GPT-6.1 Sol Enterprise Review 2026: Near-Astra Intelligence at One-Fifth the Price
GPT-6.1 Sol enterprise review: $2/$10 pricing, cached-input economics, benchmark deltas vs Astra and Opus 5.5, and which agent workloads to move now.
TLDR: GPT-6.1 Sol is the model most enterprise agent fleets should standardize on right now. It lands 1 point below GPT-6 Astra on Artificial Analysis’s Intelligence Index at less than a quarter of the cost per task ($0.72 vs $3.26), beats Claude Opus 5.5 on business-workflow automation at roughly a third of the cost, and its $0.10/M cached-input tier makes long-context agents dramatically cheaper to run. Keep Astra for the hardest scientific and computer-use work where its lead still holds. But if you’re routing routine agent traffic to Astra or Opus 5.5 today, you’re overpaying - move it to 6.1 Sol this month.
Why This Review Matters Now
OpenAI launched GPT-6 Sol on September 22, 2026. Seven days later, it replaced it.
That’s not a typo. GPT-6.1 Sol shipped September 29, superseding a model barely a week old - the fastest flagship-tier replacement cycle Artificial Analysis has ever recorded. The reason is blunt: GPT-6 Sol disappointed. Developers on Hacker News called it “worse than 5.6 Sol… far worse,” with one reporting they “used it for one day, spent the next day fixing its lousy code, then went back to 5.6.” OpenAI clearly got the message and shipped the fix in days, not quarters.
But this isn’t just a bug-fix release. GPT-6.1 Sol gains 4 points on the Intelligence Index, lands 1 point below GPT-6 Astra, and pushes out the cost-efficiency Pareto frontier at every effort level - meaning no cheaper model matches its intelligence at any setting. For enterprise buyers, the practical question changed overnight: the September debate was Astra vs Opus 5.5 at the top end. Now there’s a third option that does ~95% of the work at ~20% of the price, and your cost-per-task math from last month is already stale.
GPT-6.1 Sol at a Glance
| Dimension | GPT-6.1 Sol | GPT-6 Sol | GPT-6 Astra | Claude Opus 5.5 |
|---|---|---|---|---|
| Best for | Default agent workloads | Nothing - superseded | Hardest science + computer use | Teams standardized on Anthropic |
| API input price | $2/M tokens | $2/M tokens | $10/M tokens | ~$5/M tokens (est.) |
| API output price | $10/M tokens | $10/M tokens | $50/M tokens | ~$25/M tokens (est.) |
| Cached input price | $0.10/M (95% off) | $0.20/M (90% off) | Higher tier | Check Anthropic schedule |
| Intelligence Index | 1 pt below Astra | 4 pts below 6.1 Sol | Reference leader | Competitive, trails on workflows |
| Cost per Index task | $0.72 | $1.05 | $3.26 | ~$2+ (vendor-dependent) |
| Agentic coding (DeepSWE) | Matches Astra | 6.4pp behind | Reference | Trails on Terminal-Bench Sci |
| Workflow automation | +2.2pp over Opus 5.5 | Baseline | Slightly ahead | Reference |
| Hallucination rate (AA) | 54% (down from 60%) | 60% | Lower | Competitive |
| Availability | ChatGPT Work, Codex, API | Being replaced | ChatGPT Work, Codex, API | Claude, Bedrock, Vertex |
Pricing verified October 2, 2026 from openai.com and artificialanalysis.ai. Opus 5.5 API prices are approximate - confirm against your Anthropic agreement before budgeting.
Agentic Coding: Where the Biggest Delta Lives
This is the workload that matters most for enterprise agent fleets - code-writing agents running inside Codex, Copilot-style assistants, and CI-integrated review bots.
On DeepSWE v1.1 (long-horizon engineering tasks in real codebases), GPT-6.1 Sol matches GPT-6 Astra outright at roughly one-fifth the cost. The gap it closed is stark: +6.4 percentage points over GPT-6 Sol at lower reasoning effort and lower cost. Artificial Analysis’s Coding Agent Index tells the same story from the independent side - a 3-point gain over GPT-6 Sol, sitting just 2 points below Astra, with the xhigh effort setting actually scoring 1 point above Astra at less than 15% of the cost per task.
Here’s the catch: 6.1 Sol uses 10-30% more output tokens than GPT-6 Sol across effort settings. It thinks longer. The token bill per request rises even as the price per token stays flat - but because the intelligence gain outruns the token growth, low and medium effort settings remain Pareto-optimal for token efficiency. In plain terms: you’ll burn more tokens and still pay less per solved task.
