Claude Fable 5.1 (High) vs. Claude Sonnet 5.5 (High): Which Anthropic Model Should You Use?

For the most challenging coding, research, and physics problems, select Claude Fable 5.1 at high effort. Almost anything else should be handled at high effort with Claude Sonnet 5.5. On the Artificial Analysis Intelligence Index, Fable 5.1 beats the other models by 6 points, but it costs 4.6x more per task and is 1.8x slower at generating text. This Claude Fable 5.1 High vs Claude Sonnet 5.5 High comparison covers intelligence, pricing, speed, coding, agents, and research, so you can choose the model that fits your needs.

The numbers come from documentation from Artificial Analysis and Anthropic as of November 2026. Testers run benchmarks frequently, so check the source before spending money.

Two Anthropic Flagships, Two Different Bargains

The maximum capability is sold at Fable 5.1. Sonnet 5.5 offers that for much less. Anthropic's prices are $2 for Sonnet 5.5 and $10 for Fable 5.1 per million input tokens. According to the documentation, Fable 5.1 is rated “Slower” for latency, while Sonnet 5.5 is rated “Fast.”

Both models use adaptive thinking, and both are high effort in the API. The number of times the model reasons before it answers is determined by the effort. The more effort you use, the better the answers will be, but it also costs more and takes longer. For the adaptive thinking to be turned on and off, Fable 5.1 will be left set to stay on. If you want a closer look at what the smaller model costs, see our Claude Sonnet 5.5 pricing guide for its token rates, caching, and cost per task.

The table below shows the headline results from the Fable 5.1 high vs. Sonnet 5.5 high test.

Fable is the stronger thinker. Sonnet is the cheaper, quicker worker. Both can read the same amount of text at once.

Overall Intelligence: Fable Leads by 6 Points

Claude Fable 5.1 has an Artificial Analysis Intelligence Index score of 47 at high effort. Sonnet 5.5 scores 41 at high effort. That is a 15% difference in favor of Fable.

The index reflects the average results of nine tests covering agentic work, coding, knowledge, and reasoning. The higher the score, the more of those tasks the model gets right. These scores apply only to high effort. Other effort settings will yield different outcomes.

Not all leads are the same. One variant may outperform the other or vice versa. See below for each model's destination. If only one takeaway is required, the rule of thumb is: Fable has the higher ceiling; Sonnet is still a good performer for much less money.

Pricing: Sonnet Is 4.9x Cheaper per Token

Sonnet 5.5 has an input token price of $2/million and an output token price of $10/million. Fable 5.1 costs $10 and $50. Sonnet is one-fifth as expensive as Fable, according to Anthropic's list prices.

Artificial Analysis combines input, cached input and output prices into one number. Based on that, Sonnet 5.5 is $1.47 per million tokens, and Fable 5.1 is $7.18, which makes Sonnet 4.9 50% cheaper per token. The mix is slightly below the 5x list-price spread because cached input is cheap on both models.

A token is a small portion of text, about 3/4 of a word. Users pay for tokens sent and written back by the model. Cache reads are tokens the model has already seen and are therefore less expensive. The Batch API provides a 50% discount on both models for jobs that do not require an immediate response.

This is the crux of the Fable 5.1 vs Sonnet 5.5 price issue – Fable costs five times as much per token. For Fable’s own rates, caching, and batch discounts, see our Fable 5.1 pricing guide.

Cost per Task: Sonnet Is 4.6x Cheaper

The typical task of Intelligence Index is worth $1.64 on Sonnet 5.5 and $7.54 on Fable 5.1. This saves Sonnet $0.6 per copy in practice. Beyond token price, cost per task matters more because it reflects the tokens each model consumes.

The full Intelligence Index will cost $1,268 for Sonnet 5.5 and $6,618 for Fable 5.1. The difference increases as volume increases. At 1,000 tasks, you would spend about $1,640 on Sonnet and $7,540 on Fable. The difference is $5,900.

This accounts for AI agents repeating tasks thousands of times, and the gap adds up quickly. Those are the broken-down AI agent costs to help you estimate them pre-launch.

Token Use: Nearly Identical Workloads

Both models have a similar token-to-task ratio. The spread isn't because one model is written much more than the other; it comes from the rate card.

Number of output tokens: 66,000 (Fable 5.1). Sonnet 5.5 produces 58,000. The number of tokens used to think before answering is 34,000 for Fable and 32,000 for Sonnet. Fable is roughly 14% more output per task. Not much of a difference for a 5x price difference.

The practical outcome is straightforward. Closing the cost gap is not possible through prompt modification. Fable remains much more expensive because its tokens cost more.

Speed: Sonnet Generates 1.8x Faster

At high effort, Sonnet 5.5 produces 103 tokens per second. Fable 5.1 generates 57. Sonnet is 1.8 times faster after it begins writing.

Output speed is the rate at which text can be output after the first token is received. It takes approximately 10 seconds to get a 1,000-token answer from Sonnet and approximately 18 seconds from Fable. Sonnet averages 345.22 seconds across the full benchmark task, while Fable averages 699.52 seconds.

