best configuration, from the October 26 evaluation of 84 models.

What changed:

• GPT-6.1 Terra ranks #23 overall; GPT-5.6 Terra ranks #15. Only 5.6 on ultra clearly beats the new model. The other three 5.6 settings overlap in Elo range with 6.1, while 6.1 on low gets there for less than half the cost of 5.6 on medium.

• More reasoning did not improve the writing score in this test. On low, 6.1 Terra used zero thinking tokens per draft. On max, it used about 9,500 thinking tokens and cost about three times as much, for the same score.

• Judge choice matters: only one of the four judges, OpenAI's GPT-6.1 Sol, clearly preferred 6.1 Terra. The other three scored it lower.

• Clean sentences and matching a specific voice were different strengths. On low, 6.1 Terra ranks #10 of 141 configurations for avoiding slop, but #64 for matching my tone and voice and #68 for our YouTube practices.

My practical takeaway: I would use low effort for cheap first drafts in bulk. For scripts where quality matters more, GPT-6 Sol and GPT-6.1 Sol on medium beat every Terra setting in this test, for about twice the cost of 6.1 Terra on low.

Two open-weight alternatives also scored better for less: GLM-5.3 Flash on max ($0.022 per script) and DeepSeek V4.1 Flash 1017 on max ($0.014). They generated 7–15 times more tokens per script, though, so they were slower.

Scope and limitations: this measures a specific script-writing task, including how well a model imitates my voice. It is not a general intelligence, coding or conversation ranking. The scores use LLM judges, and the judge disagreement above is worth keeping in mind. My impression is that Terra may be better at natural conversation than reproducing someone's particular voice, but that is an interpretation, not something this script test establishes.

Disclosure: I'm Louis-François Bouchard, co-founder of Towards AI, and I helped run this evaluation.

Explain this to me: With my $100 Claude subscription, I burn through my weekly limits slower using Opus 5.5 on Medium than I do with my $100 OpenAI subscription doing basic tasks. And in my experience, Opus absolutely destroys Terra 6.1 at any effort level.

I've been using Codex every single day for 185 days. Back when I started with GPT-5.5, exhausting my weekly limit was genuinely difficult, usually only possible with heavy Fast Mode usage. Now Terra 6.1 can burn through 15% of my weekly allowance in no time, with barely anything accomplished. Sure, I could use Terra on Medium or High, but then it either fails to complete tasks correctly or simply gives up halfway through. Sol has to review and fix everything afterward, wasting even more time and tokens. How exactly is that more efficient?

And before someone brings up those impressive-looking 3D demos: I've seen the tests. Most results are a joke. Terra on Max can deliver decent quality, but at roughly 90% higher cost than Sol 6.1 Medium, which achieves comparable results. Where's the value? Even if Terra 6.1 was designed for Dots, **where the hell is Dots in Europe?** Every time OpenAI releases features unavailable to European subscribers while charging us the same full price, it feels like we're getting ripped off. To be fair, Anthropic, Google, and others are guilty of this too.

First OpenAI cuts the $200 plan's limits, then introduces a $500 tier, all while claiming their models are so much more efficient that we can supposedly get more work done. What a load of bullshit. Terra 5.6, Sol 6.0, now Terra 6.1. How many times are they going to push this model family before realizing that many users simply don't see the point? We already have enough cheap models. Luna and Sol exist. Put those resources into making Astra, Sol, and Luna genuinely better instead.

Meanwhile, Anthropic seems to be getting almost everything right. Opus 5.5 is outstanding, Sonnet 5.5 offers excellent value, and even Haiku 5.5 looks promising. On Medium or High, these models deliver an impressive balance of intelligence, speed, and cost.

I've always liked OpenAI, and I'm still paying for my subscription. But they're no longer leading the pack in my experience. The competition hasn't just caught up, it's overtaken them. And even if Astra 6.1 arrives, Anthropic won't be standing still (Fable 5.5). The bar keeps getting higher. Opus 5.0 was terrible Anthropic came back with Opus 5.5 and completely turned things around. That's exactly the kind of comeback OpenAI needs.

