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BenchLM
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Claude Fable 5.1 vs Llama 4 Maverick

Updated October 17, 2026. Rank says Claude Fable 5.1 is ahead. Price, access, and your workload can each overturn that. Public scores include evidence status and uncertainty.

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Decision reading

Claude Fable 5.1 has the higher public score, 83.18 versus 21.61, and the 90% score intervals do not overlap. 1 results are shared. Category rows resting on Estimated evidence or different benchmark sets are marked directional and do not name a winner.

Model A
Anthropic logo
Claude Fable 5.1

Anthropic

83.18/100

Supported · Public rank #4

90% interval 79.9–86.5

Model B
Meta logo
Llama 4 Maverick

Meta

21.61/100

Supported · Public rank #207

90% interval 16.9–26.3

Shared results
1
Claude Fable 5.1 only
32
Llama 4 Maverick only
0
Like-for-like categories
0 / 8
Supported: Claude Fable 5.1 and Llama 4 MaverickHow the comparison works

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.

No workload recommendation clears the current evidence threshold.

Use the matched evidence, workload costs, and sourced specifications below instead of treating a point score as a universal answer.

Show secondary and unsupported calls

  • Coding work

    Code generation, repair, and software-engineering tasks

    Not enough matched evidence

    Llama 4 Maverick is scored on Estimated evidence for coding, so the reading is directional rather than like-for-like.

    Confidence: limited

  • Agentic work

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

    Not enough matched evidence

    Llama 4 Maverick is scored on Estimated evidence for agentic, so the reading is directional rather than like-for-like.

    Confidence: limited

  • Long documents

    Prompts that approach the documented context limit

    No clear pick

    The documented context windows are equal.

    Confidence: documented

  • Chat turn cost

    1K fresh input + 500 output tokens

    Not enough matched evidence

    A complete comparable API-rate estimate is not available for both models.

    Confidence: listed-rates

  • Cache-heavy agent loop cost

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

    Not enough matched evidence

    A complete comparable API-rate estimate is not available for both models.

    Confidence: listed-rates

  • Repository review cost

    50K fresh input + 3K output tokens

    Not enough matched evidence

    A complete comparable API-rate estimate is not available for both models.

    Confidence: listed-rates

Which one for a specific job

Choose a job from the LLM Selector's task catalog to see the category row it rests on, under the same basis rules as the table below. A directional row stays directional; choosing a job never creates a winner.

The same task catalog as the LLM Selector. Each job names the evidence surface it rests on; nothing here adds a new score.

76.8Claude Fable 5.113.8Llama 4 Maverick

Directional only · BenchAlign v5.8

Claude Fable 5.1 has the higher coding point estimate. Conditional score ranges do not establish rank confidence.

Coding scores combine specific tasks and setups. Match the editor, harness, and effort to your workflow.

Open the coding leaderboardShortlist models for this job
Same basis rules as the category table below

What is actually comparable

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

3 categories rest on Estimated evidence or different evidence sets. Those rows remain visible for coverage context but do not name a winner.

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.

A shared-evidence shape is not available.

BenchLM does not draw a radar or infer missing axes when the matched evidence is too sparse.

Category results, on a stated basis

Each row shows the public-lane category score for both models: the BenchAlign v5.8 lane for agentic, coding, and knowledge, and the weighted public rows elsewhere, with the same rank each model page reports. A row is like-for-like only when both scores rest on Supported evidence or the same weighted set. Directional and not-comparable rows remain visible, but they never receive a winner.

Agentic

Directional only
Claude Fable 5.1
80.9
Supported · #2/128
Llama 4 Maverick
10.6
Estimated · #121/128
Basis
BenchAlign v5.8 lane · 12 vs 1 public rows
Reading
Directional only

Coding

Directional only
Claude Fable 5.1
76.8
Supported · #3/150
Llama 4 Maverick
13.8
Estimated · #137/150
Basis
BenchAlign v5.8 lane · 11 vs 0 public rows
Reading
Directional only

Knowledge

Directional only
Claude Fable 5.1
86.9
Supported · #2/184
Llama 4 Maverick
32.2
Estimated · #145/184
Basis
BenchAlign v5.8 lane · 4 vs 0 public rows
Reading
Directional only

Reasoning

Not comparable
Claude Fable 5.1
85.2
#4/28
Llama 4 Maverick
60.2
Unranked · 2 rankable rows
Basis
Provisional lane · 1 vs 0 weighted rows
Reading
Not comparable

Multimodal

Not comparable
Claude Fable 5.1
Not ranked
Llama 4 Maverick
56.0
Unranked · 1 rankable row
Basis
Provisional lane · 0 vs 0 weighted rows
Reading
Not comparable

