The supplied reports describe OpenAI cutting GPT-5.6 Sol API and eligible credit pricing on August 21, 2026; Reuters cites standard short-context rates falling from $5 to $4 per million input tokens and from $30 to $20 per million output tokens, reductions of 20% and roughly 33%, respectively. Enterprise DNA reports the promotion lasts through at least November 21, while citybiz instead calculates a $24 output rate; the supplied OpenAI announcement snippet is truncated, and its July 30 blog documents an earlier change that left Sol pricing unchanged. September reports concern a separate GPT-6 release, not this price cut. The snippets establish reported price changes but provide no production-usage evidence that developers switched models or moved deferrable workloads to OpenAI.
| source | object | author | score | comments |
|---|
| 🟠 reddit | API Price cut > 20% OpenAI | Babayaga1664 | 6 | 2 |
| 🟧 echo.x ⭐ | OpenAI posted: “As we continue to push the frontier of capabilities while improving efficiency, we're dropping API and credit pricing of GPT | OpenAI | — | — |
| 🟧 hn | GPT 5.6 Sol 20% price reduction | izakfr | 90 | 74 |
| 🟠 reddit | New Sol API pricing - $4 per million input tokens and $20 per million output tokens OpenAI | Dualyeti | 175 | 68 |
| 🟧 hn | OpenAI: GPT 5.6 Sol price reduction (until at least Nov 21) | tosh | 338 | 319 |
| 🟧 hn | AI Realist Radar:GPT‑5.6 Sol Pricing, Stripe's OpenRouter Deal | Airealist | 1 | 0 |
| 🟧 hn | GPT 5.6 Discounts and Jevons Paradox | Philpax | 1 | 0 |
| 🟠 reddit | GPT-6 Sol Appeared on the OpenAI API singularity | 141_1337 | 450 | 90 |
| 🟧 hn | GPT-6-sol appeared on OpenAI API | pranshuchittora | 10 | 7 |
| 🟠 reddit | Introducing GPT-6 Sol and Luna OpenAI | DemiPixel | 1128 | 189 |
| 🟧 openai | Introducing GPT-6 Sol and LunaRetrieved article excerptOpen article · Retrieved 2026-09-22T18:24:17.863121+00:00 # Introducing GPT‑6 Sol and Luna
More ways to bring frontier intelligence into the work you do every day.
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Earlier this month, we introduced [GPT‑6 Astra](https://openai.com/index/gpt-6-astra/), the most intelligent and aligned model in the world. While the most demanding and important projects still call for Astra’s full depth, work happens at different scales, rhythms, and budgets.
That’s why we’re expanding the GPT‑6 universe with **GPT‑6 Sol** and **GPT‑6 Luna.** GPT‑6 Astra introduced a new generation of intelligence—these models help distribute the benefits of that intelligence by advancing the frontier on cost efficiency. We trained GPT‑6 Sol and Luna with similar methods as GPT‑6 Astra, bringing the advances behind Astra’s state-of-the-art performance in professional work, factuality, coding, computer use, and alignment to faster, more affordable models.
The GPT‑6 models lead across the cost–intelligence curve, combining exceptional capabilities at every tier with infrastructure that delivers them efficiently at scale. Improvements in caching and inference let us serve these models at lower cost, and we’re passing those savings directly on to users and customers by **reducing API prices for Sol and Luna by 50%** compared with their GPT‑5.6 promotional pricing. Together, these improvements make advanced AI practical for more everyday tasks and applications at scale.
### GPT‑6 API pricing
| | | | |
| --- | --- | --- | --- |
| **Model** | **Input** | **Output** | **Price reduction** |
| **GPT‑6 Sol** vs. GPT‑5.6 Sol | $4 → **$2** | $20 → **$10** | 50% cheaper |
| **GPT‑6 Luna** vs. GPT‑5.6 Luna | $0.20 → **$0.10** | $1.20 → **$0.50** | 50% cheaper |
*Prices are per 1 million tokens.*
**GPT‑6 Astra** continues to be our best model across the board. Choose it when you want the best results and an uncompromising experience.
