Model comparison
GLM-4.6 vs Qwen3-Next 80B-A3B Instruct
Qwen3-Next 80B-A3B Instruct is the stronger model overall, scoring 42.5 to 40.9 on the Noometry Index.
Last verified . 19 shared benchmarks.
Alibaba (Qwen)Summary
- They share 19 benchmarks with published results for both. GLM-4.6 scores higher in 5 categories and Qwen3-Next 80B-A3B Instruct in 3 categories; 7 gaps are clear of the uncertainty.
- The widest gap is in reasoning, where Qwen3-Next 80B-A3B Instruct leads 30.1 to 23.1.
- The biggest single-benchmark swing is Kagi LLM Benchmark: 47.4% for GLM-4.6 and 66.7% for Qwen3-Next 80B-A3B Instruct.
- Qwen3-Next 80B-A3B Instruct is cheaper at $0.50 / $2 per million input/output tokens, against $0.60 / $2.20 for GLM-4.6.
- GLM-4.6 accepts more context: 205K tokens versus 131K.
Side by side
| GLM-4.6 | Qwen3-Next 80B-A3B Instruct | |
|---|---|---|
| Provider | Z.ai (Zhipu) | Alibaba (Qwen) |
| Noometry Index | 40.9 | 42.5 |
| Released | 2025-09-30 | 2025-09 |
| Weights | Open | Open |
| Context window | 205K | 131K |
| Max output | 131K | 33K |
| Input $ / M tokens | $0.60 | $0.50 |
| Output $ / M tokens | $2.20 | $2 |
| Results tracked | 29 | 25 |
Sponsored placements are available on pages like this one. Advertise on Noometry
Category by category
Coding Qwen3-Next 80B-A3B Instruct leads
GLM-4.6: 39.9 (#137), Qwen3-Next 80B-A3B Instruct: 41.9 (#101)
| Benchmark | GLM-4.6 | Qwen3-Next 80B-A3B Instruct |
|---|---|---|
| LMArena Coding | 1449 | 1441 |
| SWE-bench Verified (bash only) | 55.4% | — |
| LMArena WebDev | 1339 | — |
| SciCode | 38.4% | — |
| ALE-Bench | 340.82 | — |
Agentic & Tool Use Not comparable
GLM-4.6: 32.1 (#60), Qwen3-Next 80B-A3B Instruct: —
| Benchmark | GLM-4.6 | Qwen3-Next 80B-A3B Instruct |
|---|---|---|
| Terminal-Bench | 24.5% | — |
| Berkeley Function Calling Leaderboard | 72.4% | — |
Reasoning Qwen3-Next 80B-A3B Instruct leads
GLM-4.6: 23.1 (#168), Qwen3-Next 80B-A3B Instruct: 30.1 (#79)
| Benchmark | GLM-4.6 | Qwen3-Next 80B-A3B Instruct |
|---|---|---|
| Kagi LLM Benchmark | 47.4% | 66.7% |
| LMArena Hard Prompts | 1440 | 1428 |
| CritPt | 1.1% | — |
Math Too close to call
GLM-4.6: 36.4 (#115), Qwen3-Next 80B-A3B Instruct: 36.3 (#118)
| Benchmark | GLM-4.6 | Qwen3-Next 80B-A3B Instruct |
|---|---|---|
| LMArena Math | 1431 | 1440 |
| Omni-MATH | — | 46.7% |
| FrontierMath (Feb 2025 set) | 3.8% | — |
| FrontierMath Tier 4 (v1) | 2.1% | — |
Knowledge Qwen3-Next 80B-A3B Instruct leads
GLM-4.6: 39.6 (#118), Qwen3-Next 80B-A3B Instruct: 41.3 (#102)
| Benchmark | GLM-4.6 | Qwen3-Next 80B-A3B Instruct |
|---|---|---|
| Vectara Hallucination Rate | 9.5% | 9.3% |
| LMArena Expert | 1432 | 1416 |
| MMLU-Pro | — | 78.6% |
| GPQA (HELM) | — | 63% |
Multilingual GLM-4.6 leads
GLM-4.6: 53.6 (#62), Qwen3-Next 80B-A3B Instruct: 52.3 (#88)
| Benchmark | GLM-4.6 | Qwen3-Next 80B-A3B Instruct |
|---|---|---|
| LMArena Non-English | 1426 | 1407 |
| LMArena Chinese | 1500 | 1461 |
| LMArena French | 1459 | 1415 |
| LMArena German | 1447 | 1416 |
| LMArena Japanese | 1394 | 1398 |
| LMArena Korean | 1401 | 1366 |
| LMArena Russian | 1418 | 1403 |
| LMArena Spanish | 1436 | 1436 |
Instruction Following GLM-4.6 leads
GLM-4.6: 74.5 (#93), Qwen3-Next 80B-A3B Instruct: 71.0 (#154)
| Benchmark | GLM-4.6 | Qwen3-Next 80B-A3B Instruct |
|---|---|---|
| LMArena Instruction Following | 1410 | 1388 |
| IFEval | — | 81% |
Long Context GLM-4.6 leads
GLM-4.6: 43.4 (#91), Qwen3-Next 80B-A3B Instruct: 37.0 (#218)
| Benchmark | GLM-4.6 | Qwen3-Next 80B-A3B Instruct |
|---|---|---|
| LMArena Longer Query | 1423 | 1403 |
| Fiction.LiveBench | — | 55.6% |
Writing & Preference GLM-4.6 leads
GLM-4.6: 61.3 (#86), Qwen3-Next 80B-A3B Instruct: 58.2 (#116)
| Benchmark | GLM-4.6 | Qwen3-Next 80B-A3B Instruct |
|---|---|---|
| LMArena Text | 1440 | 1417 |
| LMArena Creative Writing | 1411 | 1333 |
| LMArena Multi-Turn | 1427 | 1415 |
| EQ-Bench Creative Writing | 1411 | — |
| WildBench | — | 80.7% |
Frequently asked questions
Is GLM-4.6 better than Qwen3-Next 80B-A3B Instruct?
Qwen3-Next 80B-A3B Instruct is the stronger model overall, scoring 42.5 to 40.9 on the Noometry Index.
Which is cheaper, GLM-4.6 or Qwen3-Next 80B-A3B Instruct?
Qwen3-Next 80B-A3B Instruct is cheaper. It lists at $0.50 per million input tokens and $2 per million output tokens; GLM-4.6 lists at $0.60 and $2.20.
Is GLM-4.6 or Qwen3-Next 80B-A3B Instruct better for coding?
Qwen3-Next 80B-A3B Instruct scores higher on coding benchmarks: 41.9 versus 39.9 in the Noometry coding category.
Which has the bigger context window?
GLM-4.6 does, with 205K tokens against 131K.
How many benchmarks do GLM-4.6 and Qwen3-Next 80B-A3B Instruct share?
19 benchmarks have published results for both models. GLM-4.6 has 29 scored results on Noometry and Qwen3-Next 80B-A3B Instruct has 25.