Multilingual

Not comparable
Claude Fable 5.1
Not ranked
Llama 4 Maverick
Not ranked
Basis
Provisional lane · 0 vs 0 weighted rows
Reading
Not comparable

Instruction following

Not comparable
Claude Fable 5.1
Not ranked
Llama 4 Maverick
50.3
#93/148
Basis
Provisional lane · 0 vs 0 weighted rows
Reading
Not comparable

Math

Not comparable
Claude Fable 5.1
96.8
Unranked · 2 rankable rows
Llama 4 Maverick
Not ranked
Basis
Provisional lane · 2 vs 0 weighted rows
Reading
Not comparable

Ranks count the models scored in each category’s lane, so the agentic, coding, and knowledge denominators (BenchAlign v5.8) differ from the provisional-lane categories. Unranked scores sit on the provisional lane’s scale but never name a winner. Like-for-like rows within 0.5 points read as a practical tie.

Supported evidence per lane · bars run 0–100Methodology

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
Llama 4 Maverick
Self-hosted; infrastructure cost varies
Fits in one request

Llama 4 Maverick has no comparable published API token rate.

Repository review

50K fresh input + 3K output tokens

Claude Fable 5.1
$0.65
Fits in one request
Llama 4 Maverick
Self-hosted; infrastructure cost varies
Fits in one request

Llama 4 Maverick has no comparable published API token rate.

Cache-heavy agent loop

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

Claude Fable 5.1
$0.75
Fits in one request
Llama 4 Maverick
Self-hosted; infrastructure cost varies
Fits in one request
Cached-input rate unavailable

Llama 4 Maverick has no comparable published API token rate.

Cached input falls back to the list input rate only where a cached rate is unpublished

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

Llama 4 Maverick

No comparable hosted API rate

Documented inputs

Claude Fable 5.1

Not sourced

Llama 4 Maverick

Not sourced

Documented outputs

Claude Fable 5.1

Not sourced

Llama 4 Maverick

Not sourced

Reasoning profile

Claude Fable 5.1

Reasoning

Llama 4 Maverick

Non-Reasoning

Weight access

Claude Fable 5.1

Proprietary

Llama 4 Maverick

Open Weight

License

Claude Fable 5.1

Proprietary

Llama 4 Maverick

Open Weight

Release date

Claude Fable 5.1

2026-09-01

Llama 4 Maverick

2025-04-05

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
Claude Fable 5.1 has the higher public score, 83.18 versus 21.61, and the 90% score intervals do not overlap.
Workload cost
A complete comparable API-rate estimate is not available for both models.
Context tradeoff
Both models list 1M.
Run the same representative tasks against both endpoints before changing production traffic.

Questions

Which is better, Claude Fable 5.1 or Llama 4 Maverick?

Claude Fable 5.1 has the higher public score, 83.18 versus 21.61, and the 90% score intervals do not overlap. That is the clearest overall quality signal in the current public data.

Which is better for coding, Claude Fable 5.1 or Llama 4 Maverick?

Claude Fable 5.1 scores higher for coding on the public lane, 76.8 to 13.8. Llama 4 Maverick is scored on Estimated evidence for coding, so the reading is directional rather than like-for-like. BenchLM does not name a winner for coding from a directional reading; read the shared benchmark rows directly and test the models on the same task set.

Which is better for agentic tasks, Claude Fable 5.1 or Llama 4 Maverick?

Claude Fable 5.1 scores higher for agentic tasks on the public lane, 80.9 to 10.6. Llama 4 Maverick is scored on Estimated evidence for agentic tasks, so the reading is directional rather than like-for-like. BenchLM does not name a winner for agentic tasks from a directional reading; read the shared benchmark rows directly and test the models on the same task set.

Which costs less, Claude Fable 5.1 or Llama 4 Maverick?

Both models do not have comparable published API token rates, so this page does not name a universal price winner.

Which has the larger context window, Claude Fable 5.1 or Llama 4 Maverick?

Both models list the same context window, 1M.

Self-host vs API cost

Estimates at 50,000 req/day · 1000 tokens/req average.