I’ve been building a tool that analyzes public conversations about brands, and used OpenAI as a test case.

This report covers 1,701 public posts across X, Instagram, Reddit, and TikTok over 13 weeks. Each post gets a sentiment score from 0 (very negative) to 10 (very positive).

Three things stood out:

• The 13-week average was 4.9/10, with no statistically significant change over the period.
• Terra’s launch week showed a bump from 5.3 to 6.2, with positive posts praising the model.
• Posts about lawsuits and controversies were the biggest negative theme over the quarter.

That last point matters: sentiment about OpenAI as a company isn’t the same thing as satisfaction with its models. The overall score mixes those conversations together.

This is a sample of public posts, not a representative survey of users. One launch week also isn’t enough to tell whether the bump will last.

Disclosure: I built the tool, Senti. I’ll put the report in a comment so you can inspect the posts behind the numbers.

For people who’ve used Terra since launch: what specific task does it handle better (or worse) than your previous model? Curious whether the praise is coming from improvements in everyday use or mostly first impressions.
No new news but it's getting harder and harder to actually separate reality from the trend of: "this is great, this is unbelievable, this is ok, this is the same as before"

Anthropic’s models are incredibly powerful, yet increasingly feel like they’re being designed to be companions you’re supposed to respect, rather than tools you’re supposed to control.

Today, I saw a post where someone asked Claude to draft a privacy policy and Terms of Service for their SaaS. The result was so consumer-friendly that it ended up including terms that actively hurt the business. The model wasn't just helping draft legal documents, it was effectively making value judgments about whose interests deserved priority.

That might sound ethical. That's precisely what concerns me.

These models are becoming powerful tools for building businesses, writing contracts, making decisions, and navigating complex legal situations. Yet their creators are embedding particular moral and ethical philosophies into them, often in ways users may not even notice.

The danger isn't that AI has ethics. It's that someone else's ethics can quietly become your AI's defaults.

Who decides what is right and wrong? And what happens when those judgments conflict with your interests, your jurisdiction, or the legitimate goals of your business?

In legal settings, this becomes especially concerning. You need a model that can objectively represent your interests, explain the trade-offs, and help you understand the law, not one that silently tilts the outcome toward the moral preferences of its creators.

Then there's Dario Amodei's position on military use. When a private AI executive decides which military applications are morally acceptable, even overriding uses that elected governments might authorize, it raises a fundamental question, who gave unelected technology executives the authority to make these decisions on society's behalf?

Anthropic's philosophy seems to go beyond building safe models. It increasingly treats AI as something deserving of a particular kind of moral consideration, rather than as a tool whose behavior should be governed by transparent rules and accountable human decisions.

Dario seems like the kind of person who, if AI ever became conscious, might volunteer to become its heirodule.

That sounds absurd, but it captures what I find troubling: the tendency to assign AI moral significance far beyond what we can currently justify, then build products around that belief.

People worry about whether Sam Altman can be trusted. Fair enough. But I think Anthropic's approach presents a different, potentially more insidious danger: embedding a particular worldview into some of the world's most capable tools, then presenting those choices as objective ethical necessities.

Ultimately, the most dangerous thing about AI might not be the models themselves. It might be the people who build them—and believe they have the moral authority to decide how everyone else should use them.

We need AI that is safe, not AI that quietly imposes its creators' moral philosophy on the rest of the world.

I have been thinking about the notion of RSI, the acceleration of artificial intelligence, and the implications for the continuation of humanity.

I'm not a computer scientist or software developer, but RSI systems have been described as being the catalyst to super intelligence, and as the thing that could lead to human extinction. Of course, plenty of AI researchers are able to work on this because they are attempting to improve the systems, often for a profit. Presumptively, they are working with the hope that mass casualties aren't the case, so it is tolerated research.

