Gemini 3.7 Flash vs GLM-5.2

Updated September 27, 2026. Rank says Gemini 3.7 Flash is ahead. Price, access, and your workload can each overturn that. Public scores include evidence status and uncertainty.

Decision reading

Gemini 3.7 Flash has the higher public score estimate, 67.66 versus 62.52, but the 90% score intervals overlap. Treat that as a lead, not a settled winner. 8 results are shared. Category rows resting on Estimated evidence or different benchmark sets are marked directional and do not name a winner.

Model A

Gemini 3.7 Flash

Google

67.66/100

Supported · Public rank #19

90% interval 63.0–72.3

Model B

GLM-5.2

Z.AI

62.52/100

Supported · Public rank #36

90% interval 55.8–69.2

Shared results: 8
Gemini 3.7 Flash only: 17
GLM-5.2 only: 17
Like-for-like categories: 3 / 8

Supported: Gemini 3.7 Flash 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 3.7 Flash
Gemini 3.7 Flash leads on the public coding lane, 60.3 to 57.2, with Supported evidence for both models, although the 90% intervals overlap.
Confidence: limited
Agentic work
Tool use, computer use, and multi-step task completion
Gemini 3.7 Flash
Gemini 3.7 Flash leads on the public agentic lane, 58.6 to 55.6, with Supported evidence for both models, although the 90% intervals overlap.
Confidence: limited
Chat turn cost
1K fresh input + 500 output tokens
Gemini 3.7 Flash
Gemini 3.7 Flash 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 3.7 Flash
Gemini 3.7 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
Repository review cost
50K fresh input + 3K output tokens
Gemini 3.7 Flash
Gemini 3.7 Flash 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.

Pick the job you would use them for

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

60.3Gemini 3.7 Flash57.2GLM-5.2

Like-for-like · BenchAlign v5.7

Gemini 3.7 Flash leads the like-for-like coding row, although the 90% intervals overlap.

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

Open the coding leaderboard Shortlist models for this job

Same basis rules as the category table below

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.

LiveCodeBench (Vals)Coding
Normalized gap 19.2
Gemini 3.7 Flash:88.7%
GLM-5.2:69.5%
Gemini 3.7 Flash source GLM-5.2 source
MMLU-Pro (Vals)Knowledge
Normalized gap 3.4
Gemini 3.7 Flash:90.1%
GLM-5.2:86.7%
Gemini 3.7 Flash source GLM-5.2 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 v5.7 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

Like-for-like

Gemini 3.7 Flash: 58.6; Supported · #20/105
GLM-5.2: 55.6; Supported · #26/105
Basis: BenchAlign v5.7 lane · 7 vs 6 public rows
Reading: Gemini 3.7 Flash leads · intervals overlap

Coding

Like-for-like

Gemini 3.7 Flash: 60.3; Supported · #17/135
GLM-5.2: 57.2; Supported · #22/135
Basis: BenchAlign v5.7 lane · 6 vs 8 public rows
Reading: Gemini 3.7 Flash leads · intervals overlap

Knowledge

Like-for-like

Gemini 3.7 Flash: 71.1; Supported · #10/158
GLM-5.2: 57.2; Supported · #40/158
Basis: BenchAlign v5.7 lane · 6 vs 6 public rows
Reading: Gemini 3.7 Flash leads · intervals overlap

Reasoning

Not comparable

Gemini 3.7 Flash: 77.8; Unranked · 5 rankable rows
GLM-5.2: 75.9; Unranked · 2 rankable rows
Basis: Provisional lane · 1 vs 0 weighted rows
Reading: Not comparable

Multimodal

Not comparable

Gemini 3.7 Flash: 82.6; #10/50
GLM-5.2: Not ranked
Basis: Provisional lane · 1 vs 0 weighted rows
Reading: Not comparable

Multilingual

Not comparable

Gemini 3.7 Flash: Not ranked
GLM-5.2: Not ranked
Basis: Provisional lane · 0 vs 0 weighted rows
Reading: Not comparable

