Model comparison
GLM-5 vs Qwen3-Next 80B-A3B Instruct
GLM-5 is the stronger model overall, scoring 45.9 to 42.5 on the Noometry Index. Qwen3-Next 80B-A3B Instruct costs 1.8× less per token, which makes it the better buy when GLM-5's lead doesn't matter for your workload.
Last verified . 19 shared benchmarks.
Alibaba (Qwen)Summary
- They share 19 benchmarks with published results for both. GLM-5 scores higher in 7 categories and Qwen3-Next 80B-A3B Instruct in 1 category; 8 gaps are clear of the uncertainty.
- The widest gap is in knowledge, where GLM-5 leads 52.2 to 41.3.
- The biggest single-benchmark swing is Kagi LLM Benchmark: 75% for GLM-5 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 $1 / $3.20 for GLM-5.
- GLM-5 accepts more context: 205K tokens versus 131K.
Side by side
| GLM-5 | Qwen3-Next 80B-A3B Instruct | |
|---|---|---|
| Provider | Z.ai (Zhipu) | Alibaba (Qwen) |
| Noometry Index | 45.9 | 42.5 |
| Released | 2026-02-11 | 2025-09 |
| Weights | Open | Open |
| Context window | 205K | 131K |
| Max output | 131K | 33K |
| Input $ / M tokens | $1 | $0.50 |
| Output $ / M tokens | $3.20 | $2 |
| Results tracked | 45 | 25 |
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Category by category
Coding GLM-5 leads
GLM-5: 49.0 (#47), Qwen3-Next 80B-A3B Instruct: 41.9 (#101)
| Benchmark | GLM-5 | Qwen3-Next 80B-A3B Instruct |
|---|---|---|
| LMArena Coding | 1461 | 1441 |
| SWE-bench Verified | 72.1% | — |
| SWE-bench Verified (bash only) | 72.8% | — |
| LMArena WebDev | 1434 | — |
| SWE-bench Multilingual | 69.7% | — |
| WeirdML | 48.2% | — |
| ALE-Bench | 765.62 | — |
Agentic & Tool Use Not comparable
GLM-5: 30.9 (#65), Qwen3-Next 80B-A3B Instruct: —
| Benchmark | GLM-5 | Qwen3-Next 80B-A3B Instruct |
|---|---|---|
| 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 Qwen3-Next 80B-A3B Instruct leads
GLM-5: 27.6 (#100), Qwen3-Next 80B-A3B Instruct: 30.1 (#79)
| Benchmark | GLM-5 | Qwen3-Next 80B-A3B Instruct |
|---|---|---|
| Kagi LLM Benchmark | 75% | 66.7% |
| LMArena Hard Prompts | 1451 | 1428 |
| ARC-AGI-2 | 4.9% | — |
| SimpleBench | 53.2% | — |
| NYT Connections (extended) | 74.8% | — |
| ARC-AGI-1 | 44.7% | — |
| Chess Puzzles | 10% | — |
| Epoch Capabilities Index | 145.84 | — |
| ForecastBench | 61 | — |
Math GLM-5 leads
GLM-5: 44.8 (#66), Qwen3-Next 80B-A3B Instruct: 36.3 (#118)
| Benchmark | GLM-5 | Qwen3-Next 80B-A3B Instruct |
|---|---|---|
| LMArena Math | 1440 | 1440 |
| MathArena Final-Answer Competitions | 65.7% | — |
| OTIS Mock AIME 2024-2025 | 80% | — |
| Omni-MATH | — | 46.7% |
| FrontierMath (Feb 2025 set) | 16.4% | — |
| FrontierMath Tier 4 (v1) | 2.1% | — |
Knowledge GLM-5 leads
GLM-5: 52.2 (#59), Qwen3-Next 80B-A3B Instruct: 41.3 (#102)
| Benchmark | GLM-5 | Qwen3-Next 80B-A3B Instruct |
|---|---|---|
| Vectara Hallucination Rate | 10.1% | 9.3% |
| LMArena Expert | 1454 | 1416 |
| GPQA Diamond | 87.8% | — |
| MMLU-Pro | — | 78.6% |
| GPQA (HELM) | — | 63% |
Multilingual GLM-5 leads
GLM-5: 53.9 (#57), Qwen3-Next 80B-A3B Instruct: 52.3 (#88)
| Benchmark | GLM-5 | Qwen3-Next 80B-A3B Instruct |
|---|---|---|
| LMArena Non-English | 1430 | 1407 |
| LMArena Chinese | 1511 | 1461 |
| LMArena French | 1455 | 1415 |
| LMArena German | 1445 | 1416 |
| LMArena Japanese | 1416 | 1398 |
| LMArena Korean | 1421 | 1366 |
| LMArena Russian | 1436 | 1403 |
| LMArena Spanish | 1452 | 1436 |
Instruction Following GLM-5 leads
GLM-5: 75.4 (#64), Qwen3-Next 80B-A3B Instruct: 71.0 (#154)
| Benchmark | GLM-5 | Qwen3-Next 80B-A3B Instruct |
|---|---|---|
| LMArena Instruction Following | 1427 | 1388 |
| IFEval | — | 81% |
Long Context GLM-5 leads
GLM-5: 44.6 (#58), Qwen3-Next 80B-A3B Instruct: 37.0 (#218)
| Benchmark | GLM-5 | Qwen3-Next 80B-A3B Instruct |
|---|---|---|
| LMArena Longer Query | 1446 | 1403 |
| Fiction.LiveBench | — | 55.6% |
| CL-bench | 18.7% | — |
Writing & Preference GLM-5 leads
GLM-5: 66.2 (#38), Qwen3-Next 80B-A3B Instruct: 58.2 (#116)
| Benchmark | GLM-5 | Qwen3-Next 80B-A3B Instruct |
|---|---|---|
| LMArena Text | 1446 | 1417 |
| LMArena Creative Writing | 1438 | 1333 |
| LMArena Multi-Turn | 1456 | 1415 |
| EQ-Bench Creative Writing | 1601 | — |
| WildBench | — | 80.7% |
Frequently asked questions
Is GLM-5 better than Qwen3-Next 80B-A3B Instruct?
GLM-5 is the stronger model overall, scoring 45.9 to 42.5 on the Noometry Index. Qwen3-Next 80B-A3B Instruct costs 1.8× less per token, which makes it the better buy when GLM-5's lead doesn't matter for your workload.
Which is cheaper, GLM-5 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 lists at $1 and $3.20.
Is GLM-5 or Qwen3-Next 80B-A3B Instruct better for coding?
GLM-5 scores higher on coding benchmarks: 49.0 versus 41.9 in the Noometry coding category.
Which has the bigger context window?
GLM-5 does, with 205K tokens against 131K.
How many benchmarks do GLM-5 and Qwen3-Next 80B-A3B Instruct share?
19 benchmarks have published results for both models. GLM-5 has 45 scored results on Noometry and Qwen3-Next 80B-A3B Instruct has 25.