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BenchLM
Anthropic logo
Model A
Claude Fable 5.1

Anthropic

82.74/100

Estimated · Public rank #1

90% interval 71.2–94.3

Claude Fable 5.1 vs Laguna S 2.1

Updated September 1, 2026. Public scores include evidence status and uncertainty. They are not guarantees for a specific workload.

Poolside logo
Model B
Laguna S 2.1

Poolside

—

Evidence status unavailable

90% interval unavailable

Decision reading

At least one model is not scored in the current public ranking lane, so the page does not name an overall quality winner.

4 results are shared. Category rows based on different benchmark sets are marked directional and do not name a winner.

Which one for your work

Recommendations appear only when a shared evidence basis or an explicit operating constraint supports the call. Secondary and unsupported use cases stay disclosed below the initial list.

  • Coding work

    Code generation, repair, and software-engineering tasks

    Claude Fable 5.1

    Claude Fable 5.1 leads on the same 1 weighted benchmark row.

    Confidence: limited

  • Chat turn cost

    1K fresh input + 500 output tokens

    Laguna S 2.1

    Laguna S 2.1 has the lower estimated token cost for this stated workload. Costs use the listed standard API rates.

    Confidence: listed-rates

  • Cache-heavy agent loop cost

    200K cached + 20K fresh input + 10K output tokens

    Laguna S 2.1

    Laguna S 2.1 has the lower estimated token cost for this stated workload. Costs use the listed standard API rates.

    Confidence: listed-rates

Show secondary and unsupported calls

  • Repository review cost

    50K fresh input + 3K output tokens

    Laguna S 2.1

    Laguna S 2.1 has the lower estimated token cost for this stated workload. Costs use the listed standard API rates.

    Confidence: listed-rates

  • Agentic work

    Tool use, computer use, and multi-step task completion

    Not enough matched evidence

    No shared weighted benchmark basis supports a winner.

    Confidence: limited

  • Long documents

    Prompts that approach the documented context limit

    No clear pick

    The documented context windows are equal.

    Confidence: documented

What is actually comparable

Shared results can support a head-to-head reading. Results present for only one model describe coverage, not superiority.

Shared results
4
Claude Fable 5.1 only
14
Laguna S 2.1 only
2
Like-for-like categories
1 / 8

Category results, on a stated basis

Each row states whether both averages use the same weighted benchmark set. Directional and not-comparable rows remain visible, but they never receive a winner in this template.

Coding

Like-for-like
Claude Fable 5.1
81.2
Laguna S 2.1
59.4
Weighted basis
1 vs 1 rows
Reading
Claude Fable 5.1 leads

Agentic

Not comparable
Claude Fable 5.1
Not measured
Laguna S 2.1
70.2
Weighted basis
0 vs 1 rows
Reading
Not comparable

Reasoning

Not comparable
Claude Fable 5.1
90.0
Laguna S 2.1
Not measured
Weighted basis
1 vs 0 rows
Reading
Not comparable

Knowledge

Not comparable
Claude Fable 5.1
65.0
Laguna S 2.1
Not measured
Weighted basis
1 vs 0 rows
Reading
Not comparable

Math

Not comparable
Claude Fable 5.1
Not measured
Laguna S 2.1
Not measured
Weighted basis
0 vs 0 rows
Reading
Not comparable

Multilingual

Not comparable
Claude Fable 5.1
Not measured
Laguna S 2.1
Not measured
Weighted basis
0 vs 0 rows
Reading
Not comparable

Multimodal

Not comparable
Claude Fable 5.1
Not measured
Laguna S 2.1
Not measured
Weighted basis
0 vs 0 rows
Reading
Not comparable

Instruction following

Not comparable
Claude Fable 5.1
Not measured
Laguna S 2.1
Not measured
Weighted basis
0 vs 0 rows
Reading
Not comparable

Shape of the matched evidence

Only shared public evidence is shown. Sparse evidence stays a ruled list rather than being closed into a radar shape.

Too few matched category axes support a radar. The ruled list below shows only shared benchmark results; positions use each benchmark’s normalized display scale when available.

What each workload costs

Three fixed token mixes turn per-token rates into comparable decisions. Each scenario states context fit and whether cached input had to fall back to the published list-input rate.

Chat turn

1K fresh input + 500 output tokens

Claude Fable 5.1
$0.035
Fits in one request
Laguna S 2.1
$0.0002
Fits in one request

Laguna S 2.1 has the lower modeled cost

Costs use the listed standard API rates.

Repository review

50K fresh input + 3K output tokens

Claude Fable 5.1
$0.65
Fits in one request
Laguna S 2.1
$0.0056
Fits in one request

Laguna S 2.1 has the lower modeled cost

Costs use the listed standard API rates.

Cache-heavy agent loop

200K cached + 20K fresh input + 10K output tokens

Claude Fable 5.1
$0.75
Fits in one request
Laguna S 2.1
$0.006
Fits in one request

Laguna S 2.1 has the lower modeled cost

Costs use the listed standard API rates.

Specification differences

Sourced differences are shown directly. Missing facts stay explicit instead of being inferred from a model name or family.

Cached-input rate

A missing cached-input rate falls back to the listed input rate only in the stated workload estimate.

