GPT-5.6 vs Claude Opus 4.8: A Split Decision (July 2026)

Most comparison pages treat GPT-5.6 as one model and crown a single winner. Neither is true. GPT-5.6 ships as three tiers (Sol, Terra, Luna) and the real question is which of the four models — including Claude Opus 4.8 — fits which task. Here is the split decision, with the output-token math the spec tables never bother to compute.

The 30-second verdict

Both models are real, verifiable July-2026 releases. Claude Opus 4.8 is Anthropic hybrid reasoning model, described as built for serious coding and AI agents. GPT-5.6 is not a single model but a three-tier family — Sol, Terra, and Luna — that OpenAI moved to general availability on July 9 2026 across ChatGPT, Codex, the API, and GitHub Copilot after a limited preview that opened June 26.

The honest answer is a split decision, not a coronation:

• Opus 4.8 wins hard software engineering. It scores 69.2% on SWE-bench Pro versus 64.6% for the strongest GPT-5.6 tier, and it is the strongest computer-use and browser agent at around 84% on Online-Mind2Web.
• GPT-5.6 Sol wins frontier agentic reasoning. Sol leads OpenAI cited Agents Last Exam eval at 53.6.
• Terra and Luna win on price. They deliver near-frontier coding scores at a fraction of Opus or Sol cost.

If you have only ever thought of this as "GPT vs Claude," start with our broader Claude vs ChatGPT overview, then come back for the model-level detail below.

Pricing: output is the number that matters

Agentic coding loops are output-heavy — the model writes far more tokens than it reads once tools, plans, and diffs start flowing. That makes the output price, not the headline input price, the number that actually drives your bill. Here is the verified July-2026 picture, side by side.

The quiet surprise: Opus 4.8 output ($25/1M) is cheaper than GPT-5.6 Sol output ($30/1M) — and Opus beats Sol on SWE-bench Pro. So on the hardest coding work, the higher-scoring model is also the cheaper one per output token. Context and max-output are a wash: all four offer a 1M-token window and 128,000 max output tokens. GPT-5.6 also carries a February 16 2026 knowledge cutoff across every tier.

One caution before you copy any of these figures elsewhere: several aggregator pages carry hallucinated GPT-5.6 specs — one listed tiers at $4/$16, $8/$32, and $14/$56 with mismatched 512K and 1.5M context sizes. Those contradict the primary numbers from OpenAI, Simon Willison, and MarkTechPost. Trust only the table above.

Benchmarks: coding vs agents

On the coding number that is most consistently sourced — SWE-bench Pro, which measures real repository diffs — Opus 4.8 leads the active leaderboard. It climbed to 69.2% from 64.3% on Opus 4.7; if you want that generational jump in detail, see Claude Opus 4.8 vs 4.7. All three GPT-5.6 tiers cluster just below it.

Flip to open-ended agentic reasoning and the ranking flips too. GPT-5.6 Sol leads OpenAI cited Agents Last Exam at 53.6, and the family introduces Programmatic Tool Calling — the model writes JavaScript that runs in an isolated V8 runtime with no network access, exposed in the Responses API. On terminal automation, GPT-5.6 is strong across the board (Terminal-Bench 2.1: Sol 88.8, Terra 87.4, Luna 84.7). Treat every one of these as vendor-reported and eval-specific — they are snapshots, not laws, and these leaderboards move within weeks.

Opus 4.8 SWE-bench Verified is reported inconsistently across leaderboards (figures range from the low-80s to about 88.6%), which is exactly why we lead with the more stable SWE-bench Pro number instead.

Sol vs Terra vs Luna

Collapsing GPT-5.6 into one row is the mistake most comparisons make. The tiers are built for different jobs:

• Sol — flagship frontier reasoning for complex agentic and scientific work. Coding Agent Index v1.1: 80.
• Terra — balanced mid-tier for production, at $2.50/$15. Coding Agent Index: 77.4.
• Luna — lightweight, high-throughput for classification, routing, and volume, at $1/$6. Coding Agent Index: 74.6.

OpenAI pitches Terra and Luna as outperforming a rival at roughly one-sixteenth the cost. On cache economics, aggregator sources report cache writes billing at 1.25x uncached input with cached reads getting a 90% discount — useful if true, but treat it as unconfirmed until you see it on OpenAI own pricing page. If you are weighing whether to move up from the last generation, our GPT-5.6 vs GPT-5.5 and GPT-5.6 launch breakdown cover the delta; the older GPT-5.5 model page has its specs.

A computed cost example

Spec tables never do the arithmetic, so here it is. Assume a realistic agentic coding session — 8K input and 2K output tokens per turn — run for 1,000 turns (8M input, 2M output total):

Two facts jump out. First, for the hardest work, Opus 4.8 is both the highest-scoring and the cheapest of the two frontier options — $90 versus $100 for Sol, while scoring higher on SWE-bench Pro. Second, Luna does the same 1,000 turns for $20, roughly a fifth of Opus, at a SWE-bench Pro score only ~6.5 points lower. That gap is the whole argument for routing: pay Opus prices only where the extra points earn their keep.

Which one for which task

The frame that beats a single-winner verdict:

• Hardest agentic coding, browser/computer use, real repo diffs → Claude Opus 4.8. Highest SWE-bench Pro, top computer-use score, and cheaper output than Sol.
• Frontier open-ended agentic reasoning, science-flavored tool loops → GPT-5.6 Sol, especially if Programmatic Tool Calling fits your stack.
• Production workloads at scale → GPT-5.6 Terra — near-frontier coding at half the cost.
• High-volume classification, routing, batch → GPT-5.6 Luna — $20 per 1,000 turns in the example above.

One hedge worth stating plainly: some sources reference a higher Anthropic model above Opus 4.8, so do not read "Opus 4.8 leads SWE-bench Pro" as "the top of Anthropic entire lineup." Within the four models this comparison is about, the routing table above holds. And because DataLLM Lab keeps all of them on one key, you do not have to pick once — you pick per request.

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