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BenchLM

GLM-4.5 vs GLM-5.1

Updated September 23, 2026. We do not rank this pair: at least one has no public score. Public scores include evidence status and uncertainty.

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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. 3 results are shared. Category rows resting on Estimated evidence or different benchmark sets are marked directional and do not name a winner.

Model A
Z.AI logo
GLM-4.5

Z.AI

—

Evidence status unavailable

90% interval unavailable

Model B
Z.AI logo
GLM-5.1

Z.AI

57.71/100

Supported · Public rank #48

90% interval 47.6–67.8

Shared results
3
GLM-4.5 only
0
GLM-5.1 only
23
Like-for-like categories
0 / 8
Supported: GLM-5.1How the comparison works

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-5.1

    GLM-5.1 has the larger documented context window.

    Confidence: documented

  • Chat turn cost

    1K fresh input + 500 output tokens

    GLM-4.5

    GLM-4.5 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-4.5

    GLM-4.5 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

    GLM-4.5 is not ranked on the public lane for coding, so no winner is named for coding.

    Confidence: limited

  • Agentic work

    Tool use, computer use, and multi-step task completion

    Not enough matched evidence

    GLM-4.5 is not ranked on the public lane for agentic, so no winner is named for agentic.

    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-4.5 does not fit this workload in one request. GLM-5.1 does not fit this workload in one request. GLM-4.5 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

Which one for a specific job

Choose a job from the LLM Selector's task catalog to see the category row it rests on, under the same basis rules as the table below. A directional row stays directional; choosing a job never creates a winner.

The same task catalog as the LLM Selector. Each job names the evidence surface it rests on; nothing here adds a new score.

—GLM-4.551.5GLM-5.1

Not comparable · BenchAlign v5.6

The coding row is not comparable on the public lane: at least one model is not measured or not ranked there.

Coding scores combine specific tasks and setups. Match the editor, harness, and effort to your workflow.

Open the coding leaderboardShortlist models for this job
Same basis rules as the category table below

What is actually comparable

Shared results can support a head-to-head reading. Results present for only one model describe coverage, not superiority.

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.

Bars run 0–100 on each benchmark’s normalized display scale

Category results, on a stated basis

Each row shows the public-lane category score for both models: the BenchAlign v5.6 lane for agentic, coding, and knowledge, and the weighted public rows elsewhere, with the same rank each model page reports. A row is like-for-like only when both scores rest on Supported evidence or the same weighted set. Directional and not-comparable rows remain visible, but they never receive a winner.

Agentic

Not comparable
GLM-4.5
Not ranked
GLM-5.1
42.1
Estimated · #43/105
Basis
BenchAlign v5.6 lane · 0 vs 9 public rows
Reading
Not comparable

Coding

Not comparable
GLM-4.5
Not ranked
GLM-5.1
51.5
Supported · #36/135
Basis
BenchAlign v5.6 lane · 1 vs 7 public rows
Reading
Not comparable

Reasoning

Not comparable
GLM-4.5
Not ranked
GLM-5.1
71.7
Unranked · 2 rankable rows
Basis
Provisional lane · 0 vs 0 weighted rows
Reading
Not comparable

Multimodal

Not comparable
GLM-4.5
Not ranked
GLM-5.1
Not ranked
Basis
Provisional lane · 0 vs 0 weighted rows
Reading
Not comparable

Knowledge

Not comparable
GLM-4.5
Not ranked
GLM-5.1
50.2
Supported · #54/160
Basis
BenchAlign v5.6 lane · 2 vs 4 public rows
Reading
Not comparable

Multilingual

Not comparable
GLM-4.5
Not ranked
GLM-5.1
Not ranked
Basis
Provisional lane · 0 vs 0 weighted rows
Reading
Not comparable

Instruction following

Not comparable
GLM-4.5
Not ranked
GLM-5.1
92.4
#4/124
Basis
Provisional lane · 0 vs 0 weighted rows
Reading
Not comparable

Math

Not comparable
GLM-4.5
Not ranked
GLM-5.1
63.8
#3/7
Basis
Provisional lane · 0 vs 4 weighted rows
Reading
Not comparable

Ranks count the models scored in each category’s lane, so the agentic, coding, and knowledge denominators (BenchAlign v5.6) differ from the provisional-lane categories. Unranked scores sit on the provisional lane’s scale but never name a winner. Like-for-like rows within 0.5 points read as a practical tie.

Supported evidence per lane · bars run 0–100Methodology

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-4.5
$0.0017
Fits in one request
GLM-5.1
$0.0036
Fits in one request

GLM-4.5 has the lower modeled cost

Costs use the listed standard API rates.

Repository review

50K fresh input + 3K output tokens

GLM-4.5
$0.0366
Fits in one request
GLM-5.1
$0.0832
Fits in one request

GLM-4.5 has the lower modeled cost

Costs use the listed standard API rates.

Cache-heavy agent loop

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

GLM-4.5
$0.154
Does not fit in one request
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-4.5 does not fit this workload in one request. GLM-5.1 does not fit this workload in one request. GLM-4.5 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.

