GPT-6.1 Sol vs GPT-6 Astra vs GPT-6 Sol: When Astra Is Worth It

As of September 30, 2026, three names hide a fairly simple choice. GPT-6.1 Sol (gpt-6.1-sol, released September 29) has the same per-token prices as GPT-6 Sol (gpt-6-sol), except that cached input now costs half as much. It also scored higher than GPT-6 Sol at every reasoning effort in Artificial Analysis's previous independent index (v4.3.2, September 30, 2026); the current v5.0 index no longer scores GPT-6 Sol. If you run GPT-6 Sol today, move that traffic to GPT-6.1 Sol after fixing the few request settings that don't carry over.

GPT-6 Astra (gpt-6-astra) is still the stronger model, and it costs five times as much per input and output token. On Artificial Analysis's Intelligence Index v5.0 (as of October 28, 2026), Astra leads GPT-6.1 Sol by 2 to 8 points at the same effort level, with the widest gap at low effort and the narrowest at max. Running GPT-6.1 Sol one level higher gets close only in the middle: Sol at high scores 50 against Astra medium's 51 at about a fifth of the benchmark cost per task. To match Astra's 55 at high or xhigh, Sol needs max effort, which costs 2.6 to 2.9 times less per task but takes more than twice as long. Astra is worth paying for in three cases: you need more than GPT-6.1 Sol's best result (Astra at max effort, 57, has no match), you're doing the hardest science work, or your own tests show GPT-6.1 Sol failing often enough that retries and review eat the price gap.

If you're comparing Astra and Sol without a version number, the short answer is the same. Astra is OpenAI's most capable GPT-6 model, and Sol is the mid-priced line, now at version 6.1 in the API. Sol costs a fifth of Astra per token and comes within 2 to 8 index points at the same effort. Prices below were rechecked against OpenAI's pricing page on October 15, 2026.

Which model to use, by what you run today

What changed between GPT-6 Sol and GPT-6.1 Sol

On price, almost nothing. OpenAI's model pages and pricing page list these Standard rates per 1M tokens for prompts up to 272K input tokens:

The only list-price change is cached input, where the discount rises from 90% to 95% of the input rate. Astra costs five times GPT-6.1 Sol on input, cache writes, and output, and ten times on cached input. OpenAI added the 12-hour cache write on October 20, 2026: it costs 2.5 times the input rate, against 1.25 times for the default 30-minute write, and above 272K input it doubles to $10 on both Sol models and $50 on Astra.

The same billing rules apply to all three. A prompt over 272K input tokens is billed at 2x the input and cache rates and 1.5x the output rate for the whole request, so GPT-6.1 Sol becomes $4 input, $0.20 cached, and $15 output, while Astra becomes $20, $2, and $75. Batch and Flex are 50% of Standard, Fast mode is 2x, and regional data residency adds 10% where it's offered. All three have a 1,050,000-token context window and a 128,000-token output limit.

Ultrafast costs six times the Standard rate on both models. At launch, only gpt-6-astra had an Ultrafast price and GPT-6.1 Sol support was listed as coming later. As of October 15, 2026, the API pricing page lists both: GPT-6.1 Sol at $12 input, $0.60 cached, $15 cache write, and $60 output per 1M tokens, and Astra at $60, $6, $75, and $300. The ChatGPT and Codex models page now lists GPT-6.1 Sol Ultrafast on Pro $500 and eligible Enterprise and Edu plans. Needing Ultrafast is no longer a reason on its own to stay on Astra.

What did change is capability. On its previous Intelligence Index (v4.3.2, as of September 30, 2026), Artificial Analysis measured GPT-6.1 Sol 4 points above GPT-6 Sol at max effort and 7 to 8 points above it at low through xhigh (the table is below). Its current v5.0 index doesn't score GPT-6 Sol, so that comparison has no newer version. OpenAI reports gains on its own coding, computer-use, and science evaluations.

GPT-6 Sol is not being shut down. The deprecations page has no entry for gpt-6-sol, and its model page is still live with a pointer to GPT-6.1 Sol as "the newer Sol model." The pricing page's main table dropped the GPT-6 Sol row, and Artificial Analysis headlined the launch as GPT-6.1 Sol replacing GPT-6 Sol. Both mean the newer model is now the recommended Sol, not that the old one is gone. Your existing gpt-6-sol requests keep working.

