Gemini 3 Pro vs GLM-5.3-Flash

Updated October 10, 2026. Rank says Gemini 3 Pro is ahead. Price, access, and your workload can each overturn that. Public scores include evidence status and uncertainty.

Decision reading

Gemini 3 Pro has the higher public point estimate, 59.42 versus 57.36. Their conditional score ranges overlap. These ranges do not establish rank confidence. 1 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 Pro

Google

59.42/100

Supported · Public rank #52

90% interval 48.9–70.0

Model B

GLM-5.3-Flash

Z.AI

57.36/100

Estimated · Public rank #55

Conditional range 47.6–67.1

Shared results: 1
Gemini 3 Pro only: 28
GLM-5.3-Flash only: 20
Like-for-like categories: 0 / 8

Supported: Gemini 3 Pro · Estimated: GLM-5.3-Flash. Conditional ranges do not establish rank confidence.How 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 3 Pro
Gemini 3 Pro 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 3 Pro is scored on Estimated evidence for coding, so the reading is directional rather than like-for-like.
Confidence: limited
Agentic work
Tool use, computer use, and multi-step task completion
Not enough matched evidence
Gemini 3 Pro 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
A complete comparable API-rate estimate is not available for both models.
Confidence: rate-fallback
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.

42.9Gemini 3 Pro49.3GLM-5.3-Flash

Directional only · BenchAlign v5.8

GLM-5.3-Flash 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 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.

CharXivMultimodal
Normalized gap 8.0
Gemini 3 Pro:81.4%
GLM-5.3-Flash:89.4%
Gemini 3 Pro source GLM-5.3-Flash 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, 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

Directional only

Gemini 3 Pro: 42.9; Estimated · #55/146
GLM-5.3-Flash: 49.3; Supported · #42/146
Basis: BenchAlign v5.8 lane · 1 vs 8 public rows
Reading: Directional only

Multimodal

Directional only

Gemini 3 Pro: 80.0; #18/54
GLM-5.3-Flash: 84.0; #14/54
Basis: Provisional lane · 2 vs 2 weighted rows
Reading: Directional only

Knowledge

Directional only

Gemini 3 Pro: 60.2; Estimated · #44/177
GLM-5.3-Flash: 60.5; Supported · #43/177
Basis: BenchAlign v5.8 lane · 0 vs 2 public rows
Reading: Directional only

Agentic

Not comparable

Gemini 3 Pro: Not ranked
GLM-5.3-Flash: 55.7; Supported · #34/123
Basis: BenchAlign v5.8 lane · 2 vs 6 public rows
Reading: Not comparable

Reasoning

Not comparable

Gemini 3 Pro: 50.2; Unranked · 4 rankable rows
GLM-5.3-Flash: 81.1; #11/28
Basis: Provisional lane · 1 vs 0 weighted rows
Reading: Not comparable

Multilingual

Not comparable

Gemini 3 Pro: Not ranked
GLM-5.3-Flash: Not ranked
Basis: Provisional lane · 0 vs 0 weighted rows
Reading: Not comparable

Instruction following

Not comparable

Gemini 3 Pro: 86.1; #43/127
GLM-5.3-Flash: Not ranked
Basis: Provisional lane · 0 vs 0 weighted rows
Reading: Not comparable

Math

Not comparable

Gemini 3 Pro: 55.7; Unranked · 2 rankable rows
GLM-5.3-Flash: Not ranked
Basis: Provisional lane · 2 vs 0 weighted rows
Reading: Not comparable

Category scores with the server-provided evidence basis for Gemini 3 Pro and GLM-5.3-Flash
Category	Gemini 3 Pro	GLM-5.3-Flash	Basis	Reading
Coding	42.9Estimated · #55/146	49.3Supported · #42/146	Directional onlyBenchAlign v5.8 lane · 1 vs 8 public rows	Directional only
Multimodal	80.0#18/54	84.0#14/54	Directional onlyProvisional lane · 2 vs 2 weighted rows	Directional only
Knowledge	60.2Estimated · #44/177	60.5Supported · #43/177	Directional onlyBenchAlign v5.8 lane · 0 vs 2 public rows	Directional only
Agentic	Not ranked	55.7Supported · #34/123	Not comparableBenchAlign v5.8 lane · 2 vs 6 public rows	Not comparable
Reasoning	50.2Unranked · 4 rankable rows	81.1#11/28	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	86.1#43/127	Not ranked	Not comparableProvisional lane · 0 vs 0 weighted rows	Not comparable
Math	55.7Unranked · 2 rankable rows	Not ranked	Not comparableProvisional lane · 2 vs 0 weighted rows	Not comparable

Ranks count the models scored in each category’s lane, so the agentic, coding, 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 3 Pro: $0.008; Fits in one request
GLM-5.3-Flash: Self-hosted; infrastructure cost varies; Fits in one request

GLM-5.3-Flash has no comparable published API token rate.

