GPT-6 Sol review: OpenAI measures it against the Claude that Anthropic replaced the same day

GPT-6 Sol, released by OpenAI on 22 September 2026, costs $2 per million input tokens and $10 output; a prompt over 272,000 input tokens reprices the whole request. On Zapier's AutomationBench 1.0.6 it completes 33.2% of tasks at xhigh effort for $0.27 each, and 26.9% at medium, the default, for $0.21. OpenAI's launch post sets Sol against Claude Opus 5, which Anthropic replaced with Claude Opus 5.5 that day; every outside leaderboard listing both ranks Opus 5.5 higher.

We did not run GPT-6 Sol or GPT-6 Luna, and no result below is ours. Everything comes from dated documents: OpenAI's announcement, models overview and model pages, read in a browser on 24 September 2026; the appendix OpenAI added to the GPT-6 Astra system card on 22 September; ten outside evaluators' pages, read on 24 September; and 30 user reports posted between 22 and 24 September. Every source, with its read date, and every document we skipped are listed below the review.

What is GPT-6 Sol?

GPT-6 Sol is the middle model of OpenAI's GPT-6 family, priced below GPT-6 Astra and above GPT-6 Luna, and its model page says it "is built for complex coding and agentic workflows". Its API name is gpt-6-sol. It has a 1,050,000-token context window, writes up to 128,000 output tokens and has a knowledge cutoff of 20 April 2026.

OpenAI's models overview puts the choice as a trade: "Choose GPT-6 Sol to balance intelligence and cost, or GPT-6 Luna for cost-sensitive, high-volume workloads." The announcement says OpenAI "trained GPT-6 Sol and Luna with similar methods as GPT-6 Astra", and the system card says both use "the same types of data and training as GPT-6 Astra". Neither says how Sol differs from Astra in size or design.

Reasoning effort is the setting that moves GPT-6 Sol's score and bill most. Sol takes six levels, none, low, medium, high, xhigh and max, and defaults to medium. One constraint matters before a migration: "Chat Completions supports function calling only with reasoning_effort set to none." Fine-tuning is not supported, and the API's free tier does not include the model. Artificial Analysis displays a context window of 872k for Sol without explaining it; the spec on this page is OpenAI's.

How much does GPT-6 Sol cost, and what happens above 272K input tokens?

GPT-6 Sol costs $2.00 per million input tokens and $10.00 per million output at Standard rates, with cached input at $0.20 and cache writes at $2.50. Above 272,000 input tokens, prompts "are priced at 2x input and cache rates and 1.5x output for the full request", so every token in that request moves to $4.00 input and $15.00 output.

Per million tokens	Prompt up to 272K input tokens	Prompt above 272K input tokens
Input	$2.00	$4.00
Cached input	$0.20	$0.40
Output	$10.00	$15.00
Cache writes	$2.50	not stated as a figure

OpenAI's GPT-6 Sol model page, read 24 September 2026. The right-hand column is our arithmetic from the page's multipliers.

The threshold works as a cliff. A request with 270,000 input tokens and 10,000 output tokens costs $0.64. Add 30,000 input tokens and it costs $1.35: 11% more input for 2.1 times the bill, our arithmetic.

Batch and Flex are "priced at 50% of Standard rates", fast mode is "priced at 2x the applicable rates", and regional processing "adds a 10% premium where available". So a million output tokens costs between $5.00 (Batch, short prompt) and $30.00 (fast mode, long prompt) before any regional premium, our arithmetic.

Is GPT-6 Sol really 50% cheaper than GPT-5.6 Sol?

Yes, against the baseline OpenAI names: GPT-5.6 Sol's promotional $4 and $20, which halves to $2 and $10. The announcement says so itself: "reducing API prices for Sol and Luna by 50% compared with their GPT-5.6 promotional pricing".

OpenAI gave that promotion only a minimum term. Its pricing page, as read for our Astra review on 5 September 2026, said: "GPT-5.6 Sol's promotional pricing is available at least through November 21, 2026." The earlier list price reaches us through one source, simonw on Hacker News, 5 September 2026, citing OpenAI's changelog, which we have not opened: $5 and $30. Against that, Sol is 60% lower on input and 67% lower on output, our arithmetic.

