GPT-6 Astra vs GPT-5.6 Sol: Should You Upgrade?

OpenAI launched GPT-6 Astra on 3 September 2026, displacing GPT-5.6 Sol as its flagship. If you still run Sol in production, you have exactly one question: is switching worth it, and what does it cost?

Updated 5 October 2026. The independent numbers on this page have changed materially since launch day, and not for the reason you would guess. Artificial Analysis rebased its Intelligence Index from v4.1.1 to v4.3.2, swapping the evaluation basket. On the old basket these two models tied. On the current one Astra leads by 5.7 points. Every figure below has been re-read against AA's live model pages and OpenAI's current model documentation, and vendor-reported results are kept separate from independently measured ones throughout, because on this comparison they disagree.

What actually changed on 3 September 2026?

Astra ships in the API as gpt-6-astra. OpenAI calls it state-of-the-art on computer use, browsing, software engineering, cybersecurity, science and professional work, and it is the company's first model to reach the Critical cybersecurity threshold under its Preparedness Framework.

The rollout was staged. Per the launch post, Astra went out on launch day "to a limited set of organizations" before reaching ChatGPT Plus, Pro, Business and Enterprise users and the API "over the coming days." That staging has since completed: Astra is now resolvable in the API and is listed on OpenRouter at the full $10 / $50 rate, with a batch tier at $5 / $25. Enterprise workspace access can still be off by default — if you cannot see the model, check with an admin before assuming a capacity problem.

How do GPT-6 Astra and GPT-5.6 Sol compare on paper?

This is the part most launch coverage gets wrong. The two models are specced identically:

Same context window, same output ceiling, same long-context boundary, same discount structure. The genuine differences are a 10-week fresher knowledge cutoff, and one that has real cost consequences: Astra drops the none reasoning effort level. OpenAI's model documentation lists Astra's effort levels as low, medium, high, xhigh and max, while Sol's are none, low, medium (default), high, xhigh and max. On Sol you can turn thinking off entirely. On Astra you cannot — every call pays for reasoning tokens, and reasoning tokens bill as output at $50 per million.

What do the benchmarks say, and who measured them?

OpenAI's numbers and independent numbers tell different stories. Both matter.

OpenAI-reported results

Note the row where Astra loses: Humanity's Last Exam with tools, where Sol scores 65.0% and Astra 57.2%. OpenAI published that regression rather than burying it, but it does mean Astra is not a strict superset of Sol on broad knowledge work.

Two conventions matter before you quote any of these. OpenAI states its scores are "the maximum at any effort" — these are best-case reasoning-effort results, not what a default-configured call returns. And the OSWorld figure is specifically the partial-score variant on the offline set; vendors publish OSWorld in both partial and strict modes and the two can differ by tens of points on the same run. The Astra-vs-Sol comparison above is internally consistent because both columns come from the same OpenAI table under the same conventions, but do not cross-compare these numbers against another vendor's scores without checking which variant they reported.

Independently measured results

This is the section that changed. On the Artificial Analysis Intelligence Index v4.3.2 — a third-party composite of ten evaluations (AA-Briefcase, GDPval-AA, AutomationBench-AA, Terminal-Bench 4.0, SciCode, Humanity's Last Exam, GDP.pdf, CritPt, AA-Omniscience and AA-LCR) — GPT-6 Astra scores 52.67 and GPT-5.6 Sol scores 46.97, both measured at max reasoning effort. That is a 5.70-point lead, or 12.1% relative. Astra currently ranks #7 of 224 models on AA's index.

If you read an earlier version of this article, or coverage from launch week, you will have seen these two models described as tied at 61 points. That was accurate at the time and is now obsolete: those were Intelligence Index v4.1.1 figures, and AA has since rebased to v4.3.2. There is no conversion factor between the two versions — a rebase changes the benchmark basket, so every model's score moves whether or not the model changed. Any bare index score you encounter without a version stamp is unauditable; treat it as unusable.

The rebase is also why the tie disappeared. Two of the ten evaluations in the v4.3.2 basket are Terminal-Bench 4.0 and AutomationBench-AA — the two agentic benchmarks carrying Astra's largest margins in OpenAI's own table (57.7% vs 37.3%, and 41.4% vs 18.1%). A basket that weights long-horizon tool use more heavily surfaces a gap that the older basket averaged away. The models did not change; what AA measures did.