## A step up across the model family
GPT‑6 Sol and Luna bring intelligence upgrades and cost efficiency to the models you already know and use across capabilities most useful for getting complex work done.
### Professional work
GPT‑6 Sol can take on difficult work tasks while giving you more room to iterate with higher usage limits and lower cost, offering more intelligence and better results versus similarly priced competitor models.
On **AutomationBench,** a test of business workflows across apps, GPT‑6 Sol at xhigh effort outperforms Claude Opus 5 at max effort at just 9% of Opus 5’s cost per task. At high effort, GPT‑6 Luna improves on its predecessor by 5.4 percentage points at 58% lower cost per task.
*In* [*AutomationBench 1.0.6*(opens in a new window)](https://zapier.com/benchmarks)*, AI agents are tested on end-to-end workflows using 47 tools across sales, marketing, operations, support, finance, and HR. The datapoint for Claude Fable 5.1 understates its actual cost, as it omits the cost of the Opus 5 fallbacks, which occurred on ~40% of tasks.*
GPT‑6 Sol also exceeds Claude Fable 5.1 at far lower cost, and even bests low-effort GPT‑6 Astra.
| **Model (and effort)** | **Score** | **Cost per task** |
| --- | --- | --- |
| GPT‑6 Sol (xhigh) | 33.2% | $0.27 |
| GPT‑6 Astra (low) | 30.3% | **3.9x** GPT‑6 Sol |
| Claude Opus 5 (max) | 26.9% | **11.1x** GPT‑6 Sol |
| Claude Fable 5.1 w/ Opus 5 Fallback (max) | 31.4% | **>8.9x** GPT‑6 Sol *(fallback cost not reported)* |
On **Agents’ Last Exam**, which evaluates agents on complex professional workflows, GPT‑6 Sol at max effort scores 56.4%, above Claude Opus 5’s highest score in the evaluation at 60% lower cost per task.
*In* [*Agents’ Last Exam V1*(opens in a new window)](https://agents-last-exam.org/)*, AI agents are evaluated on long-horizon, economically valuable tasks spanning 55 sub-industries, covering most major fields of professional work performed on a computer.*
### Factuality
The usefulness of an answer depends on getting the facts right, and we’re continuing to make progress on factual reliability. On our internal factuality evaluation, which is based on de-identified real-world conversations where users flagged mistakes by our models, GPT‑6 Sol makes about half as many mistakes as its predecessor, approaching Astra-level reliability at much lower cost. GPT‑6 Luna also improves substantially; at higher effort levels it matches GPT‑5.6 Sol at about a hundredth its cost.
*Here we evaluate factuality on de-identified ChatGPT conversations where users had flagged a factual error from a prior model. These error-inducing conversations are not representative of typical usage, where factual errors are more rare. Scores are not controlled for length; however, our verbosity sweeps showed almost no dependence on answer length.*
### Coding
This year, coding agents have begun tackling tasks with more complexity, scope, and duration than ever before. At OpenAI, our internal usage has grown exponentially. Valued at API prices, daily token usage has exceeded $600 for the median researcher and $7,000 for researchers at the 90th percentile ([Research acceleration: The view inside OpenAI](https://openai.com/index/research-acceleration-view-inside-openai/)). As coding agents take on longer and more demanding tasks, the cost of sustained use matters more. GPT‑6 Sol and Luna combine strong coding performance with lower API prices, giving developers more room to iterate and teams the confidence to be more ambitious about what they ask Codex to take on.
On **FrontierCode**, which evaluates whether coding agents produce changes ready to merge into real codebases, GPT‑6 Sol improves substantially over GPT‑5.6 Sol, and is able to match Claude Fable 5.1 xhigh at much lower cost.