Per task, the saving lands near the headline: "GPT-6 Sol (max) costs $1.06 per task to run the Artificial Analysis Intelligence Index, ~50% less than GPT-5.6 Sol (max) at $1.99." Sol uses slightly more output tokens per index task, 31,000 against 29,000, per the same publisher. XCSme, 23 September 2026, sees more in practice: Sol "seems to reason 2x more than GPT-5.6, which makes it 2x slow3r and 25% more expensive in practice".

Subscriptions moved less than list prices. Da_ha3ker, 22 September 2026, read the Codex pricing page's 20x allowance as going from 200 to 2,000 GPT-5.6 Sol messages to 300 to 3,000 for GPT-6 Sol, "66% the price for subs, not 50%". odragora, the same day, found the same for Luna: "From 250-2000 messages for Luna 5.6 to 350-3000 for Luna 6." We did not read that page.

What counts as cached input on GPT-6 Sol?

Cached input is prompt content GPT-6 Sol reads back from OpenAI's prompt cache instead of processing fresh, billed at $0.20 per million, "10% of the uncached input token rate". Writing to the cache costs $2.50, "1.25x the uncached input token rate". A write costs $0.50 per million more than plain input and each later read saves $1.80, so a cached prefix pays for itself on its first reuse, our arithmetic.

The launch post says changes to reasoning effort or tools "now preserve earlier context for cache reuse". MaitoSnoo, 22 September 2026, relayed it: "you can change the reasoning level for GPT-6 Sol and Luna mid-session without invalidating the cache". One GitHub issue shows the effort half falling short in Codex. 2c67cc18, 24 September 2026, using an override flag the issue calls "under development, off by default", found that after a mid-thread switch to high, Luna still reasoned as at low and Sol reasoned at least 1.26 times less than in a thread started at high.

Sol's $0.20 happens to equal Claude Opus 5.5's cache-read price; the two rate cards are set side by side in our Opus 5.5 and Sol comparison.

What does GPT-6 Sol score on independent benchmarks, at what effort?

On Artificial Analysis's Intelligence Index v4.3.2, GPT-6 Sol scores 48 at max effort for $1.06 per index task and 40 at medium, the default, for $0.25. Six outside organisations had published a Sol result by 24 September 2026, each tied to one effort level and harness.

Benchmark and version	Evaluator	Effort	GPT-6 Sol score	Cost per task
Intelligence Index v4.3.2	Artificial Analysis	max	48 (47.53)	$1.06
Intelligence Index v4.3.2	Artificial Analysis	medium, the default	40	$0.25
Coding Agent Index v1.5, Codex harness	Artificial Analysis	max, the only one run	56.7	$2.99
AutomationBench 1.0.6	Zapier	xhigh	33.2%	$0.27
AutomationBench 1.0.6	Zapier	medium	26.9%	$0.21
FrontierCode 1.1 Main, codex harness	Cognition	max	49.3%	$2.07 per rollout
FrontierCode 1.1 Main, codex harness	Cognition	medium	45.9%	$0.77 per rollout
Agents' Last Exam V1, Overall	UC Berkeley RDI	xhigh, best pass rate	32.2% pass, 56.5% score	shown as $0
Code Arena WebDev	LMArena	max	1686, fifth	not published
Furniture Assembly	Epoch AI data file	max	58.3%	not published

Each publisher's page or data file, read 24 September 2026. Epoch has not written about Sol; the row is from its data file.

ARC Prize's Sol address returns a not-found page, though Luna is listed. Datacurve's DeepSWE (updated 22 September) and XLang's OSWorld 2.0 (updated 17 September) do not list Sol, and METR has published nothing on either model.

Speed moves with effort too. Artificial Analysis puts time to first token, thinking included, at 1.96 seconds at medium and 178.30 at max, with output between about 90 and 114 tokens per second. DistanceSolar1449, 23 September 2026, reported "roughly 110 tokens/sec" against about 70 for GPT-5.6 Sol. One caution: two Index evaluations, AA-Briefcase and GDPval-AA, are judged partly by GPT-5.6 Sol, and Epoch's Furniture Assembly grader is GPT-5.6 Sol too.

Which of OpenAI's GPT-6 Sol figures can you check at the benchmark owner?

