Agentic
Like-for-like- Composer 2
- 61.7
- GLM-5.2
- 81.0
- Weighted basis
- 1 vs 1 rows
- Reading
- GLM-5.2 leads
Model comparison
Composer 2 vs GLM-5.2
Updated July 31, 2026. Public scores include evidence status and uncertainty. They are not guarantees for a specific workload.
21 confirmed releases in the last 30 daystrack changes
At least one model is not scored in the current public ranking lane, so the page does not name an overall quality winner.
2 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.
Agentic work
Tool use, computer use, and multi-step task completion
GLM-5.2
GLM-5.2 leads on the same 1 weighted benchmark row.
Confidence: limited
Long documents
Prompts that approach the documented context limit
GLM-5.2
GLM-5.2 has the larger documented context window.
Confidence: documented
Chat turn cost
1K fresh input + 500 output tokens
Composer 2
Composer 2 has the lower estimated token cost for this stated workload. Costs use the listed standard API rates.
Confidence: listed-rates
Show secondary and unsupported calls
Repository review cost
50K fresh input + 3K output tokens
Composer 2
Composer 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
No shared weighted benchmark basis supports a winner.
Confidence: limited
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. Composer 2 does not fit this workload in one request. Composer 2 has no published cached-input rate, so cached tokens use its listed input rate. GLM-5.2 has no published cached-input rate, so cached tokens use its listed input rate.
Confidence: rate-fallback
What is actually comparable
Shared results can support a head-to-head reading. Results present for only one model describe coverage, not superiority.
3 Composer 2 only
2 Shared
16 GLM-5.2 only
- Shared results
- 2
- Composer 2 only
- 3
- GLM-5.2 only
- 16
- Like-for-like categories
- 1 / 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- Composer 2
- 58.0
- GLM-5.2
- 62.1
- Weighted basis
- 1 vs 1 rows
- Reading
- Not comparable
Reasoning
Not comparable- Composer 2
- Not measured
- GLM-5.2
- Not measured
- Weighted basis
- 0 vs 0 rows
- Reading
- Not comparable
Knowledge
Not comparable- Composer 2
- Not measured
- GLM-5.2
- 59.6
- Weighted basis
- 0 vs 2 rows
- Reading
- Not comparable
Math
Not comparable- Composer 2
- Not measured
- GLM-5.2
- 95.9
- Weighted basis
- 0 vs 2 rows
- Reading
- Not comparable
Multilingual
Not comparable- Composer 2
- Not measured
- GLM-5.2
- Not measured
- Weighted basis
- 0 vs 0 rows
- Reading
- Not comparable
Multimodal
Not comparable- Composer 2
- Not measured
- GLM-5.2
- Not measured
- Weighted basis
- 0 vs 0 rows
- Reading
- Not comparable
Instruction following
Not comparable- Composer 2
- Not measured
- GLM-5.2
- Not measured
- Weighted basis
- 0 vs 0 rows
- Reading
- Not comparable
| Category | Composer 2 | GLM-5.2 | Weighted basis | Reading |
|---|---|---|---|---|
| Agentic | 61.7 | 81.0 | Like-for-like1 vs 1 rows | GLM-5.2 leads |
| Coding | 58.0 | 62.1 | Not comparable1 vs 1 rows | Not comparable |
| Reasoning | Not measured | Not measured | Not comparable0 vs 0 rows | Not comparable |
| Knowledge | Not measured | 59.6 | Not comparable0 vs 2 rows | Not comparable |
| Math | Not measured | 95.9 | Not comparable0 vs 2 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.
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
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
- Composer 2
- $0.00175
- Fits in one request
- GLM-5.2
- $0.0036
- Fits in one request
Composer 2 has the lower modeled cost
Costs use the listed standard API rates.
Repository review
50K fresh input + 3K output tokens
- Composer 2
- $0.0325
- Fits in one request
- GLM-5.2
- $0.0832
- Fits in one request
Composer 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
- Composer 2
- $0.135
- Does not fit in one request
- Cached input priced at the published list-input rate
- GLM-5.2
- $0.352
- Fits in one request
- Cached input priced at the published list-input rate
Composer 2 does not fit this workload in one request. Composer 2 has no published cached-input rate, so cached tokens use its listed input rate. 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.
SpecificationComposer 2GLM-5.2
Context window
Maximum documented context; output-token limits may be lower.
Composer 2
200K
GLM-5.2
1M
API model ID
Composer 2
Not sourced
GLM-5.2
Not sourced
Cached-input rate
A missing cached-input rate falls back to the listed input rate only in the stated workload estimate.
Composer 2
Not published
GLM-5.2
Not published
Documented inputs
Composer 2
Not sourced
GLM-5.2
Not sourced
Documented outputs
Composer 2
Not sourced
GLM-5.2
Not sourced
Provider availability
Composer 2
Not sourced
GLM-5.2
Not sourced
Reasoning profile
Composer 2
Reasoning
GLM-5.2
Reasoning
Weight access
Composer 2
Proprietary
GLM-5.2
Open Weight
License
Composer 2
Proprietary
GLM-5.2
Open Weight
Release date
Composer 2
2026-03-19
GLM-5.2
2026-06-16
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: $0.0325 vs $0.0832. Cache-heavy agent loop: $0.135 vs $0.352.
- Context tradeoff
- GLM-5.2 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 evidence21 rows
Agentic
Coding
SWE Multilingual
Composer 273.7%SourceGLM-5.2—Not directly comparable
SWE-Rebench
Composer 258%SourceGLM-5.2—Not directly comparable
React Native Evals
Composer 296.1%SourceGLM-5.2—Not directly comparable
Terminal-Bench 2.0
Composer 261.7%SourceGLM-5.281.0%SourceGLM-5.2 leads this result
SWE-bench Pro
Composer 2—GLM-5.262.1%SourceNot directly comparable
NL2Repo
Composer 2—GLM-5.248.9%SourceNot directly comparable
ProgramBench
Composer 2—GLM-5.263.7%SourceNot directly comparable
cursorBench32
Composer 2—GLM-5.255.0%SourceNot directly comparable
Reasoning
CritPt
Composer 2—GLM-5.220.9%SourceNot directly comparable
Knowledge
Math
Frequently asked questions
Which is better, Composer 2 or GLM-5.2?
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, Composer 2 or GLM-5.2?
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, Composer 2 or GLM-5.2?
GLM-5.2 leads the like-for-like agentic tasks comparison across 1 shared weighted benchmark row.
Which costs less, Composer 2 or GLM-5.2?
For the stated presets, chat costs $0.00175 on Composer 2 and $0.0036 on GLM-5.2; repository review costs $0.0325 and $0.0832; the cache-heavy agent loop costs $0.135 and $0.352. Composer 2 does not fit this workload in one request. Composer 2 has no published cached-input rate, so cached tokens use its listed input rate. GLM-5.2 has no published cached-input rate, so cached tokens use its listed input rate.
Which has the larger context window, Composer 2 or GLM-5.2?
GLM-5.2 has the larger documented context window: 1M, compared with 200K.
Related comparisons
Last updated July 31, 2026
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