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Data

Gemma 4 31B vs GLM-5.3

Updated November 13, 2026. Rank says GLM-5.3 is ahead. Price, access, and your workload can each overturn that. Public scores include evidence status and uncertainty.

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Decision reading

GLM-5.3 has the higher public point estimate, 60.96 versus 28.81. Their conditional score ranges do not overlap. These ranges do not establish rank confidence. 2 results are shared. Category rows resting on Estimated evidence or different benchmark sets are marked directional and do not name a winner.

Model A
Google logo
Gemma 4 31B

Google

28.81/100

Estimated · Public rank #175

Conditional range 19.1–38.5

Model B
Z.AI logo
GLM-5.3

Z.AI

60.96/100

Supported · Public rank #48

90% interval 51.2–70.7

API price per 1M tokens

Gemma 4 31B: No comparable API price

GLM-5.3: No comparable API price

Context window

Gemma 4 31B: 256K

GLM-5.3: 1M

Released

Gemma 4 31B: April 2, 2026

GLM-5.3: August 14, 2026

Shared results
2
Gemma 4 31B only
11
GLM-5.3 only
37
Like-for-like categories
3 / 8
Estimated: Gemma 4 31B · Supported: GLM-5.3. Conditional ranges do not establish rank confidence.How 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.

  • Coding work

    Code generation, repair, and software-engineering tasks

    GLM-5.3

    GLM-5.3 has the higher public coding point estimate, 53.6 to 28.8, with Supported evidence for both models. The conditional ranges do not establish rank confidence.

    Confidence: limited

  • Long documents

    Prompts that approach the documented context limit

    GLM-5.3

    GLM-5.3 has the larger documented context window.

    Confidence: documented
Show secondary and unsupported calls

  • Agentic work

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

    Not enough matched evidence

    Gemma 4 31B is scored on Estimated evidence for agentic, so the reading is directional rather than like-for-like.

    Confidence: limited

  • Chat turn cost

    1K fresh input + 500 output tokens

    Not enough matched evidence

    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

    A complete comparable API-rate estimate is not available for both models.

    Confidence: listed-rates

  • Repository review cost

    50K fresh input + 3K output tokens

    Not enough matched evidence

    A complete comparable API-rate estimate is not available for both models.

    Confidence: listed-rates

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.

28.8Gemma 4 31B53.6GLM-5.3

Like-for-like · BenchAlign v5.8

GLM-5.3 has the higher coding point estimate. Conditional score ranges do not establish rank confidence.

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

Or describe your own job

The LLM Selector reads what you need a model for and returns a shortlist, ranked in the same order the job pages publish.

Describe the task and constraints. This selector recommends models; it cannot carry out the task or access your files and accounts.

We keep what you type here, drafts included, for 180 days, and after that only the job and requirements read from it. Nothing is tied to your IP address or account. Leave out names and other personal details. Privacy

What is actually comparable

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

1 category rests on Estimated evidence or different evidence sets. Those rows remain visible for coverage context but do not name a winner.

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.

Category results, on a stated basis

Each row shows the public-lane category score for both models: the BenchAlign v5.8 lane for agentic, coding, reasoning, multimodal, 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.

Coding

Like-for-like
Gemma 4 31B
28.8
Supported · #107/162
GLM-5.3
53.6
Supported · #33/162
Basis
BenchAlign v5.8 lane · 3 vs 17 public rows
Reading
GLM-5.3 leads

Reasoning

Like-for-like
Gemma 4 31B
32.0
Supported · #109/160
GLM-5.3
57.3
Supported · #36/160
Basis
BenchAlign v5.8 lane · 1 vs 0 public rows
Reading
GLM-5.3 leads

Knowledge

Like-for-like
Gemma 4 31B
36.7
Supported · #121/180
GLM-5.3
59.9
Supported · #48/180
Basis
BenchAlign v5.8 lane · 4 vs 2 public rows
Reading
GLM-5.3 leads

Agentic

Directional only
Gemma 4 31B
12.5
Estimated · #133/135
GLM-5.3
66.7
Supported · #14/135
Basis
BenchAlign v5.8 lane · 3 vs 17 public rows
Reading
Directional only

Multimodal

Not comparable
Gemma 4 31B
47.3
Supported · #60/67
GLM-5.3
Not ranked
Basis
BenchAlign v5.8 lane · 2 vs 0 public rows
Reading
Not comparable

Multilingual

Not comparable
Gemma 4 31B
Not ranked
GLM-5.3
Not ranked
Basis
Provisional lane · 0 vs 0 weighted rows
Reading
Not comparable

Instruction following

Not comparable
Gemma 4 31B
92.9
#16/157
GLM-5.3
Not ranked
Basis
Provisional lane · 0 vs 0 weighted rows
Reading
Not comparable

Math

Not comparable
Gemma 4 31B
Not ranked
GLM-5.3
67.0
Unranked · 2 rankable rows
Basis
Provisional lane · 0 vs 2 weighted rows
Reading
Not comparable

Ranks count the models scored in each category’s lane, so the agentic, coding, reasoning, multimodal, and knowledge denominators (BenchAlign v5.8) 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

Gemma 4 31B
Self-hosted; infrastructure cost varies
Fits in one request
GLM-5.3
Self-hosted; infrastructure cost varies
Fits in one request

Gemma 4 31B has no comparable published API token rate. GLM-5.3 has no comparable published API token rate.

