In my opinion, they solved the wrong problem. The main issue I have with Codex is that the best model is insanely slow, except at nights and weekends when Silicon Valley goes to bed. I don't want a faster, smaller model (already have that with GLM and MiniMax). I want a faster, better model (at least as fast as Opus).
When they partnered with Cerebras, I kind of had a gut feeling that they wouldn't be able to use their technology for larger models because Cerebras doesn't have a track record of serving models larger than GLM.
It pains me that five days before my Codex subscription ends, I have to switch to Anthropic because despite getting less quota compared to Codex, at least I'll be able to use my quota _and_ stay in the flow.
But even Codex's slowness aside, it's just not as good of an "agentic" model as Opus: here's what drove me crazy: https://x.com/OrganicGPT/status/2021462447341830582?s=20. The Codex model (gpt-5.3-xhigh) has no idea about how to call agents smh
For a bit, waiting for LLMs was like waiting for code to compile: https://xkcd.com/303/
> more than 1000 tokens per second
Perhaps, no more?
(Not to mention, if you're waiting for one LLM, sometimes it makes sense to multi-table. I think Boris from Anthropic says he runs 5 CC instances in his terminal and another 5-10 in his browser on CC web.)
This is interesting for offloading "tiered" workloads / priority queue with coding agents.
If 60% of the work is "edit this file with this content", or "refactor according to this abstraction" then low latency - high token inference seems like a needed improvement.
Recently someone made a Claude plugin to offload low-priority work to the Anthropic Batch API [1].
Also I expect both Nvidia and Google to deploy custom silicon for inference [2]
This is closer to 5.1 mini it seems and tied to Pro account. GLM 4.7 is available on-demand on Cerebras today [1] and performs better and cheaper...
[1] https://www.cerebras.ai/blog/glm-4-7
It'll be nice when there's smarter routing between models, or easier routing, so some things get sent to the fast model, some get sent to the cheap model, some get sent to the smart model, etc.
The search for speed is vain. Often Claude Code Opus 4.6, on hard enough problems, can do the impression of acting fast without really making progresses because of lack of focus on what matters. Then you spin the much slower GPT 5.3-Codex and it fixes everything in 3 minutes of doing the right thing.
When I saw Spark my mind went to Apache Spark and wondered if we were learning all the lessons in orchestration of driver/worker and data shuffling from that space.
Wasn't aware there was an effort to move to websockets. Is there any standards work for this, or is this just happening purely within the walled OpenAI garden?
> Under the hood, we streamlined how responses stream from client to server and back, rewrote key pieces of our inference stack, and reworked how sessions are initialized so that the first visible token appears sooner and Codex stays responsive as you iterate. Through the introduction of a persistent WebSocket connection and targeted optimizations inside of Responses API, we reduced overhead per client/server roundtrip by 80%, per-token overhead by 30%, and time-to-first-token by 50%. The WebSocket path is enabled for Codex-Spark by default and will become the default for all models soon.
> Our latest frontier models have shown particular strengths in their ability to do long-running tasks, working autonomously for hours, days or weeks without intervention.
I have yet to see this (produce anything actually useful).
Agreed. Optimistically let it resolve merge conflicts in an old complex branch. Looked fine at first but was utter slop upon further review. Duplication, wildly unnecessary complexity and all.
Seems like the industry is moving further towards having low-latency/high-speed models for direct interaction, and having slow, long thinking models for longer tasks / deeper thinking.
Quick/Instant LLMs for human use (think UI).
Slow, deep thinking LLMs for autonomous agents.
Great stuff. People are getting used to agents as the interface for everything, even work as simple as "change label X to label Y". More speed on that front is welcome. The Codex "blended mode" they refer to will be useful (similar to Claude Code bouncing between haiku and opus).
I imagine it's a win-win. This could significantly help their tokenomics.
The example showing a plan being generated instantaneously is interesting. Human understanding will end up as the last, true bottleneck.
I love this! I use coding agents to generate web-based slide decks where “master slides” are just components, and we already have rules + assets to enforce corporate identity. With content + prompts, it’s straightforward to generate a clean, predefined presentation.
What I’d really want on top is an “improv mode”: during the talk, I can branch off based on audience questions or small wording changes, and the system proposes (say) 3 candidate next slides in real time. I pick one, present it, then smoothly merge back into the main deck.
Example: if I mention a recent news article / study / paper, it automatically generates a slide that includes a screenshot + a QR code link to the source, then routes me back to the original storyline.
With realtime voice + realtime code generation, this could turn the boring old presenter view into something genuinely useful.
> Today, we’re releasing a research preview of GPT‑5.3-Codex-Spark, a smaller version of GPT‑5.3-Codex, and our first model designed for real-time coding. Codex-Spark marks the first milestone in our partnership with Cerebras, which we announced in January .
Does anyone want this? Speed has never been the problem for me, in fact, higher latency means less work for me as a replaceable corporate employee. What I need is the most intelligence possible; I don't care if I have to wait a day for an answer if the answer is perfect. Small code edits, like they are presented as the use case here, I can do much better myself than trying to explain to some AI what exactly I want done.
Continue to believe that Cerebras is one of the most underrated companies of our time. It's a dinner-plate sized chip. It actually works. It's actually much faster than anything else for real workloads. Amazing
91 comments
[ 92.6 ms ] story [ 1895 ms ] thread(Yes I know they released /fast last week but I’m loving the constant oneupsmanship)
When they partnered with Cerebras, I kind of had a gut feeling that they wouldn't be able to use their technology for larger models because Cerebras doesn't have a track record of serving models larger than GLM.
It pains me that five days before my Codex subscription ends, I have to switch to Anthropic because despite getting less quota compared to Codex, at least I'll be able to use my quota _and_ stay in the flow.
But even Codex's slowness aside, it's just not as good of an "agentic" model as Opus: here's what drove me crazy: https://x.com/OrganicGPT/status/2021462447341830582?s=20. The Codex model (gpt-5.3-xhigh) has no idea about how to call agents smh
> more than 1000 tokens per second
Perhaps, no more?
(Not to mention, if you're waiting for one LLM, sometimes it makes sense to multi-table. I think Boris from Anthropic says he runs 5 CC instances in his terminal and another 5-10 in his browser on CC web.)
If 60% of the work is "edit this file with this content", or "refactor according to this abstraction" then low latency - high token inference seems like a needed improvement.
Recently someone made a Claude plugin to offload low-priority work to the Anthropic Batch API [1].
Also I expect both Nvidia and Google to deploy custom silicon for inference [2]
1: https://github.com/s2-streamstore/claude-batch-toolkit/blob/...
2: https://www.tomshardware.com/tech-industry/semiconductors/nv...
> Under the hood, we streamlined how responses stream from client to server and back, rewrote key pieces of our inference stack, and reworked how sessions are initialized so that the first visible token appears sooner and Codex stays responsive as you iterate. Through the introduction of a persistent WebSocket connection and targeted optimizations inside of Responses API, we reduced overhead per client/server roundtrip by 80%, per-token overhead by 30%, and time-to-first-token by 50%. The WebSocket path is enabled for Codex-Spark by default and will become the default for all models soon.
I have yet to see this (produce anything actually useful).
Quick/Instant LLMs for human use (think UI). Slow, deep thinking LLMs for autonomous agents.
I imagine it's a win-win. This could significantly help their tokenomics.
The example showing a plan being generated instantaneously is interesting. Human understanding will end up as the last, true bottleneck.
Nevermind. [0]
[0] https://news.ycombinator.com/item?id=35490837