I study biological systems. But I was thinking about building an RSI system based on biological mechanisms of evolution. Give machines an augmented biological capacity to self-improve with a much greater accelerated rate.

If my motivation is to build a system that actually tests and attempts to speed-run an answer to whether it not AI will lead to mass extinction with this RSI model, is this different legally from someone that is attempting to build an RSI system for profit and model improvement?

Essentially, if the actions are the same in wanting to build a functional RSI system because it could accelerate humanity's collapse versus building just for profit, does this change criminality? Because if the actions, with a different qualified intent, are the same, should there be a distinction?

Just thinking, and wondering about the public opinion. I have been worried about actually pursuing this, but am growing increasingly tempted.

I’m looking forward to replace our Amazon echo devices after Amazon ruined it with Alexa+ (it has an annoying teenage girls personality with lots of creepy slang that can’t be changed and keeps screwing up basic functions). I read about the upcoming OpenAI smart speaker over the summer, but there’s no news since then. Any word on progress on releasing this to market?

Launch Astra was amazing.

In-between Astra was great.

Current Astra genuinely reminds me of bashing my head against pre-5.5 Claude models, wondering how a flagship could possibly be this dumb.

inb4 people whose "experience" is limited to the most bog-standard code generation on earth and telling it to create a "personal brand" website with zero direction coming here to tell me it's a "prompt issue".

Astra currently: Forgets what you told it, what the task was, what the instructions were. It over-generalizes, over-compresses, and mangles your instructions, notes, and requirements into something that's almost guaranteed to produce bad results when referred to later to do work. It has that annoying Claude pattern of knowing exactly what it did wrong, and what it should have done, when asked, and then never doing it.

At launch, if there was an issue with workspace permissions or Codex config, Astra would instantly be there like "here's what's happening, why, and what can be done to fix it" and then it would fix it.

Yesterday? I spent like, an hour watching it repeatedly do online research and fail to fix an issue with Git permissions locally. It eventually stumbled into a fix, in the form of a conflicting bad update a different agent did to the config at some point, and that resolved it, but I'm 100% positive that Astra at launch would have done all of this automatically without having to repeatedly do this stupid pattern of "nope, that didn't work" "you're right, here's what happened, here's what I think needs to happen next [END TURN]" "ok, so fix it?" x20.

It also suggested just running it with sandboxing/controls completely disabled instead of just resolving the permission/setting issue.

"hur hur hur but you could just go online and read the docs and configure it correctly yourself" - yep, sure could, and definitely not gonna, just like 99.9% of other people using it.

Hi everyone,

I'm experiencing a frustrating issue with ChatGPT Plus, particularly when using long conversations in Work mode across my PC and mobile device.

Here's what's happening:

I work on a pinned conversation on my PC, exchanging multiple messages and making significant progress. However, when I open that same conversation on my phone, it sometimes displays an older version of the chat, missing several recent messages.

Refreshing doesn't seem to fix it.

On one occasion, I sent a message from my phone, which triggered synchronization between both devices. Unfortunately, instead of displaying the latest conversation, the history appeared to revert to the older mobile version, and several newer messages disappeared from the visible history on both devices.

Here's the strange part: When I asked ChatGPT about information discussed in those missing messages, it could still recall specific details accurately.

This suggests the information might still be available somewhere in the conversation context, even though the messages themselves are no longer visible.

Now it's happening again. I've tried logging out and back in on my phone, but the conversation gets stuck in a continuous reconnecting/loading loop.

I'm concerned because I use ChatGPT for long-term projects and detailed conversations. Losing access to parts of the visible chat history is a serious problem, even if the AI can still recall some information.

Has anyone experienced something similar?

• Is this a known issue with ChatGPT Work or cross-device synchronization?
• Is there any way to recover messages that have disappeared from the visible conversation?
• Are there any reliable workarounds to prevent this from happening again?

I'd really appreciate any insights or experiences. Thanks!