Knowledge
Like-for-like- GLM-5.2
- 59.6
- Inkling-Small
- 53.4
- Weighted basis
- 2 vs 2 rows
- Reading
- GLM-5.2 leads
Model comparison
GLM-5.2 vs Inkling-Small
Updated July 30, 2026. Public scores include evidence status and uncertainty. They are not guarantees for a specific workload.
At least one model is not scored in the current public ranking lane, so the page does not name an overall quality winner.
11 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
Inkling-Small
Inkling-Small 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-Small
Inkling-Small 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
Inkling-Small
Inkling-Small 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.
7 GLM-5.2 only
11 Shared
9 Inkling-Small only
- Shared results
- 11
- GLM-5.2 only
- 7
- Inkling-Small only
- 9
- Like-for-like categories
- 2 / 8
2 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.
Math
Like-for-like- GLM-5.2
- 95.9
- Inkling-Small
- 92.9
- Weighted basis
- 2 vs 2 rows
- Reading
- GLM-5.2 leads
Agentic
Directional only- GLM-5.2
- 81.0
- Inkling-Small
- 70.1
- Weighted basis
- 1 vs 2 rows
- Reading
- Directional only
Coding
Directional only- GLM-5.2
- 62.1
- Inkling-Small
- 62.4
- Weighted basis
- 1 vs 3 rows
- Reading
- Directional only
Reasoning
Not comparable- GLM-5.2
- Not measured
- Inkling-Small
- 40.1
- Weighted basis
- 0 vs 1 rows
- Reading
- Not comparable
Multilingual
Not comparable- GLM-5.2
- Not measured
- Inkling-Small
- Not measured
- Weighted basis
- 0 vs 0 rows
- Reading
- Not comparable
Multimodal
Not comparable- GLM-5.2
- Not measured
- Inkling-Small
- 76.6
- Weighted basis
- 0 vs 2 rows
- Reading
- Not comparable
Instruction following
Not comparable- GLM-5.2
- Not measured
- Inkling-Small
- 82.2
- Weighted basis
- 0 vs 1 rows
- Reading
- Not comparable
| Category | GLM-5.2 | Inkling-Small | Weighted basis | Reading |
|---|---|---|---|---|
| Knowledge | 59.6 | 53.4 | Like-for-like2 vs 2 rows | GLM-5.2 leads |
| Math | 95.9 | 92.9 | Like-for-like2 vs 2 rows | GLM-5.2 leads |
| Agentic | 81.0 | 70.1 | Directional only1 vs 2 rows | Directional only |
| Coding | 62.1 | 62.4 | Directional only1 vs 3 rows | Directional only |
| Reasoning | Not measured | 40.1 | Not comparable0 vs 1 rows | Not comparable |
| Multilingual | Not measured | Not measured | Not comparable0 vs 0 rows | Not comparable |
| Multimodal | Not measured | 76.6 | Not comparable0 vs 2 rows | Not comparable |
| Instruction following | Not measured | 82.2 | 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
AIME26
Math
HMMT Feb 2026
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-Small
- $0.0013
- Fits in one request
Inkling-Small 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-Small
- $0.03332
- Fits in one request
Inkling-Small 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-Small
- $0.0492
- Fits in one request
Inkling-Small 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-Small
Context window
Maximum documented context; output-token limits may be lower.
GLM-5.2
1M
Inkling-Small
1M
API model ID
GLM-5.2
Not sourced
Inkling-Small
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-Small
$0.116 per 1M cached input tokens
Documented inputs
GLM-5.2
Not sourced
Inkling-Small
Not sourced
Documented outputs
GLM-5.2
Not sourced
Inkling-Small
Not sourced
Provider availability
GLM-5.2
Not sourced
Inkling-Small
Not sourced
Reasoning profile
GLM-5.2
Reasoning
Inkling-Small
Hybrid
Weight access
GLM-5.2
Open Weight
Inkling-Small
Open Weight
License
GLM-5.2
Open Weight
Inkling-Small
Open Weight
Release date
GLM-5.2
2026-06-16
Inkling-Small
2026-07-30
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
- At least one model is not scored in the current public ranking lane, so the page does not name an overall quality winner.
- Workload cost
- Repository review: $0.0832 vs $0.03332. Cache-heavy agent loop: $0.352 vs $0.0492.
- 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 evidence27 rows
Agentic
Terminal-Bench 2.0
GLM-5.281%SourceInkling-Small64.7%SourceGLM-5.2 leads this result
MCP Atlas
GLM-5.276.8%SourceInkling-Small79.6%SourceInkling-Small leads this result
Toolathlon
GLM-5.248.2%SourceInkling-Small—Not directly comparable
ResearchClawBench
GLM-5.220.7%SourceInkling-Small—Not directly comparable
BrowseComp
GLM-5.2—Inkling-Small77.4%SourceNot directly comparable
Toolathlon-Verified
GLM-5.2—Inkling-Small54.4%SourceNot directly comparable
Coding
SWE-bench Pro
GLM-5.262.1%SourceInkling-Small55.9%SourceGLM-5.2 leads this result
NL2Repo
GLM-5.248.9%SourceInkling-Small—Not directly comparable
Terminal-Bench 2.0
GLM-5.281.0%SourceInkling-Small64.7%SourceGLM-5.2 leads this result
ProgramBench
GLM-5.263.7%SourceInkling-Small—Not directly comparable
cursorBench32
GLM-5.255.0%SourceInkling-Small—Not directly comparable
SWE-bench Verified
GLM-5.2—Inkling-Small80.2%SourceNot directly comparable
SciCode
GLM-5.2—Inkling-Small48.7%SourceNot directly comparable
Reasoning
Knowledge
GPQA
GLM-5.291.2%SourceInkling-Small89.5%SourceGLM-5.2 leads this result
GPQA-D
GLM-5.291.2%SourceInkling-Small89.5%SourceGLM-5.2 leads this result
HLE
GLM-5.254.7%SourceInkling-Small47.8%SourceGLM-5.2 leads this result
HLE w/o tools
GLM-5.240.5%SourceInkling-Small31.6%SourceGLM-5.2 leads this result
Math
Multimodal
Instruction following
IFBench
GLM-5.2—Inkling-Small82.2%SourceNot directly comparable
Frequently asked questions
Which is better, GLM-5.2 or Inkling-Small?
At least one model is not scored in the current public ranking lane, so the page does not name an overall quality winner. The page therefore keeps the decision tied to the specific documented workload.
Which is better for coding, GLM-5.2 or Inkling-Small?
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-Small?
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-Small?
For the stated presets, chat costs $0.0036 on GLM-5.2 and $0.0013 on Inkling-Small; repository review costs $0.0832 and $0.03332; the cache-heavy agent loop costs $0.352 and $0.0492. 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-Small?
Both models list the same context window, 1M.
Related comparisons
Last updated July 30, 2026
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