OpenAI released GPT-6 Sol and Luna on 22 September 2026 — two new tiers of the GPT-6 family (flagship Astra shipped early September; Sol/Luna debuted in July as GPT-5.6 models) — with Sol aimed at complex coding, agentic and computer-control work and Luna at high-volume lightweight tasks. Pricing is half the GPT-5.6 promotional rates (Sol $2/$10 vs $4/$20; Luna $0.10/$0.50 vs $0.20/$1.20 per 1M tokens) plus a 90% cached-input discount, live day-one in ChatGPT Work, Codex and the API; Artificial Analysis independently confirms the ~50% effective cost drop ($1.06 vs $1.99 per Intelligence Index run) but notes the savings come entirely from the price cut since output tokens per task rose slightly. OpenAI's quality claims — roughly half as many factual errors (on an internal eval it acknowledges is non-representative), lower rates of coding agents misrepresenting completed work, and cost-per-task wins over Claude Opus 5 and Fable 5 — are vendor-reported only, and notably it benchmarked Opus 5 rather than the Opus 5.5 Anthropic shipped about 90 minutes before launch. The supplied web results cover only the 22 September launch: the GPT-6.1 Sol point release and its reported 'one-fifth of Astra' pricing that the case's recent evidence and reprice decision hinge on do not appear in this search and remain uncorroborated beyond Reddit testimony.
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| 🟠 reddit ⭐ | Introducing GPT-6 Sol and Luna singularity Retrieved 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 | DemiPixel | 1331 | 313 |
| 🟠 reddit | GPT 6 Sol/Luna available on Codex/Work singularity Retrieved article excerptOpen article · Retrieved 2026-09-22T18:24:16.402189+00:00 # Prove your humanity
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| 🟠 reddit | OpenAI launches GPT-6 Sol + Luna: Astra intelligence moves down the cost curve OpenAI Retrieved article excerptOpen article · Retrieved 2026-09-22T19:23:33.605954+00:00 # Prove your humanity
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| 🟠 reddit | GPT-6 Sol surpasses Claude Opus 5 on Agents’ Last Exam at 60% lower cost singularity Retrieved article excerptOpen article · Retrieved 2026-09-22T22:25:24.932975+00:00 # Prove your humanity
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| 🟠 reddit | GPT 6 Sol and Luna on benchmarks singularity | krizzalicious49 | 45 | 10 |
| 🟠 reddit | GPT-6 Sol scores 33.2% on AutomationBench, beating Opus 5 while costing ~11× less per task singularity | 141_1337 | 31 | 7 |
| 🟠 reddit | GPT 6 Sol and Luna at half the GPT 5.6 prices singularity | No_Hovercraft6239 | 132 | 15 |
| 🟠 reddit | GPT 6 Sol and Luna Prices singularity | Ok_Barracuda_1161 | 17 | 3 |
| 🟧 hn | GPT-6 Sol and Luna | OfficialTurkey | 1723 | 819 |
| 🟧 hn | GPT-6 Sol (Max) Intelligence, Performance and Price AnalysisRetrieved article excerptOpen article · Retrieved 2026-09-22T21:23:08.354203+00:00 [Artificial Analysis](https://artificialanalysis.ai/)
K
OpenAI logo
[OpenAI](https://openai.com/)
•
[GPT-6 Sol](https://artificialanalysis.ai/models/releases/gpt-6-sol)max
•
Proprietary model
•
Released September 2026
# GPT-6 Sol (max) Intelligence, Performance & Price Analysis
Compare[Try it out](https://artificialanalysis.ai/microevals) [API Provider Benchmarks](https://artificialanalysis.ai/models/gpt-6-sol/providers)
### Model summary
#### [Intelligence](https://artificialanalysis.ai/models/gpt-6-sol#intelligence)Updated
#18 / 212
48
Artificial Analysis Intelligence Index
4 out of 4 units for Intelligence.
#### [Speed](https://artificialanalysis.ai/models/gpt-6-sol#speed)
#49 / 212
104.4
Output tokens per second
3 out of 4 units for Speed.