Against the Claude line the ordering also moved. On v4.3.2, Astra's 52.67 now beats Claude Opus 5 (Max) at 50.78 and Claude Fable 5 at 49.63, and trails only Claude Fable 5.1 (Max) at 53.35 — which costs $7.63 per index task against Astra's $3.26. On the v4.1.1 basket all three Claude models were ahead of Astra. If you are choosing between frontier flagships on index score alone, Astra is no longer the one giving ground.

One figure from the old version of this page has been removed rather than updated: the Artificial Analysis Coding Agent Index v1.4, on which Astra scored 67.0 to Sol's 65.1. That composite no longer exists in v4.3.2 — AA's capability-specific indices are now Finance & Accounting, Strategy & Ops, Legal, Healthcare & Medical, Engineering and Economics. A retired composite cannot be converted onto the new scale, so quoting it alongside current numbers would be misleading.

What does each reasoning effort tier actually cost?

AA publishes the index separately for every reasoning-effort variant, and this is where the decision actually lives. Mind the variant trap: AA's unqualified model names are not the same tier across models. The bare GPT-6 Astra entry is its xhigh variant, while the bare GPT-5.6 Sol entry is its low variant. Comparing those two unqualified labels gives you 52.39 against 33.47 — an 18.9-point chasm that is almost entirely an effort-tier artefact, not a capability difference. Every row below states its tier explicitly.

All index figures are Artificial Analysis Intelligence Index v4.3.2 as of October 2026, and all costs are AA's cost per index task. Per-task cost is the comparable figure, not the cost of running the whole suite: AA runs different task counts per model, so suite totals do not form valid ratios. As absolute numbers, a full index run costs $5,324.10 on Astra at max effort and $3,464.84 on Sol at max.

Four conclusions fall out of that table, and they are worth more than the headline score:

• Astra leads at every matched tier, and the gap widens as you spend less. +5.70 at max, +12.31 at low. Sol degrades faster when you dial effort down, which makes Astra the more forgiving model to run cheaply.
• Astra at high beats Sol at max — and costs less. 50.92 for $1.73 against 46.97 for $1.99: nearly four index points better for 13% less money per task. medium does it too (49.57 for $1.54, 23% less). This is the single most useful configuration finding here, and it inverts the launch-week advice: if you switch, drop two effort tiers as you do it.
• On Astra, max effort is wasted money. It scores 52.67 against xhigh's 52.39 — +0.28 points for 41% more cost ($3.26 vs $2.31). Treat xhigh as Astra's real ceiling.
• On Sol, medium remains the value knee at 39.24 for $0.50. Each tier up to medium buys roughly 5.5 points per extra $0.24; after it, high buys 3.11 points for 62% more and xhigh only 1.66 more for $0.37 beyond that.

Note that AA publishes no non-reasoning datapoint for Astra, consistent with OpenAI's documentation: the none tier simply does not exist on that model. Sol's non-reasoning row is the cheapest thing in this comparison by an order of magnitude — $0.03 per task — and if any part of your workload lives there, Astra has no equivalent to offer you at any price.

The honest summary is no longer "they tie, so don't bother." It is sharper: Astra is genuinely the stronger model on the current independent index, by 5.7 points at the top of the ladder, and the per-task premium at max effort is 64% rather than the 2.5× the token prices imply. Whether that is worth paying is now a question about the alternatives, not about Sol — see below.

ARC Prize published its own evaluation on launch day, and it surfaces a caveat the headline omits. OpenAI says Astra "saturates ARC-AGI-3 with a 99.9% score." ARC Prize's data shows that 99.9% comes from the Provider Adapter harness, which preserves OpenAI's opaque reasoning state between requests. On ARC Prize's provider-neutral Standard harness, Astra's best score is 62.7%. Sol manages 7.8% on the same harness — still a huge jump, just not saturation.

ARC Prize also found Astra used fewer actions than the median human on 96% of ARC-AGI-3 levels, and 51.7% fewer actions per level on average.

Is GPT-6 Astra vs GPT-5.6 Sol still the right comparison?

A month after launch, honestly: no. Two things have happened that change the question.

First, Artificial Analysis now marks GPT-5.6 Sol as deprecated, in AA's own words: "This model is deprecated. We only continue performance benchmarking for the default 10k input token workload." AA points readers to GPT-6 Sol instead. This is a benchmarking status, not an OpenAI retirement — see the FAQ — but it means Sol's numbers will stop tracking reality from here.