*In* [*FrontierCode 1.1 Main*(opens in a new window)](https://cognition.com/frontiercode)*, AI agents write code that’s graded not only on correctness but also “mergeability”: e.g., test quality, scope discipline, code style, and adherence to codebase standards.*
On **DeepSWE v1.1,** which tests performance on complex software-engineering tasks in real codebases, GPT‑6 Sol at max effort scores 68.8%, within 1.1 percentage points of Claude Fable 5’s highest score in the evaluation—69.9% at xhigh effort—at approximately 80% lower cost per task.
GPT‑6 Luna at max effort scores 66.6%, comparable to Claude Opus 5 and Fable 5 at medium effort. In these comparisons, Luna costs 93% less per task than Opus 5 and 96% less than Fable 5.
*In* [*DeepSWE 1.1*(opens in a new window)](https://deepswe.datacurve.ai/)*, AI agents solve original, long-horizon software engineering tasks.*
### Computer use
While GPT‑6 Astra remains the world’s best model for computer use, GPT‑6 Sol and Luna offer more cost-efficient performance than their predecessors. On **OSWorld 2.0 offline**, GPT‑6 Sol at xhigh effort achieves a similar score to Claude Opus 5 at medium effort—60.5% versus 60.3%—at approximately 80% lower cost per task. GPT‑6 Luna (max) is able to exceed GPT‑5.6 Sol (medium) at one tenth of its cost.
*In* [*OSWorld 2.0*(opens in a new window)](https://osworld-v2.xlang.ai/)*, AI agents attempt long-horizon computer-use workflows spanning everyday and professional tasks. We report the partial reward on the offline set from the v2026.08.08 release.*
### Collaboration style
We’ve also brought GPT‑6 Astra’s improved communication style to Sol and Luna, which we think will be especially noticeable in technical and coding conversations. Expect to see more clarity, less jargon, fewer odd turns of phrase, fewer low-value details, and slightly shorter answers overall without losing substance.
Prompt
Website’s looking clean! Could we use a Bento Box design style and add a slider between the pages in the top right? You may need to dive into the React..
GPT-5.6 Sol
Absolutely. I can get the bento feel and the top-right sliding page switcher without pulling in React—the interaction is small enough to stay fast and smooth in the current site. I’m reshaping all four pages around that system now.
---
Done — open the live site.
It now has a colorful bento-box layout across all four pages and an animated top-right Home / Play / Place / Taste slider. React wasn’t necessary; native page transitions keep it lightweight and smooth.
The built-in image tool created the food artwork. Prompt: “An energetic gouache-and-ink editorial illustration celebrating dumplings and spicy noodles, with bright colors, steam, spice, and no text or logos.”
GPT-6 Sol
I’ll reshape the pages into a bento grid and turn the top-right links into a sliding page switcher. I’ll keep the existing site and check whether that interaction needs React before changing its setup.
---
The updated site is live. Every page now has a playful bento layout, and the top-right page switcher slides between sections. I checked it on desktop and narrow mobile screens, including browser back navigation. The existing site didn’t need React for this.
*Although style is subjective, we prefer GPT‑6 Sol’s reply here. It doesn’t jump to conclusions as quickly, spends less time reiterating details that might be obvious to the asker (e.g., that the website has four pages), uses less vague language (e.g., “bento feel”, “reshaping… around that system”), is more forthcoming with what it did and didn’t check, and doesn’t unnecessarily share implementation details like its image tool prompt.*
## Improving caching for agents and long conversations
Alongside lower token prices, we’re helping developers building on GPT‑6 save more on the context their applications reuse. We’ve improved prompt caching for GPT‑6 to deliver higher cache hit rates by default, helping agents reuse more context, respond faster, and benefit from discounts of 90% on cached input-token reads.
