Coding work
Code generation, repair, and software-engineering tasks
GLM-5.3
GLM-5.3 leads on the public coding lane, 56.7 to 21, with Supported evidence for both models and non-overlapping 90% intervals.
Confidence: stronger
GLM-5.3 vs Trinity-Large-Thinking
Updated September 28, 2026. Rank says GLM-5.3 is ahead. Price, access, and your workload can each overturn that. 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
Share or export
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.
- Shared results
- 0
- GLM-5.3 only
- 24
- Trinity-Large-Thinking only
- 5
- Like-for-like categories
- 1 / 8
Estimated: GLM-5.3 and Trinity-Large-ThinkingHow the comparison works
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-5.3
GLM-5.3 has the larger documented context window.
Confidence: documented
Show secondary and unsupported calls
Agentic work
Tool use, computer use, and multi-step task completion
Not enough matched evidence
Trinity-Large-Thinking is scored on Estimated evidence for agentic, so the reading is directional rather than like-for-like.
Confidence: limitedChat turn cost
1K fresh input + 500 output tokens
Not enough matched evidence
A complete comparable API-rate estimate is not available for both models.
Confidence: listed-ratesCache-heavy agent loop cost
200K cached + 20K fresh input + 10K output tokens
Not enough matched evidence
A complete comparable API-rate estimate is not available for both models.
Confidence: rate-fallbackRepository review cost
50K fresh input + 3K output tokens
Not enough matched evidence
A complete comparable API-rate estimate is not available for both models.
Confidence: listed-rates
Which one for a specific job
Choose a job from the LLM Selector's task catalog to see the category row it rests on, under the same basis rules as the table below. A directional row stays directional; choosing a job never creates a winner.
The same task catalog as the LLM Selector. Each job names the evidence surface it rests on; nothing here adds a new score.
56.7GLM-5.321.0Trinity-Large-Thinking
Like-for-like · BenchAlign v5.7
GLM-5.3 leads the like-for-like coding row.
Coding scores combine specific tasks and setups. Match the editor, harness, and effort to your workflow.
Same basis rules as the category table below
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-5.3 only
5 Trinity-Large-Thinking only
2 categories rest on Estimated evidence or different evidence sets. Those rows remain visible for coverage context but do not name a winner.
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.
Category results, on a stated basis
Each row shows the public-lane category score for both models: the BenchAlign v5.7 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
Like-for-like- GLM-5.3
- 56.7
- Supported · #22/136
- Trinity-Large-Thinking
- 21.0
- Supported · #114/136
- Basis
- BenchAlign v5.7 lane · 13 vs 1 public rows
- Reading
- GLM-5.3 leads
Agentic
Directional only- GLM-5.3
- 67.3
- Supported · #9/111
- Trinity-Large-Thinking
- 17.1
- Estimated · #99/111
- Basis
- BenchAlign v5.7 lane · 9 vs 1 public rows
- Reading
- Directional only
Knowledge
Directional only- GLM-5.3
- 61.9
- Supported · #33/160
- Trinity-Large-Thinking
- 35.8
- Estimated · #108/160
- Basis
- BenchAlign v5.7 lane · 2 vs 2 public rows
- Reading
- Directional only
Reasoning
Not comparable- GLM-5.3
- 77.1
- #11/27
- Trinity-Large-Thinking
- 48.3
- Unranked · 2 rankable rows
- Basis
- Provisional lane · 0 vs 0 weighted rows
- Reading
- Not comparable
Multimodal
Not comparable- GLM-5.3
- Not ranked
- Trinity-Large-Thinking
- Not ranked
- Basis
- Provisional lane · 0 vs 0 weighted rows
- Reading
- Not comparable
Multilingual
Not comparable- GLM-5.3
- Not ranked
- Trinity-Large-Thinking
- Not ranked
- Basis
- Provisional lane · 0 vs 0 weighted rows
- Reading
- Not comparable
Instruction following
Not comparable- GLM-5.3
- Not ranked
- Trinity-Large-Thinking
- 66.3
- #68/124
- Basis
