Agentic
Not comparable- GLM-5.3-Flash
- Not measured
- GLM-5V-Turbo
- Not measured
- Weighted basis
- 0 vs 0 rows
- Reading
- Not comparable
GLM-5.3-Flash vs GLM-5V-Turbo
Updated August 26, 2026. Public scores include evidence status and uncertainty. They are not guarantees for a specific workload. This is a same-family comparison, so migration details appear when the source data supports them.
Decision reading
The public evidence has no benchmark result shared by both models, so it does not support a quality verdict. Use the documented cost, context, and runtime rows instead.
0 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.
Long documents
Prompts that approach the documented context limit
GLM-5.3-Flash
GLM-5.3-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
No shared weighted benchmark basis supports a winner.
Confidence: limited
Agentic work
Tool use, computer use, and multi-step task completion
Not enough matched evidence
No shared weighted benchmark basis supports a winner.
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-5V-Turbo does not fit this workload in one request. GLM-5V-Turbo 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.
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
What is actually comparable
Shared results can support a head-to-head reading. Results present for only one model describe coverage, not superiority.
13 GLM-5.3-Flash only
2 GLM-5V-Turbo only
- Shared results
- 0
- GLM-5.3-Flash only
- 13
- GLM-5V-Turbo only
- 2
- Like-for-like categories
- 0 / 8
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
Not comparable- GLM-5.3-Flash
- Not measured
- GLM-5V-Turbo
- Not measured
- Weighted basis
- 0 vs 0 rows
- Reading
- Not comparable
Reasoning
Not comparable- GLM-5.3-Flash
- Not measured
- GLM-5V-Turbo
- Not measured
- Weighted basis
- 0 vs 0 rows
- Reading
- Not comparable
Knowledge
Not comparable- GLM-5.3-Flash
- Not measured
- GLM-5V-Turbo
- Not measured
- Weighted basis
- 0 vs 0 rows
- Reading
- Not comparable
Math
Not comparable- GLM-5.3-Flash
- Not measured
- GLM-5V-Turbo
- Not measured
- Weighted basis
- 0 vs 0 rows
- Reading
- Not comparable
Multilingual
Not comparable- GLM-5.3-Flash
- Not measured
- GLM-5V-Turbo
- Not measured
- Weighted basis
- 0 vs 0 rows
- Reading
- Not comparable
Multimodal
Not comparable- GLM-5.3-Flash
- 74.7
- GLM-5V-Turbo
- Not measured
- Weighted basis
- 2 vs 0 rows
- Reading
- Not comparable
Instruction following
Not comparable- GLM-5.3-Flash
- Not measured
- GLM-5V-Turbo
- Not measured
- Weighted basis
- 0 vs 0 rows
- Reading
- Not comparable
| Category | GLM-5.3-Flash | GLM-5V-Turbo | Weighted basis | Reading |
|---|---|---|---|---|
| Agentic | Not measured | Not measured | Not comparable0 vs 0 rows | Not comparable |
| Coding | Not measured | Not measured | Not comparable0 vs 0 rows | Not comparable |
| Reasoning | Not measured | Not measured | Not comparable0 vs 0 rows | Not comparable |
| Knowledge | Not measured | Not measured | Not comparable0 vs 0 rows | Not comparable |
| Math | Not measured | Not measured | Not comparable0 vs 0 rows | Not comparable |
| Multilingual | Not measured | Not measured | Not comparable0 vs 0 rows | Not comparable |
| Multimodal | 74.7 | Not measured | Not comparable2 vs 0 rows | Not comparable |
| Instruction following | Not measured | Not measured | Not comparable0 vs 0 rows | 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.
A shared-evidence shape is not available.
BenchLM does not draw a radar or infer missing axes when the matched evidence is too sparse.
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
- GLM-5.3-Flash
- Self-hosted; infrastructure cost varies
- Fits in one request
- GLM-5V-Turbo
- $0.0032
- Fits in one request
GLM-5.3-Flash has no comparable published API token rate.
Repository review
50K fresh input + 3K output tokens
- GLM-5.3-Flash
- Self-hosted; infrastructure cost varies
- Fits in one request
- GLM-5V-Turbo
- $0.072
- 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
- GLM-5.3-Flash
- Self-hosted; infrastructure cost varies
- Fits in one request
- Cached-input rate unavailable
- GLM-5V-Turbo
- $0.304
- Does not fit in one request
- Cached input priced at the published list-input rate
GLM-5V-Turbo does not fit this workload in one request. GLM-5V-Turbo 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.
