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Model comparison

Claude Opus 4.6 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 4.6

Anthropic

67.7/100

Supported · Public rank #20

90% interval 54.8–80.7

GLM-5.2

Z.AI

62.9/100

Estimated · Public rank #41

90% interval 47.7–78.2

Claude Opus 4.6 has the higher public score estimate, 67.74 versus 62.94, but the 90% score intervals overlap. Treat that as a lead, not a settled winner.

7 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. Claude Opus 4.6 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

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
7
Claude Opus 4.6 only
26
GLM-5.2 only
11
Like-for-like categories
0 / 8

3 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 Opus 4.6
73.0
GLM-5.2
81.0
Weighted basis
3 vs 1 rows
Reading
Directional only

Coding

Directional only
Claude Opus 4.6
68.1
GLM-5.2
62.1
Weighted basis
3 vs 1 rows
Reading
Directional only

Knowledge

Directional only
Claude Opus 4.6
69.1
GLM-5.2
59.6
Weighted basis
4 vs 2 rows
Reading
Directional only

Reasoning

Not comparable
Claude Opus 4.6
Not measured
GLM-5.2
Not measured
Weighted basis
0 vs 0 rows
Reading
Not comparable

Math

Not comparable
Claude Opus 4.6
36.3
GLM-5.2
95.9
Weighted basis
2 vs 2 rows
Reading
Not comparable

Multilingual

Not comparable
Claude Opus 4.6
Not measured
GLM-5.2
Not measured
Weighted basis
0 vs 0 rows
Reading
Not comparable

Multimodal

Not comparable
Claude Opus 4.6
77.3
GLM-5.2
Not measured
Weighted basis
1 vs 0 rows
Reading
Not comparable

Instruction following

Not comparable
Claude Opus 4.6
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 Opus 4.6
$0.0175
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 Opus 4.6
$0.325
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 Opus 4.6
$1.35
Fits 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

GLM-5.2 has the lower modeled cost

Claude Opus 4.6 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.

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 4.6

1M

GLM-5.2

1M

API model ID

Claude Opus 4.6

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 4.6

Not published

GLM-5.2

Not published

Documented inputs

Claude Opus 4.6

Not sourced

GLM-5.2

Not sourced

Documented outputs

Claude Opus 4.6

Not sourced

GLM-5.2

Not sourced

Provider availability

Claude Opus 4.6

Not sourced

GLM-5.2

Not sourced

Reasoning profile

Claude Opus 4.6

Non-Reasoning

GLM-5.2

Reasoning

Weight access

Claude Opus 4.6

Proprietary

GLM-5.2

Open Weight

License

Claude Opus 4.6

Proprietary

GLM-5.2

Open Weight

Release date

Claude Opus 4.6

2026-02-01

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
Claude Opus 4.6 has the higher public score estimate, 67.74 versus 62.94, but the 90% score intervals overlap.
Workload cost
Repository review: $0.325 vs $0.0832. Cache-heavy agent loop: $1.35 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 evidence44 rows

