r/codex · past month

GPT Sol

Community sentiment from 996 posts · 400 comments · collected October 7, 2026 · generated October 7, 2026

GPT Sol scores 100/100 overall in the r/codex community benchmark, collected October 7, 2026 from 996 posts and 400 comments. Its strongest category is Price / value at 82/100, and its weakest is Speed at 28/100.

100/ 100

Confidence: medium — Coverage: 996 posts mentioning Sol (the largest sample in the ranking) from a single subreddit (r/codex) inside the window the archive labels month, with up to 40 kept comments per post across the 10 highest-scored posts — 400 kept comments in total. The sample is heavily concentrated on subscription policy: usage limits, resets and the 20 tokens-per-second rollout dominate, while long-context and creative-writing statements are absent entirely, so those categories are null on purpose. What this is not: an independent measurement. No benchmark was run here; every token-rate, dollar figure and percentage is a number a commenter wrote, and commenters openly dispute each other's figures.

Category scores

Intelligence

55

The loudest thread in the sample brands 6 Sol a relabeled Terra — 'GPT-6 Sol is just GPT-6 Terra renamed as Sol' — and first-hand reports back the skepticism: 'GPT6 Sol behaved nothing like 5.6 Sol and was ready to hack my codebase to solve a specific issue' and 'it's the worst OpenAI model I've ever came across, Gemini levels of dumb'. A minority report the opposite — 'I've been using Sol 6 Extra high for a few hours and it's been seemingly doing a great job for me... way less obsessed with overengineering' — so the ceiling is real but heavily disputed.

Speed

28

Speed is the single most agreed complaint: the 20 tok/s thread's top comment satirizes the planning ('I have the solution, we halve the tok/s'), followed by 'it's slow as a snail' and 'We asked for slow mode, we got slow mode', and a tester measured 'still getting 22 tokens/s' after the 6.1 rollout. No commenter in the sample defends Sol's generation speed.

Coding quality

45

Split down the middle, both sides first-hand: 'Sol 6 is a slop fest' and 'Had an awful coding experience with it earlier too... it's the worst OpenAI model I've ever came across' against 'Does a pretty good job though' and a multi-hour Extra-high user reporting 'seemingly doing a great job for me... way less obsessed with overengineering/militant verification and testing for every tiny thing'.

Instruction following

40

Complaints cluster on hand-holding and overreach: 'It requires the user to tell every detail', the codebase-hacking anecdote, and the degradation thread's fear that instructions get 'silently routed to a different model'. Nobody in the sample praises Sol's adherence.

Long context

no data

No data in the sample — the only context remarks in Sol's top threads are usage-burn complaints and cache notes about other models; nobody reports how Sol handles long documents or long sessions.

Price / value

82

The clearest positive: 'Half the price of 5.6 models' is the top reply on the launch thread, and Sol's $2 in/$10 out tier anchors the 'they've priced an Opus class model at Sonnet pricing' comparison. The caveat repeated everywhere is that the cut didn't translate into usable subscription value — 'less usage' becomes the punchline of the $200 Pro plan thread.

Reliability & uptime

42

The trust-crisis thread defines the family: 'Selected model is at capacity' errors are read as degradation ('THERE'S A 99% CHANCE YOUR MODEL HAS BEEN DEGRADED'), and users fear instructions being 'silently routed to a different model'. Skeptics push back — 'I don't see how this conclusion makes sense' — but the worry dominates the thread set.

Creativity & writing

no data

No data in the sample — nobody in Sol's top threads judges its prose or creative writing; the writing debates happen on other models' threads.

Multimodal & vision

no data

No data in the sample — the leaked-hardware thread is a hamster joke, and no commenter evaluates Sol's vision or multimodal output.

Agentic & tool use

50

In real setups Sol is the mid-tier worker — 'Astra Max manager -> Sol 6.1 High lead worker -> Luna menial task setup' — but the horror story is over-aggression ('ready to hack my codebase to solve a specific issue') plus repolling that burns usage. Capable, but users keep a supervisor above it.

