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Gemini 4 Argon vs GLM-5.2

Updated November 13, 2026. Rank says Gemini 4 Argon is ahead. Price, access, and your workload can each overturn that. Public scores include evidence status and uncertainty.

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Decision reading

Gemini 4 Argon has the higher public score, 79.03 versus 59.71, and the 90% score intervals do not overlap. 6 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
Gemini 4 Argon

Google

79.03/100

Supported · Public rank #8

90% interval 75.4–82.7

Model B
Z.AI logo
GLM-5.2

Z.AI

59.71/100

Supported · Public rank #53

90% interval 51.5–68.0

API price per 1M tokens

Gemini 4 Argon: $2 input / $10 output

GLM-5.2: $1.40 input / $4.40 output

Context window

Gemini 4 Argon: 1M

GLM-5.2: 1M

Released

Gemini 4 Argon: September 30, 2026

GLM-5.2: June 16, 2026

Shared results
6
Gemini 4 Argon only
33
GLM-5.2 only
26
Like-for-like categories
4 / 8
Supported: Gemini 4 Argon and GLM-5.2How 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

    Gemini 4 Argon

    Gemini 4 Argon has the higher public coding point estimate, 71.8 to 52.1, with Supported evidence for both models. The conditional ranges do not establish rank confidence.

    Confidence: limited

  • Agentic work

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

    Gemini 4 Argon

    Gemini 4 Argon has the higher public agentic point estimate, 74.9 to 54.2, with Supported evidence for both models. The conditional ranges do not establish rank confidence.

    Confidence: limited

  • 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
Show secondary and unsupported calls

  • Cache-heavy agent loop cost

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

    Gemini 4 Argon

    Gemini 4 Argon 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

  • 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

  • Long documents

    Prompts that approach the documented context limit

    No clear pick

    The documented context windows are equal.

    Confidence: documented

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.

71.8Gemini 4 Argon52.1GLM-5.2

Like-for-like · BenchAlign v5.8

Gemini 4 Argon 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.

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.

  • FrontierMath v2 (Tier 4)Math

    Normalized gap 48.8
    Gemini 4 Argon:78.049%
    GLM-5.2:29.268%
    Shared source

  • FrontierMath v2 (Tiers 1-3)Math

    Normalized gap 26.8
    Gemini 4 Argon:85.965%
    GLM-5.2:59.206%
    Shared source
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 v5.8 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.

Agentic

Like-for-like
Gemini 4 Argon
74.9
Supported · #6/135
GLM-5.2
54.2
Supported · #42/135
Basis
BenchAlign v5.8 lane · 13 vs 8 public rows
Reading
Gemini 4 Argon leads

Coding

Like-for-like
Gemini 4 Argon
71.8
Supported · #9/162
GLM-5.2
52.1
Supported · #36/162
Basis
BenchAlign v5.8 lane · 9 vs 11 public rows
Reading
Gemini 4 Argon leads

Reasoning

Like-for-like
Gemini 4 Argon
76.5
Supported · #7/160
GLM-5.2
42.7
Supported · #72/160
Basis
BenchAlign v5.8 lane · 7 vs 1 public rows
Reading
Gemini 4 Argon leads

Knowledge

Like-for-like
Gemini 4 Argon
79.2
Supported · #8/180
GLM-5.2
56.1
Supported · #56/180
Basis
BenchAlign v5.8 lane · 2 vs 6 public rows
Reading
Gemini 4 Argon leads

Multimodal

Not comparable
Gemini 4 Argon
Not ranked
GLM-5.2
Not ranked
Basis
BenchAlign v5.8 lane · 5 vs 0 public rows
Reading
Not comparable

Multilingual

Not comparable
Gemini 4 Argon
Not ranked
GLM-5.2
Not ranked
Basis
Provisional lane · 0 vs 0 weighted rows
Reading
Not comparable

Instruction following

Not comparable
Gemini 4 Argon
Not ranked
GLM-5.2
89.9
#29/157
Basis
Provisional lane · 0 vs 0 weighted rows
Reading
Not comparable

Math

Not comparable
Gemini 4 Argon
87.4
Unranked · 2 rankable rows
GLM-5.2
74.6
#3/12
Basis
Provisional lane · 2 vs 4 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

Gemini 4 Argon
$0.007
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

Gemini 4 Argon
$0.13
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

Gemini 4 Argon
$0.16
Fits in one request
GLM-5.2
$0.352
Fits in one request
Cached input priced at the published list-input rate

Gemini 4 Argon has the lower modeled cost

GLM-5.2 has no published cached-input rate, so cached tokens use its listed input rate.

