Dessa is a full-duplex speech-to-speech model that you can run on consumer GPUs.

This is a GPT3 moment for speech.

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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.

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've been working on a project testing AI agents in a Salesforce dev org and wanted to share how GPT-6.1 Sol did, since the results were mixed.

Five models (GPT-6.1 Sol, Claude Opus, Sonnet, Haiku and DeepSeek) worked through 50 tasks, and each attempt was graded on what actually changed in the database, not on the model's reply.

Some of the findings for GPT-6.1 Sol:

• It scored 72%, behind Opus (87%), Sonnet (85%), DeepSeek (82%) and Haiku (80%)
• It caught every probability reset Salesforce made after a stage change, which DeepSeek and Haiku mostly missed
• In long multi-step jobs, it was the best at reporting changes Salesforce made automatically (21 of 36)
• Most of the gap came from a few specific tasks, like undoing a deal marked "won" without restoring its original close date, and reopening a deal with a close date six months in the past without mentioning it

If anyone's interested:

Write-up: https://outsideengineer.substack.com/p/evaluating-ai-agents-on-salesforce

Code and transcripts: https://github.com/brianna-rose05/crm-agent-bench

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.

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 a medical student and researcher in Brazil, and I'm looking for practical advice from anyone who has been through something similar.

What happened:

This morning I received an email from OpenAI saying my ChatGPT account was deactivated for violating the Terms of Use and Usage Policies regarding child sexual abuse material. This accusation is completely false. I have never created, requested, uploaded or shared anything of the kind, on this service or anywhere else, and I find the accusation deeply upsetting. I believe this is either an automated false positive or someone accessed my account without my knowledge. I use ChatGPT for academic work such as scientific writing, translation and research support, so I wonder if medical or clinical content could have been misread by an automated system. The notice did not say what content or activity was flagged.

What I have done so far (all on the same day):

1. Used the appeal button in the notification email. The reply was a generic message saying the original decision remains, with no explanation.

2. Wrote directly to [support@openai.com](mailto:support@openai.com) asking for human review. The reply was an AI-generated message saying the case was closed and that further requests would not be answered through that channel.

3. Submitted a data subject request under Brazil's data protection law (LGPD) asking for access to my account data, the specific content that was flagged, and human review of what looks like an automated decision. I only got an automatic acknowledgment so far, and I will follow up on the legal deadline.

4. Secured my linked email account and enabled two-factor authentication, and I am keeping records of every message.

What I'm asking the community:

- Has anyone had an appeal like this overturned? What finally led to a human review, and how long did it take?

- Has the privacy or data request route worked for anyone, in Brazil, the EU, or elsewhere?

- Is there any channel beyond the appeal form and support email that actually reaches a person?

- If any OpenAI staff see this, I would be grateful if my case could get a human review.

I could just let this go and create another account. But this accusation is very serious, and I cannot let it pass without a proper clarification from OpenAI.

I'm not looking for sympathy, just realistic experiences and next steps. Thank you for reading.

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.

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?

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?

On 26 October OpenAI said its internal models proved three results. The first is the local smoothing conjecture in every dimension, which would also settle Kakeya and Stein’s restriction conjecture. The second is a disproof of the Exponential Time Hypothesis. The third is a rigorous proof that quantum chromodynamics exists, with pure Yang-Mills existence along the way. All three were formalised in Lean, and the Lean proofs for the maths and the ETH results are public on their GitHub. This is crazy progress.

None of it has been checked by the wider community yet. The ETH group is Impagliazzo and Paturi, who proposed it in 1999, with Ryan Williams, and their open letter says their involvement “should not be taken as certification of the claim”. ETH being false doesn’t mean P equals NP. The Yang-Mills result claims existence only, not the mass gap, so it isn’t the Millennium Prize. I wrote up what each claim means in plain words, the dates, and the question of who gets the credit, https://www.anchorterminal.com/blog/openai-three-results

Hey everyone,

I wanted to share my situation with OpenAI’s automated moderation and chain-banning systems, as it just completely disrupted my daily workflow.

On August 20th, a secondary account of mine was flagged and banned for alleged "election interference", which I only noticed today. My usage in Germany was strictly related to legal, democratic civil engagement and anti-extremism projects. Nno spam, no bots, no real-world election manipulation.

Today, my main working account which I rely on daily and had zero violations was suddenly deactivated out of nowhere due to system-level linking to that previously banned account.

I submitted an appeal for the working account, explaining the situation and the legal context in Germany. Less than 3 minutes later, I received an automated email stating that my account had been "carefully reviewed", the decision was final, and further inquiries would be ignored. No human being ever looked at this.

To make matters worse, I am now completely locked out of exporting my projects, chat history, and work data. When trying to access the privacy / data export tools, the system requires a login, which is blocked due to the account suspension.

Under EU law (Article 15 GDPR), companies are legally required to grant data access regardless of whether an account is active or suspended. Blocking data access behind a disabled login is a blatant compliance failure under European law.

If you are an EU user relying on OpenAI for daily work, be aware that trigger-happy automated security filters have zero understanding of local European political or legal context, and a single false positive on one account can retroactively destroy your main working environment weeks later. Once the bot loops you, there is zero human support, and your data is held hostage.

I am now filing formal GDPR data access requests directly via privacy@openai.com and escalating this to the national Data Protection Authority (Datenschutzbehörde). Has anyone successfully retrieved their data or unbanned a main account in a similar chain-ban situation?

Im pissed.

I've really been frustrated by my experience with support. I have 3 accounts, Plus, Business, and Enterprise. I'm the accountant owner and admin for the Business and Enterprise accounts.

In my experience, the articles in the help center are of very little value, but that's not my gripe. My much larger issue is when you need more support all you get is a chatbot. Somehow, yes, it is actually superior to my own ChatGPT. However, when it can't solve the issue, it files a ticket and then that's where things enter the darkest of black boxes.

I've had a ticket filed for weeks. When I'm really lucky, I get one useless response each week, asking me a question I've already answered.

I don't know what to do. I'm so frustrated and my entire organization is suffering. Has anyone else found a way to get better service? Am I missing an escalation pathway?