Model comparison
GLM-4.5V vs Qwen3-Next 80B-A3B Instruct
Qwen3-Next 80B-A3B Instruct is the stronger model overall, scoring 43.0 to 39.8 on the Noometry Index.
Last verified . 14 shared benchmarks.
Alibaba (Qwen)Summary
- They share 14 benchmarks with published results for both. GLM-4.5V scