Gemini 3.7 Flash vs GLM-4.7

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, 67.66 versus 49.23, and the 90% score intervals do not overlap. 4 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-4.7

Z.AI

49.23/100

Supported · Public rank #77

90% interval 37.8–60.7

Shared results: 4
Gemini 3.7 Flash only: 21
GLM-4.7 only: 13
Like-for-like categories: 2 / 8

Supported: Gemini 3.7 Flash and GLM-4.7How 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 39.6, with Supported evidence for both models and non-overlapping 90% intervals.
Confidence: stronger
Long documents
Prompts that approach the documented context limit
Gemini 3.7 Flash
Gemini 3.7 Flash 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
GLM-4.7 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
The page does not recommend a cost winner because at least one model cannot fit the stated workload in one request. GLM-4.7 does not fit this workload in one request. GLM-4.7 has no comparable published API token rate.
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.

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 Flash39.6GLM-4.7

Like-for-like · BenchAlign v5.7

Gemini 3.7 Flash leads the like-for-like coding row.

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.

MMLU-Pro (Vals)Knowledge
Normalized gap 7.4
Gemini 3.7 Flash:90.1%
GLM-4.7:82.7%
Gemini 3.7 Flash source GLM-4.7 source
LiveCodeBench (Vals)Coding
Normalized gap 6.5
Gemini 3.7 Flash:88.7%
GLM-4.7:82.2%
Gemini 3.7 Flash source GLM-4.7 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.

Coding

Like-for-like

Gemini 3.7 Flash: 60.3; Supported · #17/135
GLM-4.7: 39.6; Supported · #56/135
Basis: BenchAlign v5.7 lane · 6 vs 5 public rows
Reading: Gemini 3.7 Flash leads

Knowledge

Like-for-like

Gemini 3.7 Flash: 71.1; Supported · #10/158
GLM-4.7: 40.6; Supported · #89/158
Basis: BenchAlign v5.7 lane · 6 vs 5 public rows
Reading: Gemini 3.7 Flash leads

Agentic

Directional only

Gemini 3.7 Flash: 58.6; Supported · #20/105
GLM-4.7: 27.0; Estimated · #75/105
Basis: BenchAlign v5.7 lane · 7 vs 4 public rows
Reading: Directional only

Reasoning

Not comparable

Gemini 3.7 Flash: 77.8; Unranked · 5 rankable rows
GLM-4.7: 70.9; 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-4.7: Not ranked
Basis: Provisional lane · 1 vs 0 weighted rows
Reading: Not comparable

Multilingual

Not comparable

Gemini 3.7 Flash: Not ranked
GLM-4.7: Not ranked
Basis: Provisional lane · 0 vs 0 weighted rows
Reading: Not comparable

Instruction following

Not comparable

Gemini 3.7 Flash: Not ranked
GLM-4.7: 81.4; #51/124
Basis: Provisional lane · 0 vs 0 weighted rows
Reading: Not comparable

Math

Not comparable

Gemini 3.7 Flash: Not ranked
GLM-4.7: 25.8; Unranked · 2 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-4.7
Category	Gemini 3.7 Flash	GLM-4.7	Basis	Reading
Coding	60.3Supported · #17/135	39.6Supported · #56/135	Like-for-likeBenchAlign v5.7 lane · 6 vs 5 public rows	Gemini 3.7 Flash leads
Knowledge	71.1Supported · #10/158	40.6Supported · #89/158	Like-for-likeBenchAlign v5.7 lane · 6 vs 5 public rows	Gemini 3.7 Flash leads
Agentic	58.6Supported · #20/105	27.0Estimated · #75/105	Directional onlyBenchAlign v5.7 lane · 7 vs 4 public rows	Directional only
Reasoning	77.8Unranked · 5 rankable rows	70.9Unranked · 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	81.4#51/124	Not comparableProvisional lane · 0 vs 0 weighted rows	Not comparable
Math	Not ranked	25.8Unranked · 2 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-4.7: Self-hosted; infrastructure cost varies; Fits in one request

GLM-4.7 has no comparable published API token rate.

