Knowledge
Like-for-like- Gemini 2.5 Pro
- 27.4
- GLM-5.2
- 59.6
- Weighted basis
- 2 vs 2 rows
- Reading
- GLM-5.2 leads
Model comparison
Gemini 2.5 Pro 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
GLM-5.2 has the higher public score estimate, 62.94 versus 56.59, but the 90% score intervals overlap. Treat that as a lead, not a settled 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.
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
Gemini 2.5 Pro
Gemini 2.5 Pro 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
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
What is actually comparable
Shared results can support a head-to-head reading. Results present for only one model describe coverage, not superiority.
5 Gemini 2.5 Pro only
2 Shared
16 GLM-5.2 only
- Shared results
- 2
- Gemini 2.5 Pro only
- 5
- 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.
Agentic
Not comparable- Gemini 2.5 Pro
- Not measured
- GLM-5.2
- 81.0
- Weighted basis
- 0 vs 1 rows
- Reading
- Not comparable
Coding
Not comparable- Gemini 2.5 Pro
- 63.8
- GLM-5.2
- 62.1
- Weighted basis
- 1 vs 1 rows
- Reading
- Not comparable
Reasoning
Not comparable- Gemini 2.5 Pro
- Not measured
- GLM-5.2
- Not measured
- Weighted basis
- 0 vs 0 rows
- Reading
- Not comparable
Math
Not comparable- Gemini 2.5 Pro
- 11.6
- GLM-5.2
- 95.9
- Weighted basis
- 2 vs 2 rows
- Reading
- Not comparable
Multilingual
Not comparable- Gemini 2.5 Pro
- Not measured
- GLM-5.2
- Not measured
- Weighted basis
- 0 vs 0 rows
- Reading
- Not comparable
Multimodal
Not comparable- Gemini 2.5 Pro
- Not measured
- GLM-5.2
- Not measured
- Weighted basis
- 0 vs 0 rows
- Reading
- Not comparable
Instruction following
Not comparable- Gemini 2.5 Pro
- Not measured
- GLM-5.2
- Not measured
- Weighted basis
- 0 vs 0 rows
- Reading
- Not comparable
| Category | Gemini 2.5 Pro | GLM-5.2 | Weighted basis | Reading |
|---|---|---|---|---|
| Knowledge | 27.4 | 59.6 | Like-for-like2 vs 2 rows | GLM-5.2 leads |
| Agentic | Not measured | 81.0 | Not comparable0 vs 1 rows | Not comparable |
| Coding | 63.8 | 62.1 | Not comparable1 vs 1 rows | Not comparable |
| Reasoning | Not measured | Not measured | Not comparable0 vs 0 rows | Not comparable |
| Math | 11.6 | 95.9 | Not comparable2 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.
HLE
Knowledge
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
- Gemini 2.5 Pro
- $0.00625
- Fits in one request
- GLM-5.2
- $0.0036
- 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
- Gemini 2.5 Pro
- $0.0925
- Fits in one request
- GLM-5.2
- $0.0832
- 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
- Gemini 2.5 Pro
- $0.15
- Fits in one request
- GLM-5.2
- $0.352
- Fits in one request
- Cached input priced at the published list-input rate
Gemini 2.5 Pro 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.
SpecificationGemini 2.5 ProGLM-5.2
Context window
Maximum documented context; output-token limits may be lower.
Gemini 2.5 Pro
GLM-5.2
1M
API model ID
Gemini 2.5 Pro
gemini-2.5-pro
Google Gemini API pricingGLM-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.
Gemini 2.5 Pro
$0.125 per 1M cached input tokens
Google Gemini API pricingGLM-5.2
Not published
Documented inputs
Gemini 2.5 Pro
Not sourced
GLM-5.2
Not sourced
Documented outputs
Gemini 2.5 Pro
Not sourced
GLM-5.2
Not sourced
Provider availability
Gemini 2.5 Pro
Not sourced
GLM-5.2
Not sourced
Reasoning profile
Gemini 2.5 Pro
Non-Reasoning
GLM-5.2
Reasoning
Weight access
Gemini 2.5 Pro
Proprietary
GLM-5.2
Open Weight
License
Gemini 2.5 Pro
Proprietary
GLM-5.2
Open Weight
Release date
Gemini 2.5 Pro
2025-03-01
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
- GLM-5.2 has the higher public score estimate, 62.94 versus 56.59, but the 90% score intervals overlap.
- Workload cost
- Repository review: $0.0925 vs $0.0832. Cache-heavy agent loop: $0.15 vs $0.352.
- 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 evidence23 rows
Agentic
Gert Labs
Gemini 2.5 Pro42.01%SourceGLM-5.2—Not directly comparable
Terminal-Bench 2.0
Gemini 2.5 Pro—GLM-5.281%SourceNot directly comparable
MCP Atlas
Gemini 2.5 Pro—GLM-5.276.8%SourceNot directly comparable
Toolathlon
Gemini 2.5 Pro—GLM-5.248.2%SourceNot directly comparable
ResearchClawBench
Gemini 2.5 Pro—GLM-5.220.7%SourceNot directly comparable
Coding
SWE-bench Verified
Gemini 2.5 Pro63.8%SourceGLM-5.2—Not directly comparable
Vibe Code Bench
Gemini 2.5 Pro0.40%SourceGLM-5.2—Not directly comparable
SWE-bench Pro
Gemini 2.5 Pro—GLM-5.262.1%SourceNot directly comparable
NL2Repo
Gemini 2.5 Pro—GLM-5.248.9%SourceNot directly comparable
Terminal-Bench 2.0
Gemini 2.5 Pro—GLM-5.281.0%SourceNot directly comparable
ProgramBench
Gemini 2.5 Pro—GLM-5.263.7%SourceNot directly comparable
cursorBench32
Gemini 2.5 Pro—GLM-5.255.0%SourceNot directly comparable
Reasoning
CritPt
Gemini 2.5 Pro—GLM-5.220.9%SourceNot directly comparable
Knowledge
Math
FrontierMath v2 (Tiers 1-3)
Gemini 2.5 Pro14.138%SourceGLM-5.2—Not directly comparable
FrontierMath v2 (Tier 4)
Gemini 2.5 Pro4.167%SourceGLM-5.2—Not directly comparable
AIME26
Gemini 2.5 Pro—GLM-5.299.2%SourceNot directly comparable
HMMT Nov 2025
Gemini 2.5 Pro—GLM-5.294.4%SourceNot directly comparable
HMMT Feb 2026
Gemini 2.5 Pro—GLM-5.292.5%SourceNot directly comparable
MMAnswerBench
Gemini 2.5 Pro—GLM-5.291.0%SourceNot directly comparable
Frequently asked questions
Which is better, Gemini 2.5 Pro or GLM-5.2?
GLM-5.2 has the higher public score estimate, 62.94 versus 56.59, but the 90% score intervals overlap. The higher estimate is not a decisive winner because the uncertainty ranges overlap.
Which is better for coding, Gemini 2.5 Pro 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, Gemini 2.5 Pro or GLM-5.2?
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, Gemini 2.5 Pro or GLM-5.2?
For the stated presets, chat costs $0.00625 on Gemini 2.5 Pro and $0.0036 on GLM-5.2; repository review costs $0.0925 and $0.0832; the cache-heavy agent loop costs $0.15 and $0.352. GLM-5.2 has no published cached-input rate, so cached tokens use its listed input rate.
Which has the larger context window, Gemini 2.5 Pro or GLM-5.2?
Both models list the same context window, 1M.
Related comparisons
Last updated July 31, 2026
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