GPT-6 Astra wins on coding (100 vs 97). GPT-5.6 Sol wins on price ($2 vs $10/1M input). For most workflows, GPT-6 Astra is the stronger default — openai's frontier answer to fable 5.1 — computer-use and agentic-coding leader at $10/$50.
Last verified Sep 4, 2026/Model data modified Sep 4, 2026
Rankings refresh dailyScored on 6 criteriaNo paid rankings
OpenAIPremium
Input cost
$10.00/1M
Context
1.1M tokens
Speed
Deliberate
Clear recommendation block
The shortest way to see the safest default, the lower-cost option, and the specialist pick before you read deeper.
GPT-5.6 Sol is the better pick when response speed matters more than maximum reasoning depth.
OpenAIPremium
Best for
Frontier agentic coding, deep research, and hardest reasoning tasks
Price
$2.00/1M
Context
1.1M tokens
Why this page recommends it
GPT-6 Astra leads on coding with a score of 100 vs 97 for GPT-5.6 Sol.
GPT-5.6 Sol is cheaper at $2/1M input tokens vs $10/1M for GPT-6 Astra.
GPT-6 Astra is the stronger default for coding tasks.
Decision notes
Go with GPT-6 Astra if you want one model to handle coding and research — it targets computer and browser use, long-horizon agentic coding, and frontier math and science work.
Switch to GPT-5.6 Sol when your work is mostly frontier agentic coding and deep research; on that narrower brief it is the better tool.
GPT-5.6 Sol is the more cost-efficient option at $2/1M input — GPT-6 Astra costs 5x more per input token, so the gap is worth taking seriously wherever token volume rather than peak quality drives the bill.
Interactive decision lab
Test the recommendation against your priority
Switch the scoring lens to see whether the top answer changes when you care more about cost, speed, or long-document work.
#1GPT-6 Astra91 pts
#2GPT-5.6 Sol89 pts
Quality first
GPT-6 Astra
OpenAI / Premium / Sep 4, 2026
91
OpenAI's frontier answer to Fable 5.1 — computer-use and agentic-coding leader at $10/$50.
Ranks models by the broadest mix of coding, writing, research, and long-context usefulness.
You are cost-sensitive or latency-bound — GPT-5.6 Sol is a fifth of the price — or you need it today in an Enterprise workspace, where it is off by default, or for offensive-security work, which it refuses outside OpenAI Daybreak.
Recommended comparisons
The fastest way to see where the recommendation shifts when your priority changes.
Capability scores are out of 100 and reflect our own weighting of published benchmarks and production signals — see how we evaluate models. “Est. month” assumes 10M input and 2M output tokens at list price, with no batch or caching discounts applied, so treat it as a ceiling.
The case for each model
What each one is genuinely good at, where it falls down, and the situations we would steer you away from it — not just the headline score.
OpenAI's September 3, 2026 frontier release — the first GPT-6 model and OpenAI's answer to Claude Fable 5.1 two days earlier. State of the art on computer use (OSWorld 2.0 72.6% in ~47% less time than GPT-5.6 Sol), agentic coding (Terminal-Bench 4.0 57.9%), and frontier math (FrontierMath Tier 4 97.6%). $10/$50 per 1M tokens, 1.05M context, 128K output, knowledge cutoff April 30, 2026.
Input
$10.00/1M
Output
$50.00/1M
Context
1.1M tokens
Speed
Deliberate
What people actually use it for
Computer-use agents that fill forms, update CRMs, run QA in a browser — 72.6% on OSWorld 2.0 at ~40 minutes per task vs Sol's 75
Agentic coding in Codex with cross-context notes — 57.9% Terminal-Bench 4.0, ahead of Claude Fable 5.1 (55.8%)
Frontier math and science — 97.6% FrontierMath Tier 4 and 64.6% Terminal-Bench Science, both well clear of every rival OpenAI tested
Where it wins
OSWorld 2.0 72.6% vs 65.7% for GPT-5.6 Sol, in roughly 47% less time per task — the new computer-use ceiling
Terminal-Bench 4.0 57.9% — ahead of Claude Fable 5.1 (55.8%), Opus 5 (52.3%) and GPT-5.6 Sol (37.3%)
FrontierMath Tier 4 (v2) 97.6% vs Fable 5.1's 87.8%; Terminal-Bench Science 64.6% vs 52.6%; GPQA Diamond 96.0%
1.05M context with 96.3% on OpenAI MRCR 8-needle at 512K–1M (Sol: 73.8%) and 128K max output
OpenAI's lowest misaligned-outcome rates to date: 2.4% on its computer-use safety benchmark vs 22.0% for Sol, 0% scope-creep on impossible cyber tasks vs 48%
Where it falls down
$10/$50 per 1M — five times GPT-5.6 Sol's $2/$10 and the same premium as Claude Fable 5.1; Fast mode doubles it again
Trails Claude Fable 5.1 on Humanity's Last Exam with tools (57.2% vs 65.0%) and on the Artificial Analysis Intelligence Index (61.2 vs 65.7)
OpenAI published no SWE-bench Verified or SWE-bench Pro figure at launch, so it does not appear on our SWE-bench leaderboard
Rolling out over days, not instantly: Enterprise access is off by default, advanced cyber tasks are refused outside OpenAI Daybreak, and the safety layer can pause or stop legitimate agent runs
OpenAI's own system card finds its written reasoning harder to monitor than Sol's
Skip it if
You are cost-sensitive or latency-bound — GPT-5.6 Sol is a fifth of the price — or you need it today in an Enterprise workspace, where it is off by default, or for offensive-security work, which it refuses outside OpenAI Daybreak.
