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Model comparison

Claude Opus 4.5 Thinking 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 Opus 4.5 Thinking

Anthropic

56.4/100

Estimated · Public rank #78

90% interval 44.9–67.9

GLM-5.2

Z.AI

62.9/100

Estimated · Public rank #41

90% interval 47.7–78.2

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 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.

  • Long documents

    Prompts that approach the documented context limit

    GLM-5.2

    GLM-5.2 has the larger documented context window.

    Confidence: documented

Show secondary and unsupported calls

  • Coding work

    Code generation, repair, and software-engineering tasks

    Not enough matched evidence

    No shared weighted benchmark basis supports a winner.

    Confidence: limited

  • 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

  • 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. Claude Opus 4.5 Thinking does not fit this workload in one request. GLM-5.2 has no published cached-input rate, so cached tokens use its listed input rate. Claude Opus 4.5 Thinking has no comparable published API token rate.

    Confidence: rate-fallback

  • 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
0
Claude Opus 4.5 Thinking only
1
GLM-5.2 only
18
Like-for-like categories
0 / 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.

Agentic

Not comparable
Claude Opus 4.5 Thinking
Not measured
GLM-5.2
81.0
Weighted basis
0 vs 1 rows
Reading
Not comparable

Coding

Not comparable
Claude Opus 4.5 Thinking
Not measured
GLM-5.2
62.1
Weighted basis
0 vs 1 rows
Reading
Not comparable

Reasoning

Not comparable
Claude Opus 4.5 Thinking
Not measured
GLM-5.2
Not measured
Weighted basis
0 vs 0 rows
Reading
Not comparable

Knowledge

Not comparable
Claude Opus 4.5 Thinking
Not measured
GLM-5.2
59.6
Weighted basis
0 vs 2 rows
Reading
Not comparable

Math

Not comparable
Claude Opus 4.5 Thinking
Not measured
GLM-5.2
95.9
Weighted basis
0 vs 2 rows
Reading
Not comparable

Multilingual

Not comparable
Claude Opus 4.5 Thinking
Not measured
GLM-5.2
Not measured
Weighted basis
0 vs 0 rows
Reading
Not comparable

Multimodal

Not comparable
Claude Opus 4.5 Thinking
Not measured
GLM-5.2
Not measured
Weighted basis
0 vs 0 rows
Reading
Not comparable

Instruction following

Not comparable
Claude Opus 4.5 Thinking
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.

A shared-evidence shape is not available.

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

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 Opus 4.5 Thinking
API rate not published
Fits in one request
GLM-5.2
$0.0036
Fits in one request

Claude Opus 4.5 Thinking has no comparable published API token rate.

Repository review

50K fresh input + 3K output tokens

Claude Opus 4.5 Thinking
API rate not published
Fits in one request
GLM-5.2
$0.0832
Fits in one request

Claude Opus 4.5 Thinking has no comparable published API token rate.

Cache-heavy agent loop

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

Claude Opus 4.5 Thinking
API rate not published
Does not fit in one request
Cached-input rate unavailable
GLM-5.2
$0.352
Fits in one request
Cached input priced at the published list-input rate

Claude Opus 4.5 Thinking does not fit this workload in one request. GLM-5.2 has no published cached-input rate, so cached tokens use its listed input rate. Claude Opus 4.5 Thinking 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 Opus 4.5 Thinking

200K

GLM-5.2

1M

API model ID

Claude Opus 4.5 Thinking

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.

Claude Opus 4.5 Thinking

No comparable hosted API rate

GLM-5.2

Not published

Documented inputs

Claude Opus 4.5 Thinking

Not sourced

GLM-5.2

Not sourced

Documented outputs

Claude Opus 4.5 Thinking

Not sourced

GLM-5.2

Not sourced

Provider availability

Claude Opus 4.5 Thinking

Not sourced

GLM-5.2

Not sourced

Reasoning profile

Claude Opus 4.5 Thinking

Reasoning

GLM-5.2

Reasoning

Weight access

Claude Opus 4.5 Thinking

Proprietary

GLM-5.2

Open Weight

License

Claude Opus 4.5 Thinking

Proprietary

GLM-5.2

Open Weight

Release date

Claude Opus 4.5 Thinking

2025-11-01

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
A complete comparable API-rate estimate is not available for both models.
Context tradeoff
GLM-5.2 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 evidence19 rows

Agentic

  • Terminal-Bench 2.0

    Claude Opus 4.5 Thinking—
    GLM-5.281%
    Source

    Not directly comparable

  • MCP Atlas

    Claude Opus 4.5 Thinking—
    GLM-5.276.8%
    Source

    Not directly comparable

  • Toolathlon

    Claude Opus 4.5 Thinking—
    GLM-5.248.2%
    Source

    Not directly comparable

  • ResearchClawBench

    Claude Opus 4.5 Thinking—
    GLM-5.220.7%
    Source

    Not directly comparable

Coding

  • Vibe Code Bench

    Claude Opus 4.5 Thinking20.63%
    Source
    GLM-5.2—

    Not directly comparable

  • SWE-bench Pro

    Claude Opus 4.5 Thinking—
    GLM-5.262.1%
    Source

    Not directly comparable

  • NL2Repo

    Claude Opus 4.5 Thinking—
    GLM-5.248.9%
    Source

    Not directly comparable

  • Terminal-Bench 2.0

    Claude Opus 4.5 Thinking—
    GLM-5.281.0%
    Source

    Not directly comparable

  • ProgramBench

    Claude Opus 4.5 Thinking—
    GLM-5.263.7%
    Source

    Not directly comparable

  • cursorBench32

    Claude Opus 4.5 Thinking—
    GLM-5.255.0%
    Source

    Not directly comparable

Reasoning

  • CritPt

    Claude Opus 4.5 Thinking—
    GLM-5.220.9%
    Source

    Not directly comparable

Knowledge

  • GPQA

    Claude Opus 4.5 Thinking—
    GLM-5.291.2%
    Source

    Not directly comparable

  • GPQA-D

    Claude Opus 4.5 Thinking—
    GLM-5.291.2%
    Source

    Not directly comparable

  • HLE

    Claude Opus 4.5 Thinking—
    GLM-5.254.7%
    Source

    Not directly comparable

  • HLE w/o tools

    Claude Opus 4.5 Thinking—
    GLM-5.240.5%
    Source

    Not directly comparable

Math

  • AIME26

    Claude Opus 4.5 Thinking—
    GLM-5.299.2%
    Source

    Not directly comparable

  • HMMT Nov 2025

    Claude Opus 4.5 Thinking—
    GLM-5.294.4%
    Source

    Not directly comparable

  • HMMT Feb 2026

    Claude Opus 4.5 Thinking—
    GLM-5.292.5%
    Source

    Not directly comparable

  • MMAnswerBench

    Claude Opus 4.5 Thinking—
    GLM-5.291.0%
    Source

    Not directly comparable

Frequently asked questions

Which is better, Claude Opus 4.5 Thinking 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, Claude Opus 4.5 Thinking or GLM-5.2?

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

Which is better for agentic tasks, Claude Opus 4.5 Thinking or GLM-5.2?

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 Opus 4.5 Thinking or GLM-5.2?

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 Opus 4.5 Thinking or GLM-5.2?

GLM-5.2 has the larger documented context window: 1M, compared with 200K.

Related comparisons

Compare API pricing Read the methodology Open the model selector

Last updated July 31, 2026

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