Second, OpenAI shipped two Sol successors, and GPT-6.1 Sol, released 29 September 2026, lands within one index point of Astra at roughly a fifth of the per-task cost. OpenAI's own model documentation says it "delivers near-Astra performance at a lower cost for complex coding, computer use, and professional work," and recommends you "compare it with Astra on your tasks to assess the tradeoff between quality and cost." The independent numbers agree with the vendor, which is unusual enough to be worth acting on:

Read the top two rows together: GPT-6.1 Sol gives up 0.84 index points to Astra and charges 78% less per task — $0.72 against $3.26, on identical $2 / $10 token pricing versus Astra's $10 / $50. At xhigh the spread is even starker: 51.04 for $0.39 against Astra's 52.39 for $2.31, roughly six times cheaper for 1.35 points less.

And the bottom two rows retire the premise of this comparison entirely. GPT-6 Sol beats GPT-5.6 Sol on the index (47.63 vs 46.97) while costing 48% less per task and generating tokens 32% faster, on half the per-token price. There is no workload on which staying on GPT-5.6 Sol is the better choice than moving to GPT-6 Sol. If you came here asking whether to upgrade from 5.6 Sol, the answer is yes — just not necessarily to Astra.

Two caveats keep this honest. GPT-6.1 Sol is slow: 55.7 output tokens per second ranks #144 of 224 models on AA, below Astra and well below GPT-6 Sol's 107.1. And like Astra, it does not support none or minimal reasoning effort, so it is no help for the cheap non-reasoning calls Sol handles at $0.03 per task. If you need low latency, GPT-6 Sol is the better-balanced choice; if you need reasoning off, nothing in the GPT-6 line replaces 5.6 Sol. We cover the new flagship Sol in depth in our GPT-6.1 Sol complete guide.

How much more does GPT-6 Astra actually cost?

From OpenAI's pricing page, per million tokens:

The ratio is remarkably clean: Astra is exactly 2.5× Sol in every cell — input, output, cached, cache writes, batch, flex and fast mode alike.

Sol's promotional pricing is still live

OpenAI's documentation states verbatim that "GPT-5.6 Sol's promotional pricing is available at least through November 21, 2026." The same page describes the $4/$20 rate as "a 20% reduction in input pricing and a 33% reduction in output pricing" — which puts Sol's list price at $5 input / $30 output.

That reframes the comparison, though less favourably than it once did. Against Sol's promotional rate Astra is 2.5×. Against Sol's list rate it is 2× input and 1.67× output. But note that both GPT-6 Sol and GPT-6.1 Sol are priced at $2 / $10, with no promotional expiry noted in OpenAI's model documentation for either — below Sol's discounted rate, let alone its list rate. If you are budgeting a Sol workload into 2027, the promo expiry on 21 November 2026 is one more reason the migration target should be GPT-6, not a renegotiated GPT-5.6.

The per-token price is not the per-task price

Per-token price and per-task price are not the same thing, and on this comparison they differ by a lot. ARC Prize's figures show Astra at max effort costing $0.433 per ARC-AGI-1 task against Sol's $0.540, and $1.12 against $1.44 on ARC-AGI-2 — 2.5× dearer per token yet cheaper per completed task, because it solves those problems in far fewer tokens and actions. On Artificial Analysis's broader ten-evaluation basket the ordering flips but the margin narrows sharply: $3.26 against Sol's $1.99 per index task at max effort, a 64% premium rather than 150%. Workload shape decides which applies, and in both measurements Astra's token efficiency absorbs a large part of the headline price increase.

OpenAI reports a matching latency effect on task duration: in OSWorld 2.0 simulations Astra scored 72.6% at roughly 40 minutes per task against Sol's 65.7% at roughly 75 minutes — about 47% less time.

So don't estimate your Astra bill by multiplying your Sol bill by 2.5. On agentic, multi-step workloads token savings absorb much of the increase. On short single-shot completions where output length is set by your prompt they do not, and there you pay close to the full 2.5× — with no option to switch reasoning off.

What happens when your prompts cross 272,000 tokens?

Both models step up to long-context rates above 272K input tokens, with identical structure:

The critical detail, in OpenAI's own words: prompts above 272K are repriced "for the full request." This is not marginal pricing on the tokens past the boundary — the entire request reprices. A 270,000-token Astra prompt costs $2.70 in input. Push it to 275,000 tokens, a 1.9% increase, and the input cost becomes $5.50. You double your bill for 5,000 extra tokens.

