Model comparison
GLM-5 vs Qwen3.6 27B
GLM-5 is the stronger model overall, scoring 46.1 to 42.2 on the Noometry Index.
Last verified . 4 shared benchmarks.
Alibaba (Qwen)Summary
- They share 4 benchmarks with published results for both. GLM-5 scores higher in 3 categories and Qwen3.6 27B in 2 categories; 4 gaps are clear of the uncertainty.
- The widest gap is in writing & preference, where GLM-5 leads 66.0 to 50.3.
- The biggest single-benchmark swing is Chess Puzzles: 10% for GLM-5 and 22% for Qwen3.6 27B.
- Qwen3.6 27B is cheaper at $0.60 / $3.60 per million input/output tokens, against $1 / $3.20 for GLM-5.
- Qwen3.6 27B accepts more context: 262K tokens versus 205K.
Side by side
| GLM-5 | Qwen3.6 27B | |
|---|---|---|
| Provider | Z.ai (Zhipu) | Alibaba (Qwen) |
| Noometry Index | 46.1 | 42.2 |
| Released | 2026-02-11 | 2026-04-22 |
| Weights | Open | Open |
| Context window | 205K | 262K |
| Max output | 131K | 66K |
| Input $ / M tokens | $1 | $0.60 |
| Output $ / M tokens | $3.20 | $3.60 |
| Results tracked | 45 | 11 |
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Category by category
Coding GLM-5 leads
GLM-5: 49.0 (#52), Qwen3.6 27B: 39.1 (#163)
| Benchmark | GLM-5 | Qwen3.6 27B |
|---|---|---|
| SWE-bench Verified | 72.1% | — |
| SWE-bench Verified (bash only) | 72.8% | — |
| LMArena WebDev | 1434 | — |
| SWE-bench Multilingual | 69.7% | — |
| SciCode | — | 37.3% |
| WeirdML | 48.2% | — |
| LMArena Coding | 1461 | — |
| ALE-Bench | 765.62 | — |
Agentic & Tool Use Not comparable
GLM-5: 31.1 (#71), Qwen3.6 27B: —
| Benchmark | GLM-5 | Qwen3.6 27B |
|---|---|---|
| Terminal-Bench | 52.4% | — |
| τ²-bench Airline | 82.5% | — |
| τ²-bench Banking | 9.8% | — |
| τ²-bench Retail | 73.7% | — |
| τ²-bench Telecom | 86.8% | — |
| Vending-Bench 2 | 4,432 | — |
Reasoning GLM-5 leads
GLM-5: 27.6 (#116), Qwen3.6 27B: 25.0 (#153)
| Benchmark | GLM-5 | Qwen3.6 27B |
|---|---|---|
| Chess Puzzles | 10% | 22% |
| Epoch Capabilities Index | 145.83 | 146.5 |
| ARC-AGI-2 | 4.9% | — |
| SimpleBench | 53.2% | — |
| Kagi LLM Benchmark | 75% | — |
| NYT Connections (extended) | 74.8% | — |
| ARC-AGI-1 | 44.7% | — |
| CritPt | — | 0.9% |
| LMArena Hard Prompts | 1452 | — |
| Mystery Game Puzzles | — | 7% |
| DTBench | — | 78.1% |
| LMCA | — | 34.5% |
| ForecastBench | 61 | — |
Math Qwen3.6 27B leads
GLM-5: 46.4 (#71), Qwen3.6 27B: 48.5 (#62)
| Benchmark | GLM-5 | Qwen3.6 27B |
|---|---|---|
| OTIS Mock AIME 2024-2025 | 80% | 91.1% |
| FrontierMath (Tiers 1-3) | — | 35.1% |
| MathArena Final-Answer Competitions | 65.7% | — |
| LMArena Math | 1440 | — |
| FrontierMath (Feb 2025 set) | 16.4% | — |
| FrontierMath Tier 4 (v1) | 2.1% | — |
Knowledge Too close to call
GLM-5: 52.3 (#64), Qwen3.6 27B: 52.4 (#63)
| Benchmark | GLM-5 | Qwen3.6 27B |
|---|---|---|
| GPQA Diamond | 87.8% | 85.9% |
| Vectara Hallucination Rate | 10.1% | — |
| LMArena Expert | 1454 | — |
Multilingual Not comparable
GLM-5: 53.7 (#58), Qwen3.6 27B: —
| Benchmark | GLM-5 | Qwen3.6 27B |
|---|---|---|
| LMArena Non-English | 1430 | — |
| LMArena Chinese | 1511 | — |
| LMArena French | 1455 | — |
| LMArena German | 1445 | — |
| LMArena Japanese | 1416 | — |
| LMArena Korean | 1423 | — |
| LMArena Russian | 1436 | — |
| LMArena Spanish | 1454 | — |
Instruction Following Not comparable
GLM-5: 75.2 (#67), Qwen3.6 27B: —
| Benchmark | GLM-5 | Qwen3.6 27B |
|---|---|---|
| LMArena Instruction Following | 1428 | — |
Long Context Not comparable
GLM-5: 44.7 (#60), Qwen3.6 27B: —
| Benchmark | GLM-5 | Qwen3.6 27B |
|---|---|---|
| CL-bench | 18.7% | — |
| LMArena Longer Query | 1446 | — |
Writing & Preference GLM-5 leads
GLM-5: 66.0 (#38), Qwen3.6 27B: 50.3 (#181)
| Benchmark | GLM-5 | Qwen3.6 27B |
|---|---|---|
| LMArena Text | 1446 | — |
| LMArena Creative Writing | 1439 | — |
| EQ-Bench Creative Writing | 1601 | — |
| EQ-Bench 4 | — | 1026 |
| LMArena Multi-Turn | 1456 | — |
Frequently asked questions
Is GLM-5 better than Qwen3.6 27B?
GLM-5 is the stronger model overall, scoring 46.1 to 42.2 on the Noometry Index.
Which is cheaper, GLM-5 or Qwen3.6 27B?
Qwen3.6 27B is cheaper. It lists at $0.60 per million input tokens and $3.60 per million output tokens; GLM-5 lists at $1 and $3.20.
Is GLM-5 or Qwen3.6 27B better for coding?
GLM-5 scores higher on coding benchmarks: 49.0 versus 39.1 in the Noometry coding category.
Which has the bigger context window?
Qwen3.6 27B does, with 262K tokens against 205K.
How many benchmarks do GLM-5 and Qwen3.6 27B share?
4 benchmarks have published results for both models. GLM-5 has 45 scored results on Noometry and Qwen3.6 27B has 11.