Agentic
Not comparable- GLM-4.7
- 45.7
- GLM-5-Turbo
- Not measured
- Weighted basis
- 2 vs 0 rows
- Reading
- Not comparable
Model comparison
GLM-4.7 vs GLM-5-Turbo
Updated July 29, 2026. Public scores include evidence status and uncertainty. They are not guarantees for a specific workload.
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.
No workload recommendation clears the current evidence threshold.
Use the matched evidence, workload costs, and sourced specifications below instead of treating a point score as a universal answer.
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
Long documents
Prompts that approach the documented context limit
No clear pick
The documented context windows are equal.
Confidence: documented
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-5-Turbo does not fit this workload in one request. GLM-5-Turbo has no published cached-input rate, so cached tokens use its listed input rate. GLM-4.7 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-4.7 only
1 GLM-5-Turbo only
- Shared results
- 0
- GLM-4.7 only
- 13
- GLM-5-Turbo only
- 1
- 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-4.7
- 75.4
- GLM-5-Turbo
- Not measured
- Weighted basis
- 3 vs 0 rows
- Reading
- Not comparable
Reasoning
Not comparable- GLM-4.7
- Not measured
- GLM-5-Turbo
- Not measured
- Weighted basis
- 0 vs 0 rows
- Reading
- Not comparable
Knowledge
Not comparable- GLM-4.7
- 51.8
- GLM-5-Turbo
- Not measured
- Weighted basis
- 3 vs 0 rows
- Reading
- Not comparable
Math
Not comparable- GLM-4.7
- 1.8
- GLM-5-Turbo
- Not measured
- Weighted basis
- 2 vs 0 rows
- Reading
- Not comparable
Multilingual
Not comparable- GLM-4.7
- Not measured
- GLM-5-Turbo
- Not measured
- Weighted basis
- 0 vs 0 rows
- Reading
- Not comparable
Multimodal
Not comparable- GLM-4.7
- Not measured
- GLM-5-Turbo
- Not measured
- Weighted basis
- 0 vs 0 rows
- Reading
- Not comparable
Instruction following
Not comparable- GLM-4.7
- Not measured
- GLM-5-Turbo
- Not measured
- Weighted basis
- 0 vs 0 rows
- Reading
- Not comparable
| Category | GLM-4.7 | GLM-5-Turbo | Weighted basis | Reading |
|---|---|---|---|---|
| Agentic | 45.7 | Not measured | Not comparable2 vs 0 rows | Not comparable |
| Coding | 75.4 | Not measured | Not comparable3 vs 0 rows | Not comparable |
| Reasoning | Not measured | Not measured | Not comparable0 vs 0 rows | Not comparable |
| Knowledge | 51.8 | Not measured | Not comparable3 vs 0 rows | Not comparable |
| Math | 1.8 | Not measured | Not comparable2 vs 0 rows | Not comparable |
| Multilingual | Not measured | Not measured | Not comparable0 vs 0 rows | Not comparable |
| Multimodal | Not measured | Not measured | Not comparable0 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-4.7
- Self-hosted; infrastructure cost varies
- Fits in one request
- GLM-5-Turbo
- $0.0032
- Fits in one request
GLM-4.7 has no comparable published API token rate.
Repository review
50K fresh input + 3K output tokens
- GLM-4.7
- Self-hosted; infrastructure cost varies
- Fits in one request
- GLM-5-Turbo
- $0.072
- 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
- GLM-4.7
- Self-hosted; infrastructure cost varies
- Does not fit in one request
- Cached-input rate unavailable
- GLM-5-Turbo
- $0.304
- Does not fit in one request
- Cached input priced at the published list-input rate
GLM-4.7 does not fit this workload in one request. GLM-5-Turbo does not fit this workload in one request. GLM-5-Turbo has no published cached-input rate, so cached tokens use its listed input rate. GLM-4.7 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-4.7GLM-5-Turbo
Context window
Maximum documented context; output-token limits may be lower.
GLM-4.7
200K
GLM-5-Turbo
200K
API model ID
GLM-4.7
Not sourced
GLM-5-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-4.7
No comparable hosted API rate
GLM-5-Turbo
Not published
Documented inputs
GLM-4.7
Not sourced
GLM-5-Turbo
Not sourced
Documented outputs
GLM-4.7
Not sourced
GLM-5-Turbo
Not sourced
Provider availability
GLM-4.7
Not sourced
GLM-5-Turbo
Not sourced
Reasoning profile
GLM-4.7
Reasoning
GLM-5-Turbo
Reasoning
Weight access
GLM-4.7
Open Weight
GLM-5-Turbo
Proprietary
License
GLM-4.7
Open Weight
GLM-5-Turbo
Proprietary
Release date
GLM-4.7
2025-10-01
GLM-5-Turbo
2026-03-01
If you already use one of these models
- 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
- Both models list 200K.
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 evidence14 rows
Agentic
Terminal-Bench 2.0
GLM-4.741%SourceGLM-5-Turbo—Not directly comparable
BrowseComp
GLM-4.752%SourceGLM-5-Turbo—Not directly comparable
VITA-Bench
GLM-4.715.5%SourceGLM-5-Turbo—Not directly comparable
Gert Labs
GLM-4.739.95%SourceGLM-5-Turbo—Not directly comparable
Claw-Eval
GLM-4.7—GLM-5-Turbo55.8%SourceNot directly comparable
Coding
Knowledge
Math
Frequently asked questions
Which is better, GLM-4.7 or GLM-5-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-4.7 or GLM-5-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-4.7 or GLM-5-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-4.7 or GLM-5-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-4.7 or GLM-5-Turbo?
Both models list the same context window, 200K.
Related comparisons
Last updated July 29, 2026
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