GPT-5.3 Codex & Claude Opus 4.6: 2 NEW Models Dropped Today (Full Breakdown)!

We just witnessed the most intense AI release date in history. Enthropic and OpenAI went head-to-head in what could only be described as an allout coding agent war. Enthropic dropped Cloud Opus 4. 6 at 9:45 a. m. Pacific. And then OpenAI, not wanting to be upstaged, launched GPT 5. 3 Codeex just 20 minutes later. This wasn't a coincidence. These companies were supposed to coordinate a 10:00 a. m. release. But Enthropic blinked first, moving their announcement to grab headlines, and OpenAI scrambled to respond. So, what did each company actually ship? Which model won? And what does this mean for developers and the future of AI powered work? Let's break down both releases, compare the benchmarks head-to-head, and figure out who's actually leading this race. So, let's get into it. Let's start with

Entropics release, Cloud Opus 4. 6. This is their smartest model yet, and it brings three massive upgrades. First, elite coding capabilities, better planning, longer sustained work on agentic tasks, more reliable operation in large code bases, and significantly improved code review and debugging. Second, a million token context window. The first time an Opus class model has had this. That's roughly 750,000 words of context. You could feed in multiple novels and it would track everything. Third, realworld performance that actually delivers. Companies are reporting that Opus 4. 6 genuinely follows through on complex tasks without constant handholding. Now let's talk about where Enthropic is claiming victory on the GDP vala which tests economically valuable knowledge work across finance legal and professional domains. Claude Opus 4. 6 destroys the competition. It beats OpenAI's GPT 5. 2 by 144 ELO points. In practical terms, that means it wins about 70% of head-to-head comparisons. Against his own model, Opus 4. 5, the gap is even wider at 190 ELO points. On Terminal Bench 2. 0, which measures realworld agentic coding and terminal skills, Opus 4. 6 achieved the highest score in the industry at launch. On humanity's last exam, which is a difficult reasoning test, it leads all frontier models. And on browser comp, which tests a model's ability to find obscure information online, Opus 4. 6 outperforms everything else on the market. But what's really cool is long context retrieval. On the eight needle 1 million variant test, essentially hiding information in a massive hay stack of text, Opus 4. 6 scores 76%. For reference, their previous model, Sonnet 4. 5, only hit 18. 5%. That's not a small improvement. That's a massive leap in context handling. Enthropic didn't just ship a better model. They rebuilt the entire ecosystem around it. On the API, developers now get adaptive thinking. The model decides when to use deeper reasoning instead of it being all or nothing. Effort controls now comes with four levels, low, medium, high, and max to balance intelligence, speed, and cost, and context compaction. Auto summarization when approaching context limits so agents can run longer tasks. and then 120k output tokens which generate massive outputs in a single request for product users. Cloud code can now support agent teams, multiple AI agents working in parallel on different subtasks coordinating autonomously. There's also claude in Excel which got major upgrades for multi-step workflows and longunning tasks and claude in PowerPoint launches in research preview reading your brand guidelines and layouts to generate onbrand decks. Oh, and pricing it stays the same. $5 per million input tokens and $25 per million output tokens. 20 minutes after

Enthropic's announcement, OpenAI fired back with GPT 5. 3 Codeex. And let's be clear, this wasn't a rush response. This was kind of a coordinated counterpunch that OpenAI had been preparing for a while. GPT 5. 3 Codeex combines the Frontier Coding performance of GPT 5. 2 codecs with the reasoning and professional knowledge of GPT 5. 2 to all-in-one model that's also 25% faster than its predecessors. But OpenAI made a bold claim. This is their first model that was instrumental in creating itself. Early versions of GPT 5. 3 Codeex, debugged its own training runs, managed deployment infrastructure, and diagnosed test results. The Codeex team said that they were blown away by how much the model accelerated its own development. All right, let's get into the numbers because this is where things get interesting. on software bench pro which is a hard software engineering evaluation which spans about four languages GPT 5. 3 codec scores 57% that is the state-of-the-art according to open AAI on terminal bench 2. 0 the same benchmark anthropic claimed victory on GPT 5. 3 codec scores 77. 3% that's a 13point jump over GPT 5. 2 codeex and according to early reports higher than cloud opus 4. 6 six score. One developer said GPT 5. 3 absolutely demolished Enthropic on this benchmark. On OS World, an Agentic computer use benchmark where models complete productivity tasks in visual desktop environments. GPD 5. 3 CEX hits 64%. OpenAI also emphasized efficiency. GPT 5. 3 Codeex uses less than half the tokens of its older models for equivalent task, plus is 25% faster per token. That's a huge deal for cost and throughput. But here's where OpenAI is really betting on GPT 5. 3 Codex. It's not going to be just a coding agent anymore. OpenAI explicitly states that Codeex goes from an agent that can write and review code to an agent that could do nearly anything developers and professionals can do on a computer. It handles the entire software cycle, debugging, deploying, monitoring, writing PRDS, editing, copy, user research, and so on. To prove this, OpenAI had GPT 5. 3 codecs autonomously build two full browser games over millions of tokens. A racing game with eight maps, different races, and powerups, plus a diving game with multiple reefs, oxygen and pressure management, and hazards. These ran for days without human intervention. This is open AI signaling. We're not just better at code. We're building a general purpose digital coworker. Right now

