Model comparison
GLM-5.1 vs Qwen3.8 27B
GLM-5.1 is the stronger model overall, scoring 47.8 to 46.0 on the Noometry Index. Qwen3.8 27B costs 1.9× less per token, which makes it the better buy when GLM-5.1's lead doesn't matter for your workload.
Last verified . 25 shared benchmarks.
Alibaba (Qwen)Summary
- They share 25 benchmarks with published results for both. GLM-5.1 scores higher in 6 categories and Qwen3.8 27B in 3 categories; 7 gaps are clear of the uncertainty.
- The widest gap is in knowledge, where GLM-5.1 leads 54.9 to 41.6.
- The biggest single-benchmark swing is NYT Connections (extended): 77.7% for GLM-5.1 and 54.5% for Qwen3.8 27B.
- Qwen3.8 27B is cheaper at $0.99 / $1.49 per million input/output tokens, against $1.40 / $4.40 for GLM-5.1.
- Qwen3.8 27B accepts more context: 262K tokens versus 200K.
Side by side
| GLM-5.1 | Qwen3.8 27B | |
|---|---|---|
| Provider | Z.ai (Zhipu) | Alibaba (Qwen) |
| Noometry Index | 47.8 | 46.0 |
| Released | 2026-04-07 | 2026-08-14 |
| Weights | Open | Open |
| Context window | 200K | 262K |
| Max output | 131K | 33K |
| Input $ / M tokens | $1.40 | $0.99 |
| Output $ / M tokens | $4.40 | $1.49 |
| Results tracked | 41 | 31 |
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Category by category
Coding Qwen3.8 27B leads
GLM-5.1: 48.7 (#55), Qwen3.8 27B: 50.5 (#44)
| Benchmark | GLM-5.1 | Qwen3.8 27B |
|---|---|---|
| LMArena WebDev | 1508 | 1593 |
| SciCode | 43.8% | 46.6% |
| LMArena Coding | 1485 | 1482 |
| SWE-bench Verified | 74.2% | — |
| WeirdML | 57.1% | — |
| ALE-Bench | 887.1 | — |
Agentic & Tool Use Qwen3.8 27B leads
GLM-5.1: 24.9 (#113), Qwen3.8 27B: 32.9 (#57)
| Benchmark | GLM-5.1 | Qwen3.8 27B |
|---|---|---|
| APEX-Agents | 40.9% | 47.5% |
| ExploitBench | 18.1% | — |
| GBAEval | 0% | — |
| Vending-Bench 2 | 5,634 | — |
Reasoning Qwen3.8 27B leads
GLM-5.1: 39.1 (#60), Qwen3.8 27B: 41.0 (#54)
| Benchmark | GLM-5.1 | Qwen3.8 27B |
|---|---|---|
| NYT Connections (extended) | 77.7% | 54.5% |
| CritPt | 4.6% | 5.4% |
| LMArena Hard Prompts | 1472 | 1460 |
| Epoch Capabilities Index | 149.84 | 149.38 |
| ARC-AGI-2 | — | 42.4% |
| SimpleBench | 55.1% | — |
| ARC-AGI-1 | — | 87.5% |
| Chess Puzzles | 19% | — |
| Thematic Generalization | 69.8% | — |
| DTBench | — | 88% |
| LMCA | — | 41.4% |
| Surface Evolver Bench | — | 45% |
Math GLM-5.1 leads
GLM-5.1: 49.7 (#60), Qwen3.8 27B: 37.1 (#161)
| Benchmark | GLM-5.1 | Qwen3.8 27B |
|---|---|---|
| ProofBench | 22.2% | 16% |
| LMArena Math | 1473 | 1456 |
| FrontierMath (Tiers 1-3) | 36.8% | — |
| MathArena Final-Answer Competitions | 67.1% | — |
| OTIS Mock AIME 2024-2025 | 93.3% | — |
| FrontierMath (Feb 2025 set) | 33.4% | — |
| FrontierMath Tier 4 (v1) | 12.5% | — |
Knowledge GLM-5.1 leads
GLM-5.1: 54.9 (#50), Qwen3.8 27B: 41.6 (#109)
| Benchmark | GLM-5.1 | Qwen3.8 27B |
|---|---|---|
| LMArena Expert | 1476 | 1482 |
| GPQA Diamond | 89.9% | — |
| SimpleQA Verified | 34% | — |
Multimodal Not comparable
GLM-5.1: —, Qwen3.8 27B: 41.3 (#37)
| Benchmark | GLM-5.1 | Qwen3.8 27B |
|---|---|---|
| LMArena Vision | — | 1271 |
Multilingual GLM-5.1 leads
GLM-5.1: 55.0 (#36), Qwen3.8 27B: 53.7 (#60)
| Benchmark | GLM-5.1 | Qwen3.8 27B |
|---|---|---|
| LMArena Non-English | 1447 | 1430 |
| LMArena Chinese | 1515 | 1504 |
| LMArena French | 1474 | 1465 |
| LMArena German | 1465 | 1438 |
| LMArena Japanese | 1434 | 1384 |
| LMArena Korean | 1418 | 1393 |
| LMArena Russian | 1454 | 1415 |
| LMArena Spanish | 1469 | 1448 |
Instruction Following Too close to call
GLM-5.1: 76.3 (#42), Qwen3.8 27B: 75.8 (#53)
| Benchmark | GLM-5.1 | Qwen3.8 27B |
|---|---|---|
| LMArena Instruction Following | 1451 | 1439 |
Long Context Too close to call
GLM-5.1: 44.9 (#53), Qwen3.8 27B: 44.3 (#70)
| Benchmark | GLM-5.1 | Qwen3.8 27B |
|---|---|---|
| LMArena Longer Query | 1466 | 1450 |
Writing & Preference GLM-5.1 leads
GLM-5.1: 66.9 (#31), Qwen3.8 27B: 65.8 (#43)
| Benchmark | GLM-5.1 | Qwen3.8 27B |
|---|---|---|
| LMArena Text | 1461 | 1441 |
| LMArena Creative Writing | 1453 | 1384 |
| EQ-Bench Creative Writing | 1592 | 1671 |
| LMArena Multi-Turn | 1472 | 1441 |
Frequently asked questions
Is GLM-5.1 better than Qwen3.8 27B?
GLM-5.1 is the stronger model overall, scoring 47.8 to 46.0 on the Noometry Index. Qwen3.8 27B costs 1.9× 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 Qwen3.8 27B?
Qwen3.8 27B is cheaper. It lists at $0.99 per million input tokens and $1.49 per million output tokens; GLM-5.1 lists at $1.40 and $4.40.
Is GLM-5.1 or Qwen3.8 27B better for coding?
Qwen3.8 27B scores higher on coding benchmarks: 50.5 versus 48.7 in the Noometry coding category.
Which has the bigger context window?
Qwen3.8 27B does, with 262K tokens against 200K.
How many benchmarks do GLM-5.1 and Qwen3.8 27B share?
25 benchmarks have published results for both models. GLM-5.1 has 41 scored results on Noometry and Qwen3.8 27B has 31.