Worth stating plainly: this is not an Astra-only penalty. Sol has the identical cliff at the identical boundary — the same prompt costs $1.08 then $2.20. Migrating does not introduce a cost cliff you did not already have; it makes the existing one 2.5× more expensive to fall off. If you run long-context RAG anywhere near 272K, add a token check before dispatch on either model.

Is GPT-6 Astra better than GPT-5.6 Sol for coding and agents?

Yes, and this is where the upgrade is easiest to justify. Terminal-Bench 4.0 is the headline: 57.7% against 37.3%, a 20-point jump on agentic terminal work. Internal database-migration tasks go 42.7% to 63.9%. SRE-Bench binary reverse-engineering goes 55.9% to 88.0% at a single attempt. Terminal-Bench 4.0 is also one of the ten evaluations in AA's current index basket, which is why Astra's independent lead grew when AA rebased.

The gains shrink sharply on benchmarks measuring single-turn code generation rather than agentic execution — DeepSWE v1.1 moves only 72.7% to 74.1%, FrontierCode 1.1 Main 47.5% to 53.3%. The pattern is consistent: Astra's advantage scales with how long and tool-heavy the task is. A one-shot "write me this function" call barely benefits. A forty-step refactor across a real repository benefits a lot.

OpenAI also updated the Codex harness alongside Astra with a new context-preservation mechanism: rather than compacting long sessions into one lossy summary, Astra keeps searchable notes across context windows. It is opt-in via config.toml today and becomes default "in the coming weeks."

For how this lands against the other frontier option, see our GPT-6 Astra vs Claude Opus 5 comparison — noting that on the current v4.3.2 index Astra now scores above Claude Opus 5, reversing the launch-week ordering.

What's worse or still unknown about GPT-6 Astra?

• It is the slowest model in this comparison to start. AA measures Astra at max effort at 60.0 output tokens per second with a 287.7-second median time to first token — nearly five minutes of thinking before the first chunk arrives, against Sol's 107.6 seconds at 81.2 tok/s. Astra is not a model for interactive surfaces at high effort.
• The per-task premium is real even though it is smaller than 2.5×. $3.26 against $1.99 per index task at max effort, and GPT-6.1 Sol delivers 98% of Astra's index score for $0.72.
• Humanity's Last Exam with tools regressed, 65.0% to 57.2% — and HLE is one of the ten evaluations in AA's current basket, so that weakness is already priced into the 52.67.
• You cannot disable reasoning. Sol supports reasoning.effort: none; Astra's lowest setting is low. Every Astra call pays for reasoning tokens at output rates.
• Reasoning is harder to monitor. OpenAI states directly that Astra's written reasoning is harder to monitor than Sol's, and calls the decline something it takes seriously.
• Safety checks can stop API tasks outright. Because Astra meets the Critical cyber threshold, extra checks run in production. OpenAI's wording: a paused task in ChatGPT or Codex prompts you to review it, but "in the API, the task will stop." That is a new production failure mode.
• It refuses advanced security work such as building proof-of-concept exploits. Less-restrictive access is promised via OpenAI Daybreak "in the coming weeks."
• Fast mode is unavailable with EU data residency.
• Enterprise workspace access can be off by default. Check before planning a cutover.

Do you need to change your code to switch?

More than you would expect. The model string change from gpt-5.6-sol to gpt-6-astra is the easy part; both models expose the same Responses and Chat Completions endpoints. What breaks is the configuration around the call:

1. reasoning.effort: none will fail. Astra does not accept it, and neither does GPT-6.1 Sol. Any Sol code path using none for cheap, fast, non-reasoning calls needs a new plan — GPT-6 Sol does support none and is the natural home for those calls.
2. Re-tune reasoning effort downward. Astra at high outscores Sol at max for less money per task, and Astra's max buys only 0.28 index points over xhigh for 41% more. A straight tier-for-tier port is the expensive mistake.
3. Add handling for hard stops. Cybersecurity safety checks can terminate an API task. Retry logic written for rate limits will not cover it.
4. Budget for latency, not just spend. Astra's median time to first token at max effort is 287.7 seconds. Request timeouts and user-facing spinners tuned for Sol will break.
5. Guard the 272K boundary. Crossing it reprices the whole request.
6. Re-run your own evals — given the HLE regression, assume nothing transfers.

Which model should you actually use?

