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Model comparison

Claude Mythos 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 Mythos 5

Anthropic

83.0/100

Supported · Public rank #1

90% interval 79.3–86.7

GLM-5.2

Z.AI

62.9/100

Estimated · Public rank #41

90% interval 47.7–78.2

Claude Mythos 5 has the higher public score, 82.98 versus 62.94, and the 90% score intervals do not overlap.

6 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
6
Claude Mythos 5 only
9
GLM-5.2 only
12
Like-for-like categories
1 / 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.

Knowledge

Like-for-like
Claude Mythos 5
68.5
GLM-5.2
59.6
Weighted basis
2 vs 2 rows
Reading
Claude Mythos 5 leads

Agentic

Directional only
Claude Mythos 5
87.0
GLM-5.2
81.0
Weighted basis
3 vs 1 rows
Reading
Directional only

Coding

Directional only
Claude Mythos 5
89.7
GLM-5.2
62.1
Weighted basis
2 vs 1 rows
Reading
Directional only

Reasoning

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

Math

Not comparable
Claude Mythos 5
97.6
GLM-5.2
95.9
Weighted basis
1 vs 2 rows
Reading
Not comparable

Multilingual

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

Multimodal

Not comparable
Claude Mythos 5
93.5
GLM-5.2
Not measured
Weighted basis
1 vs 0 rows
Reading
Not comparable

Instruction following

Not comparable
Claude Mythos 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 Mythos 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 Mythos 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 Mythos 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 Mythos 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 Mythos 5

$1 per 1M cached input tokens

Claude API pricing

GLM-5.2

Not published

Reasoning profile

Claude Mythos 5

Reasoning

GLM-5.2

Reasoning

Weight access

Claude Mythos 5

Proprietary

GLM-5.2

Open Weight

License

Claude Mythos 5

Proprietary

GLM-5.2

Open Weight

Release date

Claude Mythos 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 Mythos 5 has the higher public score, 82.98 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 evidence27 rows

Agentic

  • Terminal-Bench 2.0

    Claude Mythos 588%
    Source
    GLM-5.281%
    Source

    Claude Mythos 5 leads this result

  • OSWorld-Verified

    Claude Mythos 585%
    Source
    GLM-5.2—

    Not directly comparable

  • BrowseComp

    Claude Mythos 588%
    Source
    GLM-5.2—

    Not directly comparable

  • ExploitGym

    Claude Mythos 517.5%
    Source
    GLM-5.2—

    Not directly comparable

  • MCP Atlas

    Claude Mythos 5—
    GLM-5.276.8%
    Source

    Not directly comparable

  • Toolathlon

    Claude Mythos 5—
    GLM-5.248.2%
    Source

    Not directly comparable

  • ResearchClawBench

    Claude Mythos 5—
    GLM-5.220.7%
    Source

    Not directly comparable

Coding

  • SWE-bench Verified

    Claude Mythos 595.5%
    Source
    GLM-5.2—

    Not directly comparable

  • SWE-bench Pro

    Claude Mythos 580.3%
    Source
    GLM-5.262.1%
    Source

    Claude Mythos 5 leads this result

  • Terminal-Bench 2.0

    Claude Mythos 588.0%
    Source
    GLM-5.281.0%
    Source

    Claude Mythos 5 leads this result

  • NL2Repo

    Claude Mythos 5—
    GLM-5.248.9%
    Source

    Not directly comparable

  • ProgramBench

    Claude Mythos 5—
    GLM-5.263.7%
    Source

    Not directly comparable

  • cursorBench32

    Claude Mythos 5—
    GLM-5.255.0%
    Source

    Not directly comparable

Reasoning

  • CritPt

    Claude Mythos 5—
    GLM-5.220.9%
    Source

    Not directly comparable

Knowledge

  • GPQA

    Claude Mythos 594.1%
    Source
    GLM-5.291.2%
    Source

    Claude Mythos 5 leads this result

  • HLE

    Claude Mythos 564.5%
    Source
    GLM-5.254.7%
    Source

    Claude Mythos 5 leads this result

  • HLE w/o tools

    Claude Mythos 559%
    Source
    GLM-5.240.5%
    Source

    Claude Mythos 5 leads this result

  • GPQA-D

    Claude Mythos 5—
    GLM-5.291.2%
    Source

    Not directly comparable

Math

  • USAMO 2026

    Claude Mythos 597.6%
    Source
    GLM-5.2—

    Not directly comparable

  • AIME26

    Claude Mythos 5—
    GLM-5.299.2%
    Source

    Not directly comparable

  • HMMT Nov 2025

    Claude Mythos 5—
    GLM-5.294.4%
    Source

    Not directly comparable

  • HMMT Feb 2026

    Claude Mythos 5—
    GLM-5.292.5%
    Source

    Not directly comparable

  • MMAnswerBench

    Claude Mythos 5—
    GLM-5.291.0%
    Source

    Not directly comparable

Multilingual

  • SWE Multilingual

    Claude Mythos 592.2%
    Source
    GLM-5.2—

    Not directly comparable

Multimodal

  • SWE-bench Multimodal

    Claude Mythos 554.9%
    Source
    GLM-5.2—

    Not directly comparable

  • CharXiv

    Claude Mythos 593.5%
    Source
    GLM-5.2—

    Not directly comparable

  • CharXiv w/o tools

    Claude Mythos 588.9%
    Source
    GLM-5.2—

    Not directly comparable

Frequently asked questions

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

Claude Mythos 5 has the higher public score, 82.98 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 Mythos 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 Mythos 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 Mythos 5 or GLM-5.2?

For the stated presets, chat costs $0.035 on Claude Mythos 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 Mythos 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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