Agentic
Directional only- Gemini 3 Pro
- 58.6
- Estimated · #26/153
- GLM-5.2
- 58.5
- Supported · #29/153
- Basis
- BenchAlign lane · 2 vs 6 public rows
- Reading
- Directional only
Gemini 3 Pro vs GLM-5.2
Updated September 14, 2026. Public scores include evidence status and uncertainty. They are not guarantees for a specific workload.
Decision reading
The public evidence has no benchmark result shared by both models, so it does not support a quality verdict. Use the documented cost, context, and runtime rows instead.
0 results are shared. Category rows resting on Estimated evidence or different benchmark sets are marked directional and do not name a winner.
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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
Gemini 3 Pro
Gemini 3 Pro 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
Cache-heavy agent loop cost
200K cached + 20K fresh input + 10K output tokens
GLM-5.2
GLM-5.2 has the lower estimated token cost for this stated workload. Gemini 3 Pro 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
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
Gemini 3 Pro is scored on Estimated evidence for coding, so the reading is directional rather than like-for-like.
Confidence: limited
Agentic work
Tool use, computer use, and multi-step task completion
Not enough matched evidence
Gemini 3 Pro is scored on Estimated evidence for agentic, so the reading is directional rather than like-for-like.
Confidence: limited
What is actually comparable
Shared results can support a head-to-head reading. Results present for only one model describe coverage, not superiority.
12 Gemini 3 Pro only
25 GLM-5.2 only
- Shared results
- 0
- Gemini 3 Pro only
- 12
- GLM-5.2 only
- 25
- Like-for-like categories
- 0 / 8
4 categories rest on Estimated evidence or different evidence sets. Those rows remain visible for coverage context but do not name a winner.
Category results, on a stated basis
Each row shows the public-lane category score for both models: the BenchAlign lane for agentic, coding, and knowledge, and the weighted public rows elsewhere, with the same rank each model page reports. A row is like-for-like only when both scores rest on Supported evidence or the same weighted set. Directional and not-comparable rows remain visible, but they never receive a winner.
Coding
Directional only- Gemini 3 Pro
- 60.0
- Estimated · #22/152
- GLM-5.2
- 61.0
- Supported · #19/152
- Basis
- BenchAlign lane · 1 vs 8 public rows
- Reading
- Directional only
Knowledge
Directional only- Gemini 3 Pro
- 64.7
- Estimated · #25/183
- GLM-5.2
- 60.7
- Supported · #35/183
- Basis
- BenchAlign lane · 0 vs 6 public rows
- Reading
- Directional only
Instruction following
Directional only- Gemini 3 Pro
- 86.1
- #39/123
- GLM-5.2
- 89.8
- #22/123
- Basis
- Provisional lane · 0 vs 0 weighted rows
- Reading
- Directional only
Reasoning
Not comparable- Gemini 3 Pro
- 36.7
- Unranked · 3 rankable rows
- GLM-5.2
- 74.8
- Unranked · 2 rankable rows
- Basis
- Provisional lane · 1 vs 0 weighted rows
- Reading
- Not comparable
Math
Not comparable- Gemini 3 Pro
- 55.4
- Unranked · 2 rankable rows
- GLM-5.2
- 80.7
- Unranked · 4 rankable rows
- Basis
- Provisional lane · 2 vs 2 weighted rows
- Reading
- Not comparable
Multilingual
