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Model comparison

GLM-5.1 vs GLM-5.2

Updated July 31, 2026. Public scores include evidence status and uncertainty. They are not guarantees for a specific workload. This is a same-family comparison, so migration details appear when the source data supports them.

21 confirmed releases in the last 30 daystrack changes
GLM-5.1

Z.AI

66.8/100

Supported · Public rank #21

90% interval 56.9–76.7

GLM-5.2

Z.AI

62.9/100

Estimated · Public rank #41

90% interval 47.7–78.2

GLM-5.1 has the higher public score estimate, 66.84 versus 62.94, but the 90% score intervals overlap. Treat that as a lead, not a settled winner.

11 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

    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

  • Chat turn cost

    1K fresh input + 500 output tokens

    No clear pick

    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. GLM-5.1 does not fit this workload in one request. GLM-5.1 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

  • Repository review cost

    50K fresh input + 3K output tokens

    No clear pick

    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
11
GLM-5.1 only
9
GLM-5.2 only
7
Like-for-like categories
0 / 8

4 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
GLM-5.1
65.4
GLM-5.2
81.0
Weighted basis
2 vs 1 rows
Reading
Directional only

Coding

Directional only
GLM-5.1
61.3
GLM-5.2
62.1
Weighted basis
2 vs 1 rows
Reading
Directional only

Knowledge

Directional only
GLM-5.1
52.3
GLM-5.2
59.6
Weighted basis
1 vs 2 rows
Reading
Directional only

Math

Directional only
GLM-5.1
62.0
GLM-5.2
95.9
Weighted basis
4 vs 2 rows
Reading
Directional only

Reasoning

Not comparable
GLM-5.1
Not measured
GLM-5.2
Not measured
Weighted basis
0 vs 0 rows
Reading
Not comparable

Multilingual

Not comparable
GLM-5.1
Not measured
GLM-5.2
Not measured
Weighted basis
0 vs 0 rows
Reading
Not comparable

Multimodal

Not comparable
GLM-5.1
Not measured
GLM-5.2
Not measured
Weighted basis
0 vs 0 rows
Reading
Not comparable

Instruction following

Not comparable
GLM-5.1
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

GLM-5.1
$0.0036
Fits in one request
GLM-5.2
$0.0036
Fits in one request

Modeled costs are equal

Costs use the listed standard API rates.

Repository review

50K fresh input + 3K output tokens

GLM-5.1
$0.0832
Fits in one request
GLM-5.2
$0.0832
Fits in one request

Modeled costs are equal

Costs use the listed standard API rates.

Cache-heavy agent loop

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

GLM-5.1
$0.352
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

GLM-5.1 does not fit this workload in one request. GLM-5.1 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.

GLM-5.1

203K

GLM-5.2

1M

API model ID

GLM-5.1

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.

GLM-5.1

Not published

GLM-5.2

Not published

Documented inputs

GLM-5.1

Not sourced

GLM-5.2

Not sourced

Documented outputs

GLM-5.1

Not sourced

GLM-5.2

Not sourced

Provider availability

GLM-5.1

Not sourced

GLM-5.2

Not sourced

Reasoning profile

GLM-5.1

Reasoning

GLM-5.2

Reasoning

Weight access

GLM-5.1

Open Weight

GLM-5.2

Open Weight

License

GLM-5.1

Open Weight

GLM-5.2

Open Weight

Release date

GLM-5.1

2026-04-07

GLM-5.2

2026-06-16

If you are considering the documented upgrade path
Deployment change
Both entries list Z.AI as the provider. Confirm endpoint, model ID, limits, and feature support before switching.
Quality signal
GLM-5.1 has the higher public score estimate, 66.84 versus 62.94, but the 90% score intervals overlap.
Workload cost
Repository review: $0.0832 vs $0.0832. Cache-heavy agent loop: $0.352 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.

