GPT-5.6 Sol vs Fable 5

Benchmark head-to-head

Only one evaluator scores both of these models under a single published methodology: Artificial Analysis, whose Intelligence Index v4.1 runs the same nine evaluations (GDPval-AA v2, τ³-Banking, Terminal-Bench v2.1, SciCode, Humanity's Last Exam, GPQA Diamond, CritPt, AA-Omniscience, AA-LCR) against every model on its board, then reports a Coding Index and an Agentic Index alongside. Those three are the rows below — GPT-5.6 Sol and Fable 5 as captured in our 2026-07-21 snapshot, with both Intelligence Index figures re-confirmed live on the Artificial Analysis model pages (Sol at 59, Fable at 60, matching the snapshot's 58.9 and 59.9 within rounding). We do not pair OpenAI's own headline numbers against Anthropic's, because the two are produced on different harnesses and are not directly comparable.

Artificial Analysis reports these for the max-effort configuration of each model, and the Intelligence Index is a rolled-up composite — a single number over nine evaluations — so it hides where within the suite each model pulls ahead or falls back. Read the three indices as a directional cross-vendor snapshot, not a task-by-task verdict. The margins here are especially tight — none exceeds 1.2 points — so treat this as a near-tie in which each model owns a different index, not a ranking. Both are reasoning models scored with thinking enabled.

Two things are deliberately left off. First, we do not pair vendor suites: OpenAI publishes its own figures for the GPT-5.6 line and Anthropic reports Fable 5 as state-of-the-art on nearly all of its own software-engineering and knowledge-work evals — but those runs use different harnesses, effort settings and scaffolds, so setting them side by side would manufacture a precision that isn't there. Second, there is no LLM Index Score row: our own LiveBench-based composite covers Fable 5 (79.82) but GPT-5.6 Sol is absent from the snapshot we score, so a like-for-like native number does not exist for this pair — we show what a single source produced for both, and nothing invented to fill the gap.

The two models, side by side

What the gap costs

Take a steady load — 10M input and 2M output tokens a day, the shape of a sustained agentic-coding workload — against each model's first-party API price:

Prices are OpenAI's own API rate for GPT-5.6 Sol ($5 / $30) and Anthropic's for Fable 5 ($10 / $50) — double on input, two-thirds more on output. The token counts assume an identical split on both, a simplification: the two use different tokenizers, so the same text splits into different numbers of tokens on each and the real gap moves a few percent either way. Both discount cache hits — Sol to $0.50 per 1M input, Fable 5 to $1. One asymmetry runs the other way: Sol prompts above 272K input tokens move to a 2x input / 1.5x output tier, while Fable 5 carries no long-context surcharge — so on very long-context work the price gap narrows.

Which should you pick?

Before you commit

• The margins are tiny — Fable 5 leads the Intelligence Index by 1.0 point, Sol leads Coding by 0.9 and Agentic by 1.2 — and each is a rolled-up composite. On this evidence neither model is decisively stronger; a task that leans hard on one specific capability can diverge from what the indices imply.
• The two use different tokenizers, so cost and context comparisons are approximate: '1M tokens' of your text is not the same amount of text on each model.
• Neither ships open weights — both are proprietary and hosted-only, so neither is an option if you need to self-host, run offline, or pin a frozen version. The usual open-vs-closed axis does not apply here; this is closed-vs-closed.
• Fable 5 ships a safety layer that routes some sensitive cybersecurity and biology queries to Opus 4.8 instead — on average under 5% of sessions (Artificial Analysis lists the model as 'Opus 4.8 Fallback'). For most workloads it is invisible, but it means a small slice of Fable traffic is answered by a different model.
• Sol and Fable 5 are built by different labs — SDKs, tool interfaces, Realtime/Batch surfaces, data-handling terms and regional availability differ, and those often decide the pick more than a sub-1.5-point index gap.

The full read

Every earlier comparison on this site has had a clean axis: open weights against closed, a cost-balanced tier against the frontier, two trims of one family. This one has none of them. GPT-5.6 Sol and Fable 5 are both closed, both hosted-only, both fully multimodal, both carry a roughly 1M-token window, and both are reasoning models — Sol the top of OpenAI's GPT-5.6 line above Terra and Luna, Fable 5 the safe-for-general-use member of Anthropic's Mythos class, a step above the Opus line. Strip away the labels and you are comparing two vendors' best commercial models, priced a factor apart, that happen to land within a point of each other.

On the measurable side the result is a split, not a ranking. Artificial Analysis — the only evaluator that runs both under one methodology — gives Fable 5 the overall Intelligence Index by 59.9 to 58.9, a single point on the broad knowledge-and-reasoning aggregate. But GPT-5.6 Sol takes the other two: the Coding Index 77.4 to 76.5 and the Agentic Index 54 to 52.8. So the pricier model wins the headline composite and loses coding and agentic — the two indices most production work actually leans on. None of the three margins clears 1.2 points; on this evidence the two are interchangeable on raw capability, each simply owning a different index.

Then price pulls the two apart, and it pulls in the cheaper model's favour. GPT-5.6 Sol lists at $5 / $30 per million tokens against Fable 5's $10 / $50 — double on input, two-thirds more on output. On a sustained 10M-input / 2M-output day that is $110 against $200, or $3,300 against $6,000 a month, for a model that already leads two of the three indices. This is the unusual case where paying more does not buy the scoreboard. What the premium does buy is real but narrow: Fable 5 owns the intelligence composite by a point, returns tokens faster (Artificial Analysis clocks it at 73 tokens per second to Sol's 65), and comes with the Anthropic stack — Claude Code, computer use, availability across Bedrock, Google Cloud and Microsoft Foundry — plus a safety layer that quietly routes a small slice of sensitive queries to Opus 4.8.

It is worth being explicit about why the benchmark table stops at the three Artificial Analysis indices. OpenAI publishes its own figures for the GPT-5.6 line, and Anthropic reports Fable 5 as state-of-the-art on nearly all of its own software-engineering and knowledge-work evaluations. Both can look convincing in isolation, but they come off different harnesses, effort settings and scaffolds, so setting one lab's self-run numbers beside the other's would manufacture a precision the underlying runs do not support. The one place both are measured identically — the Artificial Analysis Coding and Agentic indices — is where Sol's edge actually shows up, and it is the honest version of the story.

The practical rule follows from a near-tie rather than a winner. Default to GPT-5.6 Sol: it is half the price and wins the coding and agentic indices, which covers most of what teams ship. Escalate to Fable 5 when the one-point intelligence-composite edge, the faster output at max effort, or the Anthropic ecosystem and its safety routing are worth paying double for — or, as often as not, simply because you already live inside one vendor's SDK, data terms and support relationship. These are vendor-reported and third-party figures, not our own hands-on runs, so read them as a well-sourced starting point and confirm on the workload you actually have; with margins this thin, your own traffic will settle it faster than any index.

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