Developers also have more ways to measure and optimize their caching performance:
- **Monitor and diagnose.** The [Prompt Caching Dashboard(opens in a new window)](https://platform.openai.com/usage?usage_section=prompt-caching) shows how much input is cached and how that changes over time. The [diagnostics tool(opens in a new window)](https://developers.openai.com/api/docs/guides/prompt-caching/diagnostics) helps explain missed opportunities for caching and what to fix.
- **Adjust reasoning effort and tool availability without breaking cache.** Increase [reasoning effort(opens in a new window)](https://developers.openai.com/api/docs/guides/reasoning#change-reasoning-mid-conversation) for harder tasks or lower it for simpler follow-ups, and [enable or disable tools(opens in a new window)](https://developers.openai.com/api/docs/guides/prompt-caching#how-to-optimize-prompt-caching) as your agent’s needs change. Both controls now preserve earlier context for cache reuse.
- **Optimize which prefixes get cached.** Explicit breakpoints let developers choose where cached prompt prefixes end. This gives developers more control over cache reuse and can improve performance.
GitHub reports that, over the past several months, these improvements have reduced the share of prompt tokens requiring fresh processing by more than 50% across billions of requests to OpenAI models, helping Copilot respond faster.
## Continuing to improve alignment
GPT‑6 Sol and Luna build on the alignment work introduced with Astra, our most aligned model to date. In our alignment evaluations, both Sol and Luna show improvements over their GPT‑5.6 counterparts, including lower rates of misleading claims about their coding work.
The evaluations below deliberately test challenging situations and do not measure failure rates in typical use. See the [system card(opens in a new window)](https://deploymentsafety.openai.com/gpt-6-astra) for the full results.
## Availability
GPT‑6 Sol and GPT‑6 Luna are available in ChatGPT Work and Codex starting today for all Plus, Pro, Business, Enterprise, and Edu users. Free and Go users can access GPT‑6 Luna in the desktop app. These models are not yet available in Chat. In the OpenAI API, they are | OpenAI | — | — |
| 🟧 hn | GPT-6 Sol and Luna push the cost-efficiency frontier | wertyk | 4 | 0 |
| 🟠 reddit | Theory: Sol is the new Terra and should be compared to Sonnet not Opus OpenAI | TraditionalHome8852 | 61 | 33 |
| 🟧 hn | GPT-6 Sol and Luna push the cost efficiency frontier by halving token cost | theanonymousone | 1 | 0 |
| 🟠 reddit | Claude Opus 5.5 can’t really be considered a cheaper model than OpenAI new Sol model. It’s roughly twice as expensive as GPT‑6 Sol, yet it only reduces output tokens by about 19% per task and cuts reasoning/indexing tokens by about half. ClaudeAI | RFOK | 0 | 7 |
| 🟠 reddit | Cost Efficiency chart of the GPT-6 Models OpenAI | Tall_Abrocoma_3533 | 39 | 10 |
2026-09-23T17:55:33Z
grounded: known/medium — Scott already holds the relevant position in Model Perishability: model repricing requires swappable providers and re-evaluation; this reported cut gives a conc
2026-09-23T17:48:50Z
The question 'will the 5.6 Sol cut shift workloads' was overtaken by events: OpenAI's retrieved first-party announcement launched GPT-6 Sol and Luna at half the promotional 5.6 pricing ($4→$2 / $20→$10 Sol) plus 90% cached-input discounts and default cache improvements, making the 5.6 discount-horizon question moot and resetting the relevant price point. The cost-efficiency-frontier story is now a GPT-6 pricing/adoption story, not a 5.6 discount story.
2026-09-23T15:28:49Z
evidence attached: reddit.post.1wo7q53 — Third-party Artificial Analysis cost-efficiency data circulating for the GPT-6 lineup is measured context for whether Sol's price cut shifts model selection.
2026-09-23T08:21:46Z
evidence attached: reddit.post.1wnz9s1 — The comparison adds context on relative task economics between GPT-6 Sol and Opus, though it is not independent production-usage evidence.