Instruction following

Not comparable

Gemini 3.7 Flash: Not ranked
GLM-5.2: 88.5; #23/124
Basis: Provisional lane · 0 vs 0 weighted rows
Reading: Not comparable

Math

Not comparable

Gemini 3.7 Flash: Not ranked
GLM-5.2: 80.7; Unranked · 4 rankable rows
Basis: Provisional lane · 0 vs 2 weighted rows
Reading: Not comparable

Category scores with the server-provided evidence basis for Gemini 3.7 Flash and GLM-5.2
Category	Gemini 3.7 Flash	GLM-5.2	Basis	Reading
Agentic	58.6Supported · #20/105	55.6Supported · #26/105	Like-for-likeBenchAlign v5.7 lane · 7 vs 6 public rows	Gemini 3.7 Flash leads · intervals overlap
Coding	60.3Supported · #17/135	57.2Supported · #22/135	Like-for-likeBenchAlign v5.7 lane · 6 vs 8 public rows	Gemini 3.7 Flash leads · intervals overlap
Knowledge	71.1Supported · #10/158	57.2Supported · #40/158	Like-for-likeBenchAlign v5.7 lane · 6 vs 6 public rows	Gemini 3.7 Flash leads · intervals overlap
Reasoning	77.8Unranked · 5 rankable rows	75.9Unranked · 2 rankable rows	Not comparableProvisional lane · 1 vs 0 weighted rows	Not comparable
Multimodal	82.6#10/50	Not ranked	Not comparableProvisional lane · 1 vs 0 weighted rows	Not comparable
Multilingual	Not ranked	Not ranked	Not comparableProvisional lane · 0 vs 0 weighted rows	Not comparable
Instruction following	Not ranked	88.5#23/124	Not comparableProvisional lane · 0 vs 0 weighted rows	Not comparable
Math	Not ranked	80.7Unranked · 4 rankable rows	Not comparableProvisional lane · 0 vs 2 weighted rows	Not comparable

Ranks count the models scored in each category’s lane, so the agentic, coding, and knowledge denominators (BenchAlign v5.7) 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 3.7 Flash: $0.00262; Fits in one request
GLM-5.2: $0.0036; Fits in one request

Gemini 3.7 Flash has the lower modeled cost

Costs use the listed standard API rates.

Repository review

50K fresh input + 3K output tokens

Gemini 3.7 Flash: $0.04875; Fits in one request
GLM-5.2: $0.0832; Fits in one request

Gemini 3.7 Flash 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 3.7 Flash: $0.0675; Fits in one request
GLM-5.2: $0.352; Fits in one request; Cached input priced at the published list-input rate

Gemini 3.7 Flash has the lower modeled cost

GLM-5.2 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.

SpecificationGemini 3.7 FlashGLM-5.2

Context window

Maximum documented context; output-token limits may be lower.

Gemini 3.7 Flash

Google Gemini 3.7 Flash API documentation

GLM-5.2

API model ID

Gemini 3.7 Flash

gemini-3.7-flash

Google Gemini 3.7 Flash API documentation

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.

Gemini 3.7 Flash

$0.075 per 1M cached input tokens

Google Gemini API pricing

GLM-5.2

Not published

Documented inputs

Gemini 3.7 Flash

text, image, video, audio, pdf

Google Gemini 3.7 Flash API documentation

GLM-5.2

Not sourced

Documented outputs

Gemini 3.7 Flash

text

Google Gemini 3.7 Flash API documentation

GLM-5.2

Not sourced

Provider availability

Gemini 3.7 Flash

Generally Available · Gemini API, Google AI Studio, Gemini App - Spark, Gemini Enterprise App, Gemini Enterprise Agent Platform, Google Antigravity

Google DeepMind Gemini 3.7 Flash model card

GLM-5.2

Not sourced

Reasoning profile

Gemini 3.7 Flash

Reasoning

GLM-5.2

Reasoning

Weight access

Gemini 3.7 Flash

Proprietary

GLM-5.2

Open Weight

License

Gemini 3.7 Flash

Proprietary

GLM-5.2

Open Weight

Release date

Gemini 3.7 Flash

2026-08-13

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 3.7 Flash has the higher public score estimate, 67.66 versus 62.52, but the 90% score intervals overlap.
Workload cost: Repository review: $0.04875 vs $0.0832. Cache-heavy agent loop: $0.0675 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 3.7 Flash or GLM-5.2?