#### [Cost](https://artificialanalysis.ai/models/gpt-6-sol#price-cost)
In $2.00Out $10.00Cache Discount 90%
N/A
Cost per Intelligence Index task
Unknown out of 4 units for Cost.
#### [Verbosity](https://artificialanalysis.ai/models/gpt-6-sol#token-use)
#43 / 212
77M
Output tokens from Intelligence Index
2 out of 4 units for Verbosity.
### Comparison Summary
GPT-6 Sol (max) is amongst the leading models in intelligence and reasonably priced when comparing to other models of similar price. It's also faster than average and fairly concise. The model supports text and image input, outputs text, and has a 872k tokens context window.
GPT-6 Sol (max) scores 48 on the Artificial Analysis Intelligence Index, placing it well above average among comparable models (median: 25). When evaluating the Intelligence Index, it generated 77M tokens, which is fairly concise in comparison to the median of 88M.
Pricing for GPT-6 Sol (max) is $2.00 per 1M input tokens (moderately priced, median: $2.00) and $10.00 per 1M output tokens (moderately priced, median: $10.00).
At 104 tokens per second, GPT-6 Sol (max) is faster than average (75).
### Technical specifications
| | |
| --- | --- |
| Reasoning | Yes This page shows the reasoning version of this model. A non-reasoning variant may also exist. |
| Input modality | Supports: text and image |
| Output modality | Supports: text |
| Context window | 872k ~1308 A4 pages of size 12 Arial font |
### 212 models in this class
Metrics are compared against models of the same class:
- Non-reasoning models → compared only with other non-reasoning models
- Reasoning models → compared across both reasoning and non-reasoning
- Open weights models → compared only with other open weights models of the same size class:
- Tiny: ≤4B parameters
- Small: 4B–40B parameters
- Medium: 40B–150B parameters
- Large: >150B parameters
- Proprietary models → compared across proprietary and open weights models of the same price range, using a blended 3:1 input/output price ratio:
- <$0.15 per 1M tokens
- $0.15–$1 per 1M tokens
- >$1 per 1M tokens
Highlights
Updated
### [Intelligence](https://artificialanalysis.ai/models/gpt-6-sol#intelligence)
Artificial Analysis Intelligence Index · Higher is better
### [Speed](https://artificialanalysis.ai/models/gpt-6-sol#speed)
Output tokens per second · Higher is better
### [Cost per Task](https://artificialanalysis.ai/models/gpt-6-sol#price-cost)
Weighted average cost (USD) per Intelligence Index task · Lower is better
Prompt Options
## IntelligenceUpdated
### [Artificial Analysis Intelligence Index](https://artificialanalysis.ai/evaluations/artificial-analysis-intelligence-index)
Artificial Analysis Intelligence Index v4.3.2 incorporates 10 evaluations: AA-Briefcase v1.1, GDPval-AA v2.1, AutomationBench-AA, Terminal-Bench 4.0, SciCode, Humanity's Last Exam, GDP.pdf, CritPt, AA-Omniscience, AA-LCR v1.1
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### Artificial Analysis Intelligence Index
Artificial Analysis Intelligence Index v4.3.2 includes: AA-Briefcase v1.1, GDPval-AA v2.1, AutomationBench-AA, Terminal-Bench 4.0, SciCode, Humanity's Last Exam, GDP.pdf, CritPt, AA-Omniscience, AA-LCR v1.1. See [Intelligence Index methodology](https://artificialanalysis.ai/methodology/intelligence-benchmarking) for further details, including a breakdown of each evaluation and how we run them.
Open Weights / ProprietaryReasoning / Non-ReasoningText Only / Multimodal Inputs
### Artificial Analysis Intelligence Index by Open Weights / Proprietary
Artificial Analysis Intelligence Index v4.3.2 incorporates 10 evaluations: AA-Briefcase v1.1, GDPval-AA v2.1, AutomationBench-AA, Terminal-Bench 4.0, SciCode, Humanity's Last Exam, GDP.pdf, CritPt, AA-Omniscience, AA-LCR v1.1
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ProprietaryOpen WeightsOpen Weights (Commercial Use Restricted)
### Artificial Analysis Intelligence Index
Artificial Analysis Intelligence Index v4.3.2 includes: AA-Briefcase v1.1, GDPval-AA v2.1, AutomationBench-AA, Terminal-Bench 4.0, SciCode, Humanity's Last Exam, GDP.pdf, CritPt, AA-Omniscience, AA-LCR v1.1. See [Intelligence Index methodology](https://artificialanalysis.ai/methodology/intelligence-benchmarking) for further details, including a breakdown of each evaluation and how we run them.
### Open Weights
Indicates whether the model weights are available. Models are labelled as 'Commercial Use Restricted' if commercial use is limited by conditions, and as 'Non-commercial' if the license prohibits commercial use.
## [Capability Indexes](https://artificialanalysis.ai/models/capabilities)
Measures the performance of models on specific capabilities and industries
Finance & AccountingStrategy & OpsLegalEngineeringEconomics
### [Artificial Analysis Finance & Accounting Index](https://artificialanalysis.ai/models/capabilities/finance-and-accounting)
Incorporates 7 evaluations: AA-Omniscience, GDPval-AA v2.1, AA-Briefcase v1.1, Humanity's Last Exam, AutomationBench-AA, AA-LCR v1.1, GDP.pdf · Higher is better
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## [Benchmarks](https://artificialanalysis.ai/evaluations)
### Intelligence Evaluations
Intelligence evaluations measured independently by Artificial Analysis · Higher is better
CodingAgenticTool UsePrivate DatasetUser InteractionFinanceMedicalLegalIntelligence IndexLong ContextMultimodalInstruction FollowingFaithfulnessWritingBusiness[See more](https://artificialanalysis.ai/evaluations)
18 of 26 evaluations
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[AA-Briefcase v1.1](https://artificialanalysis.ai/evaluations/aa-briefcase)Updated
Agentic knowledge work, (Elo-500)/2000
[GDPval-AA v2.1](https://artificialanalysis.ai/evaluations/gdpval-aa)Updated
Agentic real-world work tasks, (Elo-500)/2000
[AutomationBench-AA](https://artificialanalysis.ai/evaluations/automationbench-aa)Updated
Agentic SaaS workflows
[Terminal-Bench 4.0](https://artificialanalysis.ai/evaluations/terminalbench-4-0)New
Agentic coding & terminal use
[SciCode](https://artificialanalysis.ai/evaluations/scicode)
Coding
[Humanity's Last Exam](https://artificialanalysis.ai/evaluations/humanitys-last-exam)
Reasoning & knowledge
[GDP.pdf](https://artificialanalysis.ai/evaluations/gdp-pdf)New
Professional document reasoning, All-pass
[CritPt](https://artificialanalysis.ai/evaluations/critpt)
Physics reasoning
[AA-Omniscience Accuracy](https://artificialanalysis.ai/evaluations/omniscience)
Knowledge
[AA-Omniscience Non-Hallucination Rate](https://artificialanalysis.ai/evaluations/omniscience)
1 - hallucination rate
[AA-LCR v1.1](https://artificialanalysis.ai/evaluations/artificial-analysis-long-context-reasoning)
Long context reasoning
[Harvey LAB-AA](https://artificialanalysis.ai/evaluations/harvey-lab-aa)
Legal agentic work, criterion pass rate
[EnterpriseOps-Gym-AA](https://artificialanalysis.ai/evaluations/enterprise-ops-gym-aa)
Agentic business operations
[AA-AnalystAgent](https://artificialanalysis.ai/evaluations/aa-analyst-agent)
Quantitative analysis on spreadsheets & documents
[𝜏³-Banking](https://artificialanalysis.ai/evaluations/tau3-banking)
Agentic tool use
[ITBench-AA](https://artificialanalysis.ai/evaluations/itbench-aa)
Kubernetes incident root-cause analysis
[MMMU-Pro](https://artificialanalysis.ai/evaluations/mmmu-pro)
Visual reasoning
[MLCR-AA](https://artificialanalysis.ai/evaluations/mlcr-aa)New
Medical long context reasoning
### Intelligence Evaluation Relevance
While model intelligence generally translates across use cases, specific evaluations may be more relevant for certain use cases.