2026-09-23T08:21:46Z
evidence attached: hn.story.49812866 — shared external link with case evidence
2026-09-23T03:22:02Z
evidence attached: reddit.post.1wntdu3 — The post discusses Sol pricing and workload selection, but offers speculation rather than independent evidence about the reported price cut’s impact.
2026-09-22T20:23:59Z
evidence attached: hn.story.49806724 — Independent Artificial Analysis coverage bears directly on whether OpenAI's newer models are changing the cost-efficiency frontier, though it is not independent validation of the reported price cut itself.
2026-09-22T19:23:24Z
evidence attached: openai.article.608eb672ebb7d160faf85854 — shared external link with case evidence
2026-09-22T18:24:10Z
evidence attached: reddit.post.1wngyux — The high-engagement post links the first-party GPT-6 Sol and Luna announcement, materially updating the existing API price-cut case and potentially superseding its scope.
2026-09-11T21:32:40Z
The new HN item repeats the possible GPT-6 Sol API appearance without access receipts or evidence of independent verification; it establishes neither a successor release nor discount-driven adoption. The GPT-5.6 price reduction remains established, but its effect on production model selection is still unproven.
2026-09-11T21:22:00Z
evidence attached: hn.story.49665088 — The reported GPT-6-sol appearance in OpenAI's API independently supports the case that the model is moving toward production availability.
2026-09-11T11:32:52Z
Only trivial engagement drift on the GPT-6 Sol appearance post (score 425→427, comments unchanged); no new production-routing or workload-migration evidence and no resolution of GPT-6 supersession status. The established temporary Sol discount remains the durable fact; adoption impact stays uncorroborated and this case should only be revisited on real usage data or a material pricing/successor development.
2026-09-10T21:43:40Z
Refreshed successor-post comments add reactions, not receipts establishing GPT-6 Sol access or supersession of GPT-5.6. The temporary GPT-5.6 discount remains established, but its effects on production routing and workload adoption remain uncorroborated.
2026-09-10T19:38:40Z
The GPT-6 Sol API-appearance claim concerns a possible successor, not corroboration of GPT-5.6 discount-driven adoption; the supplied post and reaction comments establish neither usable access nor supersession. The existing price cut remains established, with its production-routing and workload effects still unproven.
2026-09-10T18:24:04Z
evidence attached: reddit.post.1wcqwj9 — The API appearance independently supports that GPT-6 Sol is being exposed to developers, though it does not verify the reported price cut.
2026-09-09T20:38:25Z
No substantive delta changes the established temporary discount or supplies evidence of production routing or workload migration. The adoption hypothesis remains open, but the active discount window warrants sparse monitoring rather than repeated stale-discussion reviews.
2026-09-07T20:37:49Z
The temporary price cut remains established, but this review adds no evidence that it changed production routing or workload adoption. The impact hypothesis remains open within the discount window; unchanged discussion warrants weekly rather than repeated 48-hour review.
2026-09-05T20:23:35Z
There is no new substantive evidence: the temporary price cut is established, but its effect on production routing and workload adoption remains uncorroborated. The remaining discount window supports sparse monitoring, not repeated reviews of unchanged discussion.
2026-09-03T19:37:24Z
Another 48 hours adds no production-usage, routing, or workload-migration evidence; small engagement changes are repetitive amplification. The adoption-impact hypothesis remains open through the temporary discount window but now warrants review only on usage data or a material pricing change.
2026-09-01T18:51:07Z
Another 48 hours produced no production-usage, routing, or workload-migration evidence; minor engagement changes are repetitive amplification only. Keep the adoption-impact hypothesis open through the temporary discount window, but revisit only on usage data or a material pricing change.
2026-08-30T18:32:38Z
No new usage or routing evidence has appeared after 48 hours; the established temporary discount remains relevant, but its adoption-impact hypothesis is cold and should only be revisited on production data or another material pricing change.
