Gemini 2.5 Flash vs GLM-4.7

Updated September 29, 2026. Rank says GLM-4.7 is ahead. Price, access, and your workload can each overturn that. Public scores include evidence status and uncertainty.

Decision reading

GLM-4.7 has the higher public score estimate, 49.09 versus 42.97, but the 90% score intervals overlap. Treat that as a lead, not a settled winner. 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

Gemini 2.5 Flash

Google

42.97/100

Supported · Public rank #111

90% interval 27.3–58.6

Model B

GLM-4.7

Z.AI

49.09/100

Supported · Public rank #85

90% interval 37.5–60.6

Shared results: 2
Gemini 2.5 Flash only: 0
GLM-4.7 only: 15
Like-for-like categories: 0 / 8

Supported: Gemini 2.5 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.

Long documents
Prompts that approach the documented context limit
Gemini 2.5 Flash
Gemini 2.5 Flash has the larger documented context window.
Confidence: documented

Show secondary and unsupported calls

Coding work
Code generation, repair, and software-engineering tasks
Not enough matched evidence
Gemini 2.5 Flash is not ranked on the public lane for coding, so no winner is named for coding.
Confidence: limited
Agentic work
Tool use, computer use, and multi-step task completion
Not enough matched evidence
Gemini 2.5 Flash is not ranked on the public lane for agentic, so no winner is named for agentic.
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.

—Gemini 2.5 Flash39.4GLM-4.7

Not comparable · BenchAlign v5.7

The coding row is not comparable on the public lane: at least one model is not measured or not ranked there.

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.

FrontierMath v2 (Tier 4)Math
Normalized gap 4.2
Gemini 2.5 Flash:4.167%
GLM-4.7:0.000%
Shared source
FrontierMath v2 (Tiers 1-3)Math
Normalized gap 2.4
Gemini 2.5 Flash:4.844%
GLM-4.7:2.439%
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 v5.7 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.

Knowledge

Directional only

Gemini 2.5 Flash: 35.9; Estimated · #113/169
GLM-4.7: 40.5; Supported · #98/169
Basis: BenchAlign v5.7 lane · 0 vs 5 public rows
Reading: Directional only

Instruction following

Directional only

Gemini 2.5 Flash: 43.7; #93/124
GLM-4.7: 81.4; #51/124
Basis: Provisional lane · 0 vs 0 weighted rows
Reading: Directional only

Agentic

Not comparable

Gemini 2.5 Flash: Not ranked
GLM-4.7: 27.5; Estimated · #88/117
Basis: BenchAlign v5.7 lane · 0 vs 4 public rows
Reading: Not comparable

Coding

Not comparable

Gemini 2.5 Flash: Not ranked
GLM-4.7: 39.4; Supported · #63/143
Basis: BenchAlign v5.7 lane · 0 vs 5 public rows
Reading: Not comparable

Reasoning

Not comparable

Gemini 2.5 Flash: 56.5; Unranked · 2 rankable rows
GLM-4.7: 71.1; Unranked · 2 rankable rows
Basis: Provisional lane · 0 vs 0 weighted rows
Reading: Not comparable

Multimodal

Not comparable

Gemini 2.5 Flash: 57.6; Unranked · 1 rankable row
GLM-4.7: Not ranked
Basis: Provisional lane · 0 vs 0 weighted rows
Reading: Not comparable

Multilingual

Not comparable

Gemini 2.5 Flash: Not ranked
GLM-4.7: Not ranked
Basis: Provisional lane · 0 vs 0 weighted rows
Reading: Not comparable

Math

Not comparable

Gemini 2.5 Flash: 28.6; Unranked · 2 rankable rows
GLM-4.7: 25.8; Unranked · 2 rankable rows
Basis: Provisional lane · 2 vs 2 weighted rows
Reading: Not comparable

Category scores with the server-provided evidence basis for Gemini 2.5 Flash and GLM-4.7
Category	Gemini 2.5 Flash	GLM-4.7	Basis	Reading
Knowledge	35.9Estimated · #113/169	40.5Supported · #98/169	Directional onlyBenchAlign v5.7 lane · 0 vs 5 public rows	Directional only
Instruction following	43.7#93/124	81.4#51/124	Directional onlyProvisional lane · 0 vs 0 weighted rows	Directional only
Agentic	Not ranked	27.5Estimated · #88/117	Not comparableBenchAlign v5.7 lane · 0 vs 4 public rows	Not comparable
Coding	Not ranked	39.4Supported · #63/143	Not comparableBenchAlign v5.7 lane · 0 vs 5 public rows	Not comparable
Reasoning	56.5Unranked · 2 rankable rows	71.1Unranked · 2 rankable rows	Not comparableProvisional lane · 0 vs 0 weighted rows	Not comparable
Multimodal	57.6Unranked · 1 rankable row	Not ranked	Not comparableProvisional lane · 0 vs 0 weighted rows	Not comparable
Multilingual	Not ranked	Not ranked	Not comparableProvisional lane · 0 vs 0 weighted rows	Not comparable
Math	28.6Unranked · 2 rankable rows	25.8Unranked · 2 rankable rows	Not comparableProvisional lane · 2 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 2.5 Flash: $0.00155; 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 2.5 Flash: $0.0225; 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 2.5 Flash: $0.037; 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 2.5 FlashGLM-4.7