Earned insight: The effort-setting detail is the one most buyers will miss. Artificial Analysis found xhigh outperforming max by 3 points on the Coding Agent Index - the highest knob isn’t the best knob. If your platform team exposes reasoning effort as a config (and it should), default agent coding workloads to xhigh, not max. You’ll get better code for fewer dollars, which almost never happens at the same time.
The naming trap that already burned one team
Hacker News threads on launch day mixed up GPT-6.1 Sol with GPT-6.1 Astra repeatedly - “GPT-6.1 Astra is what those headlines referred to. This is GPT-6.1 Sol,” one commenter had to clarify. Your developers will make the same mistake. When you update model routing configs, pin the exact model ID (gpt-6.1-sol), not a nickname. One misrouted workload to Astra pricing costs 5x per token.
GPT-6.1 Sol Coding Strengths:
- Matches Astra on DeepSWE v1.1 at ~20% of the cost
- 12-point jump on Terminal-Bench 4.0 (independent benchmark)
- Xhigh effort beats Astra outright at <15% of cost per task
- Available in Codex today, Ultrafast 8x tier coming
GPT-6.1 Sol Coding Weaknesses:
- 10-30% more output tokens per request than 6 Sol
- Still 2 points behind Astra on AA Coding Agent Index at max
- GPT-6 Sol’s bad week means thin production track record - monitor first
Business Workflows: Beating Opus 5.5 Where It Hurts
AutomationBench tests agents on end-to-end workflows across 47 tools - sales, marketing, ops, support, finance, HR. It’s the closest public proxy to “the stuff your service agents actually do.”
GPT-6.1 Sol scores 2.2 percentage points above Claude Opus 5.5 at medium reasoning effort, at roughly a third of the cost. It’s also up 4.8 points from GPT-6 Sol at the same setting. On GDP.pdf - professional questions over complex PDFs with tables, charts, and fine print - 6.1 Sol scores higher than Opus 5.5 with fallbacks at less than half the cost per task, and approaches Astra at a fifth of it.
But read OpenAI’s footnote before you redraw your vendor map. The company admits the Claude Fable 5.1 datapoint in its charts understates actual cost because it omits fallback costs, which hit ~40% of tasks. OpenAI deserves credit for flagging this - most vendors wouldn’t. Still, the honest takeaway is narrower than the headline: 6.1 Sol beats Opus 5.5 on these workflow benchmarks, but both vendors’ numbers are vendor numbers. Run your own eval on your own workflows before migrating spend.
Warning: Don’t migrate Anthropic workloads on benchmark headlines alone. Opus 5.5’s strengths (long-context coherence, Anthropic’s enterprise agreements, Bedrock/Vortex deployment paths) don’t show up in AutomationBench scores. The cost case for 6.1 Sol is real, but switching costs - prompt re-tuning, eval rebuilds, contract renegotiation - can eat a quarter or two of savings. Pilot first, migrate second.
Computer Use and Science: Good, Not Flagship
OSWorld 2.0 measures agents driving real computer applications - the frontier for IT automation and back-office agents. GPT-6.1 Sol gains 7 percentage points over GPT-6 Sol at max effort for less than half the cost, landing within 2.1 points of Astra at roughly one-seventh the cost per task. That’s a genuine leap for a point release.
The problem is what’s still missing. On Terminal-Bench Science (data analysis, simulation, theorem proving), 6.1 Sol more than doubles GPT-6 Sol’s score - but Astra still leads at 68.1%, and OpenAI itself says Astra “should be used for the most difficult scientific research tasks.” At $5.47 per science task vs $23.21 for Opus 5.5 and $23.80 for Astra, 6.1 Sol owns the middle. The top stays flagship.
Earned insight: The $5.47 vs $23.21 Terminal-Bench Science gap is the single most misread number in this launch. It doesn’t mean science workloads get 4x cheaper - it means routine analysis (ETL checks, report generation, data QA agents) drops to Sol while only frontier research stays on Astra. In every enterprise science org I’ve seen, routine work is 80%+ of agent volume. That’s where the savings live, not in replacing Astra on the hard stuff.
Factuality and Alignment: Closer to Astra Than Sol
At low reasoning effort, GPT-6.1 Sol cuts the factual-error rate from 11.4% to 7.7% - a 32% reduction - and stays within 1.9 points of Astra across settings at under a fifth of the cost. Independently, Artificial Analysis measured hallucination rate falling from 60% to 54% on AA-Omniscience with an 8-point accuracy jump.