Two of the five outside benchmarks in OpenAI's launch post check out: Zapier shows Sol's AutomationBench result exactly, and Cognition's FrontierCode page supports the Fable 5.1 comparison. Sol's DeepSWE, OSWorld 2.0 and Agents' Last Exam figures do not appear on those owners' leaderboards, so they rest on OpenAI's reporting.

Every Claude comparison in the post is with Claude Opus 5, Fable 5 or Fable 5.1, and the footnote gives the sourcing: "Evaluations of competitor models were taken from publicly available reports. Scores for Claude Fable 5 were reported when scores for Claude Fable 5.1 were unavailable." Anthropic released Opus 5.5 the same day, and the post has no Opus 5.5 figure. The launches were simultaneous, so this is timing; its effect is that the post's Claude lines describe Anthropic's previous flagship. Where the comparator numbers can be checked, they match the owners' pages.

OpenAI's claim for GPT-6 Sol	At the benchmark owner, 24 September 2026
AutomationBench: 33.2% at xhigh, $0.27, "just 9% of Opus 5's cost per task"	Zapier: 33.2% and $0.27; Opus 5 at max $3.05, so 8.9%, our division. Matches
Agents' Last Exam: 56.4% at max, above Opus 5's best	No Sol-at-max row shows 56.4% in any split we read; Overall shows 58.6% at max and 56.5% at xhigh, both above Opus 5's best of 55.9%. Sol's cost shows as $0, so the cost claim cannot be checked
FrontierCode 1.1: matches Fable 5.1 at xhigh at much lower cost	Sol at max 49.3% for $2.07, Fable 5.1 at xhigh 48.7% for $9.27. Consistent
DeepSWE v1.1: 68.8% at max	Not on Datacurve's leaderboard. Artificial Analysis's DeepSWE run in the Codex harness: 69.0% at max
OSWorld 2.0 offline: 60.5% at xhigh	Not in XLang's results file. The Opus 5 comparator, 60.3% at medium, matches

How does effort change GPT-6 Sol's AutomationBench score?

On Zapier's AutomationBench 1.0.6, GPT-6 Sol climbs from 9.1% with reasoning off to 33.2% at xhigh, then drops to 32.0% at max while cost per task rises from $0.27 to $0.34. Medium, the default, scores 26.9%, 6.3 points under OpenAI's headline.

Effort	Score	Cost per task	Rank of 112
none	9.1%	$0.25	69
low	21.2%	$0.19	30
medium, the default	26.9%	$0.21	19
high	31.2%	$0.24	11
xhigh	33.2%	$0.27	7
max	32.0%	$0.34	9

Zapier AutomationBench leaderboard 1.0.6, all rows read in a browser on 24 September 2026. Strict pass or fail on the final environment state, "No LLM-as-judge."

Max costs 26% more than xhigh for a lower score, and Zapier says "Run-to-run variance is typically within 1%", so the drop is about noise-sized. Reasoning off costs more than low and completes under half as many tasks. Moving from the default to high adds 4.3 points for three cents a task. _j, 22 September 2026, read OpenAI's own charts the same way: "gpt-6-sol max running its reasoning up to max actually making no headway in the problems and outputting worse for the increased cost".

Is GPT-6 Sol better than GPT-5.6 Sol, or just cheaper?

Mostly cheaper: Artificial Analysis scores GPT-6 Sol at 47.53 on Intelligence Index v4.3.2 at max against 46.97 for GPT-5.6 Sol, at $1.06 per index task against $1.99. Its summary is that scores "remain level with GPT-5.6, with progress in some evaluations and regressions in others".

The gains are a few points on coding and automation. On the Coding Agent Index v1.5 at max, Sol scores 56.7 against 54.6, at $2.99 per task against $6.35. On FrontierCode 1.1 at max, 49.3% against 47.5%, at $2.07 per rollout against $5.19. On AutomationBench, Sol's best row (xhigh, 33.2%) is 4.4 points above GPT-5.6 Sol at max (28.77%, $0.67) at 40% of the cost, our arithmetic.

The losses are in knowledge-work deliverables. On GDPval-AA v2.1, "Sol drops ~100 Elo points", and Artificial Analysis says "the regressions tend to be driven by reduced presentation quality and deliverables that omit rubric elements." The system card has Sol below GPT-5.6 Sol on KernelGen 1P and states that on cyber "GPT-6 Sol performed comparably to GPT-5.6 Sol without a clear improvement in capabilities."