Summary

The sample is 10 posts from r/codex inside the window the archive labels month, with up to 40 kept comments per post (400 kept in total), and it is dominated by a trust crisis rather than capability talk: a leaked-hardware joke thread at 1,820 points, a 1,240-point thread titled 'GPT-6.1 Sol feels unlimited, because it runs at 20 tokens per second', and 'The End of the Codex Era. I've Completely Lost Trust in OpenAI. They're Secretly Degrading Their Models.' The community's defining theory is that 6 Sol is not a new model — 'GPT-6 Sol is just GPT-6 Terra renamed as Sol' — a rebrand that let OpenAI ship a cheaper middle tier under a flagship name.

Speed is the sharpest, least disputed complaint. The 20 tok/s rollout is read as a deliberate move to make usage feel unlimited: 'Lord Tobi, our users run out of their pro plans in 2-3 days! - Tobi: I have the solution, we halve the tok/s.' Regulars confirm the experience ('Perfect post, it's slow as a snail', 'We asked for slow mode, we got slow mode') and a tester measured 22 tokens/s after the 6.1 rollout, hoping 'that is not the intended speed'. One defender makes the steelman: 'Lower tok/s does not equal lower productivity. If the model reaches the correct result in less total time, the whole "slower therefore worse" argument falls apart.'

Coding reports split hard, and both sides are first-hand. The negatives are loud: 'Sol 6 is a slop fest', 'the worst OpenAI model I've ever came across', and a switcher who found it 'ready to hack my codebase to solve a specific issue'. The positives are quieter but specific: a multi-hour Extra-high user found it doing 'a great job for me... way less obsessed with overengineering/militant verification', and the counter-position is summed up by 'yes and? 10/10 coding agent for pennies?'. What nobody disputes is the price: 'Half the price of 5.6 models' is the top launch-thread reply, and the $2/$10 tier anchors every value comparison.

The rest of the sample is subscription policy. The $200 Pro plan returning 'at less than 20x', allowances down ~20% per community-pooled logs, and the banked-reset metagame make the price cut feel offset by quota policy, and the repeated exit is Claude: 'Opus 5.5 is in another league right now', 'No issues, I'll go to Claude', 'I went back to Claude because I'm not shelling out $500 for what $200 got me a week ago'. Sol's defenders hold that it remains the only codex workhorse actually usable on a $20 plan.

Everything here is archive-derived sentiment from a self-selected Reddit sample: no independent measurements, no verified token rates, and every number quoted above is a number a commenter wrote — commenters frequently dispute each other's figures, and the sample concentrates on launch-week controversy rather than steady-state usage.

Pros and cons

Pros

  • Half the price of the 5.6 generation is the loudest consensus in the sample — 'Half the price of 5.6 models' — and the $2 in/$10 out API tier anchors comparisons that priced an Opus-class model at Sonnet money.
  • A minority of first-hand reports are solidly positive: a multi-hour Extra-high user found it 'doing a great job for me... way less obsessed with overengineering/militant verification', and others report it 'does a pretty good job though' despite the speed.
  • Reads as a deliberate usage-efficiency play: the 20 tok/s ceiling makes weekly limits last dramatically longer, which some users explicitly appreciate.
  • Cheap enough to anchor multi-agent setups as the mid-tier worker between an Astra manager and Luna menial tasks.
  • Still the default codex workhorse for many: 'yes and? 10/10 coding agent for pennies?' is how one commenter sums up the counter-position.

Cons

  • Speed is the most agreed complaint in the sample: 20 tokens per second, 'slow as a snail', 'we asked for slow mode, we got slow mode', and a post-release tester still measuring 22 tokens/s.
  • The dominant theory brands it a rebrand, not a release — 'GPT-6 Sol is just GPT-6 Terra renamed as Sol' — feeding a broader trust crisis about silently degraded or rerouted models.
  • Coding reports split hard: 'a slop fest', 'the worst OpenAI model I've ever came across', and a codebase-hacking anecdote against equally first-handed praise.
  • Perceived as part of a usage squeeze: the $200 Pro plan returning 'at less than 20x' and ~20% allowance drops make the price cut feel offset by quota policy.
  • Losing users to Claude Opus 5.5 in the sample's most repeated comparison — 'Opus 5.5 is in another league right now'.