Context window

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

Gemini 2.5 Flash

Google Gemini API pricing

GLM-4.7

200K

API model ID

Gemini 2.5 Flash

gemini-2.5-flash

Google Gemini API pricing

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 2.5 Flash

$0.03 per 1M cached input tokens

Google Gemini API pricing

GLM-4.7

No comparable hosted API rate

Documented inputs

Gemini 2.5 Flash

Not sourced

GLM-4.7

Not sourced

Documented outputs

Gemini 2.5 Flash

Not sourced

GLM-4.7

Not sourced

Provider availability

Gemini 2.5 Flash

Not sourced

GLM-4.7

Not sourced

Reasoning profile

Gemini 2.5 Flash

Non-Reasoning

GLM-4.7

Reasoning

Weight access

Gemini 2.5 Flash

Proprietary

GLM-4.7

Open Weight

License

Gemini 2.5 Flash

Proprietary

GLM-4.7

Open Weight

Release date

Gemini 2.5 Flash

2025-06-17

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: GLM-4.7 has the higher public score estimate, 49.09 versus 42.97, but the 90% score intervals overlap.
Workload cost: A complete comparable API-rate estimate is not available for both models.
Context tradeoff: Gemini 2.5 Flash has the larger documented window (1M).

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

Questions

Which is better, Gemini 2.5 Flash or GLM-4.7?

GLM-4.7 has the higher public score estimate, 49.09 versus 42.97, 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 2.5 Flash or GLM-4.7?

Gemini 2.5 Flash is not ranked on the public lane for coding, so no winner is named for coding.

Which is better for agentic tasks, Gemini 2.5 Flash or GLM-4.7?

Gemini 2.5 Flash is not ranked on the public lane for agentic tasks, so no winner is named for agentic tasks.

Which costs less, Gemini 2.5 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 2.5 Flash or GLM-4.7?

Gemini 2.5 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 evidence17 rows

Agentic

Terminal-Bench 2.0
Gemini 2.5 Flash—
GLM-4.741%
Source
Not directly comparable
BrowseComp
Gemini 2.5 Flash—
GLM-4.752%
Source
Not directly comparable
VITA-Bench
Gemini 2.5 Flash—
GLM-4.715.5%
Source
Not directly comparable
Gert Labs
Gemini 2.5 Flash—
GLM-4.739.95%
Source
Not directly comparable

Coding

SWE-bench Verified
Gemini 2.5 Flash—
GLM-4.773.8%
Source
Not directly comparable
LiveCodeBench
Gemini 2.5 Flash—
GLM-4.784.9%
Source
Not directly comparable
SWE-Rebench
Gemini 2.5 Flash—
GLM-4.758.7%
Source
Not directly comparable
LiveCodeBench (Vals)
Gemini 2.5 Flash—
GLM-4.782.2%
Source
Not directly comparable
SWE-bench (Vals)
Gemini 2.5 Flash—
GLM-4.769.4%
Source
Not directly comparable

Knowledge

GPQA
Gemini 2.5 Flash—
GLM-4.785.7%
Source
Not directly comparable
MMLU-Pro
Gemini 2.5 Flash—
GLM-4.784.3%
Source
Not directly comparable
HLE
Gemini 2.5 Flash—
GLM-4.724.8%
Source
Not directly comparable
GPQA Diamond (Vals)
Gemini 2.5 Flash—
GLM-4.780.1%
Source
Not directly comparable
MMLU-Pro (Vals)
Gemini 2.5 Flash—
GLM-4.782.7%
Source
Not directly comparable

Math

FrontierMath v2 (Tiers 1-3)
Shared source
Gemini 2.5 Flash4.844%
GLM-4.72.439%
Gemini 2.5 Flash leads this result
FrontierMath v2 (Tier 4)
Shared source
Gemini 2.5 Flash4.167%
GLM-4.70.000%
Gemini 2.5 Flash leads this result
AIME 2025
Gemini 2.5 Flash—
GLM-4.795.7%
Source
Not directly comparable

17 public results · 2 shared

Related comparisons

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Last updated September 29, 2026

Gemini 2.5 Flash vs GLM-4.7

Decision reading

Which one for your work

Long documents

Coding work

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

Knowledge

Instruction following

Agentic

Coding

Reasoning

Multimodal

Multilingual

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 2.5 Flash or GLM-4.7?

Which is better for coding, Gemini 2.5 Flash or GLM-4.7?

Which is better for agentic tasks, Gemini 2.5 Flash or GLM-4.7?

Which costs less, Gemini 2.5 Flash or GLM-4.7?

Which has the larger context window, Gemini 2.5 Flash or GLM-4.7?

Benchmark evidence

Agentic

Coding

Knowledge

Math

Related comparisons

Watch Gemini 2.5 Flash vs GLM-4.7