Cached input falls back to the list input rate only where a cached rate is unpublished

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-4.5

128K

GLM-5.1

203K

API model ID

GLM-4.5

Not sourced

GLM-5.1

Not sourced

Cached-input rate

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

GLM-4.5

Not published

GLM-5.1

Not published

Documented inputs

GLM-4.5

Not sourced

GLM-5.1

Not sourced

Documented outputs

GLM-4.5

Not sourced

GLM-5.1

Not sourced

Provider availability

GLM-4.5

Not sourced

GLM-5.1

Not sourced

Reasoning profile

GLM-4.5

Non-Reasoning

GLM-5.1

Reasoning

Weight access

GLM-4.5

Proprietary

GLM-5.1

Open Weight

License

GLM-4.5

Proprietary

GLM-5.1

Open Weight

Release date

GLM-4.5

2025-06-01

GLM-5.1

2026-04-07

If you already use one of these models

Deployment change
Both entries list Z.AI as the provider. Confirm endpoint, model ID, limits, and feature support before switching.
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: $0.0366 vs $0.0832. Cache-heavy agent loop: $0.154 vs $0.352.
Context tradeoff
GLM-5.1 has the larger documented window (203K).
Run the same representative tasks against both endpoints before changing production traffic.

Questions

Which is better, GLM-4.5 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, GLM-4.5 or GLM-5.1?

GLM-4.5 is not ranked on the public lane for coding, so no winner is named for coding.

Which is better for agentic tasks, GLM-4.5 or GLM-5.1?

GLM-4.5 is not ranked on the public lane for agentic tasks, so no winner is named for agentic tasks.

Which costs less, GLM-4.5 or GLM-5.1?

For the stated presets, chat costs $0.0017 on GLM-4.5 and $0.0036 on GLM-5.1; repository review costs $0.0366 and $0.0832; the cache-heavy agent loop costs $0.154 and $0.352. GLM-4.5 does not fit this workload in one request. GLM-5.1 does not fit this workload in one request. GLM-4.5 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, GLM-4.5 or GLM-5.1?

GLM-5.1 has the larger documented context window: 203K, compared with 128K.

Self-host vs API cost

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

GLM-4.5
API / mo$2,100
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 evidence26 rows

Agentic

  • Terminal-Bench 2.0

    GLM-4.5—
    GLM-5.163.5%
    Source

    Not directly comparable

  • BrowseComp

    GLM-4.5—
    GLM-5.168%
    Source

    Not directly comparable

  • τ³-bench results

    GLM-4.5—
    GLM-5.170.6%
    Source

    Not directly comparable

  • MCP Atlas

    GLM-4.5—
    GLM-5.171.8%
    Source

    Not directly comparable

  • CyberGym

    GLM-4.5—
    GLM-5.168.7%
    Source

    Not directly comparable

  • Claw-Eval

    GLM-4.5—
    GLM-5.162.3%
    Source

    Not directly comparable

  • Gert Labs

    GLM-4.5—
    GLM-5.160.11%
    Source

    Not directly comparable

  • ResearchClawBench

    GLM-4.5—
    GLM-5.118.2%
    Source

    Not directly comparable

  • Terminal-Bench 2.1 (Vals)

    GLM-4.5—
    GLM-5.156.9%
    Source

    Not directly comparable

Coding

  • LiveCodeBench (Vals)

    GLM-4.567.4%
    Source
    GLM-5.181.4%
    Source

    GLM-5.1 leads this result

  • SWE-bench Pro

    GLM-4.5—
    GLM-5.158.4%
    Source

    Not directly comparable

  • NL2Repo

    GLM-4.5—
    GLM-5.142.7%
    Source

    Not directly comparable

  • SWE-Rebench

    GLM-4.5—
    GLM-5.162.7%
    Source

    Not directly comparable

  • Vibe Code Bench

    GLM-4.5—
    GLM-5.131.46%
    Source

    Not directly comparable

  • OpenHarmony Bench

    GLM-4.5—
    GLM-5.152.3%
    Source

    Not directly comparable

  • SWE-bench (Vals)

    GLM-4.5—
    GLM-5.176.4%
    Source

    Not directly comparable

Knowledge

  • GPQA Diamond (Vals)

    GLM-4.572.2%
    Source
    GLM-5.184.5%
    Source

    GLM-5.1 leads this result

  • MMLU-Pro (Vals)

    GLM-4.581.2%
    Source
    GLM-5.186.9%
    Source

    GLM-5.1 leads this result

  • GPQA-D

    GLM-4.5—
    GLM-5.186.2%
    Source

    Not directly comparable

  • HLE

    GLM-4.5—
    GLM-5.152.3%
    Source

    Not directly comparable

Math

  • AIME26

    GLM-4.5—
    GLM-5.195.3%
    Source

    Not directly comparable

  • HMMT Nov 2025

    GLM-4.5—
    GLM-5.194.0%
    Source

    Not directly comparable

  • HMMT Feb 2026

    GLM-4.5—
    GLM-5.182.6%
    Source

    Not directly comparable

  • MMAnswerBench

    GLM-4.5—
    GLM-5.183.8%
    Source

    Not directly comparable

  • FrontierMath v2 (Tiers 1-3)

    GLM-4.5—
    GLM-5.133.448%
    Source

    Not directly comparable

  • FrontierMath v2 (Tier 4)

    GLM-4.5—
    GLM-5.112.500%
    Source

    Not directly comparable

26 public results · 3 shared

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Last updated September 23, 2026