Agentic

  • Terminal-Bench 2.0

    Claude Opus 4.665.4%
    Source
    GLM-5.281%
    Source

    GLM-5.2 leads this result

  • BrowseComp

    Claude Opus 4.683.7%
    Source
    GLM-5.2—

    Not directly comparable

  • OSWorld-Verified

    Claude Opus 4.672.7%
    Source
    GLM-5.2—

    Not directly comparable

  • Claw-Eval

    Claude Opus 4.670.4%
    Source
    GLM-5.2—

    Not directly comparable

  • DeepSearchQA

    Claude Opus 4.673.7%
    Source
    GLM-5.2—

    Not directly comparable

  • CyberGym

    Claude Opus 4.666.6%
    Source
    GLM-5.2—

    Not directly comparable

  • Gert Labs

    Claude Opus 4.661.85%
    Source
    GLM-5.2—

    Not directly comparable

  • ResearchClawBench

    Shared source
    Claude Opus 4.619.9%
    GLM-5.220.7%

    GLM-5.2 leads this result

  • JobBench

    Claude Opus 4.636.7%
    Source
    GLM-5.2—

    Not directly comparable

  • MCP Atlas

    Claude Opus 4.6—
    GLM-5.276.8%
    Source

    Not directly comparable

  • Toolathlon

    Claude Opus 4.6—
    GLM-5.248.2%
    Source

    Not directly comparable

Coding

  • SWE-bench Verified

    Claude Opus 4.680.8%
    Source
    GLM-5.2—

    Not directly comparable

  • SWE-bench Verified*

    Claude Opus 4.675.6%
    Source
    GLM-5.2—

    Not directly comparable

  • LiveCodeBench Pro

    Claude Opus 4.670.7%
    Source
    GLM-5.2—

    Not directly comparable

  • SWE-bench Pro

    Claude Opus 4.653.4%
    Source
    GLM-5.262.1%
    Source

    GLM-5.2 leads this result

  • SWE-Rebench

    Claude Opus 4.665.3%
    Source
    GLM-5.2—

    Not directly comparable

  • React Native Evals

    Claude Opus 4.684.1%
    Source
    GLM-5.2—

    Not directly comparable

  • Vibe Code Bench

    Claude Opus 4.657.57%
    Source
    GLM-5.2—

    Not directly comparable

  • FrontierCode 1.1 Main

    Claude Opus 4.626.9%
    Source
    GLM-5.2—

    Not directly comparable

  • NL2Repo

    Claude Opus 4.6—
    GLM-5.248.9%
    Source

    Not directly comparable

  • Terminal-Bench 2.0

    Claude Opus 4.6—
    GLM-5.281.0%
    Source

    Not directly comparable

  • ProgramBench

    Claude Opus 4.6—
    GLM-5.263.7%
    Source

    Not directly comparable

  • cursorBench32

    Claude Opus 4.6—
    GLM-5.255.0%
    Source

    Not directly comparable

Reasoning

  • CritPt

    Claude Opus 4.6—
    GLM-5.220.9%
    Source

    Not directly comparable

Knowledge

  • GPQA

    Claude Opus 4.691.3%
    Source
    GLM-5.291.2%
    Source

    Claude Opus 4.6 leads this result

  • GPQA-D

    Claude Opus 4.689.2%
    Source
    GLM-5.291.2%
    Source

    GLM-5.2 leads this result

  • SuperGPQA

    Claude Opus 4.695%
    Source
    GLM-5.2—

    Not directly comparable

  • MMLU-Pro

    Claude Opus 4.682%
    Source
    GLM-5.2—

    Not directly comparable

  • MMLU-Pro (Arcee)

    Claude Opus 4.689.1%
    Source
    GLM-5.2—

    Not directly comparable

  • HLE

    Claude Opus 4.653%
    Source
    GLM-5.254.7%
    Source

    GLM-5.2 leads this result

  • HLE w/o tools

    Claude Opus 4.640%
    Source
    GLM-5.240.5%
    Source

    GLM-5.2 leads this result

  • HealthBench Hard

    Claude Opus 4.614.8%
    Source
    GLM-5.2—

    Not directly comparable

  • MedXpertQA (Text)

    Claude Opus 4.652.1%
    Source
    GLM-5.2—

    Not directly comparable

Math

  • AIME25 (Arcee)

    Claude Opus 4.699.8%
    Source
    GLM-5.2—

    Not directly comparable

  • FrontierMath v2 (Tiers 1-3)

    Claude Opus 4.640.700%
    Source
    GLM-5.2—

    Not directly comparable

  • FrontierMath v2 (Tier 4)

    Claude Opus 4.622.900%
    Source
    GLM-5.2—

    Not directly comparable

  • AIME26

    Claude Opus 4.6—
    GLM-5.299.2%
    Source

    Not directly comparable

  • HMMT Nov 2025

    Claude Opus 4.6—
    GLM-5.294.4%
    Source

    Not directly comparable

  • HMMT Feb 2026

    Claude Opus 4.6—
    GLM-5.292.5%
    Source

    Not directly comparable

  • MMAnswerBench

    Claude Opus 4.6—
    GLM-5.291.0%
    Source

    Not directly comparable

Multimodal

  • MMMU-Pro

    Claude Opus 4.677.3%
    Source
    GLM-5.2—

    Not directly comparable

  • ERQA

    Claude Opus 4.651.6%
    Source
    GLM-5.2—

    Not directly comparable

  • ScreenSpot Pro

    Claude Opus 4.683.1%
    Source
    GLM-5.2—

    Not directly comparable

  • MedXpertQA (MM)

    Claude Opus 4.664.8%
    Source
    GLM-5.2—

    Not directly comparable

Frequently asked questions

Which is better, Claude Opus 4.6 or GLM-5.2?

Claude Opus 4.6 has the higher public score estimate, 67.74 versus 62.94, but the 90% score intervals overlap. The higher estimate is not a decisive winner because the uncertainty ranges overlap.

Which is better for coding, Claude Opus 4.6 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 Opus 4.6 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 Opus 4.6 or GLM-5.2?

For the stated presets, chat costs $0.0175 on Claude Opus 4.6 and $0.0036 on GLM-5.2; repository review costs $0.325 and $0.0832; the cache-heavy agent loop costs $1.35 and $0.352. Claude Opus 4.6 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, Claude Opus 4.6 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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