Gemini 3.7 Flash has the higher public score estimate, 67.66 versus 62.52, 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 3.7 Flash or GLM-5.2?

Gemini 3.7 Flash leads the public coding lane, 60.3 to 57.2, with Supported evidence for both models, although the 90% intervals overlap.

Which is better for agentic tasks, Gemini 3.7 Flash or GLM-5.2?

Gemini 3.7 Flash leads the public agentic tasks lane, 58.6 to 55.6, with Supported evidence for both models, although the 90% intervals overlap.

Which costs less, Gemini 3.7 Flash or GLM-5.2?

For the stated presets, chat costs $0.00263 on Gemini 3.7 Flash and $0.0036 on GLM-5.2; repository review costs $0.04875 and $0.0832; the cache-heavy agent loop costs $0.0675 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 3.7 Flash or GLM-5.2?

Both models list the same context window, 1M.

Benchmark evidence

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

Browse raw public benchmark evidence42 rows

Agentic

Terminal-Bench 2.1
Gemini 3.7 Flash85.8%
Source
GLM-5.281.0%
Source
Gemini 3.7 Flash leads this result
Terminal-Bench 3.0
Gemini 3.7 Flash14.9%
Source
GLM-5.24.6%
Source
Gemini 3.7 Flash leads this result
AutomationBench
Gemini 3.7 Flash30.4%
Source
GLM-5.2—
Not directly comparable
OSWorld 2.0
Gemini 3.7 Flash47.9%
Source
GLM-5.2—
Not directly comparable
Agents' Last Exam
Gemini 3.7 Flash26.3%
Source
GLM-5.2—
Not directly comparable
Terminal-Bench 2.1 (Vals)
Gemini 3.7 Flash77.5%
Source
GLM-5.267.8%
Source
Gemini 3.7 Flash leads this result
ApprenticeBench
Gemini 3.7 Flash16%
Source
GLM-5.2—
Not directly comparable
MCP Atlas
Gemini 3.7 Flash—
GLM-5.276.8%
Source
Not directly comparable
Toolathlon
Gemini 3.7 Flash—
GLM-5.248.2%
Source
Not directly comparable
ResearchClawBench
Gemini 3.7 Flash—
GLM-5.220.7%
Source
Not directly comparable

Coding

FrontierCode 1.1 Main
Gemini 3.7 Flash43.6%
Source
GLM-5.2—
Not directly comparable
DeepSWE
Gemini 3.7 Flash65.3%
Source
GLM-5.2—
Not directly comparable
Terminal-Bench 2.1
Gemini 3.7 Flash85.8%
Source
GLM-5.281.0%
Source
Gemini 3.7 Flash leads this result
FrontierSWE v2
Gemini 3.7 Flash20.3%
Source
GLM-5.2—
Not directly comparable
LiveCodeBench (Vals)
Gemini 3.7 Flash88.7%
Source
GLM-5.269.5%
Source
Gemini 3.7 Flash leads this result
SWE-bench (Vals)
Gemini 3.7 Flash80.8%
Source
GLM-5.282.8%
Source
GLM-5.2 leads this result
SWE-bench Pro
Gemini 3.7 Flash—
GLM-5.262.1%
Source
Not directly comparable
NL2Repo
Gemini 3.7 Flash—
GLM-5.248.9%
Source
Not directly comparable
ProgramBench
Gemini 3.7 Flash—
GLM-5.263.7%
Source
Not directly comparable
cursorBench32
Gemini 3.7 Flash—
GLM-5.255.0%
Source
Not directly comparable
OpenHarmony Bench
Gemini 3.7 Flash—
GLM-5.258.4%
Source
Not directly comparable