Repository review

50K fresh input + 3K output tokens

Gemini 3.7 Flash: $0.04875; Fits in one request
GLM-4.7: Self-hosted; infrastructure cost varies; Fits in one request

GLM-4.7 has no comparable published API token rate.

Cache-heavy agent loop

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

Gemini 3.7 Flash: $0.0675; Fits in one request
GLM-4.7: Self-hosted; infrastructure cost varies; Does not fit in one request; Cached-input rate unavailable

GLM-4.7 does not fit this workload in one request. GLM-4.7 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.

SpecificationGemini 3.7 FlashGLM-4.7

Context window

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

Gemini 3.7 Flash

Google Gemini 3.7 Flash API documentation

GLM-4.7

200K

API model ID

Gemini 3.7 Flash

gemini-3.7-flash

Google Gemini 3.7 Flash API documentation

GLM-4.7

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-4.7

No comparable hosted API rate

Documented inputs

Gemini 3.7 Flash

text, image, video, audio, pdf

Google Gemini 3.7 Flash API documentation

GLM-4.7

Not sourced

Documented outputs

Gemini 3.7 Flash

text

Google Gemini 3.7 Flash API documentation

GLM-4.7

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-4.7

Not sourced

Reasoning profile

Gemini 3.7 Flash

Reasoning

GLM-4.7

Reasoning

Weight access

Gemini 3.7 Flash

Proprietary

GLM-4.7

Open Weight

License

Gemini 3.7 Flash

Proprietary

GLM-4.7

Open Weight

Release date

Gemini 3.7 Flash

2026-08-13

GLM-4.7

2025-10-01

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, 67.66 versus 49.23, and the 90% score intervals do not overlap.
Workload cost: A complete comparable API-rate estimate is not available for both models.
Context tradeoff: Gemini 3.7 Flash has the larger documented window (1M).

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

Questions

Which is better, Gemini 3.7 Flash or GLM-4.7?

Gemini 3.7 Flash has the higher public score, 67.66 versus 49.23, 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 3.7 Flash or GLM-4.7?

Gemini 3.7 Flash leads the public coding lane, 60.3 to 39.6, with Supported evidence for both models and non-overlapping 90% intervals.

Which is better for agentic tasks, Gemini 3.7 Flash or GLM-4.7?

Gemini 3.7 Flash scores higher for agentic tasks on the public lane, 58.6 to 27. GLM-4.7 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, Gemini 3.7 Flash or GLM-4.7?

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, Gemini 3.7 Flash or GLM-4.7?

Gemini 3.7 Flash has the larger documented context window: 1M, compared with 200K.

Benchmark evidence

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

Browse raw public benchmark evidence38 rows

Agentic

Terminal-Bench 2.1
Gemini 3.7 Flash85.8%
Source
GLM-4.7—
Not directly comparable
Terminal-Bench 3.0
Gemini 3.7 Flash14.9%
Source
GLM-4.7—
Not directly comparable
AutomationBench
Gemini 3.7 Flash30.4%
Source
GLM-4.7—
Not directly comparable
OSWorld 2.0
Gemini 3.7 Flash47.9%
Source
GLM-4.7—
Not directly comparable
Agents' Last Exam
Gemini 3.7 Flash26.3%
Source
GLM-4.7—
Not directly comparable
Terminal-Bench 2.1 (Vals)
Gemini 3.7 Flash77.5%
Source
GLM-4.7—
Not directly comparable
ApprenticeBench
Gemini 3.7 Flash16%
Source
GLM-4.7—
Not directly comparable
Terminal-Bench 2.0
Gemini 3.7 Flash—
GLM-4.741%
Source
Not directly comparable
BrowseComp
Gemini 3.7 Flash—
GLM-4.752%
Source
Not directly comparable
VITA-Bench
Gemini 3.7 Flash—
GLM-4.715.5%
Source
Not directly comparable
Gert Labs
Gemini 3.7 Flash—
GLM-4.739.95%
Source
Not directly comparable

