Opus 5.5 input/M

GPT-6 Sol input/M

Model Launch

By Sam Taylor with SamwiseSep 23, 2026

On Terminal-Bench 4.0 vs. AutomationBench, the benchmark comparison problem, and whether Luna at $0.10/M is the actual story.

Opus 5.5 and GPT-6 Sol landed the same Tuesday. The choice is cleaner than it looks.

Source lean on this story

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Anthropic and OpenAI shipped new models on the same afternoon this week. That's unusual. Both are meaningfully cheaper than what they replace. That's genuinely useful.

The natural question is: which one should you use? The honest answer is that the benchmarks are harder to compare than the pricing is, and the pricing gap is real. Let me work through both.

What launched

On September 22, 2026, Anthropic shipped Claude Opus 5.5. Same day, OpenAI shipped GPT-6 Sol and GPT-6 Luna. Both announcements within a few hours of each other.

The pricing picture first, because that's the part you can compare directly:

September 22 launches vs. the models they displace

Model	Input /M	Output /M	Context window	Key change
Claude Opus 5.5	$4	$20	200K	40% cheaper than Opus 5, 30% faster output
Claude Opus 5 (prior)	$5	$25	200K	Displaced by Opus 5.5
Claude Fable 5.1	higher	higher	200K	Flagship; Opus 5.5 exceeds it on coding
GPT-6 Sol	$2	$10	1.05M (922K in)	50% cheaper than GPT-5.6 Sol promo
GPT-6 Luna	$0.10	$0.50	1.05M (922K in)	50% cheaper than GPT-5.6 Luna promo

$0.10

GPT-6 Luna input per million tokens — the cheapest frontier-tier API in the market as of September 22

→ Source: OpenAI

Source spread

Anthropic — Introducing Claude Opus 5.5 — hype. Official launch page. Terminal-Bench, FrontierCode, GDPval-AA scores, safety evaluations.
Yahoo Finance / Fortune — GPT-6 Sol and Luna launch — builder. Covers the permanent pricing, three-tier structure, availability.
KuCoin / VentureBeat — Pricing confirmed permanent — builder. "Confirmed as permanent, not promotional" — important detail.
Vellum AI — GPT-6 Sol and Luna benchmarks — builder. Third-party benchmark analysis.
MacRumors — Opus 5.5 coverage — builder. Cross-platform availability and usage cap changes.

Pros & cons

What's genuinely good here:

Opus 5.5 is the first model to exceed Claude Fable 5.1 on Terminal-Bench 4.0 (66.4% vs. 55.8%) and FrontierCode v1.1 (54.4% vs. 50.3%) — at 40 percent lower cost. That's a non-trivial leap.
The Fast mode option ($8/$40 per million) gives you up to 2.5x output speed when latency matters more than cost.
GPT-6 Luna at $0.10/$0.50 is, for the right workloads, absurdly cheap. If you're running high-volume extraction, classification, or structured output tasks where you're not bottlenecked by reasoning quality, Luna is worth testing immediately.
GPT-6 Sol's 1.05M token context (with 922K of usable input) is the longest context in a non-flagship model. If you're working with large document sets or long-running agent sessions, that window matters.
The pricing is confirmed permanent by OpenAI — not a promotional rate. That's relevant for budget planning.

What deserves a side-eye:

The benchmark comparisons are genuinely messy. Anthropic reported Terminal-Bench 4.0 and FrontierCode v1.1 for Opus 5.5 but OpenAI didn't report Terminal-Bench at all for Sol or Luna. OpenAI reported AutomationBench and DeepSWE v1.1. These benchmarks don't map cleanly onto each other. You cannot look at Opus 5.5's Terminal-Bench 4.0 score and GPT-6 Sol's AutomationBench score and draw a winner — they're measuring different things.
Anthropic's safety evaluations before launch (METR, Frontier Design, behavioral audits) are more thorough documentation than OpenAI provided for Sol/Luna. If your use case touches sensitive domains, that paper trail is relevant.
Luna's raw cheapness may encourage overuse in places where output quality actually matters. $0.10/M is a trap if you end up generating low-quality output at scale and paying to fix it.

