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Model comparison

Claude Fable 5 vs GLM-5.2

Updated July 31, 2026. Public scores include evidence status and uncertainty. They are not guarantees for a specific workload.

21 confirmed releases in the last 30 daystrack changes
Claude Fable 5

Anthropic

82.7/100

Supported · Public rank #3

90% interval 79.8–85.6

GLM-5.2

Z.AI

62.9/100

Estimated · Public rank #41

90% interval 47.7–78.2

Claude Fable 5 has the higher public score, 82.73 versus 62.94, and the 90% score intervals do not overlap.

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.

  • Chat turn cost

    1K fresh input + 500 output tokens

    GLM-5.2

    GLM-5.2 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

    GLM-5.2

    GLM-5.2 has the lower estimated token cost for this stated workload. GLM-5.2 has no published cached-input rate, so cached tokens use its listed input rate.

    Confidence: rate-fallback

Show secondary and unsupported calls

  • Repository review cost

    50K fresh input + 3K output tokens

    GLM-5.2

    GLM-5.2 has the lower estimated token cost for this stated workload. Costs use the listed standard API rates.

    Confidence: listed-rates

  • Coding work

    Code generation, repair, and software-engineering tasks

    Not enough matched evidence

    The category averages use different weighted benchmark sets, so they are directional rather than like-for-like.

    Confidence: limited

  • Agentic work

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

    Not enough matched evidence

    The category averages use different weighted benchmark sets, so they are 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

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 only
7
GLM-5.2 only
14
Like-for-like categories
0 / 8

2 categories use different evidence sets. Those rows remain visible for coverage context but do not name a winner.

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.

Agentic

Directional only
Claude Fable 5
84.6
GLM-5.2
81.0
Weighted basis
2 vs 1 rows
Reading
Directional only

Coding

Directional only
Claude Fable 5
89.2
GLM-5.2
62.1
Weighted basis
2 vs 1 rows
Reading
Directional only

Reasoning

Not comparable
Claude Fable 5
Not measured
GLM-5.2
Not measured
Weighted basis
0 vs 0 rows
Reading
Not comparable

Knowledge

Not comparable
Claude Fable 5
Not measured
GLM-5.2
59.6
Weighted basis
0 vs 2 rows
Reading
Not comparable

Math

Not comparable
Claude Fable 5
Not measured
GLM-5.2
95.9
Weighted basis
0 vs 2 rows
Reading
Not comparable

Multilingual

Not comparable
Claude Fable 5
Not measured
GLM-5.2
Not measured
Weighted basis
0 vs 0 rows
Reading
Not comparable

Multimodal

Not comparable
Claude Fable 5
57.9
GLM-5.2
Not measured
Weighted basis
1 vs 0 rows
Reading
Not comparable

Instruction following

Not comparable
Claude Fable 5
Not measured
GLM-5.2
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
$0.035
Fits in one request
GLM-5.2
$0.0036
Fits in one request

GLM-5.2 has the lower modeled cost

Costs use the listed standard API rates.

Repository review

50K fresh input + 3K output tokens

Claude Fable 5
$0.65
Fits in one request
GLM-5.2
$0.0832
Fits in one request

GLM-5.2 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
$0.9
Fits in one request
GLM-5.2
$0.352
Fits in one request
Cached input priced at the published list-input rate

GLM-5.2 has the lower modeled cost

GLM-5.2 has no published cached-input rate, so cached tokens use its listed input 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-5.2

1M

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-5.2

Not published

Reasoning profile

Claude Fable 5

Reasoning

GLM-5.2

Reasoning

Weight access

Claude Fable 5

Proprietary

GLM-5.2

Open Weight

License

Claude Fable 5

Proprietary

GLM-5.2

Open Weight

Release date

Claude Fable 5

2026-06-09

GLM-5.2

2026-06-16

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, 82.73 versus 62.94, and the 90% score intervals do not overlap.
Workload cost
Repository review: $0.65 vs $0.0832. Cache-heavy agent loop: $0.9 vs $0.352.
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 evidence25 rows