Repository review

50K fresh input + 3K output tokens

Gemini 3 Pro: $0.136; Fits in one request
GLM-5.3-Flash: Self-hosted; infrastructure cost varies; Fits in one request

GLM-5.3-Flash has no comparable published API token rate.

Cache-heavy agent loop

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

Gemini 3 Pro: $0.56; Fits in one request; Cached input priced at the published list-input rate
GLM-5.3-Flash: Self-hosted; infrastructure cost varies; Fits in one request; Cached-input rate unavailable

Gemini 3 Pro has no published cached-input rate, so cached tokens use its listed input rate. GLM-5.3-Flash 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 ProGLM-5.3-Flash

Context window

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

Gemini 3 Pro

GLM-5.3-Flash

GLM-5.3-Flash model card

API model ID

Gemini 3 Pro

Not sourced

GLM-5.3-Flash

glm-5.3-flash

GLM-5.3-Flash model card

Cached-input rate

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

Gemini 3 Pro

Not published

GLM-5.3-Flash

No comparable hosted API rate

GLM-5.3-Flash model card

Documented inputs

Gemini 3 Pro

Not sourced

GLM-5.3-Flash

Not sourced

Documented outputs

Gemini 3 Pro

Not sourced

GLM-5.3-Flash

Not sourced

Provider availability

Gemini 3 Pro

Not sourced

GLM-5.3-Flash

Not sourced

Reasoning profile

Gemini 3 Pro

Non-Reasoning

GLM-5.3-Flash

Reasoning

Weight access

Gemini 3 Pro

Proprietary

GLM-5.3-Flash

Open Weight

License

Gemini 3 Pro

Proprietary

GLM-5.3-Flash

Open Weight

Release date

Gemini 3 Pro

2025-11-18

GLM-5.3-Flash

2026-08-26

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 Pro has the higher public point estimate, 59.42 versus 57.36. Their conditional score ranges overlap. These ranges do not establish rank confidence.
Workload cost: A complete comparable API-rate estimate is not available for both models.
Context tradeoff: Gemini 3 Pro has the larger documented window (2M).

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

Questions

Which is better, Gemini 3 Pro or GLM-5.3-Flash?

Gemini 3 Pro has the higher public point estimate, 59.42 versus 57.36. Their conditional score ranges overlap. These ranges do not establish rank confidence. The page therefore keeps the decision tied to the specific documented workload.

Which is better for coding, Gemini 3 Pro or GLM-5.3-Flash?

GLM-5.3-Flash scores higher for coding on the public lane, 49.3 to 42.9. Gemini 3 Pro is scored on Estimated evidence for coding, so the reading is directional rather than like-for-like. BenchLM does not name a winner for coding from a directional reading; read the shared benchmark rows directly and test the models on the same task set.

Which is better for agentic tasks, Gemini 3 Pro or GLM-5.3-Flash?

Gemini 3 Pro is not ranked on the public lane for agentic tasks, so no winner is named for agentic tasks.

Which costs less, Gemini 3 Pro or GLM-5.3-Flash?

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 Pro or GLM-5.3-Flash?

Gemini 3 Pro has the larger documented context window: 2M, compared with 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 evidence49 rows

Agentic

Gert Labs
Gemini 3 Pro63.23%
Source
GLM-5.3-Flash—
Not directly comparable
JobBench
Gemini 3 Pro11.4%
Source
GLM-5.3-Flash—
Not directly comparable
Terminal-Bench 2.1
Gemini 3 Pro—
GLM-5.3-Flash84.3%
Source
Not directly comparable
Toolathlon-Verified
Gemini 3 Pro—
GLM-5.3-Flash78.4%
Source
Not directly comparable
AutomationBench
Gemini 3 Pro—
GLM-5.3-Flash48.8%
Source
Not directly comparable
Agents' Last Exam
Gemini 3 Pro—
GLM-5.3-Flash26.3%
Source
Not directly comparable
HLE w/ tools
Gemini 3 Pro—
GLM-5.3-Flash55.3%
Source
Not directly comparable
Terminal-Bench 2.1 (Vals)
Gemini 3 Pro—
GLM-5.3-Flash62.9%
Source
Not directly comparable

Coding

Vibe Code Bench
Gemini 3 Pro14.30%
Source
GLM-5.3-Flash—
Not directly comparable
Terminal-Bench 2.1
Gemini 3 Pro—
GLM-5.3-Flash84.3%
Source
Not directly comparable
DeepSWE
Gemini 3 Pro—
GLM-5.3-Flash63.4%
Source
Not directly comparable
NL2Repo
Gemini 3 Pro—
GLM-5.3-Flash56.3%
Source
Not directly comparable
LiveCodeBench (Vals)
Gemini 3 Pro—
GLM-5.3-Flash80.5%
Source
Not directly comparable
SWE-bench (Vals)
Gemini 3 Pro—
GLM-5.3-Flash92.0%
Source
Not directly comparable
OpenHarmony Bench
Gemini 3 Pro—
GLM-5.3-Flash57.3%
Source
Not directly comparable
FrontierSWE v2
Gemini 3 Pro—
GLM-5.3-Flash18.1%
Source
Not directly comparable
Bug Hunt Bench
Gemini 3 Pro—
GLM-5.3-Flash17.7 fixes
Source
Not directly comparable

