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BenchLM
Anthropic logo
Model A
Claude Fable 5

Anthropic

80.9/100

Supported · Public rank #3

90% interval 78.7–83.1

Claude Fable 5 vs GLM-4.7

Updated September 4, 2026. Public scores include evidence status and uncertainty. They are not guarantees for a specific workload.

Z.AI logo
Model B
GLM-4.7

Z.AI

58.99/100

Supported · Public rank #79

90% interval 47.3–70.7

Decision reading

Claude Fable 5 has the higher public score, 80.9 versus 58.99, and the 90% score intervals do not overlap.

2 results are shared. Category rows resting on Estimated evidence or different benchmark sets are marked directional and do not name a winner.

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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

    Claude Fable 5 leads on the public coding lane, 76.9 to 48, with Supported evidence for both models and non-overlapping 90% intervals.

    Confidence: stronger

  • Long documents

    Prompts that approach the documented context limit

    Claude Fable 5

    Claude Fable 5 has the larger documented context window.

    Confidence: documented

Show secondary and unsupported calls

  • Agentic work

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

    Not enough matched evidence

    GLM-4.7 is scored on Estimated evidence for agentic, so the reading is directional rather than like-for-like.

    Confidence: limited

  • 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

    The page does not recommend a cost winner because at least one model cannot fit the stated workload in one request. GLM-4.7 does not fit this workload in one request. GLM-4.7 has no comparable published API token rate.

    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

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
2
Claude Fable 5 only
16
GLM-4.7 only
11
Like-for-like categories
2 / 8

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

Category results, on a stated basis

Each row shows the public-lane category score for both models: the BenchAlign 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.

Coding

Like-for-like
Claude Fable 5
76.9
Supported · #2/183
GLM-4.7
48.0
Supported · #86/183
Basis
BenchAlign lane · 10 vs 3 public rows
Reading
Claude Fable 5 leads

Knowledge

Like-for-like
Claude Fable 5
83.5
Supported · #2/181
GLM-4.7
48.1
Supported · #102/181
Basis
BenchAlign lane · 2 vs 3 public rows
Reading
Claude Fable 5 leads

Agentic

Directional only
Claude Fable 5
74.8
Supported · #3/151
GLM-4.7
51.1
Estimated · #57/151
Basis
BenchAlign lane · 4 vs 4 public rows
Reading
Directional only

Instruction following

Directional only
Claude Fable 5
78.3
#54/120
GLM-4.7
82.6
#49/120
Basis
Provisional lane · 0 vs 0 weighted rows
Reading
Directional only

Reasoning

Not comparable
Claude Fable 5
76.2
#11/22
GLM-4.7
69.9
Unranked · 2 rankable rows
Basis
Provisional lane · 0 vs 0 weighted rows
Reading
Not comparable

Math

Not comparable
Claude Fable 5
Not ranked
GLM-4.7
26.0
Unranked · 2 rankable rows
Basis
Provisional lane · 0 vs 2 weighted rows
Reading
Not comparable

Multilingual

Not comparable
Claude Fable 5
Not ranked
GLM-4.7
Not ranked
Basis
Provisional lane · 0 vs 0 weighted rows
Reading
Not comparable

Multimodal

Not comparable
Claude Fable 5
62.5
Unranked · 2 rankable rows
GLM-4.7
Not ranked
Basis
Provisional lane · 1 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) differ from the provisional-lane categories. Unranked scores sit on the lane’s scale but never name a winner. Like-for-like rows within 0.5 points read as a practical tie.

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
$0.035
Fits in one request
GLM-4.7
Self-hosted; infrastructure cost varies
Fits in one request

GLM-4.7 has no comparable published API token rate.

Repository review

50K fresh input + 3K output tokens

Claude Fable 5
$0.65
Fits in one request
GLM-4.7
Self-hosted; infrastructure cost varies
Fits in one request

GLM-4.7 has no comparable published API token rate.

Cache-heavy agent loop

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

Claude Fable 5
$0.9
Fits in one request
GLM-4.7
Self-hosted; infrastructure cost varies
Does not fit in one request
Cached-input rate unavailable

GLM-4.7 does not fit this workload in one request. GLM-4.7 has no comparable published API token rate.

Specification differences

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

Context window

Maximum documented context; output-token limits may be lower.