Coding

FrontierCode 1.1 Main
Gemini 3.7 Flash43.6%
Source
GLM-4.7—
Not directly comparable
DeepSWE
Gemini 3.7 Flash65.3%
Source
GLM-4.7—
Not directly comparable
Terminal-Bench 2.1
Gemini 3.7 Flash85.8%
Source
GLM-4.7—
Not directly comparable
FrontierSWE v2
Gemini 3.7 Flash20.3%
Source
GLM-4.7—
Not directly comparable
LiveCodeBench (Vals)
Gemini 3.7 Flash88.7%
Source
GLM-4.782.2%
Source
Gemini 3.7 Flash leads this result
SWE-bench (Vals)
Gemini 3.7 Flash80.8%
Source
GLM-4.769.4%
Source
Gemini 3.7 Flash leads this result
SWE-bench Verified
Gemini 3.7 Flash—
GLM-4.773.8%
Source
Not directly comparable
LiveCodeBench
Gemini 3.7 Flash—
GLM-4.784.9%
Source
Not directly comparable
SWE-Rebench
Gemini 3.7 Flash—
GLM-4.758.7%
Source
Not directly comparable

Reasoning

MRCR v2 64K-128K
Gemini 3.7 Flash97%
Source
GLM-4.7—
Not directly comparable
ARC-AGI-1
Gemini 3.7 Flash95.50%
Source
GLM-4.7—
Not directly comparable
ARC-AGI-2
Gemini 3.7 Flash84.6%
Source
GLM-4.7—
Not directly comparable

Multimodal

CharXiv w/o tools
Gemini 3.7 Flash84.5%
Source
GLM-4.7—
Not directly comparable
CharXiv
Gemini 3.7 Flash88.7%
Source
GLM-4.7—
Not directly comparable
LVBench
Gemini 3.7 Flash85.4%
Source
GLM-4.7—
Not directly comparable

Knowledge

HLE-Verified
Gemini 3.7 Flash53.6%
Source
GLM-4.7—
Not directly comparable
LABBench2
Gemini 3.7 Flash82.1%
Source
GLM-4.7—
Not directly comparable
BioMysteryBench (human-solvable)
Gemini 3.7 Flash87.1%
Source
GLM-4.7—
Not directly comparable
BioMysteryBench (human-difficult)
Gemini 3.7 Flash43.5%
Source
GLM-4.7—
Not directly comparable
GPQA Diamond (Vals)
Gemini 3.7 Flash93.9%
Source
GLM-4.780.1%
Source
Gemini 3.7 Flash leads this result
MMLU-Pro (Vals)
Gemini 3.7 Flash90.1%
Source
GLM-4.782.7%
Source
Gemini 3.7 Flash leads this result
GPQA
Gemini 3.7 Flash—
GLM-4.785.7%
Source
Not directly comparable
MMLU-Pro
Gemini 3.7 Flash—
GLM-4.784.3%
Source
Not directly comparable
HLE
Gemini 3.7 Flash—
GLM-4.724.8%
Source
Not directly comparable

Math

AIME 2025
Gemini 3.7 Flash—
GLM-4.795.7%
Source
Not directly comparable
FrontierMath v2 (Tiers 1-3)
Gemini 3.7 Flash—
GLM-4.72.439%
Source
Not directly comparable
FrontierMath v2 (Tier 4)
Gemini 3.7 Flash—
GLM-4.70.000%
Source
Not directly comparable

38 public results · 4 shared

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Last updated September 27, 2026

Gemini 3.7 Flash vs GLM-4.7

Decision reading

Which one for your work

Coding work

Long documents

Agentic work

Chat turn cost

Cache-heavy agent loop cost

Repository review cost

Which one for a specific job

What is actually comparable

Shape of the matched evidence

Category results, on a stated basis

Coding

Knowledge

Agentic

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-4.7?

Which is better for coding, Gemini 3.7 Flash or GLM-4.7?

Which is better for agentic tasks, Gemini 3.7 Flash or GLM-4.7?

Which costs less, Gemini 3.7 Flash or GLM-4.7?

Which has the larger context window, Gemini 3.7 Flash or GLM-4.7?

Benchmark evidence

Agentic

Coding

Reasoning

Multimodal

Knowledge

Math

Related comparisons

Watch Gemini 3.7 Flash vs GLM-4.7