GPT / ChatGPT

In the repliesQuanTradin emphasizes persistent state surviving the model switch. thelocalnode says recording rejected approaches helps avoid rebuilding failed ideas; the author agrees and links their own workflow write-up and templates. Neither reply independently reproduces the comparison.

The author promotes their own workflow. Exact Claude/Gemini versions, matched configurations and independently checked production logs are missing. Reported failures do not establish a general capability ranking or prove every model can safely share one working tree.

Original posted October 23 at 00:03:09 UTC. The author’s account and replies by QuanTradin and thelocalnode were read October 26. The workflow and comparison were not independently reproduced.

In the repliesNo human replies were visible in the opened launch thread. The linked repository describes a beta with three levels and user-supplied model access; its default 200-call limit caps requests, not the dollar cost of a round.

This is a creator’s project promotion and early comparison, not an independent ranking. The current README documents checks with Jev and DeepSeek; other model routes are not all verified. No gameplay or latency test was reproduced for this summary.

Original October 21 at 12:07 UTC; original and linked README read October 23. The current repository permits a consented server proxy for Jev, alongside direct browser-to-provider routes.

In the repliesjiabubu asks which reasoning intensity was used, a material missing configuration detail. johnlanni suggests comparing the combined time and cost of review, repair and re-review against an all-Sol run; that commenter also discloses maintaining a routing project.

One developer’s task and assessment, not an independently reproduced model ranking. The posted token-change percentages are internally unclear and are not used here.

Original October 18 and selected human replies 5 and 8 opened October 21; promotional project link omitted. No test executed by TechReadly.

In the repliesIn the earlier discussion, niccolo_21 described moving from Pro to Plus, initially generous usage and then faster depletion. Their entitlement-bug suggestion was speculation; the new support explanation is reported by the original author for a different account.

The reported compensation is separate from the weekly allowance, which follows the current plan. Earlier readings showed a 57-point counter change over 10.131 seconds within one conversation and 7.636 seconds across conversations; they do not timestamp when all underlying usage occurred.

Original posted October 17; author update labeled October 21 KST read October 21 alongside GitHub issue comment 6297631640. Support statements are relayed by the author, not independently authenticated account records.

In the repliestwendah asked why Haiku 5.5 xhigh was missing; Laprablenia asked about Gemini 3.8 Flash. Right-Performance-93 preferred Sol medium based on the same author's table, without an independent run.

The author explicitly cautioned that provider allowance percentages are not equivalent compute units. Exact prompts, repeated runs, account windows and execution logs were not verified; the table does not establish general quality rankings or API costs.

Original October 9 post and replies pevpx95, pewm3lp and pf04ai5. Model names, usage figures and quality ratings are self-reported.

In the replieslgnorant-lu reported 2/5 using Gemini Web 3.8 Flash extended with rephrased spatial questions, questioning which blue and white books the bookshelf prompt meant. The author conceded that question was problematic. The changed prompts make this a different attempt.

Single attempts with different settings, clients and providers; these user-assigned grades do not establish a model ranking or reproduce the HSS paper.

LINUX DO posts 23449203, 23450507 and 23450965, October 9. Model labels, grades and client behavior are self-reported.

In the repliessensei_von_bonzai attributed the gap to Haiku’s higher prices above 100K prompt tokens; the run’s per-turn context was not verified. _JohnWisdom questioned voxel generation as a Haiku benchmark. DesperateSunday reported little weekly-allowance movement on five smaller tasks, a different self-reported workload.

Both model names and xhigh settings are self-reported. Anthropic publishes higher per-token prices for prompts over 100K, but that does not verify this run’s aggregate token totals or cost calculation.

Original post and human replies pej3rih, pej4ybz and pejywq4. Exact prompts, raw token traces, repeats and compaction behavior were not verified; no general price or quality ranking follows.

In the repliesevilncarnate82 described manually handing Claude’s architect prompts to Antigravity and adding canary tests after incomplete work. MentalThroat7733 reported using AI Studio and Antigravity with Android Studio, or copying between Gemini and an editor for web/Python work, mainly with 3.8 Flash extended thinking and automated tests. These were different workflows, rather than tests of bocondo’s ticket.

The author’s exact Gemini version, prompts, settings and issue severity are unknown. One self-reported review comparison does not establish model accuracy or a general ranking.

Original post and substantive replies; subscription entitlements and CLI-retirement claims in comments were not verified and are not adopted here.

In the repliesCommenter ricedumpling described a separate coding workflow: use GPT Plus to develop a plan, save business logic and interface details as Markdown in the project, then have Claude Code connected to the official DeepSeek API read that plan and execute. When zzbd questioned the cache cost of switching models, ricedumpling clarified that GPT served as a consultant while execution and input caching stayed on DeepSeek; no measured savings were supplied.

The author did not identify a DeepSeek version. Evening use is a personal routine, not a verified discount schedule.

Original post and named replies on LINUX DO; spending and workflow results are self-reported, not independently tested.

In the repliesComrade-Porcupine also said GPT-6.1 Sol quickly exceeded its context and required compaction, and said they planned to use Astra for design and GPT-5.6 Sol for implementation. Automatic_Cookie42 called it an improvement over Sol 6 without describing a task or test. This is one anecdotal run: the thread provides no prompt, configuration, logs or independent reproduction. It is not a benchmark.

OpenAI’s September 29 launch page presents GPT-6.1 Sol as an upgrade to GPT-6 Sol. The company’s product claims do not verify this commenter’s experience.

One Reddit thread reviewed September 30, 2026. The performance comparison is a commenter’s single self-reported run without published logs or independent reproduction.