Reasoning

MRCR v2 64K-128K
Gemini 3.7 Flash97%
Source
GLM-5.2—
Not directly comparable
ARC-AGI-1
Gemini 3.7 Flash95.50%
Source
GLM-5.2—
Not directly comparable
ARC-AGI-2
Gemini 3.7 Flash84.6%
Source
GLM-5.2—
Not directly comparable
CritPt
Gemini 3.7 Flash—
GLM-5.220.9%
Source
Not directly comparable

Multimodal

CharXiv w/o tools
Gemini 3.7 Flash84.5%
Source
GLM-5.2—
Not directly comparable
CharXiv
Gemini 3.7 Flash88.7%
Source
GLM-5.2—
Not directly comparable
LVBench
Gemini 3.7 Flash85.4%
Source
GLM-5.2—
Not directly comparable

Knowledge

HLE-Verified
Gemini 3.7 Flash53.6%
Source
GLM-5.2—
Not directly comparable
LABBench2
Gemini 3.7 Flash82.1%
Source
GLM-5.2—
Not directly comparable
BioMysteryBench (human-solvable)
Gemini 3.7 Flash87.1%
Source
GLM-5.2—
Not directly comparable
BioMysteryBench (human-difficult)
Gemini 3.7 Flash43.5%
Source
GLM-5.2—
Not directly comparable
GPQA Diamond (Vals)
Gemini 3.7 Flash93.9%
Source
GLM-5.285.6%
Source
Gemini 3.7 Flash leads this result
MMLU-Pro (Vals)
Gemini 3.7 Flash90.1%
Source
GLM-5.286.7%
Source
Gemini 3.7 Flash leads this result
GPQA
Gemini 3.7 Flash—
GLM-5.291.2%
Source
Not directly comparable
GPQA-D
Gemini 3.7 Flash—
GLM-5.291.2%
Source
Not directly comparable
HLE
Gemini 3.7 Flash—
GLM-5.254.7%
Source
Not directly comparable
HLE w/o tools
Gemini 3.7 Flash—
GLM-5.240.5%
Source
Not directly comparable

Math

AIME26
Gemini 3.7 Flash—
GLM-5.299.2%
Source
Not directly comparable
HMMT Nov 2025
Gemini 3.7 Flash—
GLM-5.294.4%
Source
Not directly comparable
HMMT Feb 2026
Gemini 3.7 Flash—
GLM-5.292.5%
Source
Not directly comparable
MMAnswerBench
Gemini 3.7 Flash—
GLM-5.291.0%
Source
Not directly comparable

42 public results · 8 shared

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Last updated September 27, 2026

Gemini 3.7 Flash vs GLM-5.2

Decision reading

Which one for your work

Coding work

Agentic work

Chat turn cost

Cache-heavy agent loop cost

Repository review cost

Long documents

Which one for a specific job

What is actually comparable

Shape of the matched evidence

Category results, on a stated basis

Agentic

Coding

Knowledge

Reasoning

Multimodal

Multilingual

Instruction following

Math

What each workload costs

Chat turn

Repository review

Cache-heavy agent loop

Specification differences

Context window

API model ID

Cached-input rate

Documented inputs

Documented outputs

Provider availability

Reasoning profile

Weight access

License

Release date

If you already use one of these models

Questions

Which is better, Gemini 3.7 Flash or GLM-5.2?

Which is better for coding, Gemini 3.7 Flash or GLM-5.2?

Which is better for agentic tasks, Gemini 3.7 Flash or GLM-5.2?

Which costs less, Gemini 3.7 Flash or GLM-5.2?

Which has the larger context window, Gemini 3.7 Flash or GLM-5.2?

Benchmark evidence

Agentic

Coding

Reasoning

Multimodal

Knowledge

Math

Related comparisons

Watch Gemini 3.7 Flash vs GLM-5.2