Self-host vs API cost

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

GLM-5.1
API / mo$4,350
Self-host / mo$18,221
Break-even264M/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 evidence27 rows

Agentic

  • Terminal-Bench 2.0

    GLM-5.163.5%
    Source
    GLM-5.281%
    Source

    GLM-5.2 leads this result

  • BrowseComp

    GLM-5.168%
    Source
    GLM-5.2—

    Not directly comparable

  • τ³-bench results

    GLM-5.170.6%
    Source
    GLM-5.2—

    Not directly comparable

  • MCP Atlas

    GLM-5.171.8%
    Source
    GLM-5.276.8%
    Source

    GLM-5.2 leads this result

  • CyberGym

    GLM-5.168.7%
    Source
    GLM-5.2—

    Not directly comparable

  • Claw-Eval

    GLM-5.162.3%
    Source
    GLM-5.2—

    Not directly comparable

  • Gert Labs

    GLM-5.160.11%
    Source
    GLM-5.2—

    Not directly comparable

  • ResearchClawBench

    Shared source
    GLM-5.118.2%
    GLM-5.220.7%

    GLM-5.2 leads this result

  • Toolathlon

    GLM-5.1—
    GLM-5.248.2%
    Source

    Not directly comparable

Coding

  • SWE-bench Pro

    GLM-5.158.4%
    Source
    GLM-5.262.1%
    Source

    GLM-5.2 leads this result

  • NL2Repo

    GLM-5.142.7%
    Source
    GLM-5.248.9%
    Source

    GLM-5.2 leads this result

  • SWE-Rebench

    GLM-5.162.7%
    Source
    GLM-5.2—

    Not directly comparable

  • Vibe Code Bench

    GLM-5.131.46%
    Source
    GLM-5.2—

    Not directly comparable

  • Terminal-Bench 2.0

    GLM-5.1—
    GLM-5.281.0%
    Source

    Not directly comparable

  • ProgramBench

    GLM-5.1—
    GLM-5.263.7%
    Source

    Not directly comparable

  • cursorBench32

    GLM-5.1—
    GLM-5.255.0%
    Source

    Not directly comparable

Reasoning

  • CritPt

    GLM-5.1—
    GLM-5.220.9%
    Source

    Not directly comparable

Knowledge

  • GPQA-D

    GLM-5.186.2%
    Source
    GLM-5.291.2%
    Source

    GLM-5.2 leads this result

  • HLE

    GLM-5.152.3%
    Source
    GLM-5.254.7%
    Source

    GLM-5.2 leads this result

  • GPQA

    GLM-5.1—
    GLM-5.291.2%
    Source

    Not directly comparable

  • HLE w/o tools

    GLM-5.1—
    GLM-5.240.5%
    Source

    Not directly comparable

Math

  • AIME26

    GLM-5.195.3%
    Source
    GLM-5.299.2%
    Source

    GLM-5.2 leads this result

  • HMMT Nov 2025

    GLM-5.194.0%
    Source
    GLM-5.294.4%
    Source

    GLM-5.2 leads this result

  • HMMT Feb 2026

    GLM-5.182.6%
    Source
    GLM-5.292.5%
    Source

    GLM-5.2 leads this result

  • MMAnswerBench

    GLM-5.183.8%
    Source
    GLM-5.291.0%
    Source

    GLM-5.2 leads this result

  • FrontierMath v2 (Tiers 1-3)

    GLM-5.133.448%
    Source
    GLM-5.2—

    Not directly comparable

  • FrontierMath v2 (Tier 4)

    GLM-5.112.500%
    Source
    GLM-5.2—

    Not directly comparable

Frequently asked questions

Which is better, GLM-5.1 or GLM-5.2?

GLM-5.1 has the higher public score estimate, 66.84 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, GLM-5.1 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, GLM-5.1 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, GLM-5.1 or GLM-5.2?

For the stated presets, chat costs $0.0036 on GLM-5.1 and $0.0036 on GLM-5.2; repository review costs $0.0832 and $0.0832; the cache-heavy agent loop costs $0.352 and $0.352. GLM-5.1 does not fit this workload in one request. GLM-5.1 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, GLM-5.1 or GLM-5.2?

GLM-5.2 has the larger documented context window: 1M, compared with 203K.

Related comparisons

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Last updated July 31, 2026

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