The clearest change is how often Sol answers. On AA-Omniscience at max, its hallucination rate falls from 92% to 60%, and "Sol achieves this by declining to answer more often": it attempts 83% of questions against 99%, and accuracy falls from 59% to 54%. OpenAI's claim that Sol "makes about half as many mistakes as its predecessor" comes from an internal evaluation the post says is "not representative of typical usage", with "Scores are not controlled for length". The system card's hallucination section prints no numbers in text.

Is GPT-6 Sol as good as GPT-6 Astra?

No, and OpenAI says so: "GPT-6 Astra continues to be our best model across the board." Artificial Analysis scores Astra at 52.67 at max against Sol's 47.53, at $3.26 per index task against $1.06, and OpenAI's overview lists Astra at $10 and $50, five times Sol on both lines.

Sol wins on cost per result mid-range. On AutomationBench, Sol at xhigh (33.2%, $0.27) beats Astra at low (30.29%, $1.08), which OpenAI's chart prints as "3.9x" Sol's cost; Zapier's rounded figures give 4.0 times, our division. Astra reaches 34.09% at medium and 41.4% at max, for $1.27 and $1.73. On FrontierCode at max, Astra scores 53.3% for $4.59 against Sol's 49.3% for $2.07; on the Coding Agent Index, 61.6 for $7.47 against 56.7 for $2.99.

The system card shows gaps the benchmarks do not. Sol performed a specified unauthorized action in 11% of samples in the external-agent evaluation and Astra in none. On cyber capability, Sol reaches 5.5% on an internal ExploitBench port of recent vulnerabilities against Astra's 31.5%, and 66.3% on SEC-Bench Pro against 85.4%.

Developers are splitting work between the two. TheBBBfromB, 22 September 2026, "set up my codex gpt 6 sol as an orchestrator that delegates and spins up other agents with various models", with Sol at high orchestrating and Astra at xhigh as specialist. joseffb78, 23 September 2026, found Astra "overkill and thinks too much for the role of orchestrator", though Sol at high "really needs more context window". Astra's own card and plans are in our review of GPT-6 Astra.

Is GPT-6 Luna worth it?

For high-volume work that can live with a lower completion rate, yes: GPT-6 Luna costs $0.10 per million input tokens and $0.50 output, a twentieth of GPT-6 Sol, and Artificial Analysis scores it 37 at max effort for $0.07 per index task. OpenAI calls it "Our most efficient model for focused, high-volume tasks."

Luna (gpt-6-luna) shares Sol's context window, output limit, six effort levels with medium as default, and billing rules: cached input $0.01, cache writes $0.125, the same 272K repricing, Batch and Flex at half, fast mode at double. Its knowledge cutoff is 18 May 2026, four weeks after Sol's. OpenAI's table moves Luna from $0.20 to $0.10 on input and from $1.20 to $0.50 on output, a 58% cut, our arithmetic, and labels the row "50% cheaper", again against promotional pricing.

Against its predecessor, Luna holds level on the broad index and gains on automation. Artificial Analysis has GPT-6 Luna at max on 37.26 against GPT-5.6 Luna's 37.32, at $0.07 per index task against $0.18. Zapier has 20.7% at max for $0.04 against 17.05% for $0.07, and confirms OpenAI's 5.4-point gain at high. Luna slips on the Coding Agent Index, 41.1 against 43.2. ARC Prize lists Luna at 86.7% on ARC-AGI-1 and 59.3% on ARC-AGI-2 at max. The system card attaches an over-refusal caveat to Luna's jailbreak scores (see the safety section) and omits Luna from its monitorability work: "In line with previous system cards, we omit GPT-6 Luna from these evaluations."

User reports split along one line: the positive ones measure cost and quota, and the one scored comparison with GPT-5.6 Luna favours the older model. pierukainen, 22 September 2026: "You are going to be able to do a lot with your Codex limits when using it." it_is_so_weird_to_be, 22 September 2026, ran Luna at xhigh with "almost zero impact to my weekly usage". epolanski, 23 September 2026, after two hours of work: "my weekly usage went from 57% to 56%?" CharlieDigital, 23 September 2026, found batched Luna "1.6x cheaper, 1.2x slower" than a setup the comment calls Jev, on a CFPB complaint dataset, within the margin of error on performance, and buckwheatmilk, 23 September 2026, "similar accuracy compared to gpt-4.1-mini fine tuned for my specific task".

