GPT-5 for Developers
109 comments
·August 7, 2025aliljet
abossy
At my company (Charlie Labs), we've had a tremendous amount of success with context awareness over long-running tasks with GPT-5 since getting access a few weeks ago. We ran an eval to solve 10 real Github issues so that we could measure this against Claude Code and the differences were surprisingly large. You can see our write-up here:
https://charlielabs.ai/research/gpt-5
Often, our tasks take 30-45 minutes and can handle massive context threads in Linear or Github without getting tripped up by things like changes in direction part of the way through the thread.
While 10 issues isn't crazy comprehensive, we found it to be directionally very impressive and we'll likely build upon it to better understand performance going forward.
RobinL
Totally agree. At the moment I find that frontier LLMs are able to solve most of the problems I throw at them given enough context. Most of my time is spent working out what context they're missing when they fail. So the thing that would help me most is much a much more focussed ability to gather context.
For my use cases, this is mostly needing to be really home in on relevant code files, issues, discussions, PRs. I'm hopeful that GPT5 will be a step forward in this regard that isn't fully captured in the benchmark results. It's certainly promising that it can achieve similar results more cheaply than e.g. Opus.
swader999
If GPT 5 truly has 400k context, that might be all it needs to meaningfully surpass Opus.
andrewmutz
Having a large context window is very different from being able to effectively use a lot of context.
To get great results, it's still very important to manage context well. It doesn't matter if the model allows a very large context window, you can't just throw in the kitchen sink and expect good results
dimal
Even with large contexts there's diminishing returns. Just having the ability to stuff more tokens in context doesn't mean the model can effectively use it. As far as I can tell, they always reach a point in which more information makes things worse.
simonw
It's 272,000 input tokens and 128,000 output tokens.
Byamarro
More of a question is its context rot tendency than the size of its context :) LLMs are supposed to load 3 bibles into their context, but they forget what they were about to do after loading a 600LoC of locales.
AS04
400k context with 100% on the fiction livebench would make GPT-5 the undisputably best model IMHO. Don't think it will achieve that though, sadly.
tekacs
Coupled with the humungous price difference...
nadis
It's pretty vague, but the OP had this callout:
>"GPT‑5 is the strongest coding model we’ve ever released. It outperforms o3 across coding benchmarks and real-world use cases, and has been fine-tuned to shine in agentic coding products like Cursor, Windsurf, GitHub Copilot, and Codex CLI. GPT‑5 impressed our alpha testers, setting records on many of their private internal evals."
logicchains
>Between Opus aand GPT-5, it's not clear there's a substantial difference in software development expertise.
If there's no substantial difference in software development expertise then GPT-5 absolutely blows Opus out of the water due to being almost 10x cheaper.
realusername
Personally I think I'll wait for another 10x improvement for coding because with the current way it's going, they clearly need that.
fsloth
From my experience when used through IDE such as Cursor the current gen Claude model enables impressive speedruns over commodity tasks. My context is a CAD application I’ve been writing as a hobby. I used to work in that field for a decade so have a pretty good touch on how long I would expect tasks to take. I’m using mostly a similar software stack as that at previous job and am definetly getting stuff done much faster on holiday at home than at that previous work. Of course the codebase is also a lot smaller, intrinsic motivation, etc, but still.
realusername
I've done pretty much the same as you (Cursor/Claude) for our large Rails/React codebase at work and the experience has been horrific so far, I reverted back to vscode.
42lux
How often do you have to build the simple scaffolding though?
bdangubic
context awareness over long-running tasks
don’t have long-running tasks, llms or not. break the problem down into small manageable chunks and then assemble it. neither humans nor llms are good at long-running tasks.
bastawhiz
> neither humans nor llms are good at long-running tasks.
That's a wild comparison to make. I can easily work for an hour. Cursor can hardly work for a continuous pomodoro. "Long-running" is not a fixed size.
bdangubic
I just finished my workday, 8hrs with Claude Code. No single task took more than 20 minutes total. Cleared context after each task and asked it to summarize for itself the previous task before I cleared context. If I ran this as a continuous 8hr task it would have died after 35-ish minutes. Just know the limitations (like with any other tool) and you’ll be good :)
echelon
Humans can error correct.
LLMs multiply errors over time.
beoberha
A series of small manageable chunks becomes a long running task :)
If LLMs are going to act as agents, they need to maintain context across these chunks.
vaenaes
You're holding it wrong
risho
over the last week or so I have put probably close to 70 hours into playing around with cursor and claude code and a few other tools (its become my new obsession). I've been blown away by how good and reliable it is now. That said the reality is in my experience the only models that actually work in any sort of reliable way are claude models. I dont care what any benchmark says because the only thing that actually matters is actual use. I'm really hoping that this new gpt model actually works for this usecase because competition is great and the price is also great.
neuronexmachina
> That said the reality is in my experience the only models that actually work in any sort of reliable way are claude models.