- Provisional lane · 0 vs 0 weighted rows
- Reading
- Not comparable
Math
Not comparable- GLM-5.3
- Not ranked
- Trinity-Large-Thinking
- Not ranked
- Basis
- Provisional lane · 0 vs 0 weighted rows
- Reading
- Not comparable
| Category | GLM-5.3 | Trinity-Large-Thinking | Basis | Reading |
|---|---|---|---|---|
| Coding | 56.7Supported · #22/136 | 21.0Supported · #114/136 | Like-for-likeBenchAlign v5.7 lane · 13 vs 1 public rows | GLM-5.3 leads |
| Agentic | 67.3Supported · #9/111 | 17.1Estimated · #99/111 | Directional onlyBenchAlign v5.7 lane · 9 vs 1 public rows | Directional only |
| Knowledge | 61.9Supported · #33/160 | 35.8Estimated · #108/160 | Directional onlyBenchAlign v5.7 lane · 2 vs 2 public rows | Directional only |
| Reasoning | 77.1#11/27 | 48.3Unranked · 2 rankable rows | Not comparableProvisional lane · 0 vs 0 weighted rows | Not comparable |
| Multimodal | Not ranked | Not ranked | Not comparableProvisional lane · 0 vs 0 weighted rows | Not comparable |
| Multilingual | Not ranked | Not ranked | Not comparableProvisional lane · 0 vs 0 weighted rows | Not comparable |
| Instruction following | Not ranked | 66.3#68/124 | Not comparableProvisional lane · 0 vs 0 weighted rows | Not comparable |
| Math | Not ranked | 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 v5.7) differ from the provisional-lane categories. Unranked scores sit on the provisional lane’s scale but never name a winner. Like-for-like rows within 0.5 points read as a practical tie.
Supported evidence per lane · bars run 0–100Methodology
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-5.3
- Self-hosted; infrastructure cost varies
- Fits in one request
- Trinity-Large-Thinking
- $0.0007
- Fits in one request
GLM-5.3 has no comparable published API token rate.
Repository review
50K fresh input + 3K output tokens
- GLM-5.3
- Self-hosted; infrastructure cost varies
- Fits in one request
- Trinity-Large-Thinking
- $0.0152
- Fits in one request
GLM-5.3 has no comparable published API token rate.
Cache-heavy agent loop
200K cached + 20K fresh input + 10K output tokens
- GLM-5.3
- Self-hosted; infrastructure cost varies
- Fits in one request
- Cached-input rate unavailable
- Trinity-Large-Thinking
- $0.064
- Fits in one request
- Cached input priced at the published list-input rate
Trinity-Large-Thinking has no published cached-input rate, so cached tokens use its listed input rate. GLM-5.3 has no comparable published API token rate.
Cached input falls back to the list input rate only where a cached rate is unpublished
Specification differences
Sourced differences are shown directly. Missing facts stay explicit instead of being inferred from a model name or family.
SpecificationGLM-5.3Trinity-Large-Thinking
Context window
Maximum documented context; output-token limits may be lower.
GLM-5.3
Trinity-Large-Thinking
512K
API model ID
GLM-5.3
zai-org/GLM-5.3
Z.AI GLM-5.3 model cardTrinity-Large-Thinking
Not sourced
Cached-input rate
A missing cached-input rate falls back to the listed input rate only in the stated workload estimate.
GLM-5.3
No comparable hosted API rate
Z.AI GLM-5.3 model cardTrinity-Large-Thinking
Not published
Documented inputs
GLM-5.3
Not sourced
Trinity-Large-Thinking
Not sourced
Documented outputs
GLM-5.3
Not sourced
Trinity-Large-Thinking
Not sourced
Provider availability
GLM-5.3
Not sourced
Trinity-Large-Thinking
Not sourced
Reasoning profile
GLM-5.3
Reasoning
Trinity-Large-Thinking
Reasoning
Weight access
GLM-5.3
Open Weight
Trinity-Large-Thinking
Open Weight
License
GLM-5.3
Open Weight
Trinity-Large-Thinking
Open Weight
Release date
GLM-5.3
2026-08-14
Trinity-Large-Thinking
2026-03-10
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
- A complete comparable API-rate estimate is not available for both models.
- Context tradeoff
- GLM-5.3 has the larger documented window (1M).
Run the same representative tasks against both endpoints before changing production traffic.
