Model comparison
GLM-5.1 vs Trinity Large Thinking
GLM-5.1 is the stronger model overall, scoring 47.8 to 38.6 on the Noometry Index. Trinity Large Thinking costs 5.5× less per token, which makes it the better buy when GLM-5.1's lead doesn't matter for your workload.
Last verified . 22 shared benchmarks.
Arcee AISummary
- They share 22 benchmarks with published results for both. GLM-5.1 scores higher in 8 categories and Trinity Large Thinking in 0 categories; 8 gaps are clear of the uncertainty.
- The widest gap is in reasoning, where GLM-5.1 leads 39.1 to 16.9.
- The biggest single-benchmark swing is NYT Connections (extended): 77.7% for GLM-5.1 and 16.5% for Trinity Large Thinking.
- Trinity Large Thinking is cheaper at $0.25 / $0.80 per million input/output tokens, against $1.40 / $4.40 for GLM-5.1.
- Trinity Large Thinking accepts more context: 262K tokens versus 200K.
Side by side
| GLM-5.1 | Trinity Large Thinking | |
|---|---|---|
| Provider | Z.ai (Zhipu) | Arcee AI |
| Noometry Index | 47.8 | 38.6 |
| Released | 2026-04-07 | 2026-04-01 |
| Weights | Open | Open |
| Context window | 200K | 262K |
| Max output | 131K | 80K |
| Input $ / M tokens | $1.40 | $0.25 |
| Output $ / M tokens | $4.40 | $0.80 |
| Results tracked | 41 | 24 |
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Category by category
Coding GLM-5.1 leads
GLM-5.1: 48.7 (#55), Trinity Large Thinking: 34.1 (#244)
| Benchmark | GLM-5.1 | Trinity Large Thinking |
|---|---|---|
| LMArena WebDev | 1508 | 1238 |
| SciCode | 43.8% | 36.1% |
| LMArena Coding | 1485 | 1381 |
| SWE-bench Verified | 74.2% | — |
| WeirdML | 57.1% | — |
| ALE-Bench | 887.1 | — |
Agentic & Tool Use Not comparable
GLM-5.1: 24.9 (#113), Trinity Large Thinking: —
| Benchmark | GLM-5.1 | Trinity Large Thinking |
|---|---|---|
| APEX-Agents | 40.9% | — |
| ExploitBench | 18.1% | — |
| GBAEval | 0% | — |
| Vending-Bench 2 | 5,634 | — |
Reasoning GLM-5.1 leads
GLM-5.1: 39.1 (#60), Trinity Large Thinking: 16.9 (#298)
| Benchmark | GLM-5.1 | Trinity Large Thinking |
|---|---|---|
| NYT Connections (extended) | 77.7% | 16.5% |
| CritPt | 4.6% | 0.9% |
| Thematic Generalization | 69.8% | 41.6% |
| LMArena Hard Prompts | 1472 | 1350 |
| SimpleBench | 55.1% | — |
| Chess Puzzles | 19% | — |
| Surface Evolver Bench | — | 15.6% |
| Epoch Capabilities Index | 149.84 | — |
Math GLM-5.1 leads
GLM-5.1: 49.7 (#60), Trinity Large Thinking: 37.6 (#149)
| Benchmark | GLM-5.1 | Trinity Large Thinking |
|---|---|---|
| LMArena Math | 1473 | 1366 |
| FrontierMath (Tiers 1-3) | 36.8% | — |
| MathArena Final-Answer Competitions | 67.1% | — |
| OTIS Mock AIME 2024-2025 | 93.3% | — |
| ProofBench | 22.2% | — |
| FrontierMath (Feb 2025 set) | 33.4% | — |
| FrontierMath Tier 4 (v1) | 12.5% | — |
Knowledge GLM-5.1 leads
GLM-5.1: 54.9 (#50), Trinity Large Thinking: 40.9 (#113)
| Benchmark | GLM-5.1 | Trinity Large Thinking |
|---|---|---|
| LMArena Expert | 1476 | 1360 |
| GPQA Diamond | 89.9% | — |
| SimpleQA Verified | 34% | — |
| Vectara Hallucination Rate | — | 6.9% |
Multilingual GLM-5.1 leads
GLM-5.1: 55.0 (#36), Trinity Large Thinking: 46.2 (#160)
| Benchmark | GLM-5.1 | Trinity Large Thinking |
|---|---|---|
| LMArena Non-English | 1447 | 1325 |
| LMArena Chinese | 1515 | 1373 |
| LMArena French | 1474 | 1374 |
| LMArena German | 1465 | 1356 |
| LMArena Japanese | 1434 | 1311 |
| LMArena Korean | 1418 | 1306 |
| LMArena Russian | 1454 | 1337 |
| LMArena Spanish | 1469 | 1357 |
Instruction Following GLM-5.1 leads
GLM-5.1: 76.3 (#42), Trinity Large Thinking: 70.5 (#162)
| Benchmark | GLM-5.1 | Trinity Large Thinking |
|---|---|---|
| LMArena Instruction Following | 1451 | 1334 |
Long Context GLM-5.1 leads
GLM-5.1: 44.9 (#53), Trinity Large Thinking: 41.3 (#144)
| Benchmark | GLM-5.1 | Trinity Large Thinking |
|---|---|---|
| LMArena Longer Query | 1466 | 1355 |
Writing & Preference GLM-5.1 leads
GLM-5.1: 66.9 (#31), Trinity Large Thinking: 53.8 (#158)
| Benchmark | GLM-5.1 | Trinity Large Thinking |
|---|---|---|
| LMArena Text | 1461 | 1340 |
| LMArena Creative Writing | 1453 | 1320 |
| LMArena Multi-Turn | 1472 | 1342 |
| EQ-Bench Creative Writing | 1592 | — |
Frequently asked questions
Is GLM-5.1 better than Trinity Large Thinking?
GLM-5.1 is the stronger model overall, scoring 47.8 to 38.6 on the Noometry Index. Trinity Large Thinking costs 5.5× less per token, which makes it the better buy when GLM-5.1's lead doesn't matter for your workload.
Which is cheaper, GLM-5.1 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-5.1 lists at $1.40 and $4.40.
Is GLM-5.1 or Trinity Large Thinking better for coding?
GLM-5.1 scores higher on coding benchmarks: 48.7 versus 34.1 in the Noometry coding category.
Which has the bigger context window?
Trinity Large Thinking does, with 262K tokens against 200K.
How many benchmarks do GLM-5.1 and Trinity Large Thinking share?
22 benchmarks have published results for both models. GLM-5.1 has 41 scored results on Noometry and Trinity Large Thinking has 24.