### Artificial Analysis Intelligence Index
Artificial Analysis Intelligence Index v4.3.2 includes: AA-Briefcase v1.1, GDPval-AA v2.1, AutomationBench-AA, Terminal-Bench 4.0, SciCode, Humanity's Last Exam, GDP.pdf, CritPt, AA-Omniscience, AA-LCR v1.1. See [Intelligence Index methodology](https://artificialanalysis.ai/methodology/intelligence-benchmarking) for further details, including a breakdown of each evaluation and how we run them.
### AA-Briefcase v1.1Updated
AA-Briefcase EloAA-Briefcase Rubric Score (%)Analytical Quality & Presentation Elo
### AA-Briefcase Elo
AA-Briefcase v1.1 is an agentic knowledge work benchmark developed by Artificial Analysis. AA-Briefcase Elo is a combined metric that aggregates rubric pass rate, analytical quality Elo and presentation Elo · Higher is better
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### AA-Briefcase Elo
AA-Briefcase Elo is a combined metric that aggregates analytical quality Elo, presentation Elo, and rubric pass rate, with rubric performance converted into Elo via synthetic head-to-head matches. Elo and 95% confidence interval bounds are clamped at 0.
### AA-Omniscience
AA-Omniscience IndexAA-Omniscience AccuracyAA-Omniscience Hallucination Rate
### AA-Omniscience Index
AA-Omniscience Index (higher is better) measures knowledge reliability and hallucination. It rewards correct answers, penalizes hallucinations, and has no penalty for refusing to answer. Scores range from -100 to 100, where 0 means as many correct as incorrect answers, and negative scores mean more incorrect than correct.
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### AA-Omniscience Index
AA-Omniscience Index (higher is better) measures knowledge reliability and hallucination. It rewards correct answers, penalizes hallucinations, and has no penalty for refusing to answer. Scores range from -100 to 100, where 0 means as many correct as incorrect answers, and negative scores mean more incorrect than correct.
## Intelligence Index Comparisons
Intelligence Index vs. Cost per TaskIntelligence Index vs. Time per TaskIntelligence Index vs. Output SpeedIntelligence Index vs. End-to-End Response Time
### Intelligence Index vs. Cost per Intelligence Index Task
Artificial Analysis Intelligence Index · Weighted average cost (USD) per Artificial Analysis Intelligence Index task
28 of 673 models
Most attractive quadrant
Pareto line
XiaomiOpenAIAnthropicSpaceXAIGoogleMetaZ AIDeepSeek
### Cost per Intelligence Index Task
Weighted average cost per Intelligence Index task. Each evaluation’s cost is calculated from input, cache hit, cache write, reasoning, and answer token prices, divided by task count, and weighted by its Intelligence Index weight.
### Artificial Analysis Intelligence Index
Artificial Analysis Intelligence Index v4.3.2 includes: AA-Briefcase v1.1, GDPval-AA v2.1, AutomationBench-AA, Terminal-Bench 4.0, SciCode, Humanity's Last Exam, GDP.pdf, CritPt, AA-Omniscience, AA-LCR v1.1. See [Intelligence Index methodology](https://artificialanalysis.ai/methodology/intelligence-benchmarking) for further details, including a breakdown of each evaluation and how we run them.
