Model comparison
GLM-4.5V vs Trinity Large Thinking
GLM-4.5V is the stronger model overall, scoring 39.8 to 38.6 on the Noometry Index. Trinity Large Thinking costs 2.3× less per token, which makes it the better buy when GLM-4.5V's lead doesn't matter for your workload.
Last verified . 13 shared benchmarks.
Arcee AISummary
- They share 13 benchmarks with published results for both. GLM-4.5V scores higher in 2 categories and Trinity Large Thinking in 6 categories; 7 gaps are clear of the uncertainty.
- The widest gap is in reasoning, where GLM-4.5V leads 27.4 to 16.9.
- Trinity Large Thinking is cheaper at $0.25 / $0.80 per million input/output tokens, against $0.60 / $1.80 for GLM-4.5V.
- Trinity Large Thinking accepts more context: 262K tokens versus 64K.
Side by side
| GLM-4.5V | Trinity Large Thinking | |
|---|---|---|
| Provider | Z.ai (Zhipu) | Arcee AI |
| Noometry Index | 39.8 | 38.6 |
| Released | 2025-08-11 | 2026-04-01 |
| Weights | Open | Open |
| Context window | 64K | 262K |
| Max output | 16K | 80K |
| Input $ / M tokens | $0.60 | $0.25 |
| Output $ / M tokens | $1.80 | $0.80 |
| Results tracked | 15 | 24 |
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Category by category
Coding GLM-4.5V leads
GLM-4.5V: 39.5 (#155), Trinity Large Thinking: 34.1 (#244)
| Benchmark | GLM-4.5V | Trinity Large Thinking |
|---|---|---|
| LMArena Coding | 1347 | 1381 |
| LMArena WebDev | — | 1238 |
| SciCode | — | 36.1% |
Reasoning GLM-4.5V leads
GLM-4.5V: 27.4 (#119), Trinity Large Thinking: 16.9 (#298)
| Benchmark | GLM-4.5V | Trinity Large Thinking |
|---|---|---|
| LMArena Hard Prompts | 1334 | 1350 |
| Kagi LLM Benchmark | 59.8% | — |
| NYT Connections (extended) | — | 16.5% |
| CritPt | — | 0.9% |
| Thematic Generalization | — | 41.6% |
| Surface Evolver Bench | — | 15.6% |
Math Too close to call
GLM-4.5V: 37.4 (#159), Trinity Large Thinking: 37.6 (#149)
| Benchmark | GLM-4.5V | Trinity Large Thinking |
|---|---|---|
| LMArena Math | 1354 | 1366 |
Knowledge Trinity Large Thinking leads
GLM-4.5V: 37.5 (#156), Trinity Large Thinking: 40.9 (#113)
| Benchmark | GLM-4.5V | Trinity Large Thinking |
|---|---|---|
| LMArena Expert | 1353 | 1360 |
| Vectara Hallucination Rate | — | 6.9% |
Multimodal Not comparable
GLM-4.5V: 34.3 (#92), Trinity Large Thinking: —
| Benchmark | GLM-4.5V | Trinity Large Thinking |
|---|---|---|
| LMArena Vision | 1154 | — |
Multilingual Trinity Large Thinking leads
GLM-4.5V: 44.6 (#177), Trinity Large Thinking: 46.2 (#160)
| Benchmark | GLM-4.5V | Trinity Large Thinking |
|---|---|---|
| LMArena Non-English | 1303 | 1325 |
| LMArena Chinese | 1337 | 1373 |
| LMArena Russian | 1298 | 1337 |
| LMArena Spanish | 1336 | 1357 |
| LMArena French | — | 1374 |
| LMArena German | — | 1356 |
| LMArena Japanese | — | 1311 |
| LMArena Korean | — | 1306 |
Instruction Following Trinity Large Thinking leads
GLM-4.5V: 69.2 (#175), Trinity Large Thinking: 70.5 (#162)
| Benchmark | GLM-4.5V | Trinity Large Thinking |
|---|---|---|
| LMArena Instruction Following | 1311 | 1334 |
Long Context Trinity Large Thinking leads
GLM-4.5V: 39.6 (#171), Trinity Large Thinking: 41.3 (#144)
| Benchmark | GLM-4.5V | Trinity Large Thinking |
|---|---|---|
| LMArena Longer Query | 1304 | 1355 |
Writing & Preference Trinity Large Thinking leads
GLM-4.5V: 52.5 (#170), Trinity Large Thinking: 53.8 (#158)
| Benchmark | GLM-4.5V | Trinity Large Thinking |
|---|---|---|
| LMArena Text | 1333 | 1340 |
| LMArena Creative Writing | 1295 | 1320 |
| LMArena Multi-Turn | 1332 | 1342 |
Frequently asked questions
Is GLM-4.5V better than Trinity Large Thinking?
GLM-4.5V is the stronger model overall, scoring 39.8 to 38.6 on the Noometry Index. Trinity Large Thinking costs 2.3× less per token, which makes it the better buy when GLM-4.5V's lead doesn't matter for your workload.
Which is cheaper, GLM-4.5V 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-4.5V lists at $0.60 and $1.80.
Is GLM-4.5V or Trinity Large Thinking better for coding?
GLM-4.5V scores higher on coding benchmarks: 39.5 versus 34.1 in the Noometry coding category.
Which has the bigger context window?
Trinity Large Thinking does, with 262K tokens against 64K.
How many benchmarks do GLM-4.5V and Trinity Large Thinking share?
13 benchmarks have published results for both models. GLM-4.5V has 15 scored results on Noometry and Trinity Large Thinking has 24.