Claude Fable 5.1

$0.25 per 1M cached input tokens

Anthropic Fable 5.1 launch

Laguna S 2.1

$0.01 per 1M cached input tokens

Documented inputs

Claude Fable 5.1

Not sourced

Laguna S 2.1

Not sourced

Documented outputs

Claude Fable 5.1

Not sourced

Laguna S 2.1

Not sourced

Reasoning profile

Claude Fable 5.1

Reasoning

Laguna S 2.1

Reasoning

Weight access

Claude Fable 5.1

Proprietary

Laguna S 2.1

Open Weight

License

Claude Fable 5.1

Proprietary

Laguna S 2.1

Open Weight

Release date

Claude Fable 5.1

2026-09-01

Laguna S 2.1

2026-07-21

If you already use one of these models
Deployment change
The models list different providers, so authentication, endpoint behavior, limits, and feature support may change.
Quality signal
At least one model is not scored in the current public ranking lane, so the page does not name an overall quality winner.
Workload cost
Repository review: $0.65 vs $0.0056. Cache-heavy agent loop: $0.75 vs $0.006.
Context tradeoff
Both models list 1M.

Run the same representative tasks against both endpoints before changing production traffic.

Benchmark evidence

The full public result ledger is available for audit without forcing a wide desktop table onto a phone.

Browse raw public benchmark evidence20 rows

Agentic

  • Terminal-Bench 4.0

    Claude Fable 5.155.80%
    Source
    Laguna S 2.1—

    Not directly comparable

  • Terminal-Bench-Science 0.1

    Claude Fable 5.152.6%
    Source
    Laguna S 2.1—

    Not directly comparable

  • OSWorld 2.0

    Claude Fable 5.141.7%
    Source
    Laguna S 2.1—

    Not directly comparable

  • AutomationBench

    Claude Fable 5.131.4%
    Source
    Laguna S 2.1—

    Not directly comparable

  • Toolathlon-Verified

    Claude Fable 5.177.8%
    Source
    Laguna S 2.149.7%
    Source

    Claude Fable 5.1 leads this result

  • Toolathlon Verified Pass@3

    Claude Fable 5.181.5%
    Source
    Laguna S 2.1—

    Not directly comparable

  • Toolathlon Verified Pass³

    Claude Fable 5.173.1%
    Source
    Laguna S 2.1—

    Not directly comparable

  • Toolathlon Verified avg. turns

    Claude Fable 5.123.7 turns
    Source
    Laguna S 2.1—

    Not directly comparable

  • Terminal-Bench 2.0

    Claude Fable 5.1—
    Laguna S 2.170.2%
    Source

    Not directly comparable

Coding

  • SWE-bench Pro

    Claude Fable 5.181.2%
    Source
    Laguna S 2.159.4%
    Source

    Claude Fable 5.1 leads this result

  • SWE Multilingual

    Claude Fable 5.189.1%
    Source
    Laguna S 2.178.5%
    Source

    Claude Fable 5.1 leads this result

  • SWE Multimodal

    Claude Fable 5.154.7%
    Source
    Laguna S 2.1—

    Not directly comparable

  • deepSwe

    Claude Fable 5.167.4%
    Source
    Laguna S 2.140.4%
    Source

    Claude Fable 5.1 leads this result

  • ProgramBench

    Claude Fable 5.187.6%
    Source
    Laguna S 2.1—

    Not directly comparable

  • cursorBench32

    Claude Fable 5.173.4%
    Source
    Laguna S 2.1—

    Not directly comparable

  • Terminal-Bench 2.0

    Claude Fable 5.1—
    Laguna S 2.170.2%
    Source

    Not directly comparable

Reasoning

  • ARC-AGI-1

    Claude Fable 5.197.50%
    Source
    Laguna S 2.1—

    Not directly comparable

  • ARC-AGI-2

    Claude Fable 5.190%
    Source
    Laguna S 2.1—

    Not directly comparable

Knowledge

  • HLE

    Claude Fable 5.165%
    Source
    Laguna S 2.1—

    Not directly comparable

  • HLE w/o tools

    Claude Fable 5.160.9%
    Source
    Laguna S 2.1—

    Not directly comparable

Frequently asked questions

Which is better, Claude Fable 5.1 or Laguna S 2.1?

At least one model is not scored in the current public ranking lane, so the page does not name an overall quality winner. The page therefore keeps the decision tied to the specific documented workload.

Which is better for coding, Claude Fable 5.1 or Laguna S 2.1?

Claude Fable 5.1 leads the like-for-like coding comparison across 1 shared weighted benchmark row.

Which is better for agentic tasks, Claude Fable 5.1 or Laguna S 2.1?

The published evidence does not provide a shared weighted agentic tasks basis for both models, so BenchLM does not name a agentic tasks winner.

Which costs less, Claude Fable 5.1 or Laguna S 2.1?

For the stated presets, chat costs $0.035 on Claude Fable 5.1 and $0.0002 on Laguna S 2.1; repository review costs $0.65 and $0.0056; the cache-heavy agent loop costs $0.75 and $0.006. Costs use the listed standard API rates.

Which has the larger context window, Claude Fable 5.1 or Laguna S 2.1?

Both models list the same context window, 1M.

Related comparisons

Compare API pricing Read the methodology Open the model selector

Last updated September 1, 2026

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