## Token Use
Output Tokens per TaskIntelligence Index vs. Output Tokens per TaskIntelligence Index Token UseIntelligence Index vs. Token Use
### Output Tokens per Intelligence Index Task
Weighted average number of output tokens used to run one task in the Artificial Analysis Intelligence Index
28 of 673 models
AnswerReasoning
| theanonymousone | 11 | 0 |
| 🟧 echo.blog | Announces Sol and Luna availability in Work, Codex, and the API, lower token pricing, improved prompt caching, and claimed benchmark cost-pe | OpenAI | — | — |
| 🟠 reddit | 6 Sol Nerfed? singularity | Ok_Flamingo_3012 | 3 | 1 |
| 🟠 reddit | GPT‑6 Sol and Luna: Cheaper, but Worse Where It Matters OpenAI | AirportEither2456 | 8 | 20 |
| 🟠 reddit | GPT-6 Sol is NOT the replacement of GPT-5.6 Sol... OpenAI | Jarr11 | 477 | 151 |
| 🟠 reddit | Sol-6 Be super careful OpenAI | Babayaga1664 | 234 | 61 |
| 🟠 reddit | Sol and Luna feel weaker after this week's update, but Astra's usage limits seem massively improved OpenAI | Shay_Solomon | 19 | 6 |
| 🟠 reddit | OpenAI launches GPT-6.1 Sol OpenAI | ethotopia | 685 | 149 |
| 🟠 reddit | Introducing 6.1 SOL OpenAI | BitterAd6419 | 117 | 35 |
| 🟠 reddit | Opus 5.5 still leads on AA intelligence, but GPT-6.1 Sol shifts the cost frontier OpenAI | rhiever | 14 | 1 |
| 🟠 reddit | Opus 5.5 still leads on AA intelligence, but GPT-6.1 Sol shifts the cost frontier ClaudeAI | rhiever | 219 | 60 |
| 🟠 reddit | I don't understand the constant bashing of GPT 6/6.1 Sol's performance relative to 5.6 and Astra. Why are we ignoring the elephant in the room, the absurd price + efficiency gains? It costs half as much as Sonnet 5.5, 1/3rd of 5.6 Sol, 1/5th of Opus 5.5, and 1.7th of Astra. singularity | that_90s_guy | 153 | 102 |
2026-09-30T17:42:12Z
The only motion left is the 6.1 Sol threads' tail — the velocity spikes land on 1wtg4f3/1wtkr74, sibling-case content, while this launch's own evidence moves within noise (±4 pts across a week-old wave) — so the magnitude-valve spread reading prices the already-consumed launch wave, not fresh periphery. With launch 6-Sol operationally superseded by 6.1 Sol (its own case, which carries the fix-or-rebrand question and the operative LiteLLM reprice target), the episode closes: the cost claim stands absorbed as independently confirmed, the quality dispute ends unresolved-by-supersession (no independent eval will come for a dead point release), and relevance eases high→medium because the live config decision now lives in the 6.1 case.
2026-09-30T02:18:15Z
With the 6.1 Sol spinoff now a distinct case, this case contracts to the 9/22 launch itself: the cost half is settled (AA-confirmed ~50% effective drop, 90–95% cache discounts) and the attached price-vs-quality debate thread shows that cost claim has durable community uptake, while the quality-improvement claim has hardened into a testimonial-vs-vendor stalemate with no independent eval in sight — that open question now rides 6.1 Sol's clock, not this launch's. Heat holds at medium: the ~90 pts/h at 96th percentile is the 6.1 threads' tail plus one debate post in a single community, not fresh cross-platform spread, so the magnitude-valve reading reflects the already-consumed launch wave.
2026-09-30T00:38:31Z
evidence attached: reddit.post.1wtluzq — Wide price-vs-capability debate with AA data directly tests whether the Sol/Luna release's cost-reduction claim lands for agent workloads.
2026-09-29T22:56:21Z
grounded: converges/high — OpenAI's 90–95% cached-input discounts, explicit breakpoints, and near-free Luna independently arrive where Prefix-Caching Economics and the Model Barbell alrea
2026-09-29T22:49:03Z
Second-wave velocity on the 6.1 Sol launch thread (184→448 pts within hours, ~98th percentile of its cohort) plus independent AA-based cost-per-task charts reheats attention to medium — the previous '~3 pts/h, low' read undershot a thread still actively climbing — but the meaning is unchanged: 6.1 Sol moves the cost frontier while Opus 5.5 keeps the intelligence lead, and the fix-or-rebrand question awaits user validation. Reground triggered because the cached grounding covers only the 9/22 launch while the operative reprice target (6.1 Sol pricing) still rests on Reddit testimony.