Claude Fable 5.1
API / mo$45,000
Self-host / moNot listed
Break-even—
Proprietary model — self-hosting not applicable.
Llama 4 Maverick
API / mo$0
Self-host / mo$3,679
Break-even—
Model the full break-even

Benchmark evidence

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

Browse raw public benchmark evidence33 rows

Agentic

  • Terminal-Bench 4.0

    Claude Fable 5.155.80%
    Source
    Llama 4 Maverick—

    Not directly comparable

  • Terminal-Bench-Science 0.1

    Claude Fable 5.152.6%
    Source
    Llama 4 Maverick—

    Not directly comparable

  • OSWorld 2.0

    Claude Fable 5.141.7%
    Source
    Llama 4 Maverick—

    Not directly comparable

  • AutomationBench

    Claude Fable 5.131.4%
    Source
    Llama 4 Maverick—

    Not directly comparable

  • Toolathlon-Verified

    Claude Fable 5.177.8%
    Source
    Llama 4 Maverick—

    Not directly comparable

  • Toolathlon Verified Pass@3

    Claude Fable 5.181.5%
    Source
    Llama 4 Maverick—

    Not directly comparable

  • Toolathlon Verified Pass³

    Claude Fable 5.173.1%
    Source
    Llama 4 Maverick—

    Not directly comparable

  • Toolathlon Verified avg. turns

    Claude Fable 5.123.7 turns
    Source
    Llama 4 Maverick—

    Not directly comparable

  • Terminal-Bench 2.1 (Vals)

    Claude Fable 5.185.0%
    Source
    Llama 4 Maverick—

    Not directly comparable

  • ApprenticeBench

    Claude Fable 5.172%
    Source
    Llama 4 Maverick—

    Not directly comparable

  • CWE-bench v1

    Claude Fable 5.158.0%
    Source
    Llama 4 Maverick—

    Not directly comparable

  • MazeBench

    Claude Fable 5.110%
    Source
    Llama 4 Maverick0%
    Source

    Claude Fable 5.1 leads this result

Coding

  • Bug Hunt Bench

    Claude Fable 5.143.0 fixes
    Source
    Llama 4 Maverick—

    Not directly comparable

  • SWE-bench Pro

    Claude Fable 5.181.2%
    Source
    Llama 4 Maverick—

    Not directly comparable

  • SWE Multilingual

    Claude Fable 5.189.1%
    Source
    Llama 4 Maverick—

    Not directly comparable

  • SWE Multimodal

    Claude Fable 5.154.7%
    Source
    Llama 4 Maverick—

    Not directly comparable

  • DeepSWE

    Claude Fable 5.167.4%
    Source
    Llama 4 Maverick—

    Not directly comparable

  • FrontierSWE v2

    Claude Fable 5.156.3%
    Source
    Llama 4 Maverick—

    Not directly comparable

  • ProgramBench

    Claude Fable 5.187.6%
    Source
    Llama 4 Maverick—

    Not directly comparable

  • CursorBench 3.2

    Claude Fable 5.173.4%
    Source
    Llama 4 Maverick—

    Not directly comparable

  • LiveCodeBench (Vals)

    Claude Fable 5.190.5%
    Source
    Llama 4 Maverick—

    Not directly comparable

  • CursorBench 4.0

    Claude Fable 5.151.8%
    Source
    Llama 4 Maverick—

    Not directly comparable

  • PostTrainBench v1.1

    Claude Fable 5.140.2%
    Source
    Llama 4 Maverick—

    Not directly comparable

Reasoning

  • ARC-AGI-1

    Claude Fable 5.197.50%
    Source
    Llama 4 Maverick—

    Not directly comparable

  • ARC-AGI-2

    Claude Fable 5.190%
    Source
    Llama 4 Maverick—

    Not directly comparable

  • GraphWalks BFS 256K–1M

    Claude Fable 5.165.0%
    Source
    Llama 4 Maverick—

    Not directly comparable

Knowledge

  • HLE

    Claude Fable 5.165%
    Source
    Llama 4 Maverick—

    Not directly comparable

  • HLE w/o tools

    Claude Fable 5.160.9%
    Source
    Llama 4 Maverick—

    Not directly comparable

  • GPQA Diamond (Vals)

    Claude Fable 5.193.4%
    Source
    Llama 4 Maverick—

    Not directly comparable

  • MMLU-Pro (Vals)

    Claude Fable 5.192.4%
    Source
    Llama 4 Maverick—

    Not directly comparable

Instruction following

  • Gray Swan IPI (15 attempts)

    Claude Fable 5.11.0%
    Source
    Llama 4 Maverick—

    Not directly comparable

Math

  • FrontierMath v2 (Tiers 1-3)

    Claude Fable 5.190.175%
    Source
    Llama 4 Maverick—

    Not directly comparable

  • FrontierMath v2 (Tier 4)

    Claude Fable 5.187.805%
    Source
    Llama 4 Maverick—

    Not directly comparable

33 public results · 1 shared

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Last updated October 17, 2026