Knowledge
Like-for-like- GLM-5.2
- 59.6
- Inkling
- 51.6
- Weighted basis
- 2 vs 2 rows
- Reading
- GLM-5.2 leads
GLM-5.2 vs Inkling
Updated August 29, 2026. Public scores include evidence status and uncertainty. They are not guarantees for a specific workload.
Decision reading
Inkling has the higher public score estimate, 67.02 versus 63.4, but the 90% score intervals overlap. Treat that as a lead, not a settled winner.
9 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.
Chat turn cost
1K fresh input + 500 output tokens
GLM-5.2
GLM-5.2 has the lower estimated token cost for this stated workload. Costs use the listed standard API rates.
Confidence: listed-rates
Cache-heavy agent loop cost
200K cached + 20K fresh input + 10K output tokens
Inkling
Inkling has the lower estimated token cost for this stated workload. GLM-5.2 has no published cached-input rate, so cached tokens use its listed input rate.
Confidence: rate-fallback
Show secondary and unsupported calls
Repository review cost
50K fresh input + 3K output tokens
GLM-5.2
GLM-5.2 has the lower estimated token cost for this stated workload. Costs use the listed standard API rates.
Confidence: listed-rates
Coding work
Code generation, repair, and software-engineering tasks
Not enough matched evidence
The category averages use different weighted benchmark sets, so they are directional rather than like-for-like.
Confidence: limited
Agentic work
Tool use, computer use, and multi-step task completion
Not enough matched evidence
The category averages use different weighted benchmark sets, so they are directional rather than like-for-like.
Confidence: limited
Long documents
Prompts that approach the documented context limit
No clear pick
The documented context windows are equal.
Confidence: documented
What is actually comparable
Shared results can support a head-to-head reading. Results present for only one model describe coverage, not superiority.
11 GLM-5.2 only
9 Shared
6 Inkling only
- Shared results
- 9
- GLM-5.2 only
- 11
- Inkling only
- 6
- Like-for-like categories
- 1 / 8
3 categories use different evidence sets. Those rows remain visible for coverage context but do not name a winner.
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.
Agentic
Directional only- GLM-5.2
- 81.0
- Inkling
- 69.4
- Weighted basis
- 1 vs 2 rows
- Reading
- Directional only
Coding
Directional only- GLM-5.2
- 62.1
- Inkling
- 68.6
- Weighted basis
- 1 vs 2 rows
- Reading
- Directional only
Math
Directional only- GLM-5.2
- 95.9
- Inkling
- 97.1
- Weighted basis
- 2 vs 1 rows
- Reading
- Directional only
Reasoning
Not comparable- GLM-5.2
- Not measured
- Inkling
- Not measured
- Weighted basis
- 0 vs 0 rows
- Reading
- Not comparable
Multilingual
Not comparable- GLM-5.2
- Not measured
- Inkling
- Not measured
- Weighted basis
- 0 vs 0 rows
- Reading
- Not comparable
Multimodal
Not comparable- GLM-5.2
- Not measured
- Inkling
- 76.5
- Weighted basis
- 0 vs 2 rows
- Reading
- Not comparable
Instruction following
Not comparable- GLM-5.2
- Not measured
- Inkling
- 79.8
- Weighted basis
- 0 vs 1 rows
- Reading
- Not comparable
| Category | GLM-5.2 | Inkling | Weighted basis | Reading |
|---|---|---|---|---|
| Knowledge | 59.6 | 51.6 | Like-for-like2 vs 2 rows | GLM-5.2 leads |
| Agentic | 81.0 | 69.4 | Directional only1 vs 2 rows | Directional only |
| Coding | 62.1 | 68.6 | Directional only1 vs 2 rows | Directional only |
| Math | 95.9 | 97.1 | Directional only2 vs 1 rows | Directional only |
| Reasoning | Not measured | Not measured | Not comparable0 vs 0 rows | Not comparable |
| Multilingual | Not measured | Not measured | Not comparable0 vs 0 rows | Not comparable |
| Multimodal | Not measured | 76.5 | Not comparable0 vs 2 rows | Not comparable |
| Instruction following | Not measured | 79.8 | Not comparable0 vs 1 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.
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.
Terminal-Bench 2.0
Agentic
HLE
Knowledge
SWE-bench Pro
Coding
GPQA
Knowledge
AIME26
Math
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-5.2
- $0.0036
- Fits in one request
- Inkling
- $0.00421
- Fits in one request
GLM-5.2 has the lower modeled cost
Costs use the listed standard API rates.