2026-08-28T17:37:10Z
The Jevons-paradox analysis offers a plausible demand mechanism but no observed production usage, routing change, or workload migration. It therefore adds framing rather than corroboration, leaving the adoption hypothesis open and cold pending usage evidence.
2026-08-28T15:25:49Z
evidence attached: hn.story.49479633 — The analysis directly bears on whether GPT-5.6’s reported price cut changes demand and shifts the economics of deferrable inference workloads.
2026-08-28T11:24:40Z
The refreshed discussion remains repetitive reaction to the established temporary discount and adds no production evidence of changed routing, model selection, or workload migration. The adoption hypothesis remains open through the discount window but should be revisited only on usage evidence or another material pricing change.
2026-08-26T10:38:52Z
The refreshed discussion is repetitive reaction to the established temporary discount and adds no production evidence of changed routing, model selection, or deferred-workload adoption. The impact hypothesis remains open through the discount window but should now be revisited only when usage evidence or another pricing change appears.
2026-08-26T00:25:20Z
The new pricing roundup is weak duplicate coverage of the established temporary Sol discount and adds no production-routing or workload-migration evidence. The adoption hypothesis remains open but no longer merits frequent review without usage data.
2026-08-26T00:23:12Z
evidence attached: hn.story.49442514 — A directly relevant pricing roundup bears on the open GPT-5.6 Sol API pricing hypothesis, though it is weak secondary corroboration.
2026-08-25T22:33:00Z
The refreshed discussion remains consumer reaction and speculative switching intent, with no production evidence of changed routing, model selection, or deferred-workload adoption. The established temporary discount remains worth monitoring through its three-month window, but repetitive comments no longer justify frequent review.
2026-08-25T14:44:34Z
The refreshed comments remain consumer reaction and speculative switching intent, adding no production evidence of changed routing, model selection, or deferred-workload migration. The established temporary discount remains worth watching, but repetitive discussion no longer merits frequent review.
2026-08-25T13:38:07Z
The refreshed comments remain speculative consumer reaction and switching intent, with no production evidence of changed routing, model selection, or deferred-workload adoption. The established temporary discount remains worth monitoring through its three-month window, but repetitive discussion does not advance the impact hypothesis.
2026-08-25T12:36:52Z
The refreshed comments remain speculative comparisons and switching intent, with no production evidence of changed routing, model selection, or deferred-workload adoption. Repetitive discussion no longer merits hourly review; the established temporary discount should be revisited only when usage evidence appears.
2026-08-25T11:31:46Z
The refreshed discussion is repetitive switching and price-competition speculation, not production evidence of changed routing, model selection, or deferred-workload adoption. The established temporary discount remains worth watching through its three-month window, but this update does not advance the impact hypothesis.
2026-08-25T10:43:38Z
The refreshed discussion remains speculative comparison and switching intent, with no production evidence of changed routing, model selection, or deferred-workload adoption. It is repetitive amplification of the established temporary discount and does not advance the impact hypothesis.
2026-08-25T09:36:36Z
The refreshed discussion remains consumer reaction and speculative switching intent, with no production evidence of changed routing, model selection, or deferred-workload adoption. It is repetitive amplification of the established temporary discount and does not advance the impact hypothesis.
2026-08-25T08:28:56Z
The refreshed comments remain speculative consumer reaction and switching intent, with no production evidence of changed routing, model selection, or deferred-workload adoption. This is repetitive amplification of the established temporary discount and does not advance its impact hypothesis.
2026-08-25T07:30:33Z
The refreshed comments are repetitive price and switching speculation, not evidence of production routing, model-selection changes, or deferred-workload migration. The temporary discount is established, but its adoption impact remains uncorroborated.
2026-08-25T05:29:12Z
The refreshed comments remain consumer reaction and speculative switching intent, not evidence of production routing, model-selection changes, or deferred-workload migration. The established temporary price cut remains worth watching, but this repetitive amplification does not advance the adoption hypothesis.