Repository review

50K fresh input + 3K output tokens

Gemma 4 31B
Self-hosted; infrastructure cost varies
Fits in one request
GLM-5.3
Self-hosted; infrastructure cost varies
Fits in one request

Gemma 4 31B has no comparable published API token rate. GLM-5.3 has no comparable published API token rate.

Cache-heavy agent loop

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

Gemma 4 31B
Self-hosted; infrastructure cost varies
Fits in one request
Cached-input rate unavailable
GLM-5.3
Self-hosted; infrastructure cost varies
Fits in one request
Cached-input rate unavailable

Gemma 4 31B has no comparable published API token rate. GLM-5.3 has no comparable published API token 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.

Cached-input rate

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

Gemma 4 31B

No comparable hosted API rate

GLM-5.3

No comparable hosted API rate

Z.AI GLM-5.3 model card

Reasoning profile

Gemma 4 31B

Reasoning

GLM-5.3

Reasoning

Weight access

Gemma 4 31B

Open Weight

GLM-5.3

Open Weight

License

Gemma 4 31B

Open Weight

GLM-5.3

Open Weight

Release date

Gemma 4 31B

2026-04-02

GLM-5.3

2026-08-14

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
GLM-5.3 has the higher public point estimate, 60.96 versus 28.81. Their conditional score ranges do not overlap. These ranges do not establish rank confidence.
Workload cost
A complete comparable API-rate estimate is not available for both models.
Context tradeoff
GLM-5.3 has the larger documented window (1M).
Run the same representative tasks against both endpoints before changing production traffic.

Questions

Which is better, Gemma 4 31B or GLM-5.3?

GLM-5.3 has the higher public point estimate, 60.96 versus 28.81. Their conditional score ranges do not overlap. These ranges do not establish rank confidence. The page therefore keeps the decision tied to the specific documented workload.

Which is better for coding, Gemma 4 31B or GLM-5.3?

GLM-5.3 has the higher public coding point estimate, 53.6 to 28.8, with Supported evidence for both models. The conditional ranges do not establish rank confidence.

Which is better for agentic tasks, Gemma 4 31B or GLM-5.3?

GLM-5.3 scores higher for agentic tasks on the public lane, 66.7 to 12.5. Gemma 4 31B is scored on Estimated evidence for agentic tasks, so the reading is directional rather than like-for-like. BenchLM does not name a winner for agentic tasks from a directional reading; read the shared benchmark rows directly and test the models on the same task set.

Which costs less, Gemma 4 31B or GLM-5.3?

Both models do not have comparable published API token rates, so this page does not name a universal price winner.

Which has the larger context window, Gemma 4 31B or GLM-5.3?

GLM-5.3 has the larger documented context window: 1M, compared with 256K.

Self-host vs API cost

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

Gemma 4 31B
API / mo (Novita AI)$405
Self-host / mo$298
Break-even37M/day
GLM-5.3
API / mo (DeepInfra)$2,297
Self-host / mo$27,962
Break-evenNot reached at 70% load
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 evidence50 rows

Agentic

  • b3 Agent Security

    Shared source
    Gemma 4 31B83.0%
    GLM-5.393.0%

    GLM-5.3 leads this result

  • Cyber Misuse Refusal

    Shared source
    Gemma 4 31B84.0%
    GLM-5.387.0%

    GLM-5.3 leads this result

  • Gert Labs

    Gemma 4 31B35.26%
    Source
    GLM-5.3—

    Not directly comparable

  • Terminal-Bench 4.1

    Gemma 4 31B—
    GLM-5.341.20%
    Source

    Not directly comparable

  • Finch

    Gemma 4 31B—
    GLM-5.365.1%
    Source

    Not directly comparable

  • Terminal-Bench 2.1

    Gemma 4 31B—
    GLM-5.388.2%
    Source

    Not directly comparable

  • terminalBench3

    Gemma 4 31B—
    GLM-5.328.3%
    Source

    Not directly comparable

  • CyberGym

    Gemma 4 31B—
    GLM-5.384.5%
    Source

    Not directly comparable

  • ExploitGym

    Gemma 4 31B—
    GLM-5.315.0%
    Source

    Not directly comparable

  • Toolathlon-Verified

    Gemma 4 31B—
    GLM-5.373.0%
    Source

    Not directly comparable

  • AutomationBench

    Gemma 4 31B—
    GLM-5.348.2%
    Source

    Not directly comparable

  • Agents' Last Exam

    Gemma 4 31B—
    GLM-5.328.5%
    Source

    Not directly comparable

  • HLE w/ tools

    Gemma 4 31B—
    GLM-5.362.5%
    Source

    Not directly comparable

  • Terminal-Bench 2.1 (Vals)