Not comparable- Gemini 3 Pro
- Not ranked
- GLM-5.2
- Not ranked
- Basis
- Provisional lane · 0 vs 0 weighted rows
- Reading
- Not comparable
Multimodal
Not comparable- Gemini 3 Pro
- 74.2
- #17/48
- GLM-5.2
- Not ranked
- Basis
- Provisional lane · 2 vs 0 weighted rows
- Reading
- Not comparable
| Category | Gemini 3 Pro | GLM-5.2 | Basis | Reading |
|---|---|---|---|---|
| Agentic | 58.6Estimated · #26/153 | 58.5Supported · #29/153 | Directional onlyBenchAlign lane · 2 vs 6 public rows | Directional only |
| Coding | 60.0Estimated · #22/152 | 61.0Supported · #19/152 | Directional onlyBenchAlign lane · 1 vs 8 public rows | Directional only |
| Knowledge | 64.7Estimated · #25/183 | 60.7Supported · #35/183 | Directional onlyBenchAlign lane · 0 vs 6 public rows | Directional only |
| Instruction following | 86.1#39/123 | 89.8#22/123 | Directional onlyProvisional lane · 0 vs 0 weighted rows | Directional only |
| Reasoning | 36.7Unranked · 3 rankable rows | 74.8Unranked · 2 rankable rows | Not comparableProvisional lane · 1 vs 0 weighted rows | Not comparable |
| Math | 55.4Unranked · 2 rankable rows | 80.7Unranked · 4 rankable rows | Not comparableProvisional lane · 2 vs 2 weighted rows | Not comparable |
| Multilingual | Not ranked | Not ranked | Not comparableProvisional lane · 0 vs 0 weighted rows | Not comparable |
| Multimodal | 74.2#17/48 | Not ranked | Not comparableProvisional lane · 2 vs 0 weighted rows | Not comparable |
Ranks count the models scored in each category’s lane, so the agentic, coding, and knowledge denominators (BenchAlign) differ from the provisional-lane categories. Unranked scores sit on the lane’s scale but never name a winner. Like-for-like rows within 0.5 points read as a practical tie.
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.
A shared-evidence shape is not available.
BenchLM does not draw a radar or infer missing axes when the matched evidence is too sparse.
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 3 Pro
- $0.008
- 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 3 Pro
- $0.136
- 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 3 Pro
- $0.56
- Fits 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
GLM-5.2 has the lower modeled cost
Gemini 3 Pro 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.
SpecificationGemini 3 ProGLM-5.2
Context window
Maximum documented context; output-token limits may be lower.
Gemini 3 Pro
2M
GLM-5.2
1M
API model ID
Gemini 3 Pro
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.
Gemini 3 Pro
Not published
GLM-5.2
Not published
Documented inputs
Gemini 3 Pro
Not sourced
GLM-5.2
Not sourced
Documented outputs
Gemini 3 Pro
Not sourced
GLM-5.2
Not sourced
Provider availability
Gemini 3 Pro
Not sourced
GLM-5.2
Not sourced
Reasoning profile
Gemini 3 Pro
Non-Reasoning
GLM-5.2
Reasoning
Weight access
Gemini 3 Pro
Proprietary
GLM-5.2
Open Weight
License
Gemini 3 Pro
Proprietary
GLM-5.2
Open Weight
Release date
Gemini 3 Pro
2025-11-18
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
- The public evidence has no benchmark result shared by both models, so it does not support a quality verdict.
- Workload cost
- Repository review: $0.136 vs $0.0832. Cache-heavy agent loop: $0.56 vs $0.352.
- Context tradeoff
- Gemini 3 Pro has the larger documented window (2M).
