Agentic
Not comparable- GLM-5.2
- 58.4
- Supported · #31/154
- Pareto 26.9
- Not ranked
- Basis
- BenchAlign lane · 6 vs 1 public rows
- Reading
- Not comparable
GLM-5.2 vs Pareto 26.9
Decision reading
At least one model is not scored in the current public ranking lane, so the page does not name an overall quality winner.
1 results are shared. Category rows resting on Estimated evidence or different benchmark sets are marked directional and do not name a winner.
Updated September 18, 2026. We do not rank this pair: at least one has no public score. Public scores include evidence status and uncertainty.
Both of these models will change. Get the price, version and retirement notices for the pair, each with its source. Follow model changes
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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.
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
Pareto 26.9
Pareto 26.9 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
Pareto 26.9 is not ranked on the public lane for coding, so no winner is named for coding.
Confidence: limited
Agentic work
Tool use, computer use, and multi-step task completion
Not enough matched evidence
Pareto 26.9 is not ranked on the public lane for agentic, so no winner is named for agentic.
Confidence: limited
Long documents
Prompts that approach the documented context limit
Not enough matched evidence
A complete context comparison is not sourced.
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.
24 GLM-5.2 only
1 Shared
3 Pareto 26.9 only
- Shared results
- 1
- GLM-5.2 only
- 24
- Pareto 26.9 only
- 3
- Like-for-like categories
- 0 / 8
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
Not comparable- GLM-5.2
- 60.8
- Supported · #19/154
- Pareto 26.9
- Not ranked
- Basis
- BenchAlign lane · 8 vs 1 public rows
- Reading
- Not comparable
Reasoning
Not comparable- GLM-5.2
- 74.8
- Unranked · 2 rankable rows
- Pareto 26.9
- Not ranked
- Basis
- Provisional lane · 0 vs 0 weighted rows
- Reading
- Not comparable
Knowledge
Not comparable- GLM-5.2
- 60.3
- Supported · #37/184
- Pareto 26.9
- Not ranked
- Basis
- BenchAlign lane · 6 vs 1 public rows
- Reading
- Not comparable
Math
Not comparable- GLM-5.2
- 80.7
- Unranked · 4 rankable rows
- Pareto 26.9
- Not ranked
- Basis
- Provisional lane · 2 vs 0 weighted rows
- Reading
- Not comparable
Multilingual
Not comparable- GLM-5.2
- Not ranked
- Pareto 26.9
- Not ranked
- Basis
- Provisional lane · 0 vs 0 weighted rows
- Reading
- Not comparable
Multimodal
Not comparable- GLM-5.2
- Not ranked
- Pareto 26.9
- Not ranked
- Basis
- Provisional lane · 0 vs 1 weighted rows
- Reading
- Not comparable
Instruction following
Not comparable- GLM-5.2
- 88.5
- #23/124
- Pareto 26.9
- Not ranked
- Basis
- Provisional lane · 0 vs 0 weighted rows
- Reading
- Not comparable
| Category | GLM-5.2 | Pareto 26.9 | Basis | Reading |
|---|---|---|---|---|
| Agentic | 58.4Supported · #31/154 | Not ranked | Not comparableBenchAlign lane · 6 vs 1 public rows | Not comparable |
| Coding | 60.8Supported · #19/154 | Not ranked | Not comparableBenchAlign lane · 8 vs 1 public rows | Not comparable |
| Reasoning | 74.8Unranked · 2 rankable rows | Not ranked | Not comparableProvisional lane · 0 vs 0 weighted rows | Not comparable |
| Knowledge | 60.3Supported · #37/184 | Not ranked | Not comparableBenchAlign lane · 6 vs 1 public rows | Not comparable |
| Math | 80.7Unranked · 4 rankable rows | Not ranked | Not comparableProvisional lane · 2 vs 0 weighted rows | Not comparable |
| Multilingual | Not ranked | Not ranked | Not comparableProvisional lane · 0 vs 0 weighted rows | Not comparable |
| Multimodal | Not ranked | Not ranked | Not comparableProvisional lane · 0 vs 1 weighted rows | Not comparable |
| Instruction following | 88.5#23/124 | Not ranked | Not comparableProvisional lane · 0 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.
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 w/o tools
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
- Pareto 26.9
- $0.00625
- Fit state unavailable
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
- Pareto 26.9
- $0.1475
- Fit state unavailable
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
- Pareto 26.9
- $0.175
- Fit state unavailable
Pareto 26.9 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-5.2Pareto 26.9
Context window
Maximum documented context; output-token limits may be lower.