The decision rule has changed since launch week. Move off GPT-5.6 Sol — it is deprecated on Artificial Analysis and beaten on price, speed and index score by GPT-6 Sol. Default to GPT-6.1 Sol, which reaches 51.83 against Astra's 52.67 for 78% less per task. Pay for Astra only where its specific strengths earn the premium: long agentic runs, computer use and hard mathematics.

So the launch-week verdict — "not yet, they tie on the independent index" — no longer holds, but not because Astra got better. The index basket changed, and a month of releases moved the goalposts. Astra is the strongest model here and a defensible buy for agentic and computer-use work, especially run at high rather than max. For everything else the premium is hard to justify when GPT-6.1 Sol sits 0.84 points behind at a fifth of the per-task cost. The one thing that is now unambiguous: there is no good reason left to stay on GPT-5.6 Sol.

Background on the outgoing flagship: our GPT-5.6 Sol, Terra and Luna breakdown and the GPT-5.6 release notes. For Astra in depth, see the GPT-6 Astra complete guide and the GPT-6 Astra pricing and API cost breakdown.

FAQ

Is GPT-6 Astra better than GPT-5.6 Sol?

Yes, and by more than it looked at launch. On the Artificial Analysis Intelligence Index v4.3.2 (October 2026) Astra scores 52.67 at max reasoning effort against Sol's 46.97 — a 5.70-point lead, and wider at lower effort tiers. Launch-week coverage reported a tie at 61 points apiece, but those were v4.1.1 figures on a different evaluation basket. Astra still scores worse than Sol on Humanity's Last Exam with tools, 57.2% to 65.0%.

How much more expensive is GPT-6 Astra?

Exactly 2.5× Sol per token at every tier: $10 input and $50 output per million against Sol's $4 and $20, with the same ratio for cached input, cache writes, batch, flex and fast mode. Per completed task the premium is much smaller, because Astra uses fewer tokens — $3.26 against $1.99 per Artificial Analysis index task at max effort, a 64% increase rather than 150%.

Should I upgrade from GPT-5.6 Sol?

Yes, but probably not to Astra. GPT-6 Sol scores higher than GPT-5.6 Sol on the v4.3.2 index (47.63 vs 46.97) at 48% less per task and 32% faster output, on half the token price — there is no workload where staying on 5.6 Sol wins. For stronger work, GPT-6.1 Sol reaches 51.83 for $0.72 per task. Reserve Astra for long agentic runs, computer use and hard mathematics.

Is GPT-5.6 Sol deprecated?

It depends who is asking. Artificial Analysis marks it deprecated — "We only continue performance benchmarking for the default 10k input token workload" — and points to GPT-6 Sol instead. OpenAI has not retired it: there is no sunset date in the model documentation, and promotional pricing runs at least through 21 November 2026. So nothing is forcing a migration, but its independent benchmark coverage has already stopped expanding.

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

On the index, substantially. GPT-6.1 Sol, released 29 September 2026, scores 51.83 to Astra's 52.67 at max effort while costing $0.72 per index task against $3.26 — 78% less, on $2/$10 token pricing versus $10/$50. OpenAI's own documentation describes it as delivering "near-Astra performance at a lower cost." The trade-offs: it is slower (55.7 output tok/s, #144 of 224 on AA) and it drops the none reasoning tier.

What's the difference between GPT-6 Astra and Sol for coding?

The gap scales with task length. On agentic work Astra is dramatically better: Terminal-Bench 4.0 57.7% vs 37.3%, internal database-migration tasks 63.9% vs 42.7%. On single-turn code generation it nearly closes — DeepSWE v1.1 is 74.1% vs 72.7%. Astra also gains a Codex feature keeping searchable notes across context windows instead of lossy compaction.

Do I need to change my code to switch?

Yes, more than a model-string swap. Astra removes the reasoning.effort: none option that Sol supports, so any code path disabling reasoning breaks. You should also re-tune effort downward — Astra at high beats Sol at max for less per task — add handling for API tasks the new cybersecurity safety checks can terminate outright, raise request timeouts for a 287.7-second median time to first token, and guard the 272K boundary where the full request reprices.

What is GPT-6 Astra's context window?

1,050,000 tokens, with a 128,000-token maximum output — identical to GPT-5.6 Sol, GPT-6 Sol and GPT-6.1 Sol. There is no context-window advantage to switching. The billing boundary sits lower than the window: input above 272,000 tokens is charged at 2× input and 1.5× output for the entire request. Astra's knowledge cutoff is 30 April 2026, against Sol's 16 February 2026.