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 evidence37 rows
Agentic
Gert Labs
Gemini 3 Pro63.23%SourceGLM-5.2—Not directly comparable
JobBench
Gemini 3 Pro11.4%SourceGLM-5.2—Not directly comparable
Terminal-Bench 3.0
Gemini 3 Pro—GLM-5.24.6%SourceNot directly comparable
Terminal-Bench 2.0
Gemini 3 Pro—GLM-5.281%SourceNot directly comparable
MCP Atlas
Gemini 3 Pro—GLM-5.276.8%SourceNot directly comparable
Toolathlon
Gemini 3 Pro—GLM-5.248.2%SourceNot directly comparable
ResearchClawBench
Gemini 3 Pro—GLM-5.220.7%SourceNot directly comparable
Terminal-Bench 2.1 (Vals)
Gemini 3 Pro—GLM-5.267.8%SourceNot directly comparable
Coding
Vibe Code Bench
Gemini 3 Pro14.30%SourceGLM-5.2—Not directly comparable
SWE-bench Pro
Gemini 3 Pro—GLM-5.262.1%SourceNot directly comparable
NL2Repo
Gemini 3 Pro—GLM-5.248.9%SourceNot directly comparable
Terminal-Bench 2.0
Gemini 3 Pro—GLM-5.281.0%SourceNot directly comparable
ProgramBench
Gemini 3 Pro—GLM-5.263.7%SourceNot directly comparable
cursorBench32
Gemini 3 Pro—GLM-5.255.0%SourceNot directly comparable
OpenHarmony Bench
Gemini 3 Pro—GLM-5.258.4%SourceNot directly comparable
LiveCodeBench (Vals)
Gemini 3 Pro—GLM-5.269.5%SourceNot directly comparable
SWE-bench (Vals)
Gemini 3 Pro—GLM-5.282.8%SourceNot directly comparable
Reasoning
Knowledge
GPQA
Gemini 3 Pro—GLM-5.291.2%SourceNot directly comparable
GPQA-D
Gemini 3 Pro—GLM-5.291.2%SourceNot directly comparable
HLE
Gemini 3 Pro—GLM-5.254.7%SourceNot directly comparable
HLE w/o tools
Gemini 3 Pro—GLM-5.240.5%SourceNot directly comparable
GPQA Diamond (Vals)
Gemini 3 Pro—GLM-5.285.6%SourceNot directly comparable
MMLU-Pro (Vals)
Gemini 3 Pro—GLM-5.286.7%SourceNot directly comparable
Math
FrontierMath v2 (Tiers 1-3)
Gemini 3 Pro37.600%SourceGLM-5.2—Not directly comparable
FrontierMath v2 (Tier 4)
Gemini 3 Pro18.750%SourceGLM-5.2—Not directly comparable
AIME26
Gemini 3 Pro—GLM-5.299.2%SourceNot directly comparable
HMMT Nov 2025
Gemini 3 Pro—GLM-5.294.4%SourceNot directly comparable
HMMT Feb 2026
Gemini 3 Pro—GLM-5.292.5%SourceNot directly comparable
MMAnswerBench
Gemini 3 Pro—GLM-5.291.0%SourceNot directly comparable
Multimodal
MMMU-Pro
Gemini 3 Pro81%SourceGLM-5.2—Not directly comparable
MathVision
Gemini 3 Pro86.6%SourceGLM-5.2—Not directly comparable
VideoMMMU
Gemini 3 Pro87.6%SourceGLM-5.2—Not directly comparable
ScreenSpot Pro
Gemini 3 Pro72.7%SourceGLM-5.2—Not directly comparable
CharXiv
Gemini 3 Pro81.4%SourceGLM-5.2—Not directly comparable
V*
Gemini 3 Pro88.0%SourceGLM-5.2—Not directly comparable
Frequently asked questions
Which is better, Gemini 3 Pro or GLM-5.2?
The public evidence has no benchmark result shared by both models, so it does not support a quality verdict. The page therefore keeps the decision tied to the specific documented workload.
Which is better for coding, Gemini 3 Pro or GLM-5.2?
GLM-5.2 scores higher for coding on the public lane, 61 to 60. Gemini 3 Pro is scored on Estimated evidence for coding, so the reading is directional rather than like-for-like. BenchLM does not name a winner for coding from a directional reading; read the shared benchmark rows directly and test the models on the same task set.
Which is better for agentic tasks, Gemini 3 Pro or GLM-5.2?
Gemini 3 Pro scores higher for agentic tasks on the public lane, 58.6 to 58.5. Gemini 3 Pro is scored on Estimated evidence for agentic tasks, so the reading is directional rather than like-for-like. BenchLM does not name a winner for agentic tasks from a directional reading; read the shared benchmark rows directly and test the models on the same task set.
Which costs less, Gemini 3 Pro or GLM-5.2?
For the stated presets, chat costs $0.008 on Gemini 3 Pro and $0.0036 on GLM-5.2; repository review costs $0.136 and $0.0832; the cache-heavy agent loop costs $0.56 and $0.352. Gemini 3 Pro 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, Gemini 3 Pro or GLM-5.2?
Gemini 3 Pro has the larger documented context window: 2M, compared with 1M.
Related comparisons
Last updated September 14, 2026
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