Reasoning

ARC-AGI-2
Gemini 3 Pro31.1%
Source
GLM-5.3-Flash—
Not directly comparable
ARC-AGI-1
Gemini 3 Pro75.00%
Source
GLM-5.3-Flash—
Not directly comparable

Multimodal

MMMU
Gemini 3 Pro87.2%
Source
GLM-5.3-Flash—
Not directly comparable
MMMU-Pro
Gemini 3 Pro81%
Source
GLM-5.3-Flash—
Not directly comparable
RealWorldQA
Gemini 3 Pro83.3%
Source
GLM-5.3-Flash—
Not directly comparable
OmniDocBench 1.5
Gemini 3 Pro88.5%
Source
GLM-5.3-Flash—
Not directly comparable
Video-MME (with subtitle)
Gemini 3 Pro88.4%
Source
GLM-5.3-Flash—
Not directly comparable
Video-MME (w/o subtitle)
Gemini 3 Pro87.7%
Source
GLM-5.3-Flash—
Not directly comparable
MathVision
Gemini 3 Pro86.6%
Source
GLM-5.3-Flash—
Not directly comparable
We-Math
Gemini 3 Pro86.9%
Source
GLM-5.3-Flash—
Not directly comparable
DynaMath
Gemini 3 Pro85.1%
Source
GLM-5.3-Flash—
Not directly comparable
SimpleVQA
Gemini 3 Pro73.2%
Source
GLM-5.3-Flash—
Not directly comparable
MMLongBench-Doc
Gemini 3 Pro60.5%
Source
GLM-5.3-Flash—
Not directly comparable
CC-OCR
Gemini 3 Pro79.0%
Source
GLM-5.3-Flash—
Not directly comparable
AI2D_TEST
Gemini 3 Pro94.1%
Source
GLM-5.3-Flash—
Not directly comparable
CountBench
Gemini 3 Pro97.3%
Source
GLM-5.3-Flash—
Not directly comparable
RefCOCO (avg)
Gemini 3 Pro84.1%
Source
GLM-5.3-Flash—
Not directly comparable
ODINW13
Gemini 3 Pro46.3%
Source
GLM-5.3-Flash—
Not directly comparable
ERQA
Gemini 3 Pro70.5%
Source
GLM-5.3-Flash—
Not directly comparable
VideoMMMU
Gemini 3 Pro87.6%
Source
GLM-5.3-Flash—
Not directly comparable
MLVU (M-Avg)
Gemini 3 Pro83.0%
Source
GLM-5.3-Flash—
Not directly comparable
ScreenSpot Pro
Gemini 3 Pro72.7%
Source
GLM-5.3-Flash—
Not directly comparable
CharXiv
Gemini 3 Pro81.4%
Source
GLM-5.3-Flash89.4%
Source
GLM-5.3-Flash leads this result
V*
Gemini 3 Pro88.0%
Source
GLM-5.3-Flash—
Not directly comparable
OfficeQA Pro
Gemini 3 Pro—
GLM-5.3-Flash62.4%
Source
Not directly comparable
Chartography (tools)
Gemini 3 Pro—
GLM-5.3-Flash78.0%
Source
Not directly comparable
BabyVision
Gemini 3 Pro—
GLM-5.3-Flash53.4%
Source
Not directly comparable
MMVU
Gemini 3 Pro—
GLM-5.3-Flash80.5%
Source
Not directly comparable

Knowledge

GPQA Diamond (Vals)
Gemini 3 Pro—
GLM-5.3-Flash86.4%
Source
Not directly comparable
MMLU-Pro (Vals)
Gemini 3 Pro—
GLM-5.3-Flash86.1%
Source
Not directly comparable

Math

FrontierMath v2 (Tiers 1-3)
Gemini 3 Pro37.600%
Source
GLM-5.3-Flash—
Not directly comparable
FrontierMath v2 (Tier 4)
Gemini 3 Pro18.750%
Source
GLM-5.3-Flash—
Not directly comparable

49 public results · 1 shared

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Last updated October 10, 2026

Gemini 3 Pro vs GLM-5.3-Flash

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

Coding

Multimodal

Knowledge

Agentic

Reasoning

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 Pro or GLM-5.3-Flash?

Which is better for coding, Gemini 3 Pro or GLM-5.3-Flash?

Which is better for agentic tasks, Gemini 3 Pro or GLM-5.3-Flash?

Which costs less, Gemini 3 Pro or GLM-5.3-Flash?

Which has the larger context window, Gemini 3 Pro or GLM-5.3-Flash?

Benchmark evidence

Agentic

Coding

Reasoning

Multimodal

Knowledge

Math

Related comparisons

Watch Gemini 3 Pro vs GLM-5.3-Flash