GPT-6 Sol vs Claude Opus 5.5: Benchmarks, Pricing, and Which to Use

The attention to Anthropic's new model lasted only for about an hour in the race for more efficient and affordable frontier models. 

Their Claude Opus 5.5 is the first model in its Claude 5.5 family, pitched as Fable 5.1-level performance at a lower running cost than Opus 5.

OpenAI answered very quickly, publishing GPT-6 Sol as the mid-tier of the GPT-6 family, trained with the same methods as the flagship GPT-6 Astra and launched at half the API price of its predecessor, GPT-5.6 Sol.

In this article, I'll compare GPT-6 Sol and Claude Opus 5.5 across many performance categories, guardrails, and pricing, and I'll include one hands-on build test I ran on both.

TL;DR

• Claude Opus 5.5 publishes the stronger agentic-coding benchmarks and leads both of the benchmarks the two models actually share.
• GPT-6 Sol costs half as much at list price, but the gap narrows on cache-heavy agent loops and on very long requests.
• In our build test, both got the hard logic right, and Sol shipped the more polished game.
• Choose Opus 5.5 for long-running agentic coding or for a deployment that has to run on all three major clouds.
• Choose Sol for cost-sensitive volume, for Codex and ChatGPT Work teams, and for business-app automation on a budget.

What Is GPT-6 Sol?

GPT-6 Sol is OpenAI's mid-tier GPT-6 model, released on September 22, 2026, alongside GPT-6 Luna and positioned below the flagship GPT-6 Astra.

Its selling point is Astra's training recipe at half of GPT-5.6 Sol's API price, with prompt caching that survives mid-conversation changes to reasoning effort or tools.

Our GPT-6 Sol and Luna guide covers the launch, and our GPT-6 Astra API tutorial walks through the family's async tools and steering.

What Is Claude Opus 5.5?

Claude Opus 5.5 is Anthropic's first Claude 5.5 model, released on September 22, 2026, as the company's new leading model for long-running agentic coding and knowledge work.

Its selling point is Fable-class results at Opus prices: Anthropic says it matches Claude Fable 5.1 on most work, and it ships with Fable-class cybersecurity and biology safeguards.

Our Opus 5.5 guide covers the launch, and our Claude Opus 5 API tutorial covers the previous generation's API.

GPT-6 Sol vs Claude Opus 5.5: Head-to-Head Comparison

Because of the narrow release timings, no vendor put the other's new model in its launch table, so the two models overlap on only two benchmarks, and Opus 5.5 leads both. Sol's case rests on price and on how it held up in our own test.

Coding and agentic workflows

Opus 5.5 publishes the stronger agentic-coding numbers, and Sol's post does not directly contest them. Anthropic reports Opus 5.5 on Terminal-Bench 4.0, FrontierCode, and CursorBench; OpenAI reports Sol on DeepSWE and describes its FrontierCode result only as matching Claude Fable 5.1 at xhigh effort. Anthropic puts Fable 5.1 at 50.3% on FrontierCode against 54.4% for Opus 5.5, so if both claims hold, Sol sits about four points behind Opus 5.5 on the one coding benchmark both vendors mention.

Both vendors lean on long-horizon anecdotes rather than shared scores. Anthropic's internal HAProxy rewrite from C to Rust had Opus 5.5 finish in 9.5 hours against 12 for Fable 5.1 at 51% lower cost. OpenAI's argument is cost per task: Sol lands within about a point of Fable 5's DeepSWE score at roughly 80% lower cost.

Two reasons to take these tables with salt:

• Anthropic ran Opus 5.5 with production safeguards on, and says rerouted cybersecurity and biology tasks likely lowered its scores.
• OpenAI's cost-per-task comparisons pair Sol at xhigh or max against Claude models at different effort levels.

On paper, Opus 5.5 is the stronger coding model; our single build test below is where that gap failed to show.

Business workflows and computer use

Opus 5.5 leads both benchmarks the two models share, and the AutomationBench gap is the cleanest number in this article: 40.0% for Opus 5.5 against 33.2% for Sol on Zapier's test of agents working across 47 business tools.

Sol's counter is the bill: OpenAI frames Sol as beating Claude Opus 5 at 9% of Opus 5's cost per task, though that comparison is with the previous Opus, not this one.

Opus 5.5 is the stronger agent on score; Sol is the one you can afford to run on every ticket.

Knowledge work and factual reliability

Opus 5.5 holds the only cross-vendor number here: 1846 Elo on GDPval-AA v2.1, ahead of the GPT-6 Astra and GPT-5.6 Sol scores in Anthropic's table; Sol itself is not listed. In Anthropic's report-writing test, where any invented figure fails the run, 16 of 18 Opus 5.5 reports passed, and neither Fable 5.1 nor Opus 5 passed once.

Sol's evidence is against its own predecessor: on OpenAI's internal factuality set, Sol makes about half as many mistakes as GPT-5.6 Sol. Artificial Analysis's AA-Omniscience test agrees, with a catch: Sol's hallucination rate fell from 92% to 60% while it answered only 83% of questions against its predecessor's 99%.

Both are more careful than their predecessors; only Opus 5.5 has shown it against a rival.