On alignment, OpenAI reports 6.1 Sol is more transparent about broken tools, more reliable at respecting explicit restrictions, and cleaner on unauthorized outcomes during agentic tasks than 6 Sol - landing near Astra. No safety-reviewer bypass attempts, matching both siblings. Full details are in the system card addendum.
But note the test conditions: these are deliberately difficult prompts and adversarial evals, not typical usage. The direction is right. The absolute numbers still mean roughly 1 in 13 low-effort responses contains a factual error. Keep human-in-the-loop on anything customer-facing or compliance-adjacent.
Trust and Safety Strengths:
- 32% factual-error reduction at low effort vs 6 Sol
- Alignment evals near Astra, clear step up from 6 Sol
- Transparency on broken search tools improved
- System card addendum published day one
Trust and Safety Weaknesses:
- 7.7% error rate at low effort still needs human review gates
- Adversarial-eval numbers don’t predict your production error rate
- One-week-old model - no production incident history yet
Pricing Reality: The Cached-Input Story Is the Real Story
Everyone quotes $2 in / $10 out. That’s table stakes - identical to GPT-6 Sol. The number that changes budgets is cached input: $0.10 per million, a 95% discount vs standard input and a 50% cut from GPT-6 Sol’s already-discounted $0.20.
Why does this dominate the math? Because agents reuse context. Every multi-turn service agent, every coding assistant with a repo loaded, every workflow bot re-reading the same SOPs pays input price on tokens it already sent. At 95% off, the marginal cost of a long system prompt plus conversation history collapses. Artificial Analysis’s blended numbers show it: $0.72 per Intelligence Index task for 6.1 Sol vs $1.05 for 6 Sol (31% cheaper) and $3.26 for Astra - with 6.1 Sol sitting 1 point below Astra on intelligence.
| Workload pattern | What dominates cost | 6.1 Sol advantage |
|---|---|---|
| Multi-turn service agents | Cached context re-reads | 95% off repeat input - biggest win |
| Coding agents (Codex) | Long repo context + output | Output still $10/M but fewer retries needed |
| Batch PDF/document QA | Fresh input per doc | $2/M undercuts Opus-class alternatives |
| Computer-use agents | Long trajectories, many steps | 1/7th Astra cost per OSWorld task |
| Frontier research | Max-effort reasoning | Astra still wins - don’t force-fit Sol |
Pricing verified October 2, 2026. Sources: OpenAI launch announcement, Artificial Analysis analysis.
Don’t forget the Ultrafast tier coming “in the coming days” - up to 8x faster token generation in Codex. Speed pricing isn’t announced, so don’t budget it yet. But latency-sensitive agent loops (customer-facing chat, real-time copilots) should re-evaluate once it lands.
Tip: Model your agent spend on blended cost per completed task, not per-token price. Pull last month’s traces, compute your cached-input ratio (repeat context tokens / total input tokens), and apply the $0.10 rate to the cached share. Most long-context agent fleets I’ve seen run 50-70% cached input - which puts effective input cost under $1/M, not $2. That’s the number to put in the business case.
Who Should Move to GPT-6.1 Sol - and Who Shouldn’t
Good fit: You’re running OpenAI agent workloads on GPT-6 Sol, GPT-5.6 Sol, or Astra for anything other than frontier tasks. The upgrade from 6 Sol is free money - same $2/$10 sticker, 4 more intelligence points, cheaper caching. If Astra handles your routine traffic, audit it this week; every routine task on Astra costs ~4.5x what it would on 6.1 Sol. ChatGPT Work and Codex users get it automatically - no migration work.
Not a good fit: You’re mid-contract on Anthropic with Bedrock or Vertex deployment requirements 6.1 Sol can’t match, and your evals show Opus 5.5 ahead on your specific workloads. Or you need Astra’s science ceiling - drug discovery agents, theorem-proving pipelines, the genuinely hard stuff. Also: if your team just finished tuning prompts for GPT-6 Sol last week (all seven days of its reign), validate before bulk-switching. The intelligence jump changes response distributions.
Bottom Line
GPT-6.1 Sol does something rare: it makes the flagship-vs-mid-tier debate boring. At 1 point below Astra on intelligence and under a quarter of the cost per task, with workflow wins over Opus 5.5 at a third of the cost, it’s the default answer for enterprise agent workloads until someone undercuts it. Reserve Astra for frontier science and the hardest computer-use tasks. Pilot 6.1 Sol against your current models on your own evals - don’t trust any vendor’s chart, including this article’s sources. The model is a week old; your production data matters more than launch benchmarks.