Claude Fable 5

1M

Anthropic model overview

GLM-4.7

200K

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 per 1M cached input tokens

Claude API pricing

GLM-4.7

No comparable hosted API rate

Reasoning profile

Claude Fable 5

Reasoning

GLM-4.7

Reasoning

Weight access

Claude Fable 5

Proprietary

GLM-4.7

Open Weight

License

Claude Fable 5

Proprietary

GLM-4.7

Open Weight

Release date

Claude Fable 5

2026-06-09

GLM-4.7

2025-10-01

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 has the higher public score, 80.9 versus 58.99, and the 90% score intervals do not overlap.
Workload cost
A complete comparable API-rate estimate is not available for both models.
Context tradeoff
Claude Fable 5 has the larger documented window (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 evidence29 rows

Agentic

  • Terminal-Bench 3.0

    Claude Fable 534.0%
    Source
    GLM-4.7—

    Not directly comparable

  • Terminal-Bench 2.0

    Claude Fable 584.3%
    Source
    GLM-4.741%
    Source

    Claude Fable 5 leads this result

  • OSWorld-Verified

    Claude Fable 585%
    Source
    GLM-4.7—

    Not directly comparable

  • Terminal-Bench 2.1 (Vals)

    Claude Fable 580.5%
    Source
    GLM-4.7—

    Not directly comparable

  • BrowseComp

    Claude Fable 5—
    GLM-4.752%
    Source

    Not directly comparable

  • VITA-Bench

    Claude Fable 5—
    GLM-4.715.5%
    Source

    Not directly comparable

  • Gert Labs

    Claude Fable 5—
    GLM-4.739.95%
    Source

    Not directly comparable

Coding

  • SWE-bench Verified

    Claude Fable 595%
    Source
    GLM-4.773.8%
    Source

    Claude Fable 5 leads this result

  • SWE-bench Pro

    Claude Fable 580%
    Source
    GLM-4.7—

    Not directly comparable

  • FrontierSWE v2

    Claude Fable 548.0%
    Source
    GLM-4.7—

    Not directly comparable

  • FrontierCode 1.1 Main

    Claude Fable 553.5%
    Source
    GLM-4.7—

    Not directly comparable

  • Terminal-Bench 2.0

    Claude Fable 584.3%
    Source
    GLM-4.7—

    Not directly comparable

  • cursorBench31

    Claude Fable 570.6%
    Source
    GLM-4.7—

    Not directly comparable

  • cursorBench32

    Claude Fable 570.5%
    Source
    GLM-4.7—

    Not directly comparable

  • VulcanBench v3

    Claude Fable 589.5%
    Source
    GLM-4.7—

    Not directly comparable

  • LiveCodeBench (Vals)

    Claude Fable 589.8%
    Source
    GLM-4.7—

    Not directly comparable

  • SWE-bench (Vals)

    Claude Fable 595.0%
    Source
    GLM-4.7—

    Not directly comparable

  • LiveCodeBench

    Claude Fable 5—
    GLM-4.784.9%
    Source

    Not directly comparable

  • SWE-Rebench

    Claude Fable 5—
    GLM-4.758.7%
    Source

    Not directly comparable

Knowledge

  • GPQA Diamond (Vals)

    Claude Fable 593.2%
    Source
    GLM-4.7—

    Not directly comparable

  • MMLU-Pro (Vals)

    Claude Fable 591.5%
    Source
    GLM-4.7—

    Not directly comparable

  • GPQA

    Claude Fable 5—
    GLM-4.785.7%
    Source

    Not directly comparable

  • MMLU-Pro

    Claude Fable 5—
    GLM-4.784.3%
    Source

    Not directly comparable

  • HLE

    Claude Fable 5—
    GLM-4.724.8%
    Source

    Not directly comparable

Math

  • AIME 2025

    Claude Fable 5—
    GLM-4.795.7%
    Source

    Not directly comparable

  • FrontierMath v2 (Tiers 1-3)

    Claude Fable 5—
    GLM-4.72.439%
    Source

    Not directly comparable

  • FrontierMath v2 (Tier 4)

    Claude Fable 5—
    GLM-4.70.000%
    Source

    Not directly comparable

Multimodal

  • Blueprint-Bench 2

    Claude Fable 538.6%
    Source
    GLM-4.7—

    Not directly comparable

  • OfficeQA Pro

    Claude Fable 557.9%
    Source
    GLM-4.7—

    Not directly comparable

Frequently asked questions

Which is better, Claude Fable 5 or GLM-4.7?

Claude Fable 5 has the higher public score, 80.9 versus 58.99, 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 or GLM-4.7?

Claude Fable 5 leads the public coding lane, 76.9 to 48, with Supported evidence for both models and non-overlapping 90% intervals.

Which is better for agentic tasks, Claude Fable 5 or GLM-4.7?

Claude Fable 5 scores higher for agentic tasks on the public lane, 74.8 to 51.1. GLM-4.7 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 or GLM-4.7?

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 or GLM-4.7?

Claude Fable 5 has the larger documented context window: 1M, compared with 200K.

Related comparisons

Compare API pricing Read the methodology Open the model selector

Last updated September 4, 2026

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