Repository review
50K fresh input + 3K output tokens
- GLM-5.2
- $0.0832
- Fits in one request
- Inkling
- $0.10754
- Fits in one request
GLM-5.2 has the lower modeled cost
Costs use the listed standard API rates.
Cache-heavy agent loop
200K cached + 20K fresh input + 10K output tokens
- GLM-5.2
- $0.352
- Fits in one request
- Cached input priced at the published list-input rate
- Inkling
- $0.159
- Fits in one request
Inkling has the lower modeled cost
GLM-5.2 has no published cached-input rate, so cached tokens use its listed input rate.
Specification differences
Sourced differences are shown directly. Missing facts stay explicit instead of being inferred from a model name or family.
SpecificationGLM-5.2Inkling
Context window
Maximum documented context; output-token limits may be lower.
GLM-5.2
1M
Inkling
1M
API model ID
GLM-5.2
Not sourced
Inkling
Not sourced
Cached-input rate
A missing cached-input rate falls back to the listed input rate only in the stated workload estimate.
GLM-5.2
Not published
Inkling
$0.374 per 1M cached input tokens
Documented inputs
GLM-5.2
Not sourced
Inkling
Not sourced
Documented outputs
GLM-5.2
Not sourced
Inkling
Not sourced
Provider availability
GLM-5.2
Not sourced
Inkling
Not sourced
Reasoning profile
GLM-5.2
Reasoning
Inkling
Hybrid
Weight access
GLM-5.2
Open Weight
Inkling
Open Weight
License
GLM-5.2
Open Weight
Inkling
Open Weight
Release date
GLM-5.2
2026-06-16
Inkling
2026-07-15
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
- Inkling has the higher public score estimate, 67.02 versus 63.4, but the 90% score intervals overlap.
- Workload cost
- Repository review: $0.0832 vs $0.10754. Cache-heavy agent loop: $0.352 vs $0.159.
- Context tradeoff
- Both models list 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 evidence26 rows
Agentic
Terminal-Bench 3.0
GLM-5.24.6%SourceInkling—Not directly comparable
Terminal-Bench 2.0
GLM-5.281%SourceInkling63.8%SourceGLM-5.2 leads this result
MCP Atlas
GLM-5.276.8%SourceInkling74.1%SourceGLM-5.2 leads this result
Toolathlon
GLM-5.248.2%SourceInkling—Not directly comparable
ResearchClawBench
GLM-5.220.7%SourceInkling—Not directly comparable
BrowseComp
GLM-5.2—Inkling77.1%SourceNot directly comparable
Coding
SWE-bench Pro
GLM-5.262.1%SourceInkling54.3%SourceGLM-5.2 leads this result
NL2Repo
GLM-5.248.9%SourceInkling—Not directly comparable
Terminal-Bench 2.0
GLM-5.281.0%SourceInkling63.8%SourceGLM-5.2 leads this result
ProgramBench
GLM-5.263.7%SourceInkling—Not directly comparable
cursorBench32
GLM-5.255.0%SourceInkling—Not directly comparable
OpenHarmony Bench
GLM-5.258.4%SourceInkling—Not directly comparable
SWE-bench Verified
GLM-5.2—Inkling77.6%SourceNot directly comparable
Reasoning
CritPt
GLM-5.220.9%SourceInkling—Not directly comparable
Knowledge
Math
Multimodal
Instruction following
IFBench
GLM-5.2—Inkling79.8%SourceNot directly comparable
Frequently asked questions
Which is better, GLM-5.2 or Inkling?
Inkling has the higher public score estimate, 67.02 versus 63.4, but the 90% score intervals overlap. The higher estimate is not a decisive winner because the uncertainty ranges overlap.
Which is better for coding, GLM-5.2 or Inkling?
The current coding averages use different weighted benchmark sets, so BenchLM does not name a winner from them. Read the shared benchmark rows directly and test the models on the same task set.
Which is better for agentic tasks, GLM-5.2 or Inkling?
The current agentic tasks averages use different weighted benchmark sets, so BenchLM does not name a winner from them. Read the shared benchmark rows directly and test the models on the same task set.
Which costs less, GLM-5.2 or Inkling?
For the stated presets, chat costs $0.0036 on GLM-5.2 and $0.00421 on Inkling; repository review costs $0.0832 and $0.10754; the cache-heavy agent loop costs $0.352 and $0.159. GLM-5.2 has no published cached-input rate, so cached tokens use its listed input rate.
Which has the larger context window, GLM-5.2 or Inkling?
Both models list the same context window, 1M.
Related comparisons
Last updated August 29, 2026
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