2026-09-29T20:53:11Z
evidence attached: reddit.post.1wtkr74 — Independent Artificial Analysis cost-per-task data directly bears on Sol's claimed cost-frontier shift versus Opus 5.5, including caching and reasoning-effort effects.
2026-09-29T20:53:11Z
evidence attached: reddit.post.1wtksk2 — Independent Artificial Analysis-based cost-per-task chart shows the Sol 6.1 successor shifting the cost frontier while Opus 5.5 leads intelligence — bears directly on the release's lowered-cost claim.
2026-09-29T18:49:32Z
grounded: converges/high — OpenAI's 90–95% cached-input discounts, near-free Luna, and explicit cache breakpoints independently arrive where Prefix-Caching Economics and the Model Barbell
2026-09-29T18:41:43Z
GPT-6.1 Sol shipping seven days after 6-Sol reframes the case: the point release functions as tacit vendor acknowledgment that launch-week 6-Sol missed the mark — community immediately read it as 'Terra rebranded', converging with the user regression/fabrication reports — while extending the Sol-line price-cut curve to one-fifth of Astra with a 95% cached-input discount. Scott's pending gateway reprice now points at 6.1 Sol rather than 6-Sol, still gated on independent or personal validation; heat drops to low because the magnitude-valve spread was the launch wave itself, now a week cold at ~3 pts/h, and the 6.1 Sol traction (184 pts peak) is modest and confined to the same community.
2026-09-29T17:41:59Z
evidence attached: reddit.post.1wtg39p — Same GPT-6.1 Sol announcement, adding the cached-input 95% price-cut detail to the Sol-line cost case.
2026-09-29T17:41:59Z
evidence attached: reddit.post.1wtg4f3 — GPT-6.1 Sol launch extends the Sol line's aggressive price-cutting strategy to near-Astra capability at one-fifth of Astra's price.
2026-09-25T00:08:07Z
magnitude valve eligible (multi-platform, top-decile engagement) and never alerted; deterministic escalation to deliver
2026-09-24T23:33:56Z
evidence attached: reddit.post.1wpdfsh — Second independent report this week of Sol/Luna feeling weaker post-update, corroborating the degradation pattern; the Astra quota observation is disputed and secondary.
2026-09-24T23:33:56Z
evidence attached: reddit.post.1wpenup — High-traction user reports of Sol-6 fabricating completed work directly contradict the release case's quality-improvement claims.
2026-09-24T17:52:17Z
magnitude valve eligible (multi-platform, top-decile engagement) and never alerted; deterministic escalation to deliver
2026-09-24T16:38:17Z
evidence attached: reddit.post.1wp5eqa — User reports that GPT-6 Sol underperforms GPT-5.6 Sol contradict the release's capability-improvement claim — evidence re-judging the case would need.
2026-09-24T02:38:52Z
magnitude valve eligible (multi-platform, top-decile engagement) and never alerted; deterministic escalation to deliver
2026-09-24T00:31:34Z
evidence attached: reddit.post.1wojlbm — User testing across reasoning efforts reports Sol and Luna regressions versus GPT-5.6, contradicting the release's quality-improvement claim.
2026-09-23T20:15:08Z
grounded: known/medium — The radar already tracks this same GPT-6 development as an open case — radar:gpt6-prompt-cache-controls covers the GPT-6 caching update with explicit breakpoint
2026-09-23T17:00:28Z
anchor promoted to claim owner's artifact: echo.blog.dfc615803e -> reddit.post.1wngzou — All four are the same Sol/Luna launch episode; the object linking OpenAI's own announcement is the canonical anchor.
2026-09-22T20:23:58Z
evidence attached: reddit.post.1wnhb8n — A user reports possible rapid performance drift in the same emerging GPT-6 Sol episode, though the evidence is weak.
2026-09-22T19:25:28Z
case created — One actively rolling out model-family announcement unifies availability, pricing, caching, and benchmark echoes and merits near-term deployment follow-up.