For long outputs, speed matters most. All the files, reports, and multi-step agent runs make the difference even bigger.

Latency: Sonnet Responds 1.3x Faster

In 10.95 seconds, Sonnet 5.5 returns the first token. Fable 5.1 takes 14.62 seconds. As a result, Sonnet runs 1.3 times faster in response time.

This wait will include thinking time at high effort. The End-to-End response time for a 500-token reply is 16.95 seconds on Sonnet and 25.91 seconds on Fable. Neither model is instant; both require significant effort. Background jobs are better than live chat, with an 11- to 15-second wait.

If you need quick responses, try lower-effort settings before selecting a model.

Coding and Terminal Work: Fable Pulls Ahead

Fable 5.1 performed better than Sonnet 5.5 on both coding benchmarks in the index. This is the clearest example of Fable 5.1's high-effort performance.

Terminal-Bench 4.0 is an agentic coding and command-line test. The model should be able to execute commands, read output, and automatically repair issues. In fact, Fable completes 8 points more of these tasks. FrontierCode 2.0: testing software engineering in open source repositories. Fable is 4 points ahead.

In simple terms, Fable will be more likely to do a difficult, multi-step coding task without assistance. Sonnet is still a good coding model and is less expensive per attempt. To put either model behind a repeatable coding task, a coding agent builder lets you set the review step and handoff rules yourself.

Agentic Knowledge Work: Sonnet Leads

Sonnet 5.5 outperforms Fable 5.1 on both agentic knowledge work tests. That's where Claude Sonnet 5.5's high-effort performance shines.

AA-Briefcase measures agentic office-style work: analysis and presentation work. Elo is a score; the higher the score, the better. AutomationBench-AA tests workflows across business software. Sonnet scores 4 points higher on that test.

The outcome will be helpful because these are common business jobs. A cheaper, faster winning agent is a great default. If you'll be creating one, get a head start by following our guide to creating AI agents from scratch.

Research, Science, and Knowledge Reliability: Fable Leads

Fable 5.1 is much more consistent with hard facts and scientific questions. It has an AA Omniscience score of 40, 19 times Sonnet's score of 19.

Scores are awarded for correct answers and deducted for incorrect answers in AA-Omniscience. Not answering has no consequences. A higher score indicates that the model has more knowledge and less invention. HLE-Diamond is a set of very hard expert-level questions. Fable is 16 points ahead. Fable leads in Terminal-Bench-Science tests, which test research workflows in a terminal.

This section is crucial if a wrong answer will cost you a lot of money. These include scientific research, technical analysis and fact-heavy reports.

Also Read: Claude Fable 5.1 benchmarks for the vendor-reported scores behind these figures.

Physics and Document Reasoning: Fable Leads

However, Fable 5.1 dominates physics and, to a small extent, professional document reasoning. The gap between Physics and Physics is large. The document gap is very limited.

CritPt v2 features physics reasoning tests on the most difficult questions. From there, Fable scores 13 points higher. GDP.pdf checks if a model can work through the professional documents and get all parts right. Both models have scored low here, and Fable's advantage is only 2 points. The two are close on regular document work.

Context Window and Technical Specs: Identical

The basic specifications are identical on both models, and context size will not be a deciding factor. Both offer a 1M-token context window, roughly equivalent to 1500 A4 pages of text, according to Artificial Analysis. Both can support 128K output tokens each.

The real differences sit in price, speed, and benchmark results, not in specs.

Which Model Is Better and on What Basis

Neither model is always the winner. Fable 5.1 is superior in raw ability. Sonnet 5.5 is more cost-effective and fast. For the 9 bases below, Fable wins 4, Sonnet wins 4, and 1 is a tie.

*Pick the basis that matters most to your project. Then follow that row.

A Practical Decision Framework

Use Sonnet 5.5 and only move to Fable 5.1 when a task requires greater capability than that provided by Sonnet. This answers the question “which AI model should I use?” for most teams. Sonnet is the cheaper default, and benchmarks show it handles agent and business workflows well.

Select Sonnet 5.5 (High) when:

• You have high-volume or repetitive jobs, and the per-task cost adds up.
• You create automation or agents for business software.
• Your users care about speed and response time.
• You must get a good outcome on a limited budget.

Choose Fable 5.1 (High) when:

• You do difficult multiple-step coding or terminal tasks.
• You have an interest in science, technology or fact-based research.
• It is more expensive to get a wrong answer than the model.
• You tackle challenging physics and/or expert-level reasoning problems.

Try it before buying. Complete 20-50 real tasks with both models. Monitor cost per correct answer, not accuracy. Alternatively, split the tasks: send regular tasks to Sonnet and escalate challenging ones to Fable.

For more context, if you're considering other labs' models, here is our comparison of Claude Opus 5.5 vs GPT-6 Astra.

Conclusion

Fable 5.1 at maximum effort is the better model, and Sonnet 5.5 at maximum effort is the better value. Fable has a 6-point advantage in intelligence and a win in coding, research and physics. Sonnet is 4.6 times cheaper per task, 1.8 times faster, and 100% ahead of agentic knowledge work. Sonnet is indeed a good starting point for most teams.

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