How close GPT-6.1 Sol gets to Astra, by who measured it

OpenAI pitches GPT-6.1 Sol as near-Astra intelligence at a fifth of the price. Two sources back that up, measured differently, and neither is your workload.

OpenAI's own evaluations

In the GPT-6.1 Sol announcement, OpenAI reports:

• DeepSWE v1.1 (coding): on par with Astra at about a fifth of the cost, and 6.4 points above GPT-6 Sol's best score at lower effort and cost.
• OSWorld 2.0 (computer use): at max effort, within 2.1 points of Astra at about a seventh of the cost per task, and 7 points above GPT-6 Sol at under half its cost.
• Terminal-Bench Science 0.1: more than double GPT-6 Sol's score at max effort. Astra still ranks first at 68.1%, and OpenAI says the most challenging science tasks should use it. Average cost per task was $5.47 for GPT-6.1 Sol and $23.80 for Astra.
• Factual errors on hard prompts (xhigh): GPT-6.1 Sol 4.1%, GPT-6 Sol 4.5%, Astra 4.0%, at about 83% lower cost per task than Astra. The prompts in that test are deliberately hard and, in OpenAI's words, don't represent typical use.

These are the vendor's numbers from a research environment or the API; the announcement's charts don't publish every value.

Artificial Analysis, effort by effort

Artificial Analysis replaced its Intelligence Index v4.3.2 (ten evaluations) with v5.0 on October 27–28, 2026. Version 5.0 has nine evaluations covering agentic work, coding, knowledge, and science, and every score changed, so numbers you saw elsewhere before that date aren't comparable with the ones below. The v5.0 figures come from Artificial Analysis's release comparison for GPT-6.1 Sol vs GPT-6 Astra as of October 28, 2026. "Cost per task" is what Artificial Analysis paid per index task at API list prices, useful for comparing models against each other but not a forecast of your bill. "Time" is seconds per index task.

GPT-6 Sol has no v5.0 score. On the previous v4.3.2 index, as of September 30, 2026 (from Artificial Analysis's GPT-6.1 Sol analysis), the two Sol versions compared like this:

Each cell is index score · cost per index task; GPT-6 Sol at none scored 28 at $0.33. These v4.3.2 numbers can't be set against the v5.0 table above.

Two readings matter for a decision. First, on that older index GPT-6.1 Sol beat GPT-6 Sol at every effort level at the same or lower cost per task. Second, if you match GPT-6.1 Sol against Astra on v5.0 score instead of on effort label, one level up works only in the middle of the range. Sol xhigh (52) sits 3 points under Astra high (55), so reaching Astra's 55 takes Sol max:

The ratios are Astra's cost per task divided by GPT-6.1 Sol's. The ties at 55 come with a time cost: Sol max takes 858 seconds per index task against 372 for Astra high and 391 for Astra xhigh, about 2.2 to 2.3 times as long. Astra at low effort is the weakest buy of the three settings: GPT-6.1 Sol at medium scores higher (46 vs. 43) at about a quarter of the cost per task ($0.36 vs. $1.42).

The chart shows Artificial Analysis v4.3.2 figures from September 30, 2026; the tables above give the v5.0 figures as of October 28, 2026.

On Artificial Analysis's separate Coding Agent Index (as of September 30, 2026), GPT-6.1 Sol at xhigh scored 1 point above GPT-6 Astra for less than 15% of the cost per task, and xhigh beat GPT-6.1 Sol's own max setting by 3 points. At max, it sits 2 points below Astra and 3 points above GPT-6 Sol. If you run coding agents at max, test xhigh too.