Anecdotally, the tool updates in the latest Cursor (1.4) seem to have made tool usage in models like Gemini much more reliable. Previously it would struggle to make simple file edits, but now the edits work pretty much every time.
throwaway_2898
How much of the product were you able to build to say it was good/reliable? IME, 70 hours can get you to a PoC that "works", building beyond the initial set of features — like say a first draft of all the APIs — does it do well once you start layering features?
petralithic
This has been my experience. The greenfield approach works up to a point, then it just breaks.
rcarr
I think some of this might come down to stack as well. I watched a t3.gg video[1] recently about Convex[2] and how the nature of it leads to the AI getting it right first time more often. I've been playing around with it the last few days and I think I agree with him.
I think the dev workflow is going to fundamentally change because to maximise productivity out of this you need to get multiple AIs working in parallel so rather than just jumping straight into coding we're going to end up writing a bunch of tickets out in a PM tool (Linear[3] looks like it's winning the race atm) and then working out (or using the AI to work out) which ones can be run in parallel without causing merge conflicts and then pulling multiple tickets into the your IDE/Terminal and then cycling through the tabs and jumping in as needed.
Atm I'm still not really doing this but I know I need to make the switch and I'm thinking that Warp[4] might be best suited for this kind of workflow, with the occasional switch over to an IDE when you need to jump in and make some edits.
Oh also, to achieve this you need to use git worktrees[5,6,7].
[1]: https://www.youtube.com/watch?v=gZ4Tdwz1L7k
[3]: https://linear.app/
[5]: https://docs.anthropic.com/en/docs/claude-code/common-workfl...
isoprophlex
Sure sounds interesting but... Where on earth do you actually find the time to sit through a 1.5 hour yt video?!
rcarr
Jump in and start coding entire backend with stack not best suited for job and modern AI tools: most likely future hours lost.
Spend 1.5 hours now to learn from an experienced dev on a stack that is better suited for job: most likely future hours gained.
ralfd
Just replying to ask you next week what your assessment on GPT5 is.
zarzavat
The magic is the prompting/tool use/finetuning.
I find that OpenAI's reasoning models write better code and are better at raw problem solving, but Claude code is a much more useful product, even if the model itself is weaker.
Centigonal
Ditto here, except I'm using Roo and it's Claude and Gemini pro 2.5 that work for me.
null
pamelafox
I am testing out gpt-5-mini for a RAG scenario, and I'm impressed so far.
I used gpt-5-mini with reasoning_effort="minimal", and that model finally resisted a hallucination that every other model generated.
Screenshot in post here: https://bsky.app/profile/pamelafox.bsky.social/post/3lvtdyvb...
I'll run formal evaluations next.
ralfd
Q: What does a product manager do?
GPT4: Collaborating with engineering, sales, marketing, finance, external partners, suppliers and customers to ensure …… etc
GPT5: I don't know.
Upon speaking these words, AI was enlightened.
ComputerGuru
That is genuinely nice to see. What are you using for the embeddings?
pamelafox
We use text-embedding-3-large, with both quantization and MRL reduction, plus oversampling on the search to compensate for the compression.
potatolicious
This feels like honestly the biggest gain/difference. I work on things that do a lot of tool calling, and the model hallucinating fake tools is a huge problem. Worse, sometimes the model will hallucinate a response directly without ever generating the tool call.
The new training rewards that suppress hallucinations and tool-skipping hopefully push us in the right direction.
jumploops
If the model is as good as the benchmarks say, the pricing is fantastic:
Input: $1.25 / 1M tokens (cached: $0.125/1Mtok) Output: $10 / 1M tokens
For context, Claude Opus 4.1 is $15 / 1M for input tokens and $75/1M for output tokens.
The big question remains: how well does it handle tools? (i.e. compared to Claude Code)
Initial demos look good, but it performs worse than o3 on Tau2-bench airline, so the jury is still out.
addaon
> Output: $10 / 1M tokens
It's interesting that they're using flat token pricing for a "model" that is explicitly made of (at least) two underlying models, one with much lower compute costs than the other; and with use ability to at least influence (via prompt) if not choose which model is being used. I have to assume this pricing model is based on a predicted split between how often the underlying models get used; I wonder if that will hold up, if users will instead try to rouse the better model into action more than expected, or if the pricing is so padded that it doesn't matter.
mkozlows
That's how the browser-based ChatGPT works, but not the API.
simianwords
> that is explicitly made of (at least) two underlying models
what do you mean?
addaon
> a smart and fast model that answers most questions, a deeper reasoning model for harder problems, and a real-time router that quickly decides which model to use based on conversation type, complexity, tool needs, and explicit intent (for example, if you say “think hard about this” in the prompt).
croemer
> GPT‑5 also excels at long-running agentic tasks—achieving SOTA results on τ2-bench telecom (96.7%), a tool-calling benchmark released just 2 months ago.