Agentic

  • Terminal-Bench 2.0

    Claude Fable 584.3%
    Source
    GLM-5.281%
    Source

    Claude Fable 5 leads this result

  • OSWorld-Verified

    Claude Fable 585%
    Source
    GLM-5.2—

    Not directly comparable

  • MCP Atlas

    Claude Fable 5—
    GLM-5.276.8%
    Source

    Not directly comparable

  • Toolathlon

    Claude Fable 5—
    GLM-5.248.2%
    Source

    Not directly comparable

  • ResearchClawBench

    Claude Fable 5—
    GLM-5.220.7%
    Source

    Not directly comparable

Coding

  • SWE-bench Verified

    Claude Fable 595%
    Source
    GLM-5.2—

    Not directly comparable

  • SWE-bench Pro

    Claude Fable 580%
    Source
    GLM-5.262.1%
    Source

    Claude Fable 5 leads this result

  • FrontierCode 1.1 Main

    Claude Fable 553.5%
    Source
    GLM-5.2—

    Not directly comparable

  • Terminal-Bench 2.0

    Claude Fable 584.3%
    Source
    GLM-5.281.0%
    Source

    Claude Fable 5 leads this result

  • cursorBench31

    Claude Fable 570.6%
    Source
    GLM-5.2—

    Not directly comparable

  • cursorBench32

    Shared source
    Claude Fable 570.5%
    GLM-5.255.0%

    Claude Fable 5 leads this result

  • VulcanBench v3

    Claude Fable 587.0%
    Source
    GLM-5.2—

    Not directly comparable

  • NL2Repo

    Claude Fable 5—
    GLM-5.248.9%
    Source

    Not directly comparable

  • ProgramBench

    Claude Fable 5—
    GLM-5.263.7%
    Source

    Not directly comparable

Reasoning

  • CritPt

    Claude Fable 5—
    GLM-5.220.9%
    Source

    Not directly comparable

Knowledge

  • GPQA

    Claude Fable 5—
    GLM-5.291.2%
    Source

    Not directly comparable

  • GPQA-D

    Claude Fable 5—
    GLM-5.291.2%
    Source

    Not directly comparable

  • HLE

    Claude Fable 5—
    GLM-5.254.7%
    Source

    Not directly comparable

  • HLE w/o tools

    Claude Fable 5—
    GLM-5.240.5%
    Source

    Not directly comparable

Math

  • AIME26

    Claude Fable 5—
    GLM-5.299.2%
    Source

    Not directly comparable

  • HMMT Nov 2025

    Claude Fable 5—
    GLM-5.294.4%
    Source

    Not directly comparable

  • HMMT Feb 2026

    Claude Fable 5—
    GLM-5.292.5%
    Source

    Not directly comparable

  • MMAnswerBench

    Claude Fable 5—
    GLM-5.291.0%
    Source

    Not directly comparable

Multimodal

  • Blueprint-Bench 2

    Claude Fable 538.6%
    Source
    GLM-5.2—

    Not directly comparable

  • OfficeQA Pro

    Claude Fable 557.9%
    Source
    GLM-5.2—

    Not directly comparable

Frequently asked questions

Which is better, Claude Fable 5 or GLM-5.2?

Claude Fable 5 has the higher public score, 82.73 versus 62.94, 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-5.2?

The current coding averages use different weighted benchmark sets, so BenchLM does not name a winner from them. Read the shared benchmark rows directly and test the models on the same task set.

Which is better for agentic tasks, Claude Fable 5 or GLM-5.2?

The current agentic tasks averages use different weighted benchmark sets, so BenchLM does not name a winner from them. Read the shared benchmark rows directly and test the models on the same task set.

Which costs less, Claude Fable 5 or GLM-5.2?

For the stated presets, chat costs $0.035 on Claude Fable 5 and $0.0036 on GLM-5.2; repository review costs $0.65 and $0.0832; the cache-heavy agent loop costs $0.9 and $0.352. GLM-5.2 has no published cached-input rate, so cached tokens use its listed input rate.

Which has the larger context window, Claude Fable 5 or GLM-5.2?

Both models list the same context window, 1M.

Related comparisons

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Last updated July 31, 2026

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