what you're looking at is a game that GPT 5. 3 Codeex built which is a racing game which has I think about eight maps number one and has different tracks like we can see like Canyon Cole Speedway. I can change the track. Let's try this one. Maybe this one kind of looks cool. You have different characters that you can choose. How many? I think about eight or something like that. Let's try bricky. Difficulty. You can change that as well. Mean, standard. I'll do chill. Mirror mode. I don't know what mirror mode is, but I'll just keep it off. Allow clones. I'll keep that off. And you can start the race. Let's press enter. So, once again, this is the capabilities of the new models nowadays. Like, it's going to change game development. coding. It's going to change software engineering by a lot. And what's crazy is that this model was actually developed using itself. They made this model using GPT 5. 3 and it debugged. So, it's crazy. We might be getting faster model releases now because these models keep on getting better. And now that they're making themselves, just imagine what the future is going to look like. It looks like I'm winning. These guys are kind of losing. I guess I did put the difficulty on chill. So that's why I'm kind of doing good. Uh what can I do if I space jumps and everything like that a little? Do all these superpowers work? Let me try getting a superpower. But you can see this is very crazy and this is pretty good, right? And this is just made from a simple prompt and it was done autonomously by GPT 5. 3 codeex. And this is a solar system explorer that Claude Opus 4. 6 created. So what we can see number one is that holy crap guys, this is crazy. Before we used to just get simple circles. Now we can go into the actual planets, look at them. We can switch to specific planets, check them out, and it's all 3D, which is crazy. The amount of improvement we've seen with this test, I've shown this test before on my previous videos as well, is I think astronomical. Not trying to be funny or cheesy, but the models keep producing better and better results. And you can see clearly with this example here. And then this is the front-end capabilities of the model. First, I'm going to show you what GPT 5. 3 Codeex created and how it compares to Cloud Opus 4. 6. So, the prompt is here. Luckily, I was able to take this prompt and then put it into LM Marina and try it out on the Opus 4. 6 model. So, you can see the output. It follows the prompt pretty precisely. It looks pretty professional. It has all these access buttons. And then we see this that the front-end capabilities are getting better over time. I just don't get why GPT 5. 3 Codeex or the person who prompted this wanted this style cuz this style kind of looks in my opinion personally not that great. But it does pretty good job. Like it's doing the KPI and everything like that, a pricing structure. And just so you if you want to see the prompt, it's build a landing page for quiet KPI and aesthetic is soft SAS glassy cards lavender. That's why we're seeing this color. And I gave the same prompt to cloud opus 4. 6 and this is what we have. Once again the both models are producing pretty high quality results based off of the prompt. But I like this feature that cloud opus 4. 6 added personally because it looks pretty professional. And then if we scroll down all of this metrics and all this hover card all of this is pretty nice especially like the growth metrics adding the percentage change and everything like that. Then we have this. Everything looks in my opinion based off of the two basic front-end capabilities I've seen. We obviously are going to go just by our eyes. I'm going to go with Cloud Opus 4. 6 for this test, but both models are great and we're just getting better over time when it comes to front-end capabilities. So, here's the

bottom line. Both models are absolutely beast. Cloud Opus 4. 6 dominates on long context tasks and real world knowledge tasks. GPD 5. 3 Codex is faster, more efficient, and crushing it on pure coding benchmarks. The real winner, it's us. When Anthropic and OpenAI are dropping flagship models within 20 minutes of each other, competition pushes innovation into overdrive. If you want me to do a detailed head-to-head testing, running the same task on both models, comparing speed, accuracy, and cost, let me know in the comments. I'll build whatever test you want to see. Drop a like if this breakdown was helpful, and I'll see you in the next one. Make sure to subscribe to our channel. We do real tests, not just headlines. Make sure you're also subscribed to the world of AI. And don't forget to check out our newsletter for deeper breakdowns you won't see on YouTube. And I'm growing my Twitter following, so make sure you follow me on Twitter as well. Hope you guys enjoyed today's video and I'll see you in the next