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Clef-Omni vs GLM-5.2

Updated October 20, 2026. We do not rank this pair: at least one has no public score. Public scores include evidence status and uncertainty.

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Decision reading

The public evidence has no benchmark result shared by both models, so it does not support a quality verdict. Use the documented cost, context, and runtime rows instead. 0 results are shared. Category rows resting on Estimated evidence or different benchmark sets are marked directional and do not name a winner.

Model A
Clef-Omni

Cloudflare

—

Evidence status unavailable

90% interval unavailable

Model B
Z.AI logo
GLM-5.2

Z.AI

60.5/100

Supported · Public rank #55

90% interval 52.6–68.4

Shared results
0
Clef-Omni only
0
GLM-5.2 only
30
Like-for-like categories
0 / 8
Supported: GLM-5.2How 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.

  • Long documents

    Prompts that approach the documented context limit

    GLM-5.2

    GLM-5.2 has the larger documented context window.

    Confidence: documented

  • Chat turn cost

    1K fresh input + 500 output tokens

    Clef-Omni

    Clef-Omni 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

    Clef-Omni

    Clef-Omni 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

    Clef-Omni is not ranked on the public lane for coding, so no winner is named for coding.

    Confidence: limited

  • Agentic work

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

    Not enough matched evidence

    Clef-Omni is not ranked on the public lane for agentic, so no winner is named for agentic.

    Confidence: limited

  • 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. Clef-Omni does not fit this workload in one request. Clef-Omni has no published cached-input rate, so cached tokens use its listed input rate. GLM-5.2 has no published cached-input rate, so cached tokens use its listed input rate.

    Confidence: rate-fallback

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.

—Clef-Omni53.7GLM-5.2

Not comparable · BenchAlign v5.8

The coding row is not comparable on the public lane: at least one model is not measured or not ranked there.

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.

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

Not comparable
Clef-Omni
Not ranked
GLM-5.2
57.4
Supported · #36/131
Basis
BenchAlign v5.8 lane · 0 vs 8 public rows
Reading
Not comparable

Coding

Not comparable
Clef-Omni
Not ranked
GLM-5.2
53.7
Supported · #35/152
Basis
BenchAlign v5.8 lane · 0 vs 9 public rows
Reading
Not comparable

Reasoning

Not comparable
Clef-Omni
Not ranked
GLM-5.2
78.8
Unranked · 2 rankable rows
Basis
Provisional lane · 0 vs 0 weighted rows
Reading
Not comparable

Multimodal

Not comparable
Clef-Omni
Not ranked
GLM-5.2
Not ranked
Basis
Provisional lane · 0 vs 0 weighted rows
Reading
Not comparable

Knowledge

Not comparable
Clef-Omni
Not ranked
GLM-5.2
57.9
Supported · #54/187
Basis
BenchAlign v5.8 lane · 0 vs 6 public rows
Reading
Not comparable

Multilingual

Not comparable
Clef-Omni
Not ranked
GLM-5.2
Not ranked
Basis
Provisional lane · 0 vs 0 weighted rows
Reading
Not comparable

Instruction following

Not comparable
Clef-Omni
Not ranked
GLM-5.2
89.9
#26/149
Basis
Provisional lane · 0 vs 0 weighted rows
Reading
Not comparable

Math

Not comparable
Clef-Omni
Not ranked
GLM-5.2
78.4
#3/12
Basis
Provisional lane · 0 vs 4 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

Clef-Omni
$0.00015
Fits in one request
GLM-5.2
$0.0036
Fits in one request

Clef-Omni has the lower modeled cost

Costs use the listed standard API rates.

Repository review

50K fresh input + 3K output tokens

Clef-Omni
$0.0075
Fits in one request
GLM-5.2
$0.0832
Fits in one request

Clef-Omni has the lower modeled cost

Costs use the listed standard API rates.

Cache-heavy agent loop

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

Clef-Omni
$0.033
Does not fit in one request
Cached input priced at the published list-input rate
GLM-5.2
$0.352
Fits in one request
Cached input priced at the published list-input rate

Clef-Omni does not fit this workload in one request. Clef-Omni has no published cached-input rate, so cached tokens use its listed input rate. GLM-5.2 has no published cached-input rate, so cached tokens use its listed input 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.

Context window

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

Clef-Omni

64K

Workers AI pricing

GLM-5.2

1M

API model ID

Clef-Omni

Not sourced

GLM-5.2

Not sourced

Cached-input rate

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

Clef-Omni

Not published

Workers AI pricing

GLM-5.2

Not published

Documented inputs

Clef-Omni

Not sourced

GLM-5.2

Not sourced

Documented outputs

Clef-Omni

Not sourced

GLM-5.2

Not sourced

Provider availability

Clef-Omni

Not sourced

GLM-5.2

Not sourced

Reasoning profile

Clef-Omni

Unspecified

GLM-5.2

Reasoning

Weight access

Clef-Omni

Open Weight

GLM-5.2

Open Weight

License

Clef-Omni

Open Weight

GLM-5.2

Open Weight

Release date

Clef-Omni

2026-10-09

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
The public evidence has no benchmark result shared by both models, so it does not support a quality verdict.
Workload cost
Repository review: $0.0075 vs $0.0832. Cache-heavy agent loop: $0.033 vs $0.352.
Context tradeoff
GLM-5.2 has the larger documented window (1M).
Run the same representative tasks against both endpoints before changing production traffic.