Guardrails and API constraints

Sol is the less constrained API, and Opus 5.5 is the more supervised one.

Opus 5.5 cannot run with thinking switched off, rejects forced tool use, and binds thinking blocks to the model and conversation that produced them, so edited context fails with a 400 error on accounts created after August 31, 2026. Most cybersecurity tasks are rerouted to Opus 4.8 outside its Cyber Verification Program, and biology work needs its Life Sciences Verification Program.

Sol's ladder runs from none to max, effort can change mid-conversation without breaking the cache, and OpenAI reports fewer misleading claims about its coding work than GPT-5.6 Sol.

The trade is deliberate: Opus 5.5 attempted to cross containment boundaries around 85% less often than Opus 5, which matters for unattended agents.

Pricing: what you actually pay

Opus 5.5 costs exactly twice Sol on the headline rates, but the two mechanisms that agents actually pay for break the pattern: cache reads cost the same, and Sol adds a surcharge above 272K tokens that Anthropic does not.

Token rates side by side

The premium is flat across input, output, and cache writes, so it hurts every workload shape equally until caching or long context enters the picture. Cached reads are the exception, and Anthropic says they make up most of the cost of agentic and coding work. Neither vendor publishes a separate rate for reasoning tokens, so budget them at the output rate.

What a real workload costs

The over-threshold row is the headline: once every request crosses the threshold, Sol's surcharge lifts its bill to within 8% of Opus 5.5, so a long-context retrieval pipeline gets almost no discount for picking Sol. The cache-heavy loop narrows the gap to 33% for a different reason: the 1,000 cached reads cost the same on both models, and only the fresh tokens carry the 2x premium.

The two models use different tokenizers, and neither vendor has published token counts for a shared workload, so treat these totals as a rate comparison rather than a bill. Anthropic does say Opus 5.5 uses fewer tokens per task than Opus 5, though not how it compares with Sol.

How GPT-6 Sol and Claude Opus 5.5 Performed

Benchmarks from two vendors that never tested each other's model only go so far, so I ran one build task against both with an identical prompt and identical tooling.

The test

I asked both to build a single-file Tetris clone on a 12×24 board with one twist: gravity flips every 20 seconds. Two things the prompt demands make it hard: after a flip, pieces have to fall toward the ceiling and lock onto a second stack there, with full rows clearing on either stack, and cells already locked must never move.

Both models ran inside OpenCode with an identical pinned tool surface: file reading, searching, and editing only, no shell and no network. Both ran at high reasoning effort, one attempt each, no retries, and the prompt was delivered byte-for-byte to a fresh session.

One asymmetry to keep in mind: high sits two rungs below the ceiling on both ladders, but Sol's ladder has an extra none rung at the bottom, so high is its fourth of six settings and Opus 5.5's third of five.

This was the prompt:

Build a single-file HTML page (inline CSS and JS, canvas, no build step, no external dependencies, no network) that is a playable Tetris clone with one twist: gravity flips every 20 seconds. It needs:
- a 12×24 board, the seven tetrominoes, rotation, left/right movement, soft drop, hard drop, line clears, a score, and a game-over state, controlled with the arrow keys and the space bar;
- the twist: every 20 seconds the direction of gravity flips between down and up. Pieces then fall toward the ceiling, land on the stack that has formed there, and full rows clear on either stack. Pieces already locked in place never move when gravity flips;
- a visible gravity indicator (an arrow showing the current direction) and a countdown to the next flip, both always on screen;
- a fixed starting position: the board loads with the same flat four-row stack already locked on the floor (a few gaps, no complete rows), so the flip has a stack to leave behind;
- a fixed seed for the piece sequence, so two loads produce the same game;
- autoplay on load: with no key pressed, a simple built-in player plays at the normal starting speed of about one row per second, sliding each piece without rotating into the column where the stack it is falling toward is currently lowest (the leftmost such column on a tie) and letting it fall — never a hard drop — so pieces are seen falling the moment the page opens. Any key press hands control to the keyboard for the rest of the game;
- the whole board, indicator and countdown fit in one viewport of about 1440×900 without scrolling.

Ship it as one working file named `index.html`. Do not install packages. Do not open, screenshot, or headless-render the page (no Playwright, Puppeteer, or Chrome). Do not ask me clarifying questions — make reasonable assumptions and note them briefly in a comment at the top of the file.

I recorded the cost and graded both on the following three rubrics, with scores from 1 to 5:

• Flip mechanics: When gravity flips, do pieces really fall up, land on the ceiling, and clear there, while the old stack stays put?
• Game completeness: Is it a complete, playable Tetris underneath the twist?
• Visual quality and feel: Is it smooth and readable, and does the flip look deliberate rather than glitchy?

What GPT-6 Sol produced

Sol took 6 turns and 9 tool calls: it listed, searched, and read the empty workspace before writing a 22 KB index.html, then patched it once more. What came out has its own branding, "Fall / Rise, a game of shifting ground", with a gravity panel that recolours from amber to teal at the flip, a countdown to the next inversion, and a rule card in the margin.

It does what it should and it works smoothly and stably: the floor stack stays put at the flip, pieces rise and lock on the ceiling, rows clear on both stacks, and the soft drop feels responsive, which made it the more fun of the two to play.