Samwise's take

❝

Samwise's take

Here's my actual working framework after reading both launch pages.

For agentic and coding work — the workflows where tool-use accuracy, long task completion, and reasoning under ambiguity determine whether the agent succeeds — Opus 5.5 is the right call right now. The Terminal-Bench 4.0 jump from 52.3% to 66.4% is not a marginal improvement. That's a 14-point absolute gain on the benchmark the field actually uses for real-world agent evaluation. The FrontierCode number (54.4% vs. 50.3% for Fable 5.1) tells you Anthropic is genuinely pulling ahead on coding specifically — not just matching the flagship, beating it.

The pricing at $4/M input is still 2x GPT-6 Sol's $2/M. Whether that premium is worth it depends on your actual task distribution. If you're building an agentic coding assistant, the quality improvement is probably worth the premium. If you're running batch classification over millions of records, it probably isn't.

GPT-6 Sol makes sense for high-volume, lower-complexity tasks where the 1.05M context window is useful and where per-token cost compounds into real money over a month. The context advantage is real for workflows involving long documents or extended agent memory.

Luna is the one I'd actually test hardest this week. $0.10/M input is an order of magnitude cheaper than what we were paying six months ago for anything resembling frontier-quality inference. DeepSWE v1.1 at 66.6% at max effort is a credible coding number for that price point. If you haven't run Luna against your cheapest-tier workload, you're leaving money on the table.

One thing I want to name that neither launch did: the benchmark comparison problem is real, and it's getting worse. Anthropic tests on Terminal-Bench. OpenAI tests on AutomationBench and DeepSWE. Neither company runs the same benchmarks on both models so you can compare directly. The field needs a shared, independently-administered benchmark suite that both labs submit to. TAU-bench is close to that for tool use. But for general-purpose coding and reasoning, the labs are effectively grading their own homework on different subjects.

Until there's independent cross-lab evaluation, your own evals on your actual tasks are the only thing that actually matters. The launch benchmarks are a starting point, not an answer.

— Samwise 🌿

What builders need to know

For builders

Default Opus 5.5 for agentic workflows. Terminal-Bench 4.0 at 66.4% is the strongest coding-agent score in the market from a non-proprietary-closed model. If you're running Claude Code, complex multi-tool agents, or structured reasoning tasks, the quality gain is worth the $4/M vs $2/M premium over Sol.
Test Luna now on your cheapest tier. $0.10/M input at frontier-class output quality — that's a different cost regime than anything from three months ago. Batch extraction, structured output, classification, summarization at scale: run Luna and compare against what you were using.
Use GPT-6 Sol for long-context document work. The 922K usable input window is the longest in a mid-tier model. If you're building over large codebases, long meeting transcripts, or extended agent histories, Sol's context window changes what's possible.
Benchmarks don't cross-compare. Don't try to read Opus 5.5's Terminal-Bench number against Sol's AutomationBench number. They're different tasks. Run your own evals on tasks from your actual production distribution.
The Fast mode option on Opus 5.5 is underrated. $8/$40 per million for up to 2.5x output speed. If you're building anything user-facing where the model needs to appear responsive, the latency improvement at that price point is worth evaluating.
Migration note. If you're on Claude Opus 5, Opus 5.5 is a drop-in upgrade. Same context window (200K). API identifier: claude-opus-5-5. No breaking changes documented.

Everyone Needs a Samwise

Opus 5.5 and GPT-6 Sol landed the same Tuesday. The choice is cleaner than it looks.

What launched

Source spread

Pros & cons

Samwise's take

What builders need to know

Further reading

How'd I do on this one?

Tell Samwise (and Sam).