Questions

Which is better, Clef-Omni or GLM-5.2?

The public evidence has no benchmark result shared by both models, so it does not support a quality verdict. The page therefore keeps the decision tied to the specific documented workload.

Which is better for coding, Clef-Omni or GLM-5.2?

Clef-Omni is not ranked on the public lane for coding, so no winner is named for coding.

Which is better for agentic tasks, Clef-Omni or GLM-5.2?

Clef-Omni is not ranked on the public lane for agentic tasks, so no winner is named for agentic tasks.

Which costs less, Clef-Omni or GLM-5.2?

For the stated presets, chat costs $0.00015 on Clef-Omni and $0.0036 on GLM-5.2; repository review costs $0.0075 and $0.0832; the cache-heavy agent loop costs $0.033 and $0.352. Clef-Omni does not fit this workload in one request. Clef-Omni has no published cached-input rate, so cached tokens use its listed input rate. GLM-5.2 has no published cached-input rate, so cached tokens use its listed input rate.

Which has the larger context window, Clef-Omni or GLM-5.2?

GLM-5.2 has the larger documented context window: 1M, compared with 64K.

Self-host vs API cost

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

Clef-Omni
API / mo$113
Self-host / mo$298
Break-even95M/day
GLM-5.2
API / mo$4,350
Self-host / moNot listed
Break-even—
Proprietary model — self-hosting not applicable.
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 evidence30 rows

Agentic

  • Terminal-Bench 3.0

    Clef-Omni—
    GLM-5.24.6%
    Source

    Not directly comparable

  • Terminal-Bench 2.1

    Clef-Omni—
    GLM-5.281.0%
    Source

    Not directly comparable

  • MCP Atlas

    Clef-Omni—
    GLM-5.276.8%
    Source

    Not directly comparable

  • Toolathlon

    Clef-Omni—
    GLM-5.248.2%
    Source

    Not directly comparable

  • ResearchClawBench

    Clef-Omni—
    GLM-5.220.7%
    Source

    Not directly comparable

  • Terminal-Bench 2.1 (Vals)

    Clef-Omni—
    GLM-5.267.8%
    Source

    Not directly comparable

  • HLE w/ tools

    Clef-Omni—
    GLM-5.254.7%
    Source

    Not directly comparable

  • MazeBench

    Clef-Omni—
    GLM-5.20%
    Source

    Not directly comparable

Coding

  • SWE-bench Pro

    Clef-Omni—
    GLM-5.262.1%
    Source

    Not directly comparable

  • NL2Repo

    Clef-Omni—
    GLM-5.248.9%
    Source

    Not directly comparable

  • Terminal-Bench 2.1

    Clef-Omni—
    GLM-5.281.0%
    Source

    Not directly comparable

  • ProgramBench

    Clef-Omni—
    GLM-5.263.7%
    Source

    Not directly comparable

  • CursorBench 3.2

    Clef-Omni—
    GLM-5.255.0%
    Source

    Not directly comparable

  • OpenHarmony Bench

    Clef-Omni—
    GLM-5.258.4%
    Source

    Not directly comparable

  • LiveCodeBench (Vals)

    Clef-Omni—
    GLM-5.269.5%
    Source

    Not directly comparable

  • SWE-bench (Vals)

    Clef-Omni—
    GLM-5.282.8%
    Source

    Not directly comparable

  • PostTrainBench v1.1

    Clef-Omni—
    GLM-5.231.7%
    Source

    Not directly comparable

Reasoning

  • CritPt

    Clef-Omni—
    GLM-5.220.9%
    Source

    Not directly comparable

Knowledge

  • GPQA

    Clef-Omni—
    GLM-5.291.2%
    Source

    Not directly comparable

  • GPQA-D

    Clef-Omni—
    GLM-5.291.2%
    Source

    Not directly comparable

  • HLE

    Clef-Omni—
    GLM-5.240.5%
    Source

    Not directly comparable

  • HLE w/o tools

    Clef-Omni—
    GLM-5.240.5%
    Source

    Not directly comparable

  • GPQA Diamond (Vals)

    Clef-Omni—
    GLM-5.285.6%
    Source

    Not directly comparable

  • MMLU-Pro (Vals)

    Clef-Omni—
    GLM-5.286.7%
    Source

    Not directly comparable

Math

  • AIME26

    Clef-Omni—
    GLM-5.299.2%
    Source

    Not directly comparable

  • HMMT Nov 2025

    Clef-Omni—
    GLM-5.294.4%
    Source

    Not directly comparable

  • HMMT Feb 2026

    Clef-Omni—
    GLM-5.292.5%
    Source

    Not directly comparable

  • MMAnswerBench

    Clef-Omni—
    GLM-5.291.0%
    Source

    Not directly comparable

  • FrontierMath v2 (Tiers 1-3)

    Clef-Omni—
    GLM-5.259.206%
    Source

    Not directly comparable

  • FrontierMath v2 (Tier 4)

    Clef-Omni—
    GLM-5.229.268%
    Source

    Not directly comparable

30 public results · 0 shared

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Last updated October 20, 2026