# GLM-4.6 vs Trinity Large Thinking

> GLM-4.6 is the stronger model overall, scoring 41.4 to 38.6 on the Noometry Index. Trinity Large Thinking costs 2.6× less per token, which makes it the better buy when GLM-4.6's lead doesn't matter for your workload.

- Canonical page: https://noometry.com/compare/glm-4-6-vs-trinity-large-thinking
- Last updated: 2026-10-11
- Shared benchmarks: 21

## Summary

- They share 21 benchmarks with published results for both. GLM-4.6 scores higher in 7 categories and Trinity Large Thinking in 1 category; 7 gaps are clear of the uncertainty.
- The widest gap is in writing & preference, where GLM-4.6 leads 61.1 to 53.8.
- Trinity Large Thinking is cheaper at $0.25 / $0.80 per million input/output tokens, against $0.60 / $2.20 for GLM-4.6.
- Trinity Large Thinking accepts more context: 262K tokens versus 205K.

## Snapshot

| | GLM-4.6 | Trinity Large Thinking |
|---|---|---|
| Provider | Z.ai (Zhipu) | Arcee AI |
| Noometry Index | 41.4 | 38.6 |
| Rank | 135 | 185 |
| Context | 205K | 262K |
| Input $/M | $0.60 | $0.25 |
| Output $/M | $2.20 | $0.80 |
| Weights | Open | Open |

## Coding

- GLM-4.6: 40.1 (#148)
- Trinity Large Thinking: 34.1 (#244)

| Benchmark | GLM-4.6 | Trinity Large Thinking |
|---|---|---|
| LMArena WebDev | 1340 | 1238 |
| SciCode | 38.4% | 36.1% |
| LMArena Coding | 1449 | 1381 |
| SWE-bench Verified (bash only) | 55.4% | — |
| ALE-Bench | 340.82 | — |

## Agentic & Tool Use

- GLM-4.6: 32.3 (#66)
- Trinity Large Thinking: —

| Benchmark | GLM-4.6 | Trinity Large Thinking |
|---|---|---|
| Terminal-Bench | 24.5% | — |
| Berkeley Function Calling Leaderboard | 72.4% | — |

## Reasoning

- GLM-4.6: 23.7 (#172)
- Trinity Large Thinking: 16.9 (#298)

| Benchmark | GLM-4.6 | Trinity Large Thinking |
|---|---|---|
| CritPt | 1.1% | 0.9% |
| LMArena Hard Prompts | 1440 | 1350 |
| Kagi LLM Benchmark | 47.4% | — |
| NYT Connections (extended) | — | 16.5% |
| Thematic Generalization | — | 41.6% |
| Surface Evolver Bench | — | 15.6% |

## Math

- GLM-4.6: 39.1 (#111)
- Trinity Large Thinking: 37.6 (#149)

| Benchmark | GLM-4.6 | Trinity Large Thinking |
|---|---|---|
| LMArena Math | 1432 | 1366 |
| FrontierMath (Feb 2025 set) | 3.8% | — |
| FrontierMath Tier 4 (v1) | 2.1% | — |

## Knowledge

- GLM-4.6: 40.2 (#124)
- Trinity Large Thinking: 40.9 (#113)

| Benchmark | GLM-4.6 | Trinity Large Thinking |
|---|---|---|
| Vectara Hallucination Rate | 9.5% | 6.9% |
| LMArena Expert | 1431 | 1360 |

## Multilingual

- GLM-4.6: 53.5 (#66)
- Trinity Large Thinking: 46.2 (#160)

| Benchmark | GLM-4.6 | Trinity Large Thinking |
|---|---|---|
| LMArena Non-English | 1426 | 1325 |
| LMArena Chinese | 1499 | 1373 |
| LMArena French | 1459 | 1374 |
| LMArena German | 1447 | 1356 |
| LMArena Japanese | 1393 | 1311 |
| LMArena Korean | 1400 | 1306 |
| LMArena Russian | 1419 | 1337 |
| LMArena Spanish | 1436 | 1357 |

## Instruction Following

- GLM-4.6: 74.3 (#98)
- Trinity Large Thinking: 70.5 (#162)

| Benchmark | GLM-4.6 | Trinity Large Thinking |
|---|---|---|
| LMArena Instruction Following | 1410 | 1334 |

## Long Context

- GLM-4.6: 43.4 (#94)
- Trinity Large Thinking: 41.3 (#144)

| Benchmark | GLM-4.6 | Trinity Large Thinking |
|---|---|---|
| LMArena Longer Query | 1422 | 1355 |

## Writing & Preference

- GLM-4.6: 61.1 (#90)
- Trinity Large Thinking: 53.8 (#158)

| Benchmark | GLM-4.6 | Trinity Large Thinking |
|---|---|---|
| LMArena Text | 1440 | 1340 |
| LMArena Creative Writing | 1411 | 1320 |
| LMArena Multi-Turn | 1427 | 1342 |
| EQ-Bench Creative Writing | 1411 | — |

## FAQ

### Is GLM-4.6 better than Trinity Large Thinking?

GLM-4.6 is the stronger model overall, scoring 41.4 to 38.6 on the Noometry Index. Trinity Large Thinking costs 2.6× less per token, which makes it the better buy when GLM-4.6's lead doesn't matter for your workload.

### Which is cheaper, GLM-4.6 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-4.6 lists at $0.60 and $2.20.

### Is GLM-4.6 or Trinity Large Thinking better for coding?

GLM-4.6 scores higher on coding benchmarks: 40.1 versus 34.1 in the Noometry coding category.

### Which has the bigger context window?

Trinity Large Thinking does, with 262K tokens against 205K.

### How many benchmarks do GLM-4.6 and Trinity Large Thinking share?

21 benchmarks have published results for both models. GLM-4.6 has 29 scored results on Noometry and Trinity Large Thinking has 24.