Model comparison
GLM-5.1 vs Qwen3-Next 80B-A3B Instruct
GLM-5.1 is the stronger model overall, scoring 47.8 to 43.0 on the Noometry Index. Qwen3-Next 80B-A3B Instruct costs 2.5× less per token, which makes it the better buy when GLM-5.1's lead doesn't matter for your workload.
Last verified . 17 shared benchmarks.
Alibaba (Qwen)Summary
- They share 17 benchmarks with published results for both. GLM-5.1 scores higher in 8 categories and Qwen3-Next 80B-A3B Instruct in 0 categories; 8 gaps are clear of the uncertainty.
- The widest gap is in knowledge, where GLM-5.1 leads 54.9 to 41.8.
- Qwen3-Next 80B-A3B Instruct is cheaper at $0.50 / $2 per million input/output tokens, against $1.40 / $4.40 for GLM-5.1.
- GLM-5.1 accepts more context: 200K tokens versus 131K.
Side by side
| GLM-5.1 | Qwen3-Next 80B-A3B Instruct | |
|---|---|---|
| Provider | Z.ai (Zhipu) | Alibaba (Qwen) |
| Noometry Index | 47.8 | 43.0 |
| Released | 2026-04-07 | 2025-09 |
| Weights | Open | Open |
| Context window | 200K | 131K |
| Max output | 131K | 33K |
| Input $ / M tokens | $1.40 | $0.50 |
| Output $ / M tokens | $4.40 | $2 |
| Results tracked | 41 | 25 |
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Category by category
Coding GLM-5.1 leads
GLM-5.1: 48.7 (#55), Qwen3-Next 80B-A3B Instruct: 42.5 (#98)
| Benchmark | GLM-5.1 | Qwen3-Next 80B-A3B Instruct |
|---|---|---|
| LMArena Coding | 1485 | 1440 |
| SWE-bench Verified | 74.2% | — |
| LMArena WebDev | 1508 | — |
| SciCode | 43.8% | — |
| WeirdML | 57.1% | — |
| ALE-Bench | 887.1 | — |
Agentic & Tool Use Not comparable
GLM-5.1: 24.9 (#113), Qwen3-Next 80B-A3B Instruct: —
| Benchmark | GLM-5.1 | Qwen3-Next 80B-A3B Instruct |
|---|---|---|
| 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), Qwen3-Next 80B-A3B Instruct: 31.1 (#81)
| Benchmark | GLM-5.1 | Qwen3-Next 80B-A3B Instruct |
|---|---|---|
| LMArena Hard Prompts | 1472 | 1428 |
| SimpleBench | 55.1% | — |
| Kagi LLM Benchmark | — | 66.7% |
| NYT Connections (extended) | 77.7% | — |
| CritPt | 4.6% | — |
| Chess Puzzles | 19% | — |
| Thematic Generalization | 69.8% | — |
| Epoch Capabilities Index | 149.84 | — |
Math GLM-5.1 leads
GLM-5.1: 49.7 (#60), Qwen3-Next 80B-A3B Instruct: 38.8 (#126)
| Benchmark | GLM-5.1 | Qwen3-Next 80B-A3B Instruct |
|---|---|---|
| LMArena Math | 1473 | 1440 |
| FrontierMath (Tiers 1-3) | 36.8% | — |
| MathArena Final-Answer Competitions | 67.1% | — |
| OTIS Mock AIME 2024-2025 | 93.3% | — |
| ProofBench | 22.2% | — |
| Omni-MATH | — | 46.7% |
| FrontierMath (Feb 2025 set) | 33.4% | — |
| FrontierMath Tier 4 (v1) | 12.5% | — |
Knowledge GLM-5.1 leads
GLM-5.1: 54.9 (#50), Qwen3-Next 80B-A3B Instruct: 41.8 (#106)
| Benchmark | GLM-5.1 | Qwen3-Next 80B-A3B Instruct |
|---|---|---|
| LMArena Expert | 1476 | 1417 |
| GPQA Diamond | 89.9% | — |
| SimpleQA Verified | 34% | — |
| MMLU-Pro | — | 78.6% |
| Vectara Hallucination Rate | — | 9.3% |
| GPQA (HELM) | — | 63% |
Multilingual GLM-5.1 leads
GLM-5.1: 55.0 (#36), Qwen3-Next 80B-A3B Instruct: 52.1 (#93)
| Benchmark | GLM-5.1 | Qwen3-Next 80B-A3B Instruct |
|---|---|---|
| LMArena Non-English | 1447 | 1407 |
| LMArena Chinese | 1515 | 1460 |
| LMArena French | 1474 | 1413 |
| LMArena German | 1465 | 1417 |
| LMArena Japanese | 1434 | 1395 |
| LMArena Korean | 1418 | 1364 |
| LMArena Russian | 1454 | 1404 |
| LMArena Spanish | 1469 | 1435 |
Instruction Following GLM-5.1 leads
GLM-5.1: 76.3 (#42), Qwen3-Next 80B-A3B Instruct: 70.8 (#159)
| Benchmark | GLM-5.1 | Qwen3-Next 80B-A3B Instruct |
|---|---|---|
| LMArena Instruction Following | 1451 | 1389 |
| IFEval | — | 81% |
Long Context GLM-5.1 leads
GLM-5.1: 44.9 (#53), Qwen3-Next 80B-A3B Instruct: 37.0 (#223)
| Benchmark | GLM-5.1 | Qwen3-Next 80B-A3B Instruct |
|---|---|---|
| LMArena Longer Query | 1466 | 1403 |
| Fiction.LiveBench | — | 55.6% |
Writing & Preference GLM-5.1 leads
GLM-5.1: 66.9 (#31), Qwen3-Next 80B-A3B Instruct: 58.0 (#121)
| Benchmark | GLM-5.1 | Qwen3-Next 80B-A3B Instruct |
|---|---|---|
| LMArena Text | 1461 | 1417 |
| LMArena Creative Writing | 1453 | 1334 |
| LMArena Multi-Turn | 1472 | 1416 |
| EQ-Bench Creative Writing | 1592 | — |
| WildBench | — | 80.7% |
Frequently asked questions
Is GLM-5.1 better than Qwen3-Next 80B-A3B Instruct?
GLM-5.1 is the stronger model overall, scoring 47.8 to 43.0 on the Noometry Index. Qwen3-Next 80B-A3B Instruct costs 2.5× 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 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-5.1 lists at $1.40 and $4.40.
Is GLM-5.1 or Qwen3-Next 80B-A3B Instruct better for coding?
GLM-5.1 scores higher on coding benchmarks: 48.7 versus 42.5 in the Noometry coding category.
Which has the bigger context window?
GLM-5.1 does, with 200K tokens against 131K.
How many benchmarks do GLM-5.1 and Qwen3-Next 80B-A3B Instruct share?
17 benchmarks have published results for both models. GLM-5.1 has 41 scored results on Noometry and Qwen3-Next 80B-A3B Instruct has 25.