Claude Fable 5.1 vs GPT-5.6: Depth Meets Speed
We ran both frontier models on the same long-horizon coding, research, and knowledge tasks for a month — the results are more different than the benchmark charts suggest.
Quick Verdict
Claude Fable 5.1 is the depth specialist: better at root-cause fixes, long agentic sessions, and work that runs for hours — and its 75% cheaper cache reads make those sessions cheaper than the sticker price implies. GPT-5.6 Sol is the breadth-and-speed pick: cheaper per token, faster to a first answer, and stronger across images, voice, and multimodal input. Scores: Fable 5.1 9.2, GPT-5.6 9.0 — a close race decided by where your work lives.
Our original scores and the full 14-parameter table below back this up: Fable 5.1 won long-context depth and agentic stamina; GPT-5.6 took breadth, speed, and per-task economics.
The Verdict at a Glance
Choose Claude Fable 5.1 — the deep-work engine. Better codebase-wide reasoning, longer autonomous sessions, and fewer confident wrong answers on ambiguous work. The pick for engineers, researchers, and knowledge workers doing day-long tasks.
Choose GPT-5.6 Sol — the fast, broad everything-model. Images, voice, cheaper per token, and faster first drafts. The pick for mixed workflows, high throughput, and anyone who wants one model for everything.
Our scores: Fable 5.1 9.2/10 · GPT-5.6 9.0/10 — Fable 5.1 leads on depth and reliability; GPT-5.6 leads on breadth, speed, and cost.
Side-by-Side: 14 Parameters
| Parameter | Claude Fable 5.1 | GPT-5.6 Sol |
|---|---|---|
| Our Score | 9.2/10 | 9.0/10 |
| Input price (per MTok) | $10 | $4 |
| Output price (per MTok) | $50 | $20 |
| Cache reads (per MTok) | $0.25 | $0.40 |
| Context window | 1M (default & max) | 1.05M tokens |
| Max output | 128K tokens | 128K tokens |
| Reasoning controls | Adaptive thinking; per-message effort | effort: none–max (6 levels) |
| Coding | 9.5 — best root-cause repair, long refactors | 9.2 — faster first drafts |
| Knowledge work (docs/sheets/slides) | 9.4 — finish a full document from a blank page | 8.8 — strong but shorter comfort zone |
| Research (multi-step) | 9.3 — follows up on findings | 9.0 — broad and fast |
| Multimodal (images) | 8.8 — chart/PDF reading via crop-and-zoom | 9.2 — native image generation |
| Voice & breadth | Limited | Full advanced voice |
| Computer use | 9.2 — recovers from failed steps | 9.0 — reliable but stops quicker |
| Best for | Deep work: hours-long autonomous tasks | Mixed, high-throughput work |
Score Breakdown: Our 5 Dimensions
| Dimension | Fable 5.1 | GPT-5.6 | Winner |
|---|---|---|---|
| Quality of Output | 9.5 | 9.0 | Fable 5.1 — deeper, more reliable reasoning |
| Ease of Use | 8.8 | 9.0 | GPT-5.6 — faster responses, simpler setup |
| Value for Money | 8.9 | 9.3 | GPT-5.6 — far cheaper per token |
| Speed & Reliability | 8.9 | 9.1 | GPT-5.6 — faster first answers |
| Support & Docs | 8.8 | 9.0 | GPT-5.6 — larger ecosystem |
| Overall | 9.2 | 9.0 | Fable 5.1, on depth |
Benchmark Reference Data
Public evaluation scores for both models, side by side, with source attribution. Scores are shown as bars relative to 100; n/r = not reported on the same public leaderboard.
| Benchmark | Fable 5.1 | GPT-5.6 Sol | Source |
|---|---|---|---|
| SWE-bench Verified | 95.0% | n/r | [Official System Card, Anthropic] |
| SWE-bench Pro | 80.0% | 64.6% | [Independent] |
| GPQA Diamond | 92.6% | 94.6% | [Official, both vendors] |
| Terminal-Bench | 55.8% | 88.8% | [Official, both vendors] |
| LiveCodeBench | 90.52% | n/r | [Official System Card, Anthropic] |
| FrontierMath Tier 4 | n/r | 83.0% | [Independent (BenchLM)] |
| Artificial Analysis Index (max) | 59.9 | 58.9 | [Independent (AA)] |
n/r = not reported on the same public leaderboard. Benchmarks are official-vendor or third-party as noted; scores reflect each vendor's configuration and may not be directly comparable (e.g. Terminal-Bench versions differ: Fable 5.1 v4.0, GPT-5.6 Sol v2.1). Bars are visual, relative to 100.
Claude Fable 5.1 wins the long-horizon deep work
On a full-codebase refactor and a multi-day agent session, Fable 5.1 stayed coherent where GPT-5.6 started to slip. It avoids the easy shortcut of disabling a failing test, fixes root causes rather than symptoms, and says so when it is stuck instead of reporting false success — the qualities that matter when a task runs for hours and you cannot babysit every step.
GPT-5.6 wins breadth, speed, and price
GPT-5.6 Sol is cheaper per token, faster to a first draft, generates images natively, and sits in the largest ecosystem of plugins and custom agents. For a mixed week — a doc here, an image there, a quick script — one subscription covers everything, and the per-task economics are dramatically better. Its memory and speed make it the better everyday default.
Both are agentic now — the difference is stamina
Fable 5.1 and GPT-5.6 both ship working agent modes. Fable 5.1 runs deeper and quieter over longer sessions; GPT-5.6 moves faster and gives you more for the dollar. Enterprise buyers got real governance layers on both this quarter (Covered Model rules for Fable 5.1; Fast mode and lowered Luna/Terra pricing for GPT-5.6).
Pricing, Side by Side
| Model | Input | Output | Cache read |
|---|---|---|---|
| Claude Fable 5.1 | $10 / MTok | $50 / MTok | $0.25 / MTok |
| GPT-5.6 Sol | $4 / MTok | $20 / MTok | $0.40 / MTok |
| GPT-5.6 Terra | $2 / MTok | $12 / MTok | $0.20 / MTok |
| GPT-5.6 Luna | $0.20 / MTok | $1.20 / MTok | $0.02 / MTok |
Which Should You Choose?
Reliability Over a Long Workweek
Scoring single answers is easy; the harder question is how each behaves across a real working week. We tracked both through two weeks of daily professional tasks — long coding sessions, research, document and spreadsheet work — and logged failures, retries, and time-to-good-answer.
GPT-5.6 completed 88% of assigned tasks without a retry; Fable 5.1 managed 86% but its failures were softer — more often a conservative refusal or an honest "I'm stuck" than a confident wrong answer. Time-to-good-answer favored GPT-5.6 on short prompts and Fable 5.1 on long ones, where its 1M context handled the whole session in one place.
Related on StackHK
How we test: Same codebase, same prompts, 30 days (August 1 – September 1, 2026) on paid tiers. Retested at least twice a year as models ship. StackHK retests major comparisons at least once a year; prices and plans are as of September 2026. Affiliate disclosure →