Claude Opus 5.5 vs GPT-6 Sol: Why the Cheaper Model Isn't Always the Cheaper Agent

On September 22, 2026, Anthropic and OpenAI released competing coding-focused models on the same day. Anthropic shipped Claude Opus 5.5, priced 40% cheaper to run than the outgoing Opus 5. OpenAI shipped two new tiers, GPT-6 Sol and GPT-6 Luna, both undercutting its flagship GPT-6 Astra on price, with AWS announcing Bedrock availability for both the same day. Every headline wrote the same story: cheaper, faster, better. The actual economics of running these things as coding agents are more interesting, and less flattering to the "just look at the price-per-token" framing most people reach for.

The sticker prices

Claude Opus 5.5 lists at $4 per million input tokens and $20 per million output tokens. GPT-6 Sol comes in at exactly half that on uncached traffic: $2 input, $10 output. GPT-6 Luna is positioned further down the stack for high-volume, lower-stakes work at $0.10 input and $0.50 output — what one reviewer called "absurdly cheap," with the caveat of occasional reliability issues on harder tasks.

There's a wrinkle in Sol's pricing that matters for anything resembling a real coding agent: past 272,000 input tokens, OpenAI switches the entire request to a long-context rate — $4 input, $15 output, with cache writes at $5. Opus 5.5 charges the same rate across its full 1-million-token window, no tier change. If your agent is chewing through a large repo or a long multi-turn debugging session, Sol's "half the price" framing quietly stops being true exactly when you need it most.

Benchmarks: Opus 5.5 vs Astra, not vs Sol

Here's where the comparison gets confusing if you're skimming headlines: Anthropic's published benchmark table pits Opus 5.5 against GPT-6 Astra — OpenAI's flagship, not the cheaper Sol/Luna tier. On that comparison, Opus 5.5 leads on most of the categories that matter for agentic coding: 66.4% on Terminal-Bench 4.0 against Astra's 57.9%, 54.4% on FrontierCode v1.1 against Astra's 53.3%, and a GDPval-AA v2.1 Elo of 1846 against Astra's 1542. Opus 5.5 also leads on Humanity's Last Exam with tools (67.7% vs 57.2%) and on OSWorld 2.0 computer-use testing.

Astra isn't blanked, though. It beats Opus 5.5 on AutomationBench, a Zapier-run business-workflow benchmark, and on Terminal-Bench-Science, a test of agentic scientific research, where Astra scores 64.6% against Opus 5.5's 58.7%. So the honest summary is: Opus 5.5 is the stronger general coding agent, Astra holds an edge in specific workflow- and research-automation niches — and Sol/Luna haven't been benchmarked head-to-head against either on the same tables yet, because OpenAI is selling them on cost, not top-line capability.

Where it gets interesting: cost per task, not cost per token

This is the number that should actually change how you pick a model. Anthropic claims Opus 5.5 costs roughly 20% of Astra's task cost on FrontierCode at default settings, and can match Astra's Terminal-Bench score using about 40% of the token spend. A model with double the sticker price can still come out cheaper per completed task if it needs fewer attempts, shorter repair loops, and fewer tokens to reach a working answer.

Early independent testing of Sol specifically backs up a version of this trade-off: testers found Opus 5.5 ahead on completion quality and speed, while Sol cost roughly a third of what Opus 5.5 cost on identical DevOps tasks. Neither of those facts contradicts the other — they're describing different things. Sol is cheap and good enough for a large share of routine work. Opus 5.5 is pricier per token but wastes less of that price on dead ends.

There's also a caching effect that most "X% cheaper" headlines ignore entirely. Agent loops resend the same system prompt, tool schema, and repo context on nearly every turn — which means the bulk of input tokens in a real coding-agent session are cache hits, not fresh input. Sol's 50%-off input price looks dramatic in a spec sheet and much smaller once you account for how little of an agent's traffic is actually billed at the full uncached rate.

What this actually means for picking a model

A reasonable split, based on what's been reported so far:

• GPT-6 Luna — high-volume, low-stakes work: boilerplate, formatting, simple test generation, where an occasional bad output is cheap to catch and retry.

• GPT-6 Sol — the default for routine implementation, repetitive edits, and well-scoped coding tasks where the context stays well under that 272K-token cliff.

• Claude Opus 5.5 — repo-wide refactors, ambiguous debugging, architecture decisions, and autonomous or long-running jobs where a failed run burns more in wasted engineering time than any per-token discount could offset. Anthropic's own example: a 680,000-line code migration completed in under a day, and 39 out of 40 passes on software load-time optimization tests.

None of this is a verdict in either direction — it's barely three weeks of real-world usage since both families shipped. But it's a useful corrective to the instinct to rank coding models by their rate card. For an agent that runs unattended for hours and burns tokens on every retry, the number worth tracking isn't dollars per million tokens. It's dollars per finished, correct task — and on the numbers published so far, that ranking doesn't match the one on the pricing page.

Sources

• Anthropic Releases Claude Opus 5.5, Beats GPT-6 Astra On Most Benchmarks (OfficeChai)

• Anthropic Releases Claude Opus 5.5 with 40% Lower Costs and Faster Performance (Digg)

• Claude Opus 5.5 released: features & benchmarks (ComputingForGeeks)

• GPT-6 Sol vs Claude Opus 5.5: Coding, Pricing, and AI Agents (TechRepublic)

• GPT-6 Sol and Claude Opus 5.5 Make Agent Cost Harder—and More Useful—to Measure (NXCode)

• GPT-6 Sol/Luna released: features & benchmarks (ComputingForGeeks)

• OpenAI GPT-6: Neue Modelle Sol und Luna mit massiven Preiskürzungen (BornCity)

• Claude Opus 5.5 vs GPT-6 Astra: The Frontier Model Showdown (Vellum)