Model comparison
GLM-5.1 vs GPT-5.6 Terra
GPT-5.6 Terra is the stronger model overall, scoring 59.1 to 47.6 on the Noometry Index. GLM-5.1 costs 2.1× less per token, which makes it the better buy when GPT-5.6 Terra's lead doesn't matter for your workload.
Last verified . 34 shared benchmarks.
Summary
- They share 34 benchmarks with published results for both. GLM-5.1 scores higher in 2 categories and GPT-5.6 Terra in 7 categories; 6 gaps are clear of the uncertainty.
- The widest gap is in math, where GPT-5.6 Terra leads 81.5 to 49.0.
- The biggest single-benchmark swing is ProofBench: 22.2% for GLM-5.1 and 74% for GPT-5.6 Terra.
- GLM-5.1 is cheaper at $1.40 / $4.40 per million input/output tokens, against $2 / $12 for GPT-5.6 Terra.
- GPT-5.6 Terra accepts more context: 1.05M tokens versus 200K.
- GLM-5.1 has downloadable open weights; the other is API-only.
Side by side
| GLM-5.1 | GPT-5.6 Terra | |
|---|---|---|
| Provider | Z.ai (Zhipu) | OpenAI |
| Noometry Index | 47.6 | 59.1 |
| Released | 2026-04-07 | 2026-07-09 |
| Weights | Open | Proprietary |
| Context window | 200K | 1.05M |
| Max output | 131K | 128K |
| Input $ / M tokens | $1.40 | $2 |
| Output $ / M tokens | $4.40 | $12 |
| Results tracked | 41 | 52 |
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Category by category
Coding GPT-5.6 Terra leads
GLM-5.1: 48.5 (#52), GPT-5.6 Terra: 57.1 (#16)
| Benchmark | GLM-5.1 | GPT-5.6 Terra |
|---|---|---|
| LMArena WebDev | 1509 | 1520 |
| SciCode | 43.8% | 55% |
| WeirdML | 57.1% | 78.3% |
| LMArena Coding | 1486 | 1484 |
| ALE-Bench | 887.1 | 1,951 |
| SWE-bench Verified | 74.2% | — |
| DeepSWE | — | 69.6% |
| FrontierCode | — | 41.3% |
| CursorBench | — | 41.3% |
Agentic & Tool Use GPT-5.6 Terra leads
GLM-5.1: 24.8 (#106), GPT-5.6 Terra: 40.2 (#23)
| Benchmark | GLM-5.1 | GPT-5.6 Terra |
|---|---|---|
| APEX-Agents | 40.9% | 58.2% |
| Vending-Bench 2 | 5,634 | 7,343 |
| BALROG | — | 53.2% |
| ExploitBench | 18.1% | — |
| GBAEval | 0% | — |
| GDP.pdf | — | 24.7% |
Reasoning GPT-5.6 Terra leads
GLM-5.1: 38.2 (#57), GPT-5.6 Terra: 60.6 (#19)
| Benchmark | GLM-5.1 | GPT-5.6 Terra |
|---|---|---|
| SimpleBench | 55.1% | 48.9% |
| NYT Connections (extended) | 77.7% | 78.4% |
| CritPt | 4.6% | 30% |
| Chess Puzzles | 19% | 54% |
| LMArena Hard Prompts | 1472 | 1467 |
| Epoch Capabilities Index | 149.86 | 159.31 |
| ARC-AGI-2 | — | 83.9% |
| Kagi LLM Benchmark | — | 51.3% |
| ARC-AGI-1 | — | 96.5% |
| Thematic Generalization | 69.8% | — |
| Mystery Game Puzzles | — | 35% |
| DTBench | — | 93.3% |
| LMCA | — | 55% |
| Surface Evolver Bench | — | 83.8% |
Math GPT-5.6 Terra leads
GLM-5.1: 49.0 (#48), GPT-5.6 Terra: 81.5 (#10)
| Benchmark | GLM-5.1 | GPT-5.6 Terra |
|---|---|---|