GLM-5.2
1M
Pareto 26.9
N/A
API model ID
GLM-5.2
Not sourced
Pareto 26.9
pareto
Unbiased pricingCached-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
Pareto 26.9
$0.25 per 1M cached input tokens
Unbiased pricingDocumented inputs
GLM-5.2
Not sourced
Pareto 26.9
Not sourced
Documented outputs
GLM-5.2
Not sourced
Pareto 26.9
Not sourced
Provider availability
GLM-5.2
Not sourced
Pareto 26.9
Not sourced
Reasoning profile
GLM-5.2
Reasoning
Pareto 26.9
Reasoning
Weight access
GLM-5.2
Open Weight
Pareto 26.9
Proprietary
License
GLM-5.2
Open Weight
Pareto 26.9
Proprietary
Release date
GLM-5.2
2026-06-16
Pareto 26.9
2026-09-17
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 $0.1475. Cache-heavy agent loop: $0.352 vs $0.175.
- Context tradeoff
- A complete documented context comparison is not available.
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 evidence28 rows
Agentic
Terminal-Bench 3.0
GLM-5.24.6%SourcePareto 26.9—Not directly comparable
Terminal-Bench 2.0
GLM-5.281%SourcePareto 26.9—Not directly comparable
MCP Atlas
GLM-5.276.8%SourcePareto 26.9—Not directly comparable
Toolathlon
GLM-5.248.2%SourcePareto 26.9—Not directly comparable
ResearchClawBench
GLM-5.220.7%SourcePareto 26.9—Not directly comparable
Terminal-Bench 2.1 (Vals)
GLM-5.267.8%SourcePareto 26.9—Not directly comparable
Terminal-Bench 4.0
GLM-5.2—Pareto 26.951.00%SourceNot directly comparable
Coding
SWE-bench Pro
GLM-5.262.1%SourcePareto 26.9—Not directly comparable
NL2Repo
GLM-5.248.9%SourcePareto 26.9—Not directly comparable
Terminal-Bench 2.0
GLM-5.281.0%SourcePareto 26.9—Not directly comparable
ProgramBench
GLM-5.263.7%SourcePareto 26.9—Not directly comparable
cursorBench32
GLM-5.255.0%SourcePareto 26.9—Not directly comparable
OpenHarmony Bench
GLM-5.258.4%SourcePareto 26.9—Not directly comparable
LiveCodeBench (Vals)
GLM-5.269.5%SourcePareto 26.9—Not directly comparable
SWE-bench (Vals)
GLM-5.282.8%SourcePareto 26.9—Not directly comparable
DeepSWE
GLM-5.2—Pareto 26.974.0%SourceNot directly comparable
Reasoning
CritPt
GLM-5.220.9%SourcePareto 26.9—Not directly comparable
Knowledge
GPQA
GLM-5.291.2%SourcePareto 26.9—Not directly comparable
GPQA-D
GLM-5.291.2%SourcePareto 26.9—Not directly comparable
HLE
GLM-5.254.7%SourcePareto 26.9—Not directly comparable
HLE w/o tools
GLM-5.240.5%SourcePareto 26.949%SourcePareto 26.9 leads this result
GPQA Diamond (Vals)
GLM-5.285.6%SourcePareto 26.9—Not directly comparable
MMLU-Pro (Vals)
GLM-5.286.7%SourcePareto 26.9—Not directly comparable
Math
Multimodal
MMMU-Pro
GLM-5.2—Pareto 26.978%SourceNot directly comparable
Questions
Which is better, GLM-5.2 or Pareto 26.9?
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 Pareto 26.9?
Pareto 26.9 is not ranked on the public lane for coding, so no winner is named for coding.
Which is better for agentic tasks, GLM-5.2 or Pareto 26.9?
Pareto 26.9 is not ranked on the public lane for agentic tasks, so no winner is named for agentic tasks.
Which costs less, GLM-5.2 or Pareto 26.9?
For the stated presets, chat costs $0.0036 on GLM-5.2 and $0.00625 on Pareto 26.9; repository review costs $0.0832 and $0.1475; the cache-heavy agent loop costs $0.352 and $0.175. 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 Pareto 26.9?
A complete documented context-window comparison is not available.
Related comparisons
Last updated September 18, 2026
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