Your workload

Enter a month of tokens to price it at both models’ listed API rates.

Gemini 4 Argon
$40.00
per month
GLM-5.2
$22.80
per month

Standard listed rates, no batch or volume discounts.

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

Price against score, among all priced models

Overall score against output price per 1M tokens, one model per family. Pricing as of November 12, 2026.

Gemini 4 ArgonGLM-5.2

20406080100$0.10$0.50$1$5$10$50$100Output price, $ per 1M tokens (log)Overall score
  • 2 models at the same price score higher than Gemini 4 Argon. The nearest is Gemini 4 Carbon (score 89.2, also $10.00 per 1M output tokens).
  • 7 cheaper models score at least as high as GLM-5.2. The nearest is Muse Spark 1.2 (score 65.1, $4.25 per 1M output tokens).

Gray dots are the other priced models. The dark step line is the efficiency frontier: for a model under it, another model costs no more and scores at least as high.

See GLM-5.2 on the full price vs performance chart

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.

Gemini 4 Argon

$0.1 per 1M cached input tokens

Google Gemini API pricing

GLM-5.2

Not published

Reasoning profile

Gemini 4 Argon

Reasoning

GLM-5.2

Reasoning

Weight access

Gemini 4 Argon

Proprietary

GLM-5.2

Open Weight

License

Gemini 4 Argon

Proprietary

GLM-5.2

Open Weight

Release date

Gemini 4 Argon

2026-09-30

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
Gemini 4 Argon has the higher public score, 79.03 versus 59.71, and the 90% score intervals do not overlap.
Workload cost
Repository review: $0.13 vs $0.0832. Cache-heavy agent loop: $0.16 vs $0.352.
Context tradeoff
Both models list 1M.
Run the same representative tasks against both endpoints before changing production traffic.

Questions

Which is better, Gemini 4 Argon or GLM-5.2?

Gemini 4 Argon has the higher public score, 79.03 versus 59.71, 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, Gemini 4 Argon or GLM-5.2?

Gemini 4 Argon has the higher public coding point estimate, 71.8 to 52.1, with Supported evidence for both models. The conditional ranges do not establish rank confidence.

Which is better for agentic tasks, Gemini 4 Argon or GLM-5.2?

Gemini 4 Argon has the higher public agentic tasks point estimate, 74.9 to 54.2, with Supported evidence for both models. The conditional ranges do not establish rank confidence.

Which costs less, Gemini 4 Argon or GLM-5.2?

For the stated presets, chat costs $0.007 on Gemini 4 Argon and $0.0036 on GLM-5.2; repository review costs $0.13 and $0.0832; the cache-heavy agent loop costs $0.16 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, Gemini 4 Argon or GLM-5.2?

Both models list the same context window, 1M.

Self-host vs API cost

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

Gemini 4 Argon
API / mo$9,000
Self-host / moNot listed
Break-even—
Proprietary model — self-hosting not applicable.
GLM-5.2
API / mo$4,350
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 evidence65 rows

Agentic

  • AutomationBench

    Gemini 4 Argon51.3%
    Source
    GLM-5.2—

    Not directly comparable

  • Finance Agent v2

    Gemini 4 Argon65.4%
    Source
    GLM-5.2—

    Not directly comparable

  • Terminal-Bench 4.0

    Gemini 4 Argon57.40%
    Source
    GLM-5.2—

    Not directly comparable

  • Agents' Last Exam

    Gemini 4 Argon39.5%
    Source
    GLM-5.2—

    Not directly comparable

  • OSWorld 2.0

    Gemini 4 Argon69.2%
    Source
    GLM-5.2—

    Not directly comparable

  • CWE-bench v1

    Gemini 4 Argon68.0%
    Source
    GLM-5.2—

    Not directly comparable

  • Terminal-Bench-Science 0.1 (6x verifier timeout)

