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BenchLM
Model A
Claude Mythos Preview

Anthropic

—

Evidence status unavailable

90% interval unavailable

Claude Mythos Preview vs GLM-5.1

Updated August 13, 2026. Public scores include evidence status and uncertainty. They are not guarantees for a specific workload.

Model B
GLM-5.1

Z.AI

66.6/100

Supported · Public rank #26

90% interval 56.4–76.8

Decision reading

At least one model is not scored in the current public ranking lane, so the page does not name an overall quality winner.

1 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-5.1

    GLM-5.1 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

    GLM-5.1

    GLM-5.1 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

    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

  • Long documents

    Prompts that approach the documented context limit

    Not enough matched evidence

    A complete context comparison is not sourced.

    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. GLM-5.1 does not fit this workload in one request. Claude Mythos Preview has no published cached-input rate, so cached tokens use its listed input rate. GLM-5.1 has no published cached-input rate, so cached tokens use its listed input rate.

    Confidence: rate-fallback

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
1
Claude Mythos Preview only
1
GLM-5.1 only
19
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 Mythos Preview
Not measured
GLM-5.1
65.4
Weighted basis
0 vs 2 rows
Reading
Not comparable

Coding

Not comparable
Claude Mythos Preview
Not measured
GLM-5.1
61.3
Weighted basis
0 vs 2 rows
Reading
Not comparable

Reasoning

Not comparable
Claude Mythos Preview
Not measured
GLM-5.1
Not measured
Weighted basis
0 vs 0 rows
Reading
Not comparable

Knowledge

Not comparable
Claude Mythos Preview
Not measured
GLM-5.1
52.3
Weighted basis
0 vs 1 rows
Reading
Not comparable

Math

Not comparable
Claude Mythos Preview
Not measured
GLM-5.1
62.0
Weighted basis
0 vs 4 rows
Reading
Not comparable

Multilingual

Not comparable
Claude Mythos Preview
Not measured
GLM-5.1
Not measured
Weighted basis
0 vs 0 rows
Reading
Not comparable

Multimodal

Not comparable
Claude Mythos Preview
Not measured
GLM-5.1
Not measured
Weighted basis
0 vs 0 rows
Reading
Not comparable

Instruction following

Not comparable
Claude Mythos Preview
Not measured
GLM-5.1
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 Mythos Preview
$0.0875
Fit state unavailable
GLM-5.1
$0.0036
Fits in one request

GLM-5.1 has the lower modeled cost

Costs use the listed standard API rates.

Repository review

50K fresh input + 3K output tokens

Claude Mythos Preview
$1.63
Fit state unavailable
GLM-5.1
$0.0832
Fits in one request

GLM-5.1 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 Preview
$6.75
Fit state unavailable
Cached input priced at the published list-input rate
GLM-5.1
$0.352
Does not fit in one request
Cached input priced at the published list-input rate

GLM-5.1 does not fit this workload in one request. Claude Mythos Preview has no published cached-input rate, so cached tokens use its listed input rate. GLM-5.1 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 Preview

Not sourced

GLM-5.1

203K

Cached-input rate

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

Claude Mythos Preview

Not published

Anthropic Project Glasswing

GLM-5.1

Not published

Documented inputs

Claude Mythos Preview

Not sourced

GLM-5.1

Not sourced

Documented outputs

Claude Mythos Preview

Not sourced

GLM-5.1

Not sourced

Provider availability

Claude Mythos Preview

Not sourced

GLM-5.1

Not sourced

Reasoning profile

Claude Mythos Preview

Reasoning

GLM-5.1

Reasoning

Weight access

Claude Mythos Preview

Proprietary

GLM-5.1

Open Weight

License

Claude Mythos Preview

Proprietary

GLM-5.1

Open Weight

Release date

Claude Mythos Preview

2026-04-07

GLM-5.1

2026-04-07

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
At least one model is not scored in the current public ranking lane, so the page does not name an overall quality winner.
Workload cost
Repository review: $1.63 vs $0.0832. Cache-heavy agent loop: $6.75 vs $0.352.
Context tradeoff
A complete documented context comparison is not available.

