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# GPT 6\.1 Sol: Near\-Astra intelligence for a fifth of the price

972 points · 847 comments

[Full discussion](<https://news.ycombinator.com/item?id=49896586>)

[Read original](<https://openai.com/index/introducing-gpt-6-1-sol/>)

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

## Skept-o-meter & Hotness

Skept\-o\-meter: Low\. Estimated from 10 comments\.

3 comments for the summary\.

Peak rank: \#1

Time in Top 10: 23\.0 hours

Hacksnap ranks recent stories first, then orders each group by points\. Peak rank uses all retained observations\. Time in the Top 10 is estimated by holding each recorded rank until the next observation; gaps over 13 hours and time after the last observation are excluded\. Movement between observations is unknown\.

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OpenAI's vendor\-reported benchmarks put GPT\-6\.1 Sol near Astra at one\-fifth cost, while supplied discussion questions safety timing and release cadence\.

## The brief

OpenAI introduced GPT\-6\.1 Sol, an upgrade to GPT\-6 Sol that it says nearly matches GPT\-6 Astra on agentic coding, computer use and professional work at one\-fifth of Astra's standard input and output token prices\. The company reports gains on DeepSWE, GDP\.pdf, AutomationBench, OSWorld and Terminal\-Bench Science, often at lower cost per task, plus improved factuality and alignment evaluations\. Cached input is priced at $0\.10 per million tokens, and the model is available in ChatGPT Work and Codex and via API, with an Ultrafast variant promised\. The results are OpenAI's own evaluations; competitor figures come from public reports\.

- On DeepSWE v1\.1, OpenAI says GPT\-6\.1 Sol matches GPT\-6 Astra at roughly one\-fifth the cost and beats GPT\-6 Sol's best score by 6\.4 percentage points at lower reasoning effort and cost\.
- On GDP\.pdf, it scores above Opus 5\.5 with fallbacks at less than half the cost per task across tested reasoning settings and approaches Astra's state\-of\-the\-art performance at roughly one\-fifth the cost per task\.
- On AutomationBench, it scores 2\.2 percentage points above Opus 5\.5 at medium reasoning effort at roughly one\-third the cost, and 4\.8 points above GPT\-6 Sol at the same setting\.
- On OSWorld 2\.0's offline set, it outperforms GPT\-6 Sol by seven percentage points at maximum reasoning effort at less than half the cost and comes within 2\.1 points of Astra at roughly one\-seventh the cost per task\.
- On Terminal\-Bench Science 0\.1, it more than doubles GPT\-6 Sol's score at maximum effort at less than half the cost; average cost is $5\.47 per task versus $23\.21 for Opus 5\.5 and $23\.80 for Astra, with Astra highest at 68\.1%\.
- Factuality improves at low reasoning effort, reducing the share of responses with a factual error from 11\.4% to 7\.7%; OpenAI also reports lower alignment failure rates and no safety\-reviewer bypass attempts, matching Astra and Sol\.

## Discussion themes

Analyzed: 2026\-09\-30T12:01:37\.291443\+00:00

Analysis sample: Based on 100 of 100 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.

### Subscription pricing and API cost tradeoffs

Commenters debate OpenAI's new $500 Pro plan, reduced allowances on the $200 plan, cached\-input pricing, and whether higher prices reflect unprofitability or push users to cheaper alternatives\.

Sources: [Comment 49896762](<https://news.ycombinator.com/item?id=49896762>) · [Comment 49897140](<https://news.ycombinator.com/item?id=49897140>) · [Comment 49897406](<https://news.ycombinator.com/item?id=49897406>) · [Comment 49897415](<https://news.ycombinator.com/item?id=49897415>) · [Comment 49897420](<https://news.ycombinator.com/item?id=49897420>) · [Comment 49897435](<https://news.ycombinator.com/item?id=49897435>) · [Comment 49897641](<https://news.ycombinator.com/item?id=49897641>) · [Comment 49897858](<https://news.ycombinator.com/item?id=49897858>) · [Comment 49898356](<https://news.ycombinator.com/item?id=49898356>) · [Comment 49899103](<https://news.ycombinator.com/item?id=49899103>) · [Comment 49902009](<https://news.ycombinator.com/item?id=49902009>)

### DeepSeek and open\-weight alternatives

Commenters report switching to DeepSeek, Qwen, and GLM for cost and performance, compare them with frontier models, and discuss local GPU options and data\-retention concerns\.

Sources: [Comment 49896820](<https://news.ycombinator.com/item?id=49896820>) · [Comment 49898356](<https://news.ycombinator.com/item?id=49898356>) · [Comment 49898532](<https://news.ycombinator.com/item?id=49898532>) · [Comment 49899103](<https://news.ycombinator.com/item?id=49899103>) · [Comment 49899379](<https://news.ycombinator.com/item?id=49899379>) · [Comment 49902009](<https://news.ycombinator.com/item?id=49902009>)

### Benchmark meaningfulness and independent tests

Commenters question whether benchmarks still matter, share independent comparisons such as pelican and image\-to\-HTML tests, and note incremental or task\-specific results\.

Sources: [Comment 49897423](<https://news.ycombinator.com/item?id=49897423>) · [Comment 49898129](<https://news.ycombinator.com/item?id=49898129>) · [Comment 49898638](<https://news.ycombinator.com/item?id=49898638>) · [Comment 49899440](<https://news.ycombinator.com/item?id=49899440>) · [Comment 49901504](<https://news.ycombinator.com/item?id=49901504>)

### Task\-specific strengths in coding, 3D, and image\-to\-HTML

Commenters compare models for coding, 3D graphics in Blender, image\-to\-HTML generation, and end\-to\-end prototyping, noting that strengths depend on the workload\.

Sources: [Comment 49896732](<https://news.ycombinator.com/item?id=49896732>) · [Comment 49897054](<https://news.ycombinator.com/item?id=49897054>) · [Comment 49898950](<https://news.ycombinator.com/item?id=49898950>) · [Comment 49899440](<https://news.ycombinator.com/item?id=49899440>) · [Comment 49901371](<https://news.ycombinator.com/item?id=49901371>)

### Context compaction and token usage

Commenters discuss frequent context compaction, token\-heavy model usage, subagent costs, and context\-window limits as practical constraints\.

Sources: [Comment 49897249](<https://news.ycombinator.com/item?id=49897249>) · [Comment 49898591](<https://news.ycombinator.com/item?id=49898591>) · [Comment 49899103](<https://news.ycombinator.com/item?id=49899103>) · [Comment 49899193](<https://news.ycombinator.com/item?id=49899193>) · [Comment 49899391](<https://news.ycombinator.com/item?id=49899391>)

### Rapid model release cadence

Commenters note weekly model releases, the shortest model life, and speculate about RSI, panic releases, or competitive pressure driving the pace\.

Sources: [Comment 49896665](<https://news.ycombinator.com/item?id=49896665>) · [Comment 49896671](<https://news.ycombinator.com/item?id=49896671>) · [Comment 49896722](<https://news.ycombinator.com/item?id=49896722>) · [Comment 49897155](<https://news.ycombinator.com/item?id=49897155>) · [Comment 49897181](<https://news.ycombinator.com/item?id=49897181>) · [Comment 49899124](<https://news.ycombinator.com/item?id=49899124>)

## Sources & coverage

AI-generated summary · 2026\-09\-29T18:01:05\.310524\+00:00

Based on 3 of 3 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.