Specification differences
Sourced differences are shown directly. Missing facts stay explicit instead of being inferred from a model name or family.
SpecificationGLM-5.3-FlashGLM-5V-Turbo
Context window
Maximum documented context; output-token limits may be lower.
GLM-5.3-Flash
GLM-5V-Turbo
200K
API model ID
GLM-5.3-Flash
glm-5.3-flash
GLM-5.3-Flash model cardGLM-5V-Turbo
Not sourced
Cached-input rate
A missing cached-input rate falls back to the listed input rate only in the stated workload estimate.
GLM-5.3-Flash
No comparable hosted API rate
GLM-5.3-Flash model cardGLM-5V-Turbo
Not published
Documented inputs
GLM-5.3-Flash
Not sourced
GLM-5V-Turbo
Not sourced
Documented outputs
GLM-5.3-Flash
Not sourced
GLM-5V-Turbo
Not sourced
Provider availability
GLM-5.3-Flash
Not sourced
GLM-5V-Turbo
Not sourced
Reasoning profile
GLM-5.3-Flash
Reasoning
GLM-5V-Turbo
Non-Reasoning
Weight access
GLM-5.3-Flash
Open Weight
GLM-5V-Turbo
Proprietary
License
GLM-5.3-Flash
Open Weight
GLM-5V-Turbo
Proprietary
Release date
GLM-5.3-Flash
2026-08-26
GLM-5V-Turbo
2026-03-01
If you are choosing between sibling variants
- Deployment change
- Both entries list Z.AI as the provider. Confirm endpoint, model ID, limits, and feature support before switching.
- Quality signal
- The public evidence has no benchmark result shared by both models, so it does not support a quality verdict.
- Workload cost
- A complete comparable API-rate estimate is not available for both models.
- Context tradeoff
- GLM-5.3-Flash has the larger documented window (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 evidence15 rows
Agentic
Terminal-Bench 2.1
GLM-5.3-Flash84.3%SourceGLM-5V-Turbo—Not directly comparable
Toolathlon-Verified
GLM-5.3-Flash78.4%SourceGLM-5V-Turbo—Not directly comparable
AutomationBench
GLM-5.3-Flash48.8%SourceGLM-5V-Turbo—Not directly comparable
Agents' Last Exam
GLM-5.3-Flash26.3%SourceGLM-5V-Turbo—Not directly comparable
HLE w/ tools
GLM-5.3-Flash55.3%SourceGLM-5V-Turbo—Not directly comparable
Claw-Eval
GLM-5.3-Flash—GLM-5V-Turbo53.8%SourceNot directly comparable
Gert Labs
GLM-5.3-Flash—GLM-5V-Turbo30.76%SourceNot directly comparable
Coding
Multimodal
OfficeQA Pro
GLM-5.3-Flash62.4%SourceGLM-5V-Turbo—Not directly comparable
CharXiv
GLM-5.3-Flash89.4%SourceGLM-5V-Turbo—Not directly comparable
Chartography (tools)
GLM-5.3-Flash78.0%SourceGLM-5V-Turbo—Not directly comparable
BabyVision
GLM-5.3-Flash53.4%SourceGLM-5V-Turbo—Not directly comparable
MMVU
GLM-5.3-Flash80.5%SourceGLM-5V-Turbo—Not directly comparable
Frequently asked questions
Which is better, GLM-5.3-Flash or GLM-5V-Turbo?
The public evidence has no benchmark result shared by both models, so it does not support a quality verdict. The page therefore keeps the decision tied to the specific documented workload.
Which is better for coding, GLM-5.3-Flash or GLM-5V-Turbo?
The published evidence does not provide a shared weighted coding basis for both models, so BenchLM does not name a coding winner.
Which is better for agentic tasks, GLM-5.3-Flash or GLM-5V-Turbo?
The published evidence does not provide a shared weighted agentic tasks basis for both models, so BenchLM does not name a agentic tasks winner.
Which costs less, GLM-5.3-Flash or GLM-5V-Turbo?
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, GLM-5.3-Flash or GLM-5V-Turbo?
GLM-5.3-Flash has the larger documented context window: 1M, compared with 200K.
Related comparisons
Last updated August 26, 2026
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