2026-08-25T04:27:53Z
The refreshed discussion remains speculative about switching and price competition, with no production evidence of changed routing, model selection, or deferred-workload adoption. It is repetitive amplification of the established temporary discount and does not advance the impact hypothesis.
2026-08-25T03:32:28Z
The refreshed discussion remains speculative about switching and future pricing, with no production evidence of changed routing, model selection, or deferred-workload adoption. It is repetitive amplification of the established temporary discount and does not advance the impact hypothesis.
2026-08-25T02:28:08Z
The refreshed comments remain speculative consumer reaction and switching intent, adding no evidence of production routing, model-selection changes, or deferred-workload migration. The established temporary price cut remains worth watching, but repetitive discussion does not advance the adoption hypothesis.
2026-08-25T01:24:25Z
The refreshed discussion remains speculative switching intent and consumer reaction, with no production evidence of changed routing, model selection, or deferred-workload adoption. The established price cut remains worth watching over its three-month window, but repetitive amplification does not advance the impact hypothesis.
2026-08-25T00:29:14Z
The refreshed comments remain consumer reactions and speculative switching intent, adding no production evidence of changed routing, model selection, or deferred-workload adoption. The established temporary price cut remains relevant, but repetitive amplification does not advance its downstream-impact hypothesis.
2026-08-24T23:32:01Z
The refreshed discussion remains speculative consumer reaction and switching intent, adding no production routing, model-selection, or deferred-workload evidence. The established temporary price cut still has no demonstrated adoption impact, so repetitive amplification does not advance the case.
2026-08-24T22:32:19Z
The refreshed discussion remains speculative about switching and long-term pricing, without evidence of production routing changes or deferred-workload migration. The temporary Sol price cut is established, but its adoption impact has still not advanced.
2026-08-24T21:35:57Z
Refreshed comments remain consumer reaction and speculative switching intent, with no production routing, model-selection, or deferred-workload evidence. The pricing event is established and already surfaced, but the downstream adoption hypothesis has not advanced.
2026-08-24T20:45:09Z
grounded: converges/medium — The repricing reinforces Scott’s Model Perishability position and could alter task-aware routing through his LiteLLM gateway and paid OpenAI API usage. It warra
2026-08-24T20:42:00Z
The attached first-party pricing page resolves the factual conflict: GPT-5.6 Sol has a temporary reduction to $4/M input and $20/M output tokens. The case now cleanly concerns adoption and routing effects, for which discussion still supplies no production evidence.
2026-08-24T19:26:37Z
evidence attached: hn.story.49421074 — OpenAI's first-party pricing page independently confirms the reported GPT-5.6 Sol reduction, directly strengthening the open inference-economics case.
2026-08-24T18:26:22Z
The refreshed comments remain complaints about limits, quality, and pricing rather than evidence resolving the disputed Sol discount or showing production workload migration. The case remains blocked on its factual premise and gains no maturity from repetitive amplification.
2026-08-24T16:30:32Z
The refreshed comments add only subscription-limit and quality complaints, not a primary-source resolution of the conflicting Sol pricing record or evidence of production workload migration. The disputed premise continues to block the downstream routing hypothesis.
2026-08-24T15:26:31Z
The newly attached Reddit post independently repeats the reported $4/$20 Sol pricing but offers no primary-source link and does not resolve the conflict with the latest grounding. There is still no production evidence of routing changes or workload migration, so the case cannot advance.
2026-08-24T15:22:43Z
evidence attached: reddit.post.1vx5mrz — The reported GPT-5.6 Sol API pricing directly bears on whether OpenAI's price change is real and could affect model-selection economics.
2026-08-24T01:27:17Z
The refreshed discussion is repetitive speculation and supplies neither a primary-source resolution of the conflicting Sol pricing record nor evidence of production workload migration. The case remains blocked at its factual premise despite broader topic heat.