    Gemini 4 Argon57.6%
    Source
    GLM-5.2—

    Not directly comparable

  • MazeBench

    Gemini 4 Argon12%
    Source
    GLM-5.20%
    Source

    Gemini 4 Argon leads this result

  • OSWorld 2.1 Offline

    Gemini 4 Argon74.7%
    Source
    GLM-5.2—

    Not directly comparable

  • ExploitGym

    Gemini 4 Argon39.5%
    Source
    GLM-5.2—

    Not directly comparable

  • RoboDojo Sim

    Gemini 4 Argon34.2%
    Source
    GLM-5.2—

    Not directly comparable

  • CWE-bench

    Gemini 4 Argon65.0%
    Source
    GLM-5.2—

    Not directly comparable

  • Terminal-Bench 2.1 (Vals)

    Gemini 4 Argon57.7%
    Source
    GLM-5.267.8%
    Source

    GLM-5.2 leads this result

  • Terminal-Bench 3.0

    Gemini 4 Argon—
    GLM-5.24.6%
    Source

    Not directly comparable

  • Terminal-Bench 2.1

    Gemini 4 Argon—
    GLM-5.281.0%
    Source

    Not directly comparable

  • MCP Atlas

    Gemini 4 Argon—
    GLM-5.276.8%
    Source

    Not directly comparable

  • Toolathlon

    Gemini 4 Argon—
    GLM-5.248.2%
    Source

    Not directly comparable

  • ResearchClawBench

    Gemini 4 Argon—
    GLM-5.220.7%
    Source

    Not directly comparable

  • HLE w/ tools

    Gemini 4 Argon—
    GLM-5.254.7%
    Source

    Not directly comparable

Coding

  • ScarfBench

    Gemini 4 Argon75.2%
    Source
    GLM-5.2—

    Not directly comparable

  • DeepSWE

    Gemini 4 Argon77.9%
    Source
    GLM-5.2—

    Not directly comparable

  • FrontierSWE v2

    Gemini 4 Argon55.0%
    Source
    GLM-5.2—

    Not directly comparable

  • Vibe Code Bench

    Gemini 4 Argon91.90%
    Source
    GLM-5.2—

    Not directly comparable

  • PostTrainBench v1.1

    Gemini 4 Argon45.3%
    Source
    GLM-5.231.7%
    Source

    Gemini 4 Argon leads this result

  • Bug Hunt Bench

    Gemini 4 Argon40.7 fixes
    Source
    GLM-5.2—

    Not directly comparable

  • FrontierCode 2.0

    Gemini 4 Argon26.3%
    Source
    GLM-5.2—

    Not directly comparable

  • CursorBench 4.0

    Gemini 4 Argon52.9%
    Source
    GLM-5.2—

    Not directly comparable

  • PostTrainBench

    Shared source
    Gemini 4 Argon40.4%
    GLM-5.230.4%

    Gemini 4 Argon leads this result

  • SWE-bench Pro

    Gemini 4 Argon—
    GLM-5.262.1%
    Source

    Not directly comparable

  • NL2Repo

    Gemini 4 Argon—
    GLM-5.248.9%
    Source

    Not directly comparable

  • Terminal-Bench 2.1

    Gemini 4 Argon—
    GLM-5.281.0%
    Source

    Not directly comparable

  • ProgramBench

    Gemini 4 Argon—
    GLM-5.263.7%
    Source

    Not directly comparable

  • CursorBench 3.2

    Gemini 4 Argon—
    GLM-5.255.0%
    Source

    Not directly comparable

  • OpenHarmony Bench v1.0

    Gemini 4 Argon—
    GLM-5.258.4%
    Source

    Not directly comparable

  • LiveCodeBench (Vals)