Yes, but it does worse than o3 on the airline version of that benchmark. The prose is totally cherry picker.
Fogest
How does the cost compare though? From my understanding o3 is pretty expensive to run. Is GPT-5 less costly? If so if the performance is close to o3 but cheaper, then it may still be a good improvement.
low_tech_punk
I find it strange that GPT-5 is cheaper than GPT-4.1 in input token and is only slightly more expensive in output token. Is it marketing or actually reflecting the underlying compute resources?
AS04
Very likely to be an actual reflection. That's probably their real achievement here and the key reason why they are actually publishing it as GPT-5. More or less the best or near to it on everything while being one model, substantially cheaper than the competition.
bn-l
Maybe with the router mechanism (to mini or standard) they estimate the average cost will be a lot lower for chatgpt because the capable model won’t be answering dumb questions and then they pass that on to devs?
jstummbillig
I mean... they themselves included that information in the post. It's not exactly a gotcha.
mehmetoguzderin
Context-free grammar and regex support are exciting. I wonder what, or whether, there are differences from the Lark-like CFG of llguidance, which powers the JSON schema of the OpenAI API [^1].
[^1]: https://github.com/guidance-ai/llguidance/blob/f4592cc0c783a...
msp26
Yeah that was the only exciting part of the announcement for me haha. Can't wait to play around with it.
I'm already running into a bunch of issues with the structured output APIs from other companies like Google and OpenAI have been doing a great job on this front.
chrisweekly
> "I'm already running into a bunch of issues with the structured output APIs from other companies like Google and OpenAI have been doing a great job on this front."
This run-on sentence swerved at the end; I really can't tell what your point is. Could you reword it for clarity?
nadis
"When producing frontend code for web apps, GPT‑5 is more aesthetically-minded, ambitious, and accurate. In side-by-side comparisons with o3, GPT‑5 was preferred by our testers 70% of the time."
That's really interesting to me. Looking forward to trying GPT-5!
null
hrpnk
The github issue showed in the livestream is getting lots of traction: https://github.com/openai/openai-python/issues/2472
It was (attempted to be) solved by a human before, yet not merged... With all the great coding models OpenAI has access to, their SDK team still feels too small for the needs.
zaronymous1
Can anyone explain to me why they've removed parameter controls for temperature and top-p in reasoning models, including gpt-5? It strikes me that it makes it harder to build with these to do small tasks requiring high-levels of consistency, and in the API, I really value the ability to set certain tasks to a low temp.
catigula
I thought we were going to have AGI by now.
RS-232
No shot. LLMs are simple text predictors and they are too stupid to get us to real AGI.
To achieve AGI, we will need to be capable of high fidelity whole brain simulations that model the brain's entire physical, chemical, and biological behavior. We won't have that kind of computational power until quantum computers are mature.
brookst
Are you saying that only (human?) biological brains can be GI, and that whatever intelligence is, it would emerge from a pure physics-based simulation?
Both of those seem questionable, multiplying them together seems highly unlikely.
nawgz
I don't really see any relationship between being able to model/simulate the brain and being able to exceed the brain in intelligence, can you explain more about that? Simulations sound like more of a computational and analytic problem with regards to having an accurate model.
Maybe your point is that until we understand our own intelligence, which would be reflected in such a simulation, it would be difficult to improve upon it.
evantbyrne
It will be interesting to see if humans can manage to bioengineer human-level general intelligence into another species before computers.
machiaweliczny
I call bullshit. No need for simulation. Can be achieved via RL with some twist
bopbopbop7
“some twist” is doing a lot of heavy lifting in that statement.
IAmGraydon
Not going to happen any time soon, if ever. LLMs are extremely useful, but the intelligence part is an illusion that nearly everyone appears to have fallen for.
jonplackett
This POV is just the opposite extremity - and it’s equally nuts. If you haven’t seen any intelligence at all in an LLm you just aren’t looking.
Between Opus aand GPT-5, it's not clear there's a substantial difference in software development expertise. The metric that I can't seem to get past in my attempts to use the systems is context awareness over long-running tasks. Producing a very complex, context-exceeding objective is a daily (maybe hourly) ocurrence for me. All I care about is how these systems manage context and stay on track over extended periods of time.
What eval is tracking that? It seems like it's potentially the most imporatnt metric for real-world software engineering and not one-shot vibe prayers.