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Model comparison

Gemini 3.5 Flash vs GLM-5.2

Updated July 31, 2026. Public scores include evidence status and uncertainty. They are not guarantees for a specific workload.

21 confirmed releases in the last 30 daystrack changes
Gemini 3.5 Flash

Google

63.9/100

Estimated · Public rank #37

90% interval 53.2–74.6

GLM-5.2

Z.AI

62.9/100

Estimated · Public rank #41

90% interval 47.7–78.2

Gemini 3.5 Flash has the higher public score estimate, 63.89 versus 62.94, but the 90% score intervals overlap. Treat that as a lead, not a settled winner.

9 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.

  • Coding work

    Code generation, repair, and software-engineering tasks

    GLM-5.2

    GLM-5.2 leads on the same 1 weighted benchmark row.

    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

  • Cache-heavy agent loop cost

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

    Gemini 3.5 Flash

    Gemini 3.5 Flash 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

Show secondary and unsupported calls

  • 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

  • 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

  • Long documents

    Prompts that approach the documented context limit

    No clear pick

    The documented context windows are equal.

    Confidence: documented

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
9
Gemini 3.5 Flash only
13
GLM-5.2 only
9
Like-for-like categories
1 / 8

2 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.

Coding

Like-for-like
Gemini 3.5 Flash
55.1
GLM-5.2
62.1
Weighted basis
1 vs 1 rows
Reading
GLM-5.2 leads

Agentic

Directional only
Gemini 3.5 Flash
77.2
GLM-5.2
81.0
Weighted basis
2 vs 1 rows
Reading
Directional only

Knowledge

Directional only
Gemini 3.5 Flash
40.2
GLM-5.2
59.6
Weighted basis
1 vs 2 rows
Reading
Directional only

Reasoning

Not comparable
Gemini 3.5 Flash
74.7
GLM-5.2
Not measured
Weighted basis
2 vs 0 rows
Reading
Not comparable

Math

Not comparable
Gemini 3.5 Flash
32.9
GLM-5.2
95.9
Weighted basis
2 vs 2 rows
Reading
Not comparable

Multilingual

Not comparable
Gemini 3.5 Flash
Not measured
GLM-5.2
Not measured
Weighted basis
0 vs 0 rows
Reading
Not comparable

Multimodal

Not comparable
Gemini 3.5 Flash
83.8
GLM-5.2
Not measured
Weighted basis
2 vs 0 rows
Reading
Not comparable

Instruction following

Not comparable
Gemini 3.5 Flash
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

Gemini 3.5 Flash
$0.006
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 3.5 Flash
$0.102
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 3.5 Flash
$0.15
Fits in one request
GLM-5.2
$0.352
Fits in one request
Cached input priced at the published list-input rate

Gemini 3.5 Flash has the lower modeled cost

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.

Gemini 3.5 Flash

1M

Google Gemini API pricing

GLM-5.2

1M

Cached-input rate

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

Gemini 3.5 Flash

$0.15 per 1M cached input tokens

Google Gemini API pricing

GLM-5.2

Not published

Documented inputs

Gemini 3.5 Flash

Not sourced

GLM-5.2

Not sourced

Documented outputs

Gemini 3.5 Flash

Not sourced

GLM-5.2

Not sourced

Provider availability

Gemini 3.5 Flash

Not sourced

GLM-5.2

Not sourced

Reasoning profile

Gemini 3.5 Flash

Reasoning

GLM-5.2

Reasoning

Weight access

Gemini 3.5 Flash

Proprietary

GLM-5.2

Open Weight

License

Gemini 3.5 Flash

Proprietary

GLM-5.2

Open Weight

Release date

Gemini 3.5 Flash

2026-05-19

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 3.5 Flash has the higher public score estimate, 63.89 versus 62.94, but the 90% score intervals overlap.
Workload cost
Repository review: $0.102 vs $0.0832. Cache-heavy agent loop: $0.15 vs $0.352.
Context tradeoff
Both models list 1M.

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

Benchmark evidence

The full public result ledger is available for audit without forcing a wide desktop table onto a phone.