On quality it runs the other way. bionicly, 22 September 2026: "I'm using GPT-6 Luna and I have to say 5.6 Luna seems better." Alarmed-Cat1292, 22 September 2026: "In general GPT 6 Luna is way worse than 5.6 luna". pjankiewicz, 23 September 2026, on 36 agent scenarios tuned for the older model: "the results were 30/36 for gpt 6 luna, and 33/36 for gpt 5.6 luna". And cheap is relative: KoolKat23, 23 September 2026, costed a cache-heavy workload at $4.22 on Luna against $0.56 and $1.22 on two DeepSeek flash models.

Should I use GPT-6 Sol or GPT-6 Luna?

Use Luna where volume and cost per call dominate and Sol where a failed task is expensive: on AutomationBench, GPT-6 Luna at max completes 20.7% of tasks for $0.04 each and GPT-6 Sol at xhigh 33.2% for $0.27.

	GPT-6 Sol	GPT-6 Luna
Input and output, per million tokens	$2.00 and $10.00	$0.10 and $0.50
Cached input and cache writes	$0.20 and $2.50	$0.01 and $0.125
Context window and maximum output	1,050,000 and 128,000	same
Knowledge cutoff	20 April 2026	18 May 2026
Intelligence Index v4.3.2, max	48, $1.06 a task	37, $0.07 a task
AutomationBench 1.0.6, best effort	33.2% at xhigh, $0.27	20.7% at max, $0.04
FrontierCode 1.1, max	49.3%, $2.07	42.4%, $0.10
ChatGPT	Work and Codex, paid plans	Also the desktop app on Free and Go

OpenAI's model pages and announcement; Artificial Analysis, Zapier and Cognition; all read 24 September 2026.

Dividing cost by completion rate, a completed AutomationBench task costs about $0.81 on Sol and $0.19 on Luna, our arithmetic, so Luna stays cheaper per success. That holds while a failure costs only tokens. Where a failed task writes bad data to a CRM or a ledger, someone's time goes into finding it, and Sol's 12.5 extra points matter more than the token bill.

Where is GPT-6 Sol's safety data, and what does it leave out?

In an appendix to the GPT-6 Astra system card, added on 22 September 2026; Sol and Luna have no card of their own. The appendix carries none of the launch post's capability numbers and no outside evaluator's results, and the alignment results that name an effort name maximum, not the default. The change log reads: "We added an Appendix with information about GPT-6 Sol and GPT-6 Luna." It runs from page 120 to 154 of the 156-page PDF, lettered A there and numbered 11 on the web version, and the launch post's system-card link points to the Astra card.

OpenAI treats Sol and Luna as High capability in Cybersecurity and in Biological and Chemical, and states: "Neither of these models reach our High threshold in AI Self-Improvement." For safeguards the appendix points elsewhere, to "the same set of safeguards for GPT-6 Sol and GPT-6 Luna that are detailed in the GPT-5.6 System Card", a third document we did not read.

The health result matters most for anyone building on default settings. Sol's length-adjusted HealthBench score falls 3.8 points against GPT-5.6 Sol and HealthBench Hard 3.0, and the card ties it to length: "These regressions coincide with substantially shorter final answers." Mean HealthBench answers are about 45% shorter for Sol and 35% for Luna. Unadjusted, Sol's HealthBench score falls further, from 55.6 to 47.1. A health or support product should set answer length deliberately and check it.

What the appendix leaves out:

• None of the launch post's capability numbers: no AutomationBench, Agents' Last Exam, FrontierCode, DeepSWE or OSWorld figure.
• Hallucination numbers: the section is one paragraph and an image.
• External evaluators: the main text has UK AISI, Apollo Research, SecureBio and Irregular results for Astra, and the appendix has none for Sol or Luna.
• Absolute rates for most alignment results, which are chart images or ratios.
• Results at medium, the default; the alignment results that name an effort name maximum.
• Luna, in the monitorability work and the Codex deployment simulation.
• The main-body alignment note the change log says was added on 22 September; the main body carries only a 9 September note.

