Anthropic and OpenAI Ship on the Same Day: Two Different Bets on Where AI Pricing Goes Next
The same Tuesday, two different tiers
Anthropic released Claude Opus 5.5 on September 22, 2026, describing it as performing at the level of Claude Fable 5.1, the company's higher tier, while costing 40 percent less to run than Opus 5. On the same day, OpenAI released GPT-6 Sol and GPT-6 Luna, the two models below its flagship GPT-6 Astra, cutting their API prices in half. Astra, which OpenAI launched on September 3 and still calls its best model across the board, did not get cheaper.
The overlap in timing means both companies' comparison tables were built against a moving target. Anthropic's benchmark table for Opus 5.5 includes GPT-6 Astra and GPT-5.6 Sol as competitor reference points rather than GPT-6 Sol or Luna, because those hadn't shipped yet when the table was assembled. OpenAI's announcement for Sol and Luna compares them against Claude Opus 5 and Claude Fable 5.1 rather than Opus 5.5, for the same reason. Each company's newest model is being read against the other's outgoing generation.
What actually got cheaper
The price cuts land at different points in each company's lineup. Opus 5.5 now costs $4 per million input tokens and $20 per million output tokens, down 20 percent from Opus 5's $5 and $25. The larger savings sit in cached reads, which Anthropic says make up most agentic and coding workloads: those tokens fell from $0.50 to $0.20 per million, a 60 percent cut. Anthropic says the combined effect, including that Opus 5.5 uses fewer tokens per task, works out to roughly 40 percent lower cost on typical workloads. A faster mode is priced separately at $8 and $40 per million tokens for up to 2.5 times the speed.
GPT-6 Sol dropped from $4 and $20 per million tokens to $2 and $10, exactly half. GPT-6 Luna dropped from $0.20 and $1.20 to $0.10 and $0.50, also half. Both cuts are measured against GPT-5.6's promotional pricing rather than list pricing, which OpenAI states directly. Astra, priced separately at launch, was not mentioned in the pricing table for this release at all.
So the two announcements describe different strategies. Anthropic priced down its second-highest tier and framed the result as buying Fable-class output at Opus prices. OpenAI priced down its two cheapest tiers and left the top of its lineup where it was, framing Sol and Luna as ways to bring Astra's training techniques to everyday budgets rather than as a replacement for Astra itself.
Numbers that check out, and numbers that don't
Both companies cite results from AutomationBench, a Zapier-built test of business workflows across 47 connected apps, and two of the numbers match exactly. Anthropic's table lists Claude Opus 5 at 26.9 percent and Claude Fable 5.1 at 31.4 percent. OpenAI's table, published the same day, lists the identical figures for the identical models: Opus 5 at 26.9 percent and Fable 5.1 at 31.4 percent. For an industry where every company runs its own version of a benchmark under conditions the other side can't fully see, two competitors independently reporting the same scores for the same models is a small but real point of confirmation.
Not every figure holds up as cleanly. Anthropic's table puts GPT-6 Astra at 41.4 percent on AutomationBench. OpenAI's own table, in the same release, puts GPT-6 Astra at 30.3 percent, run at "low" reasoning effort so its cost per task would be comparable to Sol. The gap is not necessarily a contradiction. Neither company states the effort setting Anthropic used when it recorded 41.4 percent, and AutomationBench scores on this kind of agent shift with reasoning effort more than most benchmarks. But it means the same model, on the same test, appears twice in the public record eleven points apart, and a reader comparing the two tables at face value would draw the wrong conclusion about where Astra stands.
A similar gap shows up on OSWorld 2.0, a computer-use benchmark. Anthropic's table has Claude Opus 5 scoring 74.0 percent, partial credit, with no effort level specified for the competitor row. OpenAI's release states that Claude Opus 5 at medium effort scores 60.3 percent on the offline version of the same benchmark, which it uses to show GPT-6 Sol at xhigh effort reaching a comparable 60.5 percent at a much lower cost. Fourteen points separate the two reported scores for the same Anthropic model on what both companies call the same test. Effort level again looks like the likely explanation, but neither release makes that explicit for the competitor's number, which is exactly the kind of gap a reader should notice before citing either figure as settled.
Where each company's own model is concerned, the numbers are more legible because effort settings are disclosed. On DeepSWE v1.1, a software-engineering benchmark, GPT-6 Sol at max effort scores 68.8 percent, which OpenAI places within 1.1 percentage points of Claude Fable 5's best recorded score of 69.9 percent at xhigh effort, achieved at roughly 80 percent lower cost per task. GPT-6 Luna at max effort scores 66.6 percent, which OpenAI calls comparable to Opus 5 and Fable 5 at medium effort, at 93 and 96 percent lower cost respectively. A footnote on that comparison discloses that Fable 5's score was used because Fable 5.1's wasn't available, a small but useful piece of transparency about what's being measured against what.
What each company says about safety
Anthropic frames Opus 5.5 as its first release since chief executive Dario Amodei's public call to pace frontier AI development against safety practices. The company reports that Opus 5.5 scored better than any recent Claude model on its automated behavioral audit, a suite of roughly 2,000 simulated scenarios, and that it attempted to cross containment boundaries around 85 percent less often than Opus 5 or Claude Mythos 5.1 in a new evaluation built for that purpose.
Opus 5.5 launches with cybersecurity and biology safeguards similar to those on Claude Fable 5.1, meaning many cyber tasks route to an older model, Opus 4.8, unless the user is enrolled in a verification program. It also carries preserved thinking, an anti-distillation measure that blocks attempts to extract a model's reasoning by editing its prior context.
OpenAI's release for Sol and Luna states that both models show improvements over their GPT-5.6 predecessors on internal alignment evaluations, including lower rates of what it calls misleading claims about coding work, and points to a system card for the full results. The webpage itself displays chart placeholders for five evaluation categories, coding deception, broken search, reviewer bypass, warning circumvention, and unauthorized interaction, without rendering the underlying numbers in the page's text. That leaves Anthropic's safety claims for Opus 5.5 easier to check against a specific number than OpenAI's claims for Sol and Luna, at least from the announcement page alone.
Who can use what, today
Availability is where the two releases diverge most practically. Claude Opus 5.5 is live now across Amazon Web Services, Google Cloud, and Microsoft Azure, and developers can call it immediately as claude-opus-5-5 on the Claude Platform. GPT-6 Sol and Luna are live in ChatGPT Work and in Codex today for Plus, Pro, Business, Enterprise, and Edu subscribers, callable in the API as gpt-6-sol and gpt-6-luna. Free and Go users get access only to Luna, and only in the desktop app. Neither Sol nor Luna is available yet in ChatGPT's standard Chat mode, and OpenAI says the rollout inside ChatGPT Work and Codex is staggered through the day rather than immediate for every account.
What's still open
Anthropic has said Claude Sonnet 5.5 and Claude Haiku 5.5 will follow "in the coming weeks," carrying many of the same efficiency and safety changes down through the rest of its lineup. OpenAI's release covers Sol and Luna but says nothing new about GPT-6 Terra, the middle tier between them, or about whether Astra's pricing will move.
The two companies have now each cut prices on part of their lineup within hours of each other twice in three weeks, counting Astra's September 3 launch, and the AutomationBench and OSWorld discrepancies above are worth revisiting once both sides publish fuller methodology notes rather than announcement-page summaries.
Author
David Borish writes The AI Spectator and is the author of The Tony Hawk Paradox: When Video Games Predict Reality. More of his work is available at davidborish.com
