DevConsensus
Models / GPT-5
Model · HN opinions Nov 2025 – Nov 2026

GPT-5: what developers actually say

We read every Hacker News comment from Nov 2025 – Nov 2026 that gave a real opinion on GPT-5. 54 of the 62 opinions come from people who say they use it themselves.

57%approval, all opinions
–approval, last 90 days
#36of 45 models ranked
58different developers

GPT-5 has 57% approval across 62 opinions from 58 authors; 54 opinions are firsthand. Code quality is the main point of disagreement at 13 positive and 14 negative, while reliability is 8 positive to 5 negative and autonomy is 3 to 0. Recent monthly approval is volatile and based on small samples: 20% in July, 0% in August, 50% in September and 100% in October, on 1 to 5 opinions per month. Those recent samples are too small to show a stable direction.

Pick it if

  • Want dependable JSON output under strict constraints
  • Want an agent that explores codebases through git
  • Value reliable tool use with some autonomy

Look elsewhere if

  • Need consistent design intent on large projects
  • Work mainly in Lisp or niche languages
  • Need long context and stable behavior over many turns

Strengths and weaknesses

Each row counts opinions that mentioned the topic. Bars to the right of the line are praise, bars to the left are complaints.

PraiseComplaints
Code quality
14
13
Reliability
5
8
Autonomy
3

How opinion has moved

050100Nov '25Dec '25Jan '26May '26Jul '26
Monthly approval (line) and number of opinions (bars). Months with fewer than 3 opinions are left out.

What developers say

Praise

Says the original GPT-5 adhered closely to prompts, which they praise.HN comment · Oct 4, 2026
Says GPT-5 and its early post-trained releases were excellent at following task instructions.HN comment · Sep 17, 2026
Reports GPT-5 and later never erred filling structured JSON under many constraints across hundreds of runs.HN comment · Jul 6, 2026
Says mid-tier GPT-5 is about as competitive as Sonnet for their workflow.HN comment · Jun 2, 2026
Says GPT-5.x explores codebases best through git commands, once arguments are passed correctly.HN comment · May 8, 2026
Finds GPT-5.x code competent, though its conversational voice is grating.HN comment · May 8, 2026
Says a GPT-5 model agent fixed nearly every contrived Linux failure they built, even on first try.HN comment · Apr 16, 2026
Says GPT-5 at high reasoning in Codex made them switch from Claude Code.HN comment · Feb 23, 2026

Complaints

Says GPT-5 kept running wrong uv commands or using pip despite AGENTS.md instructions.HN comment · Sep 9, 2026
Says GPT-5 handled R coding terribly a year ago, which led them to switch to Gemini.HN comment · Aug 19, 2026
Found early GPT-5 added flashy, overwrought frontend styling and needed heavy prompting to stop it.HN comment · Jul 10, 2026
Finds GPT-5 models nitpicky and over-defensive in code, though useful for review and math.HN comment · Jul 2, 2026
Finds GPT-5 noticeably weaker at writing Lisp than at mainstream languages.HN comment · May 12, 2026
Says GPT-5 makes decent skeletons but cheats, hacks around edge cases, and loses design intent on larger work.HN comment · May 11, 2026
Says ChatGPT 5 lacked the memory span to keep context for a complex agent project.HN comment · Mar 17, 2026
Unhappy with the GPT-5 series: pedantic, drifts off-topic, and stops following system instructions after a few turns.HN comment · Jan 30, 2026

Summaries are paraphrased. Each links to the original comment.

FAQ

Is GPT-5 worth it for coding?

The data has no pricing, but opinions are mixed: approval is 57% across 62 opinions and code quality is 13 positive to 14 negative. Reliability is stronger at 8 positive to 5 negative.

Is GPT-5 better than Claude Sonnet 4.5?

Head-to-head preference is thin: one developer preferred GPT-5 over Claude Sonnet 4.5, while two preferred Sonnet 4.5 instead. Overall approval is 57%, so the data does not show a clear winner.

What are GPT-5's main weaknesses?

Code quality is the main area of concern at 13 positive and 14 negative. Developers also cite drift, lost design intent on larger work, and weaker Lisp performance. Long context and stable instruction following over many turns also draw complaints.