    Gemma 4 31B—
    GLM-5.371.5%
    Source

    Not directly comparable

  • Hermes Index

    Gemma 4 31B—
    GLM-5.339.2%
    Source

    Not directly comparable

  • Hermes Bench

    Gemma 4 31B—
    GLM-5.371.8%
    Source

    Not directly comparable

  • MazeBench

    Gemma 4 31B—
    GLM-5.30%
    Source

    Not directly comparable

  • CWE-bench

    Gemma 4 31B—
    GLM-5.342.0%
    Source

    Not directly comparable

Coding

  • LiveCodeBench v6

    Gemma 4 31B80.0%
    Source
    GLM-5.3—

    Not directly comparable

  • SWE-Rebench

    Gemma 4 31B41.6%
    Source
    GLM-5.3—

    Not directly comparable

  • React Native Evals

    Gemma 4 31B75.2%
    Source
    GLM-5.3—

    Not directly comparable

  • Terminal-Bench 2.1

    Gemma 4 31B—
    GLM-5.388.2%
    Source

    Not directly comparable

  • terminalBench3

    Gemma 4 31B—
    GLM-5.328.3%
    Source

    Not directly comparable

  • DeepSWE

    Gemma 4 31B—
    GLM-5.366.9%
    Source

    Not directly comparable

  • NL2Repo

    Gemma 4 31B—
    GLM-5.358%
    Source

    Not directly comparable

  • ProgramBench

    Gemma 4 31B—
    GLM-5.319.0%
    Source

    Not directly comparable

  • FrontierSWE

    Gemma 4 31B—
    GLM-5.378.1%
    Source

    Not directly comparable

  • sweMarathon

    Gemma 4 31B—
    GLM-5.342.5%
    Source

    Not directly comparable

  • PostTrainBench (lab runs)

    Gemma 4 31B—
    GLM-5.339.8%
    Source

    Not directly comparable

  • VulcanBench v3

    Gemma 4 31B—
    GLM-5.378.3%
    Source

    Not directly comparable

  • OpenHarmony Bench v1.0

    Gemma 4 31B—
    GLM-5.360.8%
    Source

    Not directly comparable

  • FrontierSWE v2

    Gemma 4 31B—
    GLM-5.330.2%
    Source

    Not directly comparable

  • LiveCodeBench (Vals)

    Gemma 4 31B—
    GLM-5.380.5%
    Source

    Not directly comparable

  • SWE-bench (Vals)

    Gemma 4 31B—
    GLM-5.395.4%
    Source

    Not directly comparable

  • Bug Hunt Bench

    Gemma 4 31B—
    GLM-5.319.0 fixes
    Source

    Not directly comparable

  • CursorBench 4.0

    Gemma 4 31B—
    GLM-5.342.6%
    Source

    Not directly comparable

  • PostTrainBench

    Gemma 4 31B—
    GLM-5.334.5%
    Source

    Not directly comparable

  • OpenHarmony Bench v1.2

    Gemma 4 31B—
    GLM-5.360.65%
    Source

    Not directly comparable

Reasoning

  • MRCRv2

    Gemma 4 31B66.4%
    Source
    GLM-5.3—

    Not directly comparable

Multimodal

  • ParseBench-1

    Gemma 4 31B62.40%
    Source
    GLM-5.3—

    Not directly comparable

  • MMMU-Pro

    Gemma 4 31B76.9%
    Source
    GLM-5.3—

    Not directly comparable

Knowledge

  • GPQA

    Gemma 4 31B84.3%
    Source
    GLM-5.3—

    Not directly comparable

  • MMLU-Pro

    Gemma 4 31B85.2%
    Source
    GLM-5.3—

    Not directly comparable

  • HLE

    Gemma 4 31B26.5%
    Source
    GLM-5.3—

    Not directly comparable

  • HLE w/o tools

    Gemma 4 31B19.5%
    Source
    GLM-5.3—

    Not directly comparable

  • GPQA Diamond (Vals)

    Gemma 4 31B—
    GLM-5.388.1%
    Source

    Not directly comparable

  • MMLU-Pro (Vals)

    Gemma 4 31B—
    GLM-5.386.8%
    Source

    Not directly comparable

Instruction following

  • Gray Swan IPI (15 attempts)

    Gemma 4 31B—
    GLM-5.331.5%
    Source

    Not directly comparable

Math

  • FrontierMath v2 (Tiers 1-3)

    Gemma 4 31B—
    GLM-5.368.772%
    Source

    Not directly comparable

  • FrontierMath v2 (Tier 4)

    Gemma 4 31B—
    GLM-5.329.268%
    Source

    Not directly comparable

50 public results · 2 shared

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Last updated November 13, 2026