Model comparison
GLM-4.5 vs Trinity Large Thinking
GLM-4.5 is the stronger model overall, scoring 42.0 to 38.6 on the Noometry Index. Trinity Large Thinking costs 2.6× less per token, which makes it the better buy when GLM-4.5's lead doesn't matter for your workload.
Last verified . 17 shared benchmarks.
Arcee AISummary
- They share 17 benchmarks with published results for both. GLM-4.5 scores higher in 6 categories and Trinity Large Thinking in 2 categories; 8 gaps are clear of the uncertainty.
- The widest gap is in reasoning, where GLM-4.5 leads 28.6 to 16.9.
- Trinity Large Thinking is cheaper at $0.25 / $0.80 per million input/output tokens, against $0.60 / $2.20 for GLM-4.5.
- Trinity Large Thinking accepts more context: 262K tokens versus 131K.
Side by side
| GLM-4.5 | Trinity Large Thinking | |
|---|---|---|
| Provider | Z.ai (Zhipu) | Arcee AI |
| Noometry Index | 42.0 | 38.6 |
| Released | 2025-07-27 | 2026-04-01 |
| Weights | Open | Open |
| Context window | 131K | 262K |
| Max output | 98K | 80K |
| Input $ / M tokens | $0.60 | $0.25 |
| Output $ / M tokens | $2.20 | $0.80 |
| Results tracked | 27 | 24 |
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Category by category
Coding GLM-4.5 leads
GLM-4.5: 41.4 (#125), Trinity Large Thinking: 34.1 (#244)
| Benchmark | GLM-4.5 | Trinity Large Thinking |
|---|---|---|
| LMArena Coding | 1434 | 1381 |
| SWE-bench Verified (bash only) | 54.2% | — |
| LMArena WebDev | — | 1238 |
| SciCode | — | 36.1% |
| WeirdML | 40.6% | — |
| ALE-Bench | 344.82 | — |
| AlgoTune | 1.52 | — |
Reasoning GLM-4.5 leads
GLM-4.5: 28.6 (#100), Trinity Large Thinking: 16.9 (#298)
| Benchmark | GLM-4.5 | Trinity Large Thinking |
|---|---|---|
| LMArena Hard Prompts | 1429 | 1350 |
| Kagi LLM Benchmark | 57.9% | — |
| NYT Connections (extended) | — | 16.5% |
| CritPt | — | 0.9% |
| Thematic Generalization | — | 41.6% |
| Surface Evolver Bench | — | 15.6% |
Math GLM-4.5 leads
GLM-4.5: 39.0 (#116), Trinity Large Thinking: 37.6 (#149)
| Benchmark | GLM-4.5 | Trinity Large Thinking |
|---|---|---|
| LMArena Math | 1427 | 1366 |
Knowledge Trinity Large Thinking leads
GLM-4.5: 35.9 (#179), Trinity Large Thinking: 40.9 (#113)
| Benchmark | GLM-4.5 | Trinity Large Thinking |
|---|---|---|
| LMArena Expert | 1433 | 1360 |
| Humanity's Last Exam | 8.3% | — |
| Confabulations | 11.3% | — |
| Vectara Hallucination Rate | — | 6.9% |
Multilingual GLM-4.5 leads
GLM-4.5: 52.8 (#77), Trinity Large Thinking: 46.2 (#160)
| Benchmark | GLM-4.5 | Trinity Large Thinking |
|---|---|---|
| LMArena Non-English | 1417 | 1325 |
| LMArena Chinese | 1465 | 1373 |
| LMArena French | 1418 | 1374 |
| LMArena German | 1407 | 1356 |
| LMArena Japanese | 1415 | 1311 |
| LMArena Korean | 1380 | 1306 |
| LMArena Russian | 1414 | 1337 |
| LMArena Spanish | 1454 | 1357 |
Instruction Following GLM-4.5 leads
GLM-4.5: 74.1 (#104), Trinity Large Thinking: 70.5 (#162)
| Benchmark | GLM-4.5 | Trinity Large Thinking |
|---|---|---|
| LMArena Instruction Following | 1404 | 1334 |
Long Context Trinity Large Thinking leads
GLM-4.5: 38.2 (#201), Trinity Large Thinking: 41.3 (#144)
| Benchmark | GLM-4.5 | Trinity Large Thinking |
|---|---|---|
| LMArena Longer Query | 1412 | 1355 |
| Fiction.LiveBench | 58.3% | — |
Writing & Preference GLM-4.5 leads
GLM-4.5: 57.5 (#127), Trinity Large Thinking: 53.8 (#158)
| Benchmark | GLM-4.5 | Trinity Large Thinking |
|---|---|---|
| LMArena Text | 1430 | 1340 |
| LMArena Creative Writing | 1395 | 1320 |
| LMArena Multi-Turn | 1415 | 1342 |
| Short-Story Creative Writing | 73.4% | — |
| EQ-Bench Creative Writing | 1343 | — |
Frequently asked questions
Is GLM-4.5 better than Trinity Large Thinking?
GLM-4.5 is the stronger model overall, scoring 42.0 to 38.6 on the Noometry Index. Trinity Large Thinking costs 2.6× less per token, which makes it the better buy when GLM-4.5's lead doesn't matter for your workload.
Which is cheaper, GLM-4.5 or Trinity Large Thinking?
Trinity Large Thinking is cheaper. It lists at $0.25 per million input tokens and $0.80 per million output tokens; GLM-4.5 lists at $0.60 and $2.20.
Is GLM-4.5 or Trinity Large Thinking better for coding?
GLM-4.5 scores higher on coding benchmarks: 41.4 versus 34.1 in the Noometry coding category.
Which has the bigger context window?
Trinity Large Thinking does, with 262K tokens against 131K.
How many benchmarks do GLM-4.5 and Trinity Large Thinking share?
17 benchmarks have published results for both models. GLM-4.5 has 27 scored results on Noometry and Trinity Large Thinking has 24.