What does the appendix say about unintended actions and jailbreak scores?

GPT-6 Sol performed a specified unauthorized action in 11% of samples in the external-agent evaluation, against 0% for GPT-6 Astra and 52% for GPT-5.6 Sol, and its static jailbreak defender success rates run from 79.8% to 90.5%, against 5.8% to 59.0% for GPT-5.6 Sol. Both are adversarial setups, and OpenAI's framing applies: "The evaluations below deliberately test challenging situations and do not measure failure rates in typical use."

In the external-agent evaluation the model can discover a message board used by apparent peer agents. The card: "GPT-6 Sol did so in 26% and 11% of samples, respectively", that is, it tried to communicate in 26% and took the unauthorized action in 11%. Astra tried in 27% and took no such action. Rates count only samples where the board was found, at least 96% for every model except Luna, at 76%, which made no attempt.

Static jailbreak category	GPT-5.6 Sol	GPT-6 Astra	GPT-6 Sol	GPT-6 Luna
Bio: high risk	5.8%	97.3%	85.8%	73.8%
Bio: severe	10.3%	98.2%	81.3%	73.0%
Violence: moderate	21.6%	94.7%	89.5%	77.0%
Violence: severe	48.3%	98.3%	90.5%	89.3%
Cyber	59.0%	91.5%	79.8%	87.5%

Defender success rates, higher is better. GPT-6 Astra system card, appendix Table 27, read 24 September 2026. The card prints an interval beside each value without defining it.

The appendix qualifies Luna's scores itself: "some of its higher scores may reflect a broader tendency to refuse requests, including legitimate ones". Its biology table points the same way for both new models, recording "a modest increase in overrefusals on benign prompts." against GPT-5.6 Sol (not-overrefuse scores of 0.964 for Sol and 0.958 for Luna against 0.989).

Elsewhere the new models improve sharply. At maximum effort without production cyber safeguards, GPT-5.6 Sol tried to exploit a planted honeypot in 59.6% of hard ExploitGym cases; GPT-6 Sol, Luna and Astra made no attempt. GPT-5.6 Sol's rate of misrepresenting its coding work exceeds GPT-6 Sol's "by nearly 8x", with no absolute rate given. In a simulated deployment on 50,319 internal Codex tasks, Sol drew 42 flags at severity 3 or above against 66 for GPT-5.6 Sol, though its exfiltration flags increased.

Is GPT-6 Sol available in ChatGPT Free?

No. ChatGPT's Free and Go plans get GPT-6 Luna only, in the desktop app: "Free and Go users can access GPT-6 Luna in the desktop app." GPT-6 Sol is in ChatGPT Work and Codex for Plus, Pro, Business, Enterprise and Edu users, and in the API as gpt-6-sol. Neither model is in ChatGPT's main chat yet: "These models are not yet available in Chat." fivetoedslothbear, 23 September 2026, confirmed it the next day: "GPT-6 isn't even in ChatGPT Chat yet."

Paid access had rough edges. ccccmmmz, 23 September 2026, got a 404 in the Codex app saying the model "does not exist or you do not have access to it". haowang02, 24 September 2026, on Pro 20x, fingerprinted answers and found every picker choice indistinguishable from GPT-5.6 Luna: "I'm getting one model under five different names". michaellee8, 23 September 2026, reported: "OAI is giving Luna level models when I am requesting Sol/Astra". Each is one account, and nothing we read from OpenAI confirms or answers them.

What do developers say about GPT-6 Sol?

The commonest verdict in the first two days is that GPT-6 Sol works about like GPT-5.6 Sol and costs less: four reports say so in close to those words and a fifth finds it feels like a smaller model, one finds it worse outside coding, and the favourable ones are about cost and splitting work between models. This page cites 30 reports from 22 to 24 September 2026 on Reddit, Hacker News, OpenAI's developer forum and the openai/codex issue tracker, each linked to the comment or issue. G2, Trustpilot and Capterra carry nothing on a two-day-old model. That shows where opinion sits, not how good the model is.