Browse raw public benchmark evidence31 rows

Agentic

  • Terminal-Bench 2.0

    Gemini 3.5 Flash76.2%
    Source
    GLM-5.281%
    Source

    GLM-5.2 leads this result

  • MCP Atlas

    Gemini 3.5 Flash83.6%
    Source
    GLM-5.276.8%
    Source

    Gemini 3.5 Flash leads this result

  • Toolathlon

    Gemini 3.5 Flash56.5%
    Source
    GLM-5.248.2%
    Source

    Gemini 3.5 Flash leads this result

  • OSWorld-Verified

    Gemini 3.5 Flash78.4%
    Source
    GLM-5.2—

    Not directly comparable

  • Finance Agent v2

    Gemini 3.5 Flash57.9%
    Source
    GLM-5.2—

    Not directly comparable

  • Gert Labs

    Gemini 3.5 Flash61.85%
    Source
    GLM-5.2—

    Not directly comparable

  • ResearchClawBench

    Shared source
    Gemini 3.5 Flash18.0%
    GLM-5.220.7%

    GLM-5.2 leads this result

Coding

  • Terminal-Bench 2.0

    Gemini 3.5 Flash76.2%
    Source
    GLM-5.281.0%
    Source

    GLM-5.2 leads this result

  • SWE-bench Pro

    Gemini 3.5 Flash55.1%
    Source
    GLM-5.262.1%
    Source

    GLM-5.2 leads this result

  • Vibe Code Bench

    Gemini 3.5 Flash48.68%
    Source
    GLM-5.2—

    Not directly comparable

  • cursorBench31

    Gemini 3.5 Flash49.8%
    Source
    GLM-5.2—

    Not directly comparable

  • cursorBench32

    Shared source
    Gemini 3.5 Flash48.8%
    GLM-5.255.0%

    GLM-5.2 leads this result

  • NL2Repo

    Gemini 3.5 Flash—
    GLM-5.248.9%
    Source

    Not directly comparable

  • ProgramBench

    Gemini 3.5 Flash—
    GLM-5.263.7%
    Source

    Not directly comparable

Reasoning

  • MRCRv2

    Gemini 3.5 Flash77.3%
    Source
    GLM-5.2—

    Not directly comparable

  • MRCR 1M

    Gemini 3.5 Flash26.6%
    Source
    GLM-5.2—

    Not directly comparable

  • ARC-AGI-2

    Gemini 3.5 Flash72.1%
    Source
    GLM-5.2—

    Not directly comparable

  • CritPt

    Gemini 3.5 Flash—
    GLM-5.220.9%
    Source

    Not directly comparable

Knowledge

  • GPQA-D

    Gemini 3.5 Flash92.7%
    Source
    GLM-5.291.2%
    Source

    Gemini 3.5 Flash leads this result

  • HLE

    Gemini 3.5 Flash40.2%
    Source
    GLM-5.254.7%
    Source

    GLM-5.2 leads this result

  • GPQA

    Gemini 3.5 Flash—
    GLM-5.291.2%
    Source

    Not directly comparable

  • HLE w/o tools

    Gemini 3.5 Flash—
    GLM-5.240.5%
    Source

    Not directly comparable

Math

  • FrontierMath v2 (Tiers 1-3)

    Gemini 3.5 Flash38.966%
    Source
    GLM-5.2—

    Not directly comparable

  • FrontierMath v2 (Tier 4)

    Gemini 3.5 Flash14.583%
    Source
    GLM-5.2—

    Not directly comparable

  • AIME26

    Gemini 3.5 Flash—
    GLM-5.299.2%
    Source

    Not directly comparable

  • HMMT Nov 2025

    Gemini 3.5 Flash—
    GLM-5.294.4%
    Source

    Not directly comparable

  • HMMT Feb 2026

    Gemini 3.5 Flash—
    GLM-5.292.5%
    Source

    Not directly comparable

  • MMAnswerBench

    Gemini 3.5 Flash—
    GLM-5.291.0%
    Source

    Not directly comparable

Multimodal

  • CharXiv

    Gemini 3.5 Flash84.2%
    Source
    GLM-5.2—

    Not directly comparable

  • MMMU-Pro

    Gemini 3.5 Flash83.6%
    Source
    GLM-5.2—

    Not directly comparable

  • Blueprint-Bench 2

    Gemini 3.5 Flash33.6%
    Source
    GLM-5.2—

    Not directly comparable

Frequently asked questions

Which is better, Gemini 3.5 Flash or GLM-5.2?

Gemini 3.5 Flash has the higher public score estimate, 63.89 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, Gemini 3.5 Flash or GLM-5.2?

GLM-5.2 leads the like-for-like coding comparison across 1 shared weighted benchmark row.

Which is better for agentic tasks, Gemini 3.5 Flash 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, Gemini 3.5 Flash or GLM-5.2?

For the stated presets, chat costs $0.006 on Gemini 3.5 Flash and $0.0036 on GLM-5.2; repository review costs $0.102 and $0.0832; the cache-heavy agent loop costs $0.15 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 3.5 Flash or GLM-5.2?

Both models list the same context window, 1M.

Related comparisons

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Last updated July 31, 2026

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