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# GPT\-6\.1 Sol replaces GPT\-6 Sol after just 7 days, with near\-Astra intelligence

34 points · 32 comments

[Full discussion](<https://news.ycombinator.com/item?id=49906669>)

[Read original](<https://artificialanalysis.ai/articles/gpt-6-1-sol-replaces-gpt-6-sol-after-just-7-days-with-near-astra-intelligence>)

Category: [Models & Products](<https://hacksnap.live/?category=models-products>)

## Skept-o-meter & Hotness

Skept\-o\-meter: Low\. Estimated from 6 comments\.

6 comments for the summary\.

Peak rank: \#6

Time in Top 10: 19\.9 hours

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GPT\-6\.1 Sol nears Astra at a fraction of the cost, per Artificial Analysis, but commenters question whether benchmark gains translate into noticeable real\-world improvement\.

## The brief

Artificial Analysis reports that GPT\-6\.1 Sol, released seven days after GPT\-6 Sol, scores one point below GPT\-6 Astra on its Intelligence Index while costing less than a quarter per task\. The model matches GPT\-6 Sol's $2/$10 per million token pricing but raises the cache\-read discount to 95%, and it improves on agentic, coding, and knowledge benchmarks\. Artificial Analysis says all effort levels push the cost\-efficiency Pareto frontier, though GPT\-6\.1 Sol uses 10–30% more output tokens than GPT\-6 Sol\.

- GPT\-6\.1 Sol gains 4 Intelligence Index points over GPT\-6 Sol and 5 over GPT\-5\.6 Sol, landing 1 point below GPT\-6 Astra\.
- At max effort it costs $0\.72 per Intelligence Index task vs $3\.26 for GPT\-6 Astra, 31% less than GPT\-6 Sol and 64% less than GPT\-5\.6 Sol\.
- It uses \~10–30% more output tokens than GPT\-6 Sol across effort levels, but low and medium effort are Pareto optimal for token efficiency\.
- Coding Agent Index: \+3 points over GPT\-6 Sol at max effort, 2 points below GPT\-6 Astra; xhigh scores 1 point above GPT\-6 Astra for \<15% cost per task and outperforms max by 3 points\.
- AA\-Omniscience: \+8 accuracy points at max effort, hallucination rate falls from 60% to 54%\.
- AA\-Briefcase: \~80 Elo improvement, driven by rubric and Analytical Quality Elo, while Presentation Elo falls slightly\.

## Discussion themes

Analyzed: 2026\-09\-30T12:02:46\.149333\+00:00

Analysis sample: Based on 6 of 6 usable stored comments. Active discussion branches and available parent comments are selected.

This sample may omit parts of the full thread. Selected themes do not measure community opinion or how common a view is.

### Per\-model thinking\-mode comparisons

One user wants Artificial Analysis to break down high, medium and other effort settings within a single model, since most people do not switch models often\.

Sources: [Comment 49907124](<https://news.ycombinator.com/item?id=49907124>)

### Subscription limits and pricing

Commenters flag usage limits and subscription practices as a practical concern, with one saying governance bodies ignore them\.

Sources: [Comment 49906755](<https://news.ycombinator.com/item?id=49906755>) · [Comment 49907073](<https://news.ycombinator.com/item?id=49907073>)

### Benchmark\-to\-practice gap

One commenter reports no noticeable performance jump since version 5\.5, questioning whether benchmark gains translate to real work\.

Sources: [Comment 49906755](<https://news.ycombinator.com/item?id=49906755>)

### Competitive landscape beyond two leaders

The frontier is described as largely two\-horse, but other vendors such as Grok, Facebook and Chinese labs are said to be close behind; an internal OpenAI model is also mentioned\.

Sources: [Comment 49907157](<https://news.ycombinator.com/item?id=49907157>) · [Comment 49907190](<https://news.ycombinator.com/item?id=49907190>)

## Sources & coverage

AI-generated summary · 2026\-09\-30T12:02:46\.66535\+00:00

Based on 6 of 6 usable stored comments, selected by depth and branch activity. This is a sample of the discussion. Article text may also be shortened.

Generated using deepseek\-ai/DeepSeek\-V4\.1\-Flash. Check the linked sources for full context.