Run the same representative tasks against both endpoints before changing production traffic.

Self-host vs API cost

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

Claude Mythos Preview
API / mo$112,500
Self-host / moNot listed
Break-even—
Proprietary model — self-hosting not applicable.
GLM-5.1
API / mo$4,350
Self-host / mo$18,221
Break-even264M/day
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 evidence21 rows

Agentic

  • CyberGym

    Shared source
    Claude Mythos Preview83.1%
    GLM-5.168.7%

    Claude Mythos Preview leads this result

  • ExploitGym

    Claude Mythos Preview17.5%
    Source
    GLM-5.1—

    Not directly comparable

  • Terminal-Bench 2.0

    Claude Mythos Preview—
    GLM-5.163.5%
    Source

    Not directly comparable

  • BrowseComp

    Claude Mythos Preview—
    GLM-5.168%
    Source

    Not directly comparable

  • τ³-bench results

    Claude Mythos Preview—
    GLM-5.170.6%
    Source

    Not directly comparable

  • MCP Atlas

    Claude Mythos Preview—
    GLM-5.171.8%
    Source

    Not directly comparable

  • Claw-Eval

    Claude Mythos Preview—
    GLM-5.162.3%
    Source

    Not directly comparable

  • Gert Labs

    Claude Mythos Preview—
    GLM-5.160.11%
    Source

    Not directly comparable

  • ResearchClawBench

    Claude Mythos Preview—
    GLM-5.118.2%
    Source

    Not directly comparable

Coding

  • SWE-bench Pro

    Claude Mythos Preview—
    GLM-5.158.4%
    Source

    Not directly comparable

  • NL2Repo

    Claude Mythos Preview—
    GLM-5.142.7%
    Source

    Not directly comparable

  • SWE-Rebench

    Claude Mythos Preview—
    GLM-5.162.7%
    Source

    Not directly comparable

  • Vibe Code Bench

    Claude Mythos Preview—
    GLM-5.131.46%
    Source

    Not directly comparable

Knowledge

  • GPQA-D

    Claude Mythos Preview—
    GLM-5.186.2%
    Source

    Not directly comparable

  • HLE

    Claude Mythos Preview—
    GLM-5.152.3%
    Source

    Not directly comparable

Math

  • AIME26

    Claude Mythos Preview—
    GLM-5.195.3%
    Source

    Not directly comparable

  • HMMT Nov 2025

    Claude Mythos Preview—
    GLM-5.194.0%
    Source

    Not directly comparable

  • HMMT Feb 2026

    Claude Mythos Preview—
    GLM-5.182.6%
    Source

    Not directly comparable

  • MMAnswerBench

    Claude Mythos Preview—
    GLM-5.183.8%
    Source

    Not directly comparable

  • FrontierMath v2 (Tiers 1-3)

    Claude Mythos Preview—
    GLM-5.133.448%
    Source

    Not directly comparable

  • FrontierMath v2 (Tier 4)

    Claude Mythos Preview—
    GLM-5.112.500%
    Source

    Not directly comparable

Frequently asked questions

Which is better, Claude Mythos Preview or GLM-5.1?

At least one model is not scored in the current public ranking lane, so the page does not name an overall quality winner. The page therefore keeps the decision tied to the specific documented workload.

Which is better for coding, Claude Mythos Preview or GLM-5.1?

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 Mythos Preview or GLM-5.1?

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 Mythos Preview or GLM-5.1?

For the stated presets, chat costs $0.0875 on Claude Mythos Preview and $0.0036 on GLM-5.1; repository review costs $1.63 and $0.0832; the cache-heavy agent loop costs $6.75 and $0.352. GLM-5.1 does not fit this workload in one request. Claude Mythos Preview has no published cached-input rate, so cached tokens use its listed input rate. GLM-5.1 has no published cached-input rate, so cached tokens use its listed input rate.

Which has the larger context window, Claude Mythos Preview or GLM-5.1?

A complete documented context-window comparison is not available.

Related comparisons

Compare API pricing Read the methodology Open the model selector

Last updated August 13, 2026

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