Our verdict
The new computer-use and agentic-coding ceiling, and OpenAI's first model priced like a Mythos-class Claude. Astra beats Claude Fable 5.1 on Terminal-Bench 4.0 (57.9% vs 55.8%), Terminal-Bench Science (64.6% vs 52.6%) and FrontierMath Tier 4 (97.6% vs 87.8%), and it is the only model with a credible OSWorld 2.0 result above 70%. It loses to Fable 5.1 on Humanity's Last Exam and on Artificial Analysis's index, costs five times GPT-5.6 Sol, and ships without a SWE-bench number. If your work is computer use, browser agents or math, it is the pick; for everyday coding at scale, Sol at $2/$10 remains the value default.
Released September 3, 2026. API ID gpt-6-astra; rolling out over the coming days to ChatGPT Plus, Pro, Business and Enterprise (usage inside existing allowances; GPT-6 Astra Pro for Pro/Business/Enterprise; Enterprise off by default), the OpenAI API, Microsoft Azure and Amazon Bedrock. Standard API pricing $10/$50 per 1M tokens; Fast mode is up to 2x speed at 2x price; cache reads and writes have separate rates. Model docs list 1,050,000 context, 128,000 max output, knowledge cutoff April 30, 2026, reasoning efforts up to 'max'. Published launch numbers (Astra / GPT-5.6 Sol / Fable 5.1 / Opus 5): OSWorld 2.0 72.6 / 65.7 / — / 70.2; Terminal-Bench 4.0 57.9 / 37.3 / 55.8 / 52.3; Terminal-Bench Science 0.1 64.6 / 22.4 / 52.6 / 30.0; FrontierMath Tier 4 v2 97.6 / 83.0 / 87.8 / 73.2; GPQA Diamond 96.0 / 94.6 / 93.7 / 93.7; Humanity's Last Exam w/ tools 57.2 / — / 65.0 / 63.6; AutomationBench 41.4 / 18.1 / 31.4 / 26.9; DeepSWE v1.1 74.1 / 72.7 / 67.4 / 73.7; ARC-AGI-2 95.0 / 92.5 / 90.0 / 90.4; ARC-AGI-3 99.9 (OpenAI responses-API harness; ARC Prize's stateless runs score far lower) / 7.8 / — / 30.2; ExploitBench 100.0 / 78.5 / — / 70; SRE-Bench 88.0 / 55.9; Artificial Analysis Intelligence Index v4.1.1 61.2 / 60.9 / 65.7 / 63.1. Meets the Critical threshold for cybersecurity under OpenAI's Preparedness Framework; advanced cyber workflows gated behind OpenAI Daybreak. All figures from OpenAI's launch post and model docs, verified September 4, 2026.
The flagship of OpenAI's GPT-5.6 family — its most capable reasoning and agentic-coding model, with an 'ultra' mode that spawns sub-agents for long autonomous workflows.
Input
$2.00/1M
Output
$10.00/1M
Context
1.1M tokens
Speed
Deliberate
What people actually use it for
Tool-heavy terminal coding — 88.8% on Terminal-Bench 2.1 (91.9% in ultra mode, a state of the art)
Agentic web research at 90.4% BrowseComp with top-tier GPQA Diamond science reasoning (94.6%)
Long autonomous workflows using ultra mode's sub-agent orchestration
Where it wins
Terminal-Bench 2.1 leader at 88.8% (91.9% ultra) — the top OpenAI agentic-coding result
94.6% GPQA Diamond and 90.4% BrowseComp — frontier science reasoning and agentic browsing
Artificial Analysis Coding Agent Index leader at 80 points, near Fable 5 intelligence at roughly one-third the cost
Where it falls down
Trails Claude Opus 5 badly on repository-level engineering (SWE-bench Pro 64.6% vs 79.2%)
Long-context surcharge ($10/$45 above 272K) and 2–3x ultra-mode costs stack up fast
Skip it if
Repo-level coding is the main job — Opus 5 leads SWE-bench Pro by ~15 points — or you're cost-sensitive (Terra is 60% cheaper at 1–4 points off).