Questions
Which is better, GLM-5.3 or Trinity-Large-Thinking?
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, GLM-5.3 or Trinity-Large-Thinking?
GLM-5.3 leads the public coding lane, 56.7 to 21, with Supported evidence for both models and non-overlapping 90% intervals.
Which is better for agentic tasks, GLM-5.3 or Trinity-Large-Thinking?
GLM-5.3 scores higher for agentic tasks on the public lane, 67.3 to 17.1. Trinity-Large-Thinking 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, GLM-5.3 or Trinity-Large-Thinking?
Both models do not have comparable published API token rates, so this page does not name a universal price winner.
Which has the larger context window, GLM-5.3 or Trinity-Large-Thinking?
GLM-5.3 has the larger documented context window: 1M, compared with 512K.
Benchmark evidence
The full public result ledger is available for audit without forcing a wide desktop table onto a phone.
Browse raw public benchmark evidence29 rows
Agentic
Terminal-Bench 2.1
GLM-5.388.2%SourceTrinity-Large-Thinking—Not directly comparable
terminalBench3
GLM-5.328.3%SourceTrinity-Large-Thinking—Not directly comparable
CyberGym
GLM-5.384.5%SourceTrinity-Large-Thinking—Not directly comparable
ExploitGym
GLM-5.315.0%SourceTrinity-Large-Thinking—Not directly comparable
Toolathlon-Verified
GLM-5.373.0%SourceTrinity-Large-Thinking—Not directly comparable
AutomationBench
GLM-5.348.2%SourceTrinity-Large-Thinking—Not directly comparable
Agents' Last Exam
GLM-5.328.5%SourceTrinity-Large-Thinking—Not directly comparable
HLE w/ tools
GLM-5.362.5%SourceTrinity-Large-Thinking—Not directly comparable
Terminal-Bench 2.1 (Vals)
GLM-5.371.5%SourceTrinity-Large-Thinking—Not directly comparable
Gert Labs
GLM-5.3—Trinity-Large-Thinking32.55%SourceNot directly comparable
Coding
Terminal-Bench 2.1
GLM-5.388.2%SourceTrinity-Large-Thinking—Not directly comparable
terminalBench3
GLM-5.328.3%SourceTrinity-Large-Thinking—Not directly comparable
DeepSWE
GLM-5.366.9%SourceTrinity-Large-Thinking—Not directly comparable
NL2Repo
GLM-5.358%SourceTrinity-Large-Thinking—Not directly comparable
ProgramBench
GLM-5.319.0%SourceTrinity-Large-Thinking—Not directly comparable
FrontierSWE
GLM-5.378.1%SourceTrinity-Large-Thinking—Not directly comparable
sweMarathon
GLM-5.342.5%SourceTrinity-Large-Thinking—Not directly comparable
PostTrain Bench
GLM-5.339.8%SourceTrinity-Large-Thinking—Not directly comparable
VulcanBench v3
GLM-5.378.3%SourceTrinity-Large-Thinking—Not directly comparable
OpenHarmony Bench
GLM-5.360.8%SourceTrinity-Large-Thinking—Not directly comparable
FrontierSWE v2
GLM-5.330.2%SourceTrinity-Large-Thinking—Not directly comparable
LiveCodeBench (Vals)
GLM-5.380.5%SourceTrinity-Large-Thinking—Not directly comparable
SWE-bench (Vals)
GLM-5.395.4%SourceTrinity-Large-Thinking—Not directly comparable
SWE-bench Verified*
GLM-5.3—Trinity-Large-Thinking63.2%SourceNot directly comparable
Knowledge
GPQA Diamond (Vals)
GLM-5.388.1%SourceTrinity-Large-Thinking—Not directly comparable
MMLU-Pro (Vals)
GLM-5.386.8%SourceTrinity-Large-Thinking—Not directly comparable
GPQA-D
GLM-5.3—Trinity-Large-Thinking76.3%SourceNot directly comparable
MMLU-Pro (Arcee)
GLM-5.3—Trinity-Large-Thinking83.4%SourceNot directly comparable
Math
AIME25 (Arcee)
GLM-5.3—Trinity-Large-Thinking96.3%SourceNot directly comparable
29 public results · 0 shared
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Last updated September 28, 2026