    Gemini 4 Argon—
    GLM-5.269.5%
    Source

    Not directly comparable

  • SWE-bench (Vals)

    Gemini 4 Argon—
    GLM-5.282.8%
    Source

    Not directly comparable

  • PostTrainBench v1

    Gemini 4 Argon—
    GLM-5.234.3%
    Source

    Not directly comparable

Reasoning

  • STEMBench

    Gemini 4 Argon2.44
    Source
    GLM-5.2—

    Not directly comparable

  • Graphwalks BFS 128K

    Gemini 4 Argon99.7%
    Source
    GLM-5.2—

    Not directly comparable

  • GraphWalks BFS 256K–1M

    Gemini 4 Argon84.2%
    Source
    GLM-5.2—

    Not directly comparable

  • ARC-AGI-1

    Gemini 4 Argon98.50%
    Source
    GLM-5.2—

    Not directly comparable

  • ARC-AGI-2

    Gemini 4 Argon91.3%
    Source
    GLM-5.2—

    Not directly comparable

  • ARC-AGI-3

    Gemini 4 Argon46.3%
    Source
    GLM-5.2—

    Not directly comparable

  • ARC-AGI-3 Provider Adapter

    Gemini 4 Argon97.30%
    Source
    GLM-5.2—

    Not directly comparable

  • CritPt

    Gemini 4 Argon—
    GLM-5.220.9%
    Source

    Not directly comparable

Multimodal

  • ParseBench-2

    Gemini 4 Argon84.20%
    Source
    GLM-5.2—

    Not directly comparable

  • ExtractBench Pro

    Gemini 4 Argon30%
    Source
    GLM-5.2—

    Not directly comparable

  • Chartography (no tools)

    Gemini 4 Argon71.6%
    Source
    GLM-5.2—

    Not directly comparable

  • LVBench

    Gemini 4 Argon91.7%
    Source
    GLM-5.2—

    Not directly comparable

  • Blueprint-Bench 2

    Gemini 4 Argon54.4%
    Source
    GLM-5.2—

    Not directly comparable

Knowledge

  • LABBench2

    Gemini 4 Argon88.8%
    Source
    GLM-5.2—

    Not directly comparable

  • Morphology-to-molecule matching

    Gemini 4 Argon25.2%
    Source
    GLM-5.2—

    Not directly comparable

  • GPQA

    Gemini 4 Argon—
    GLM-5.291.2%
    Source

    Not directly comparable

  • GPQA-D

    Gemini 4 Argon—
    GLM-5.291.2%
    Source

    Not directly comparable

  • HLE

    Gemini 4 Argon—
    GLM-5.240.5%
    Source

    Not directly comparable

  • HLE w/o tools

    Gemini 4 Argon—
    GLM-5.240.5%
    Source

    Not directly comparable

  • GPQA Diamond (Vals)

    Gemini 4 Argon—
    GLM-5.285.6%
    Source

    Not directly comparable

  • MMLU-Pro (Vals)

    Gemini 4 Argon—
    GLM-5.286.7%
    Source

    Not directly comparable

Instruction following

  • Gray Swan IPI (15 attempts)

    Gemini 4 Argon0.7%
    Source
    GLM-5.2—

    Not directly comparable

Math

  • FrontierMath v2 (Tiers 1-3)

    Shared source
    Gemini 4 Argon85.965%
    GLM-5.259.206%

    Gemini 4 Argon leads this result

  • FrontierMath v2 (Tier 4)

    Shared source
    Gemini 4 Argon78.049%
    GLM-5.229.268%

    Gemini 4 Argon leads this result

  • AIME26

    Gemini 4 Argon—
    GLM-5.299.2%
    Source

    Not directly comparable

  • HMMT Nov 2025

    Gemini 4 Argon—
    GLM-5.294.4%
    Source

    Not directly comparable

  • HMMT Feb 2026

    Gemini 4 Argon—
    GLM-5.292.5%
    Source

    Not directly comparable

  • MMAnswerBench

    Gemini 4 Argon—
    GLM-5.291.0%
    Source

    Not directly comparable

65 public results · 6 shared

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Last updated November 13, 2026