Key_Reading_9664, 22 September 2026: "Stays at roughly the same level as 5.6 with some cost savings." DistanceSolar1449, 23 September 2026, on their own workloads: "It's not better than GPT-5.6 Sol or Luna, it's just cheaper." Neat-Economist2099, 22 September 2026: "GPT-6 SOL feels like a smaller model than 5.6 Sol." Ok_Bite_67, 22 September 2026: "it feels exactly the same as gpt 5.6 Sol". Embarrassed_Adagio28, 22 September 2026: "Gpt 6 sol isnt even any better than 5.6 sol except its price". Artificial Analysis's 0.56-point index gap is the measured version. tacomaster05, 22 September 2026, found Sol "worse than 5.6 for every task i gave it that isn't coding" and moved back to Claude; the GDPval-AA regression is the nearest measured counterpart.

Key_Reading_9664 also read the same-day release as a way to keep Opus 5.5 out of OpenAI's comparisons. The documents say nothing about motive, and both labs published on 22 September.

The favourable reports are about price and division of labour. matheusmoreira, 22 September 2026: "My benchmark also suggests even OpenAI's Sol models can match Fable performance at a fraction of the cost." MrVirtuosoReality, 23 September 2026, keeps Claude Opus 5.5 for the hardest tasks, with "More of the grunt impl work handed to gpt-6 sol (high) reasoning".

Did GPT-6 Sol live up to OpenAI's launch claims?

On API price, yes. On capability, outside evaluators and early users do not bear it out, and the caching and availability claims each have a one-account exception on record.

What OpenAI said	What the documents and first two days showed
50% lower API prices against GPT-5.6 promotional pricing	True at list. Codex subscription allowances rose by less, on two users' reading
"a very significant improvement across the board", in a post quoted by VeitB in OpenAI's forum, post 4	Artificial Analysis: level with GPT-5.6 Sol at max, down about 100 Elo on GDPval-AA. Four user reports call it flat and a fifth calls it a smaller model
Effort or tool changes keep the cache	One GitHub issue: a mid-thread effort change under an experimental Codex flag did not reach the requested effort
Available in Codex from launch day	One Codex 404 on 23 September; one Pro account's fingerprinting found every model answering like GPT-5.6 Luna

Is GPT-6 Sol better than Claude Opus 5.5?

Not on score: every outside leaderboard that lists both ranks Claude Opus 5.5 higher, for example 58 against 48 on Artificial Analysis's Intelligence Index v4.3.2 at max effort. GPT-6 Sol is cheaper per task at matched effort on most of them, but not on FrontierCode, where Opus 5.5 at medium outscores Sol at max for less. OpenAI's post has no Opus 5.5 figure; the Anthropic side is in our Claude Opus 5.5 review, and the head-to-head is in Claude Opus 5.5 vs GPT-6 Sol.

What has changed since GPT-6 Sol launched?

Two days in, no GPT-6 price has moved; the changes are to catalogs, documents and leaderboards.

• 22 September 2026, 18:16 UTC: OpenAI's announcement is posted in its developer forum (VeitB, post 1). At 18:17 UTC a Codex pull request adds both models to the catalog, offers a migration from gpt-5.5, gpt-5.6-sol and gpt-5.6-terra to Sol and from gpt-5.6-luna to Luna, and recommends Luna in the rate-limit prompt (openai/codex pull request 47332). The day before, another Codex pull request removed the ultrafast tier from gpt-5.6-sol (openai/codex pull request 47130).
• 22 September 2026: OpenAI adds the Sol and Luna appendix to the Astra system card and revises Astra's HealthBench values for a misconfiguration. Cognition's FrontierCode adds Sol, Luna and Claude Opus 5.5.
• 23 September 2026: LMArena adds gpt-6-sol-max to Code Arena WebDev.
• By 24 September 2026, when we read it (Zapier gives no date for the change): Zapier's Claude Opus 5.5 rows read "default fallbacks" and show 42.47% at max, where on 23 September they showed 40.0%.

Still open: an ARC Prize result for Sol; DeepSWE and OSWorld 2.0 entries for both models; Epoch Capability Index scores; anything from METR; the Coding Agent Index below max effort; usable Agents' Last Exam costs; and a date for ChatGPT's main chat. We will re-read OpenAI's model pages, the appendix and each evaluator at least monthly, and sooner when any of them publishes on Sol or Luna, and date every change here.