In these launch-week runs GPT-6.1 Sol also generated output faster, 59 to 66 tokens per second versus 44 to 51 for Astra. Speed has moved since. Artificial Analysis's model pages on October 26, 2026 had Astra as fast or faster at every effort level, and by October 28, 2026 its release comparison showed the reverse again: GPT-6.1 Sol at 61, 59, 70, 70, and 81 tokens per second from low to max effort, against 48, 59, 63, 68, and 71 for Astra. GPT-6.1 Sol is now as fast or faster at every effort level. Faster output doesn't mean a shorter task, though: at max, GPT-6.1 Sol still takes 858 seconds per index task against Astra's 711. For why GPT-6.1 Sol can feel slow in Codex and what to change, see GPT-6.1 Sol Slow in Codex or API? Causes and Tested Speed Fixes.

Where the gap is still real

A 2- or 3-point gap on a nine-evaluation average can hide a larger gap on one kind of work. Terminal-Bench Science is the clearest example in OpenAI's own data: Astra is first there, and OpenAI points hard science work to Astra. The same evaluation is now part of Artificial Analysis's v5.0 index, where Astra leads GPT-6.1 Sol at every effort level (63.3% vs. 58.1% at max). The other hard limit is the top of the scale. Astra at max (57) scored above anything GPT-6.1 Sol reached, so if your task only succeeds at Astra max, no GPT-6.1 Sol setting replaces it.

OpenAI's Codex documentation also describes Astra as better at asking focused questions and keeping the original goal and constraints in view across long workflows. That's a vendor description, but it points to where to look in your own tests: long, loosely specified tasks where a wrong assumption early costs you the whole run.

What "a fifth of the price" means for a real request

The one-fifth figure is exact for Standard input, cache writes, and output. It isn't exact for your bill, for two reasons: cached input is 10x apart, and the models don't emit the same number of tokens for the same task.

Here is one agent-loop request with identical token counts on all three models: 200,000 input tokens, of which 150,000 are cache hits and 50,000 are uncached, plus 20,000 output tokens. Standard rates, under 272K input, cache writes left out.

• GPT-6.1 Sol: 0.05M × $2 + 0.15M × $0.10 + 0.02M × $10 = $0.100 + $0.015 + $0.200 = $0.315
• GPT-6 Sol: $0.100 + 0.15M × $0.20 + $0.200 = $0.100 + $0.030 + $0.200 = $0.330
• GPT-6 Astra: 0.05M × $10 + 0.15M × $1 + 0.02M × $50 = $0.50 + $0.15 + $1.00 = $1.65

Astra costs about 5.2x more for this request, slightly more than 5x because of the cache-heavy input. The larger the cached share of your input, the further the ratio climbs above 5x, since the cached part alone is 10x apart.

Against GPT-6 Sol, the saving is small and can reverse. In its v4.3.2 runs (September 30, 2026), Artificial Analysis found GPT-6.1 Sol uses roughly 10–30% more output tokens than GPT-6 Sol at the same effort. If this request produced 24,000 output tokens instead of 20,000 (20% more), the extra 4,000 tokens add $0.04 and GPT-6.1 Sol would cost $0.355, more than GPT-6 Sol's $0.330. On those index tasks, GPT-6.1 Sol still came out the same or cheaper per task at every effort level, but an output-heavy call can go the other way. The reason to switch from GPT-6 Sol is higher quality at about the same cost, not a lower bill.

Long prompts, Batch, and Flex don't change the ratios between the models, because the same multipliers apply to all three. For a line-by-line Astra bill with cache writes and the 272K threshold, see GPT-6 Astra API Pricing: $10/$50 per 1M Tokens, Caching Costs.

Is Astra worth it for your tasks? Count cost per accepted result

Token prices and benchmark averages tell you what an attempt costs. What you pay for is a result you can use. The number to compare is:

cost per accepted result = (model cost per attempt + review cost per attempt) ÷ acceptance rate

Take the request above and some hypothetical acceptance rates. If nobody reviews the output and failures are caught automatically, Astra at 90% acceptance costs $1.65 ÷ 0.9 = $1.83 per accepted result. GPT-6.1 Sol stays cheaper as long as it accepts more than $0.315 ÷ $1.83 ≈ 17% of attempts. On model cost alone, Astra rarely wins in this example.

Add $3 of human review per attempt, a few minutes of an engineer's time, and the picture changes. Astra becomes ($1.65 + $3) ÷ 0.9 = $5.17 per accepted result. GPT-6.1 Sol at 80% acceptance costs ($0.315 + $3) ÷ 0.8 = $4.14 and still wins, but its break-even falls to $3.315 ÷ $5.17 ≈ 64%. When review time dominates the cost, the per-token discount matters less and the acceptance rate decides.