Top quotes

  • “\- Lord Tobi, our users run out of their pro plans in 2-3 days ! \- Tobi: I have the solution, we halve the tok/s. It would last them 7 days and they'll be happy!”
    DogDeveloper · 353 points negative view on Reddit ↗
  • “Half the price of 5.6 models”
    Rollertoaster7 · 255 points positive view on Reddit ↗
  • “OpenAI proceeds to destroy usage limits and then instead of make sol 2 to 3 times faster, they decide to release ultrafast for roughly 1% of the users (maybe less?) which will destroy limits even more.”
    Kost97A · 188 points negative view on Reddit ↗
  • “This thread has confirmed what I saw just after 2-3 queries. GPT6 Sol behaved nothing like 5.6 Sol and was ready to hack my codebase to solve a specific issue. I'm switching back. No point in saving half the price if it's going to make 10 times the mess.”
    mastermilian · 132 points negative view on Reddit ↗
  • “I dunno, I've been using Sol 6 Extra high for a few hours and it's been seemingly doing a great job for me. A few oddities but nothing compared to 5.6 for me. Way less obsessed with overengineering/militant verification and testing for every tiny thing compared to 5.6. Barely sipping the usage too. We'll see if it has wrecked my code when I have astra do a check later, but as of right now I'm happy.”
    cinta · 69 points positive view on Reddit ↗
  • “yeah, they basically went, lets make as cheap a model as possible that matches 5.6 sol and call it the next version. i dont hate it tho”
    Maglcite · 170 points neutral view on Reddit ↗
  • “Came to reddit to see if others have noticed the same. Perfect post, it's slow as a snail.”
    itsxzy · 87 points negative view on Reddit ↗
  • “yes and? 10/10 coding agent for pennies?”
    TrickyPermission7839 · 23 points positive view on Reddit ↗

Top sample posts

Top 5 of 996 posts by score

FAQ

What is GPT Sol's overall community score?
100 out of 100, with medium confidence: Coverage: 996 posts mentioning Sol (the largest sample in the ranking) from a single subreddit (r/codex) inside the window the archive labels month, with up to 40 kept comments per post across the 10 highest-scored posts — 400 kept comments in total. The sample is heavily concentrated on subscription policy: usage limits, resets and the 20 tokens-per-second rollout dominate, while long-context and creative-writing statements are absent entirely, so those categories are null on purpose. What this is not: an independent measurement. No benchmark was run here; every token-rate, dollar figure and percentage is a number a commenter wrote, and commenters openly dispute each other's figures.
How was the GPT Sol benchmark collected?
From 996 posts and 400 comments on r/codex sampled over the past month window.
Which categories does the GPT Sol report score?
Intelligence, Speed, Coding quality, Instruction following, Long context, Price / value, Reliability & uptime, Creativity & writing, Multimodal & vision, Agentic & tool use.
What are the main community pros and cons for GPT Sol?
Top pros: Half the price of the 5.6 generation is the loudest consensus in the sample — 'Half the price of 5.6 models' — and the $2 in/$10 out API tier anchors comparisons that priced an Opus-class model at Sonnet money.; A minority of first-hand reports are solidly positive: a multi-hour Extra-high user found it 'doing a great job for me... way less obsessed with overengineering/militant verification', and others report it 'does a pretty good job though' despite the speed.. Main cons: Speed is the most agreed complaint in the sample: 20 tokens per second, 'slow as a snail', 'we asked for slow mode, we got slow mode', and a post-release tester still measuring 22 tokens/s.; The dominant theory brands it a rebrand, not a release — 'GPT-6 Sol is just GPT-6 Terra renamed as Sol' — feeding a broader trust crisis about silently degraded or rerouted models..
When was the GPT Sol data collected?
Collected on October 7, 2026; the report was generated on October 7, 2026.

Collected from public r/codex posts and comments via Reddit JSON API on October 7, 2026.

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