Our verdict
OpenAI's strongest model and the terminal-workflow leader. Sol beats everything on Terminal-Bench and agentic browsing, but Claude Opus 5 remains the better pick for repository-level software engineering.
First frontier model family to clear a customer-by-customer US government review: limited preview June 26, full public release July 9, 2026. Pricing $5/$30 ($10/$45 above 272K context). Knowledge cutoff Feb 16, 2026.
UseRightAI recommendations are based on practical decision factors people actually feel in day-to-day use.
Newsletter
Get updates when gpt-6 astra vs gpt-5.6 sol changes
Useful if you care about ranking shifts, pricing changes, or a better recommendation appearing in this decision path.
No spam. Useful updates only. Affiliate disclosures always clearly labeled.
FAQ
Is GPT-6 Astra better than GPT-5.6 Sol?
GPT-6 Astra wins on more of the categories we score — coding, research, long context — so it is the better default of the two. GPT-5.6 Sol is the better pick when your work is mostly frontier agentic coding and deep research. Neither is universally "better": GPT-6 Astra is aimed at computer and browser use and long-horizon agentic coding, GPT-5.6 Sol at frontier agentic coding and deep research.
Which is cheaper — GPT-6 Astra or GPT-5.6 Sol?
GPT-5.6 Sol is cheaper at $2/1M input and $10/1M output. GPT-6 Astra costs $10/1M input and $50/1M output.
Which has a larger context window — GPT-6 Astra or GPT-5.6 Sol?
Both GPT-6 Astra and GPT-5.6 Sol have the same 1.05M context window.
Is GPT-6 Astra or GPT-5.6 Sol better for coding?
GPT-6 Astra is better for coding with a score of 100 vs GPT-5.6 Sol's 97 (out of 100). GPT-6 Astra is the overall coding leader in this directory at 100/100.
Which is faster — GPT-6 Astra or GPT-5.6 Sol?
Both GPT-6 Astra and GPT-5.6 Sol have similar speed profiles — rated deliberate. Neither will be the bottleneck if latency is your deciding factor.
What are the downsides of GPT-6 Astra?
$10/$50 per 1M — five times GPT-5.6 Sol's $2/$10 and the same premium as Claude Fable 5.1; Fast mode doubles it again. Trails Claude Fable 5.1 on Humanity's Last Exam with tools (57.2% vs 65.0%) and on the Artificial Analysis Intelligence Index (61.2 vs 65.7). OpenAI published no SWE-bench Verified or SWE-bench Pro figure at launch, so it does not appear on our SWE-bench leaderboard. Avoid it if you are cost-sensitive or latency-bound — GPT-5.6 Sol is a fifth of the price — or you need it today in an Enterprise workspace, where it is off by default, or for offensive-security work, which it refuses outside OpenAI Daybreak. That is the main case for looking at GPT-5.6 Sol instead.
What are the downsides of GPT-5.6 Sol?
Trails Claude Opus 5 badly on repository-level engineering (SWE-bench Pro 64.6% vs 79.2%). Long-context surcharge ($10/$45 above 272K) and 2–3x ultra-mode costs stack up fast. Avoid it if repo-level coding is the main job — Opus 5 leads SWE-bench Pro by ~15 points — or you're cost-sensitive (Terra is 60% cheaper at 1–4 points off). Against GPT-6 Astra specifically, the gap shows up most on coding (100 vs 97).
What does a month of real work cost on GPT-6 Astra vs GPT-5.6 Sol?
Take a moderate workload of 10M input and 2M output tokens a month. GPT-6 Astra runs $200.00 (at $10/1M in and $50/1M out); GPT-5.6 Sol runs $40.00 (at $2/1M in and $10/1M out). That is a $160.00/month difference — GPT-5.6 Sol is the cheaper of the two at this volume, and the gap scales linearly as you send more. Output tokens dominate the bill on both, so prompt length matters far less than response length.
Can I use GPT-6 Astra and GPT-5.6 Sol together?
Yes, and for most teams that beats picking one. A common split is GPT-6 Astra for computer and browser use and long-horizon agentic coding, with GPT-5.6 Sol handling frontier agentic coding and deep research. Routing high-volume, low-stakes calls to GPT-5.6 Sol at $2/1M and reserving GPT-6 Astra for the hard cases is usually the cheapest arrangement that does not cost you quality.