Rating: 4.5 / 5 for general enterprise agent workloads. 3.5 / 5 for frontier research, where Astra keeps its crown.
Your 30-day action: route one production agent workload to gpt-6.1-sol at xhigh effort, measure cost per completed task against your current model for two weeks, then decide the fleet migration.
FAQ
Does GPT-6.1 Sol replace GPT-6 Sol entirely?
Yes. Artificial Analysis confirms GPT-6.1 Sol replaces GPT-6 Sol after just 7 days on the market - the fastest flagship-tier supersession they’ve recorded. Pricing is identical at $2/M input and $10/M output, with an improved 95% cache discount (up from 90%). There’s no cost reason to stay on 6 Sol and a 4-point intelligence reason to leave. Update your model IDs to gpt-6.1-sol; OpenAI hasn’t announced a hard deprecation date for the 6 Sol endpoint, but a 7-day replacement cycle signals where this is going.
How much cheaper is GPT-6.1 Sol than GPT-6 Astra in practice?
At the token level, it’s exactly one-fifth: $2 vs $10 input, $10 vs $50 output. Per completed task, Artificial Analysis measures $0.72 vs $3.26 on the Intelligence Index - roughly 22% of Astra’s cost for 1 point less intelligence. On specific workloads the ratio varies: one-seventh on OSWorld computer-use tasks, roughly a third on AutomationBench vs Opus 5.5, and over 75% cheaper on Terminal-Bench Science ($5.47 vs $23.80). Your ratio depends on cached-input share - fleets with 50-70% cached context see effective input costs under $1/M.
Is GPT-6.1 Sol better than Claude Opus 5.5 for enterprise agents?
On published workflow benchmarks, yes: +2.2pp on AutomationBench at a third of the cost, higher GDP.pdf scores at less than half the cost per task. But those are OpenAI’s numbers on OpenAI-chosen benchmarks, and Opus 5.5’s advantages (Anthropic enterprise agreements, Bedrock/Vertex deployment, long-context coherence on your specific data) don’t appear in benchmark tables. If you’re an Anthropic shop, the cost gap justifies a pilot, not a migration. Run your own evals on your own workflows - switching costs can erase two quarters of token savings.
What is the $0.10 cached input pricing and why does it matter?
Cached input tokens - prompt prefixes the model has already processed, like system prompts and conversation history - cost $0.10 per million instead of $2, a 95% discount. This is up from 90% ($0.20) on GPT-6 Sol. It matters because agents re-send context constantly: a service agent re-reading the same SOPs, a coding assistant with a loaded repo, a workflow bot carrying conversation history. At 95% off repeat context, long-context agent architectures that were marginally economical become clearly economical. Compute your fleet’s cached-input ratio before budgeting - it’s the biggest lever in the 6.1 Sol business case.
Can I use GPT-6.1 Sol in ChatGPT and Codex today?
Yes for ChatGPT Work (Plus, Pro, Business, Enterprise, and Edu tiers) and Codex - available since September 29, 2026. No for regular ChatGPT Chat - OpenAI says 6.1 Sol is “not yet available in Chat” with no timeline given. API developers use the gpt-6.1-sol model ID. The Ultrafast tier (up to 8x faster generation in Codex) was announced as “coming in the coming days” at DevDay 2026 - not yet live as of October 2, with no pricing disclosed.
Should we still pilot GPT-6 Astra?
For frontier work, yes. Astra leads on Terminal-Bench Science (68.1% - OpenAI explicitly recommends it for the hardest research), holds a 2-point edge on the Coding Agent Index at max effort, and has a longer production track record. For everything else, piloting Astra first in October 2026 means paying ~4.5x per task for ~1 intelligence point. The sensible enterprise posture: default new agent pilots to 6.1 Sol, escalate to Astra only when evals show the task needs it. Revisit if Astra gets its own price cut - the price war is clearly still moving.
What happened with GPT-6 Sol - should we worry about model churn?
GPT-6 Sol lasted 7 days before replacement, and early user reports were rough - developers called it worse than its predecessor, with one reporting a day of use followed by a day of fixing its code. OpenAI shipped the fix fast, which is encouraging, but the churn risk is real: prompts tuned for 6 Sol may behave differently on 6.1, and a vendor willing to replace a model in a week may do it again. Protect yourself the standard way - pin exact model IDs, keep eval suites that catch regressions, and never auto-advance production traffic to a vendor’s newest endpoint without a canary.
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