2026-08-23T00:23:24Z
The refreshed comments are repetitive speculation and add neither production-adoption evidence nor a primary-source resolution of the conflicting Sol pricing record. The disputed pricing premise still blocks any judgment about downstream routing or workload migration.
2026-08-22T16:33:20Z
The refreshed comments remain repetitive speculation and provide neither production-adoption evidence nor a primary-source resolution of the conflicting Sol pricing record. The factual premise remains unsettled, so the downstream routing hypothesis cannot advance.
2026-08-22T14:39:58Z
The refreshed discussion remains repetitive speculation and adds neither production-adoption evidence nor a primary-source resolution of the conflicting Sol pricing record. The factual premise remains unsettled, so the downstream routing hypothesis cannot advance.
2026-08-22T11:28:59Z
The refreshed comments are repetitive switching and margin speculation, not evidence of production adoption or workload migration. The unresolved conflict over whether Sol itself received the temporary discount still blocks interpretation of the downstream routing hypothesis.
2026-08-22T10:30:13Z
The refreshed discussion remains repetitive speculation about competitive switching and pass-through pricing, with no production-routing evidence or new primary source resolving whether Sol itself received the reported discount. The factual conflict remains the gating issue, so the adoption hypothesis cannot advance.
2026-08-22T08:30:54Z
The refreshed discussion remains speculative and adds no production-routing or workload-migration evidence. More importantly, the record still contains an unresolved factual conflict between the reconstructed OpenAI/Reuters Sol discount report and the latest grounding that assigns the cuts only to Terra and Luna, so the adoption hypothesis cannot advance.
2026-08-22T07:26:30Z
grounded: known/medium — The case’s Sol price-cut premise is contradicted by the grounding: the reported reductions apply to Terra and Luna, while Sol’s standard pricing stayed unchange
2026-08-22T07:24:18Z
The refreshed comments add only speculative switching intent contingent on OpenRouter passing through the discount, not evidence of production routing or workload migration. The cached grounding now directly conflicts with the attached OpenAI testimony and reported $4/$20 Sol pricing, so the pricing facts need regrounding while the adoption hypothesis remains open.
2026-08-22T06:24:13Z
The refreshed discussion adds only margin speculation and a subscription-limit question; it provides no production evidence that the temporary Sol repricing is changing API routing, model selection, or deferrable workloads.
2026-08-22T05:32:51Z
grounded: known/high — This directly affects Scott’s active cost-tiered and task-aware routing through LiteLLM: Terra and especially Luna’s lower prices could change tier aliases and
2026-08-22T05:31:12Z
The temporary direct OpenAI price cut is now established, removing uncertainty about whether the pricing event occurred. The consequential hypothesis remains open because there is still no independent production evidence of changed routing, model selection, or deferred-workload adoption.
2026-08-22T05:22:27Z
evidence attached: hn.story.49396590 — The official OpenAI model page provides first-party confirmation of the reported roughly 20% GPT-5.6 Sol API price reduction, materially strengthening the inference-economics case.
2026-08-22T01:30:14Z
grounded: known/medium — Scott already treats API repricing as an input to swappable, task-aware routing in Model Perishability and his LiteLLM-based systems. This case bears on those a
2026-08-22T01:28:59Z
The official three-month Sol price cut is established and the cached grounding understates that fact, but there is still no independent production evidence that it is changing model selection or shifting deferrable workloads.
2026-08-22T01:26:25Z
grounded: known/medium — The decision rule is already explicit in Task-aware multi-provider model routing and AI Unit Economics, while LLM Report actively tracks model pricing. This cou
2026-08-22T01:24:39Z
origin walked (codex/luna, conf 0.96): anchor reddit.post.1vuxlw7 -> echo.x.e83a0a16c9 by OpenAI
2026-08-22T01:23:28Z
case created — The reported price cut is a concrete and economically material API change distinct from the existing Cerebras-powered inference-tier case.