So Astra earns its price when a failed attempt is expensive: a person has to review every run, a wrong result ships before anyone notices, or the task only succeeds at Astra max. For routine work with automated checks, GPT-6.1 Sol wins even with a much lower pass rate.

To measure this on your own work:

1. Pick 20 to 30 recent tasks with a clear pass or fail, drawn from the traffic you actually run.
2. Run GPT-6.1 Sol at your current effort and one level higher (and at max if you run Astra at high or xhigh), and Astra at your current effort. For coding agents, include GPT-6.1 Sol at xhigh even if you use max.
3. Record input, cached input, and output tokens from each response's usage data, and price them with the table above.
4. Count accepted results and add your review cost per attempt.
5. Move traffic to the cheapest setting per accepted result, and keep Astra only for the task types where it stays ahead.

If your GPT-6 Sol baseline includes image inputs from before September 25, rerun it first. OpenAI fixed an image-encoding bug that day that had degraded GPT-6 Sol's image understanding in the API and Codex, including computer use, so older numbers understate GPT-6 Sol on visual tasks.

If you are also weighing Astra against Anthropic's top model, GPT-6 Astra vs Claude Fable 5.1: Same Price, 1.5x the Tokens compares the two for coding agents and API work, including a same-prompt test run on October 26, 2026, which found that Fable 5.1 counts about 1.5x as many tokens as Astra for the same English text, so identical $10/$50 list prices still produce different bills. Since November 9, 2026, Anthropic's top model has been Fable 5.2; Claude Fable 5.2 vs GPT-6 Astra: When Each Costs Less per Task compares it with Astra.

Check these before swapping gpt-6-sol for gpt-6.1-sol

Most requests only need the new model ID. According to OpenAI's GPT-6 migration guide, these are the ones that don't:

The first two rows are what usually break: a request that sent none to gpt-6-sol will fail on gpt-6.1-sol, and so will a Chat Completions request with tools. If you used none for speed, measure low on GPT-6.1 Sol before switching. GPT-6 Sol remains available if low adds too much latency. For the request changes themselves, step by step, see GPT-6.1 Sol: Pricing, Access, and What Breaks When You Switch.

In Codex and ChatGPT Work

The API prices above don't apply to ChatGPT plans. In Codex and ChatGPT Work, models use ChatGPT credits, and the per-token table doesn't convert into credit usage. Check your plan's pricing page for that.

GPT-6.1 Sol is rolling out to Plus, Pro, Business, Enterprise, and Edu, in Codex (desktop app and CLI) and in ChatGPT Work on the web and mobile. Enterprise and Edu workspaces keep it off until an administrator turns it on. Free and Go plans aren't included at launch. GPT-6.1 Sol is available in Work and Codex, not in regular Chat. Standard and Fast modes work, and Ultrafast is available on Pro $500 and eligible Enterprise and Edu plans.

Regular Chat works differently. Since early October 2026, according to OpenAI's help article on GPT-6 in ChatGPT, the GPT-6 option in Chat runs on GPT-6 Sol from Instant through Extra High thinking. GPT-6 Pro, powered by Astra, appears for Pro $100, Pro $200, Business, and Enterprise plans. Plus plans get Astra in ChatGPT Work and Codex, not in Chat.

In the CLI, pick the model with codex -m gpt-6.1-sol or codex -m gpt-6-astra. OpenAI's own advice for Codex matches the API data: use GPT-6.1 Sol for repeated, long-running work across code, apps, and documents, and keep Astra for the hardest end-to-end work. Reasoning effort in Codex ranges from Light to Ultra, and OpenAI notes that effort levels don't map exactly between model generations. A task you ran on GPT-6 Sol Medium may need a different setting on GPT-6.1 Sol, so try a familiar task one step lower and adjust.

GPT-6.1 Sol vs GPT-6 Astra FAQ

Astra vs Sol: which should I use?

Use Sol by default and Astra when Sol falls short. In the API, the current Sol is GPT-6.1 Sol: $2 input and $10 output per 1M tokens against Astra's $10 and $50. On Artificial Analysis's Intelligence Index v5.0 (October 28, 2026) it lands 2 to 8 points below Astra at the same effort. One step higher it comes within a point of Astra medium, and it needs max effort to match Astra's 55 at high or xhigh. Astra earns the extra cost at max effort, on the hardest science work, and when a failed attempt is expensive to review. For how Sol's $2 / $10 compares with Claude, Gemini, Grok, and DeepSeek in the same band, see AI API Price Comparison: Cheapest Model by Input and Output Mix.

Is GPT-6.1 Sol better than GPT-6 Astra?

Not at the same effort level, where Astra scores 2 to 8 points higher on Artificial Analysis's Intelligence Index v5.0 as of October 28, 2026. Matched on score instead, GPT-6.1 Sol at high (50) sits 1 point under Astra medium (51) at about a fifth of the cost per task, and GPT-6.1 Sol at max ties Astra high and xhigh at 55 for 2.6 to 2.9 times less, though each task takes more than twice as long. That makes GPT-6.1 Sol the better buy for most work where a slower run is acceptable. Only Astra at max (57) has no GPT-6.1 Sol equivalent.

In ChatGPT, should I pick Sol or Astra?

In regular Chat you don't pick them by name. The GPT-6 option runs on GPT-6 Sol, and Astra appears as GPT-6 Pro on Pro $100, Pro $200, Business, and Enterprise plans. In ChatGPT Work and Codex you can choose GPT-6.1 Sol or Astra directly. OpenAI's own advice there matches the API data: GPT-6.1 Sol for repeated, long-running work, Astra for the hardest end-to-end tasks.

How does GPT-6 Astra compare with GPT-5.6 Sol?

GPT-5.6 Sol is the previous Sol generation. On October 15, 2026, OpenAI's pricing page listed it under Cyber models at a promotional $4 input, $0.40 cached, $5 cache write, and $20 output per 1M tokens, available at least through November 21, 2026. That is 40% of Astra's token price but twice GPT-6.1 Sol's, so for new general-purpose work GPT-6.1 Sol is both newer and cheaper. In ChatGPT, GPT-5.6 Sol and GPT-5.6 Sol Pro stay selectable, and explicit GPT-5.6 Sol selections don't move to GPT-6. For that generation's trade-offs, see GPT-5.6 Sol vs Terra vs Luna.

Is GPT-6.1 Sol only a few points better than GPT-6 Sol?

It depends on the effort level. At max effort, Artificial Analysis measured 4 points on its previous Intelligence Index (v4.3.2) and 3 on its Coding Agent Index, both as of September 30, 2026. At low through xhigh the v4.3.2 gap is 7 to 8 points, and GPT-6.1 Sol at xhigh beat GPT-6 Sol at max by 6 points on coding agents. The current v5.0 index no longer scores GPT-6 Sol, so these remain the latest side-by-side figures, alongside OpenAI's own evaluations. The upgrade is largest below max effort, which is where most production traffic runs.

Is GPT-6 Astra the same thing as GPT-6?

GPT-6 is a family of models. Astra (September 3) is the most capable, Sol is the mid-priced model now in its 6.1 version, and Luna is the fastest and cheapest. In regular ChatGPT Chat, Astra appears as "GPT-6 Pro" on Pro, Business, and Enterprise plans. For the full lineup, including Luna and GPT-5.6 Terra, see GPT-6 Sol vs Terra vs Luna vs Astra: Which Model Should You Use?. That comparison predates GPT-6.1 Sol.

Should I cancel my Astra usage entirely?

Probably not. Move the work that GPT-6.1 Sol handles at an acceptable pass rate. On Artificial Analysis's v5.0 data, GPT-6.1 Sol one level higher covers Astra low and medium, while Astra high and xhigh are matched only by GPT-6.1 Sol max, which is cheaper but slower per task. Keep Astra for tasks that need its max effort, for hard science, and anywhere a failed attempt costs you real review time. If you're also weighing a cheaper model from another vendor, Claude Sonnet 5.5 vs GPT-6 Astra: When the Cheaper Model Wins covers that trade-off.