Knowledge
Directional only- GLM-5.2
- 59.6
- o1-pro
- 79.0
- Weighted basis
- 2 vs 1 rows
- Reading
- Directional only
Model comparison
GLM-5.2 vs o1-pro
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
GLM-5.2 has the higher public score estimate, 62.94 versus 45.01, but the 90% score intervals overlap. Treat that as a lead, not a settled winner.
1 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-5.2
GLM-5.2 has the larger documented context window.
Confidence: documented
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
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
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
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. o1-pro does not fit this workload in one request. GLM-5.2 has no published cached-input rate, so cached tokens use its listed input rate. o1-pro 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.
17 GLM-5.2 only
1 Shared
- Shared results
- 1
- GLM-5.2 only
- 17
- o1-pro only
- 0
- Like-for-like categories
- 0 / 8
1 category uses 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
Not comparable- GLM-5.2
- 81.0
- o1-pro
- Not measured
- Weighted basis
- 1 vs 0 rows
- Reading
- Not comparable
Coding
Not comparable- GLM-5.2
- 62.1
- o1-pro
- Not measured
- Weighted basis
- 1 vs 0 rows
- Reading
- Not comparable
Reasoning
Not comparable- GLM-5.2
- Not measured
- o1-pro
- Not measured
- Weighted basis
- 0 vs 0 rows
- Reading
- Not comparable
Math
Not comparable- GLM-5.2
- 95.9
- o1-pro
- Not measured
- Weighted basis
- 2 vs 0 rows
- Reading
- Not comparable
Multilingual
Not comparable- GLM-5.2
- Not measured
- o1-pro
- Not measured
- Weighted basis
- 0 vs 0 rows
- Reading
- Not comparable
Multimodal
Not comparable- GLM-5.2
- Not measured
- o1-pro
- Not measured
- Weighted basis
- 0 vs 0 rows
- Reading
- Not comparable
Instruction following
Not comparable- GLM-5.2
- Not measured
- o1-pro
- Not measured
- Weighted basis
- 0 vs 0 rows
- Reading
- Not comparable
| Category | GLM-5.2 | o1-pro | Weighted basis | Reading |
|---|---|---|---|---|
| Knowledge | 59.6 | 79.0 | Directional only2 vs 1 rows | Directional only |
| Agentic | 81.0 | Not measured | Not comparable1 vs 0 rows | Not comparable |
| Coding | 62.1 | Not measured | Not comparable1 vs 0 rows | Not comparable |
| Reasoning | Not measured | Not measured | Not comparable0 vs 0 rows | Not comparable |
| Math | 95.9 | 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.
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.
GPQA
Knowledge
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
- o1-pro
- $0.45
- 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
- o1-pro
- $9.30
- 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
- o1-pro
- $39.00
- Does not fit in one request
- Cached input priced at the published list-input rate
o1-pro does not fit this workload in one request. GLM-5.2 has no published cached-input rate, so cached tokens use its listed input rate. o1-pro 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-5.2o1-pro
Context window
Maximum documented context; output-token limits may be lower.
GLM-5.2
1M
o1-pro
200K
API model ID
GLM-5.2
Not sourced
o1-pro
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
o1-pro
Not published
Documented inputs
GLM-5.2
Not sourced
o1-pro
Not sourced
Documented outputs
GLM-5.2
Not sourced
o1-pro
Not sourced
Provider availability
GLM-5.2
Not sourced
o1-pro
Not sourced
Reasoning profile
GLM-5.2
Reasoning
o1-pro
Reasoning
Weight access
GLM-5.2
Open Weight
o1-pro
Proprietary
License
GLM-5.2
Open Weight
o1-pro
Proprietary
Release date
GLM-5.2
2026-06-16
o1-pro
2024-12-01
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
- GLM-5.2 has the higher public score estimate, 62.94 versus 45.01, but the 90% score intervals overlap.
- Workload cost
- Repository review: $0.0832 vs $9.30. Cache-heavy agent loop: $0.352 vs $39.00.
- 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 evidence18 rows
Agentic
Coding
SWE-bench Pro
GLM-5.262.1%Sourceo1-pro—Not directly comparable
NL2Repo
GLM-5.248.9%Sourceo1-pro—Not directly comparable
Terminal-Bench 2.0
GLM-5.281.0%Sourceo1-pro—Not directly comparable
ProgramBench
GLM-5.263.7%Sourceo1-pro—Not directly comparable
cursorBench32
GLM-5.255.0%Sourceo1-pro—Not directly comparable
Reasoning
CritPt
GLM-5.220.9%Sourceo1-pro—Not directly comparable
Knowledge
Math
Frequently asked questions
Which is better, GLM-5.2 or o1-pro?
GLM-5.2 has the higher public score estimate, 62.94 versus 45.01, 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 o1-pro?
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-5.2 or o1-pro?
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-5.2 or o1-pro?
For the stated presets, chat costs $0.0036 on GLM-5.2 and $0.45 on o1-pro; repository review costs $0.0832 and $9.30; the cache-heavy agent loop costs $0.352 and $39.00. o1-pro does not fit this workload in one request. GLM-5.2 has no published cached-input rate, so cached tokens use its listed input rate. o1-pro has no published cached-input rate, so cached tokens use its listed input rate.
Which has the larger context window, GLM-5.2 or o1-pro?
GLM-5.2 has the larger documented context window: 1M, compared with 200K.
Related comparisons
Last updated July 31, 2026
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