The matchup is no longer hypothetical
Until this week, comparing GPT-5.6 to Claude meant guessing. Now OpenAI has previewed the GPT-5.6 family — Sol, Terra, and Luna — with published pricing and its own benchmark chart, so the comparison is real. And OpenAI drew the line itself: on Terminal-Bench 2.1, its agentic-coding evaluation, it placed Sol directly against Claude's Mythos-class model. That is the fight this article breaks down, focused on the two configurations that define Claude's top tier: Fable 5, the Mythos-class ceiling at $10/$50 per 1M tokens, and Opus 4.8 run at max effort, its deepest-reasoning setting at $5/$25. The short version is that the winner depends on which question you ask — benchmark, per-token price, or real cost per task — and those three questions give three different answers. You can pull the live Claude rates any time on the price comparison tool.
The benchmark OpenAI published: Terminal-Bench 2.1
OpenAI led its preview with agentic coding, and the numbers are specific. On Terminal-Bench 2.1, GPT-5.6 Sol in its high-effort Ultra mode scores 91.9%, base Sol scores 88.8%, and both Claude's Mythos-class model and GPT-5.5 sit at 88.0%. Read plainly: base Sol edges the Mythos-class model by 0.8 points, and Sol Ultra opens a 3.9-point lead — a margin OpenAI characterizes as meaningful on this benchmark. One honest caveat belongs up front. OpenAI's chart labels the Anthropic entry as Claude Mythos 5; on TokenRate the Mythos-class Claude model is Fable 5, and OpenAI did not include a Terminal-Bench 2.1 score for Opus 4.8 at all, which is why its cell in the table above is blank rather than a number. So the cleanest factual statement is this: on OpenAI's own agentic-coding chart, GPT-5.6 Sol leads the Mythos-class Claude model, narrowly at base and clearly in Ultra mode. As always, a vendor's chosen benchmark is a starting point — the score that decides your stack is the one you measure on your own prompts.
Opus 4.8 on max effort: the quality-per-dollar bar
The word max matters, because Opus 4.8 ships with an Effort Control dial whose default is already high effort. At max effort the model reasons harder and writes longer, holding its 5× output multiplier and making the $25 output price hit more often — blending to roughly $21 per 1M tokens at a 1:4 agent ratio. In return you get deeper reasoning chains and fewer wrong turns on long tasks. OpenAI did not publish an Opus 4.8 Terminal-Bench 2.1 number, so we cannot line it up head-to-head on that chart, but on TokenRate's blended quality index Opus 4.8 has been the top-tier quality-per-dollar reasoning model: a strong score at a 5× multiplier and a $25 output price that undercuts GPT-5.6 Sol's $30. That is the bar Sol has to clear on cost, not just on capability. For the full breakdown of how effort settings reshape the bill, see our Effort Control deep-dive, and model your own effort-versus-cost trade-off on the API cost estimator.
Fable 5: the Mythos-class ceiling
Fable 5 sits above the Sonnet and Opus stack as Anthropic's Mythos-class flagship, at $10/$50 per 1M tokens — the ceiling for frontier reasoning and the longest-horizon agents, where capability outranks the bill. It is almost certainly the model OpenAI's Terminal-Bench 2.1 chart refers to as Claude Mythos 5 at 88.0%, though the naming differs and Anthropic has not published a single directly comparable head-to-head number of its own. The framing tells the story: at double Opus 4.8's input price, Fable 5 is not the value pick — it is the model you reach for when you would rather pay 5× than risk a miss on the hardest problems. Against that ceiling, GPT-5.6 Sol Ultra's 91.9% is OpenAI's claim to the frontier crown on agentic coding. Whether that claim survives contact with real, non-benchmark workloads is exactly what the preview period is for.
The pricing reality: Sol versus Claude
On sticker economics, the picture is clear and it does not favor Sol. GPT-5.6 Sol keeps GPT-5.5's exact rate — $5 input, $30 output, a 6× multiplier. Opus 4.8 at max effort is $5 input, $25 output, a 5× multiplier. Same input price, cheaper output, lower multiplier: per output token, Opus 4.8 is the more economical way to buy top-tier reasoning, just as it was against GPT-5.5. Fable 5 is more expensive than both at $10/$50, by design. So if you stare only at the rate card, Sol is the priciest of the three per output token, and a developer who benchmarks on input price alone will badly underestimate the gap — at a 6× multiplier, output dominates the bill. That is the conventional read, and for GPT-5.5 it was the end of the story. For Sol, it is not, because of one number OpenAI put next to the price.
The token-efficiency twist that could flip it
OpenAI's sharpest claim is not about price or benchmarks — it is about tokens. On ExploitBench², it reports that Sol reaches results competitive with the Mythos preview using only about one-third of the output tokens. This matters enormously, because your bill is token count multiplied by price, not price alone. A model that answers in a third of the output tokens can be cheaper per completed task even at a higher per-token rate. Work it through: Sol's $30 output at one-third the tokens implies an effective output spend near $10 of Opus-equivalent volume per task, which would undercut Opus 4.8's $25-at-full-verbosity — if the one-third figure generalizes beyond the benchmark. That is a large if, and it is the entire ballgame. It also cuts against Opus 4.8 at max effort specifically, because max effort pushes Opus toward more output tokens, not fewer. The takeaway is not that Sol is cheaper; it is that per-token price no longer settles the question. Cost per task does, and you cannot read cost per task off a rate card — you measure it. Put your real ratio and volumes into the side-by-side comparison and the cost estimator once you have Sol access.
How to decide for your workload
Three questions, three answers, and you need all three. First, capability: on OpenAI's published agentic-coding benchmark Sol leads, so if your product is code and you can use Ultra mode, Sol has a real claim — verify it on your own hardest tasks, not on Terminal-Bench. Second, per-token price: Opus 4.8 at max effort wins, at $25 output and a 5× multiplier versus Sol's $30 and 6×, so a verbose, high-output workload still favors Opus on the rate card. Third, cost per task: this is the tiebreaker, and it hinges on whether Sol's one-third-the-output-tokens efficiency holds for you. Run the same fixed set of your real prompts through Sol and through Opus 4.8 at the effort level you would actually deploy, count the output tokens each generates, and multiply by the respective prices. The model with the lower total is your answer — and it may differ by task type, which is an argument for routing rather than a single default. Fable 5 stays in reserve for the frontier problems where you want the ceiling regardless of cost.
The verdict
As of the GPT-5.6 preview, here is the honest scorecard. On OpenAI's own agentic-coding benchmark, GPT-5.6 Sol leads Claude's Mythos-class model — narrowly at base, clearly in Ultra mode. On per-output-token price, Opus 4.8 at max effort still wins, cheaper and at a lower multiplier. On real cost per task, it is genuinely undecided and depends on whether Sol's token-efficiency claim survives your workload, so the responsible move is to measure rather than assume. Fable 5 remains the ceiling for the hardest frontier work where capability outranks cost. If you want a single default today, Opus 4.8 at a tuned effort level is still the safest quality-per-dollar bet at the top tier; if your work is agentic coding and you can get Sol access, it is the first OpenAI flagship in a while with a benchmark lead worth testing. For the full model-side detail — tiers, Ultra mode, release timeline — see GPT-5.6 Sol, Terra and Luna: release date, pricing and benchmarks.