| FrontierMath (Tiers 1-3) | 36.8% | 86% |
| OTIS Mock AIME 2024-2025 | 93.3% | 99.7% |
| ProofBench | 22.2% | 74% |
| LMArena Math | 1472 | 1463 |
| FrontierMath Tier 4 | — | 70.7% |
| MathArena Final-Answer Competitions | 67.1% | — |
| FrontierMath (Feb 2025 set) | 33.4% | — |
| FrontierMath Tier 4 (v1) | 12.5% | — |
Knowledge GPT-5.6 Terra leads
GLM-5.1: 55.0 (#45), GPT-5.6 Terra: 61.3 (#27)
| Benchmark | GLM-5.1 | GPT-5.6 Terra |
|---|---|---|
| GPQA Diamond | 89.9% | 93.3% |
| SimpleQA Verified | 34% | 43.2% |
| LMArena Expert | 1477 | 1492 |
Multimodal Not comparable
GLM-5.1: —, GPT-5.6 Terra: 47.5 (#10)
| Benchmark | GLM-5.1 | GPT-5.6 Terra |
|---|---|---|
| LMArena Vision | — | 1271 |
| Blueprint-Bench 2 | — | 30.8% |
| Furniture Assembly | — | 54.2% |
| LMArena Document | — | 1472 |
Multilingual Too close to call
GLM-5.1: 55.2 (#34), GPT-5.6 Terra: 54.5 (#42)
| Benchmark | GLM-5.1 | GPT-5.6 Terra |
|---|---|---|
| LMArena Non-English | 1448 | 1439 |
| LMArena Chinese | 1516 | 1514 |
| LMArena French | 1478 | 1476 |
| LMArena German | 1466 | 1462 |
| LMArena Japanese | 1436 | 1446 |
| LMArena Korean | 1423 | 1420 |
| LMArena Russian | 1453 | 1450 |
| LMArena Spanish | 1467 | 1455 |
Instruction Following Too close to call
GLM-5.1: 76.5 (#41), GPT-5.6 Terra: 76.6 (#38)
| Benchmark | GLM-5.1 | GPT-5.6 Terra |
|---|---|---|
| LMArena Instruction Following | 1451 | 1453 |
Long Context Too close to call
GLM-5.1: 44.9 (#51), GPT-5.6 Terra: 44.4 (#64)
| Benchmark | GLM-5.1 | GPT-5.6 Terra |
|---|---|---|
| LMArena Longer Query | 1467 | 1452 |
Writing & Preference GPT-5.6 Terra leads
GLM-5.1: 67.2 (#29), GPT-5.6 Terra: 70.4 (#21)
| Benchmark | GLM-5.1 | GPT-5.6 Terra |
|---|---|---|
| LMArena Text | 1462 | 1446 |
| LMArena Creative Writing | 1454 | 1411 |
| EQ-Bench Creative Writing | 1592 | 1855 |
| LMArena Multi-Turn | 1473 | 1451 |
| EQ-Bench 4 | — | 1234 |
Frequently asked questions
Is GLM-5.1 better than GPT-5.6 Terra?
GPT-5.6 Terra is the stronger model overall, scoring 59.1 to 47.6 on the Noometry Index. GLM-5.1 costs 2.1× less per token, which makes it the better buy when GPT-5.6 Terra's lead doesn't matter for your workload.
Which is cheaper, GLM-5.1 or GPT-5.6 Terra?
GLM-5.1 is cheaper. It lists at $1.40 per million input tokens and $4.40 per million output tokens; GPT-5.6 Terra lists at $2 and $12.
Is GLM-5.1 or GPT-5.6 Terra better for coding?
GPT-5.6 Terra scores higher on coding benchmarks: 57.1 versus 48.5 in the Noometry coding category.
Which has the bigger context window?
GPT-5.6 Terra does, with 1.05M tokens against 200K.
How many benchmarks do GLM-5.1 and GPT-5.6 Terra share?
34 benchmarks have published results for both models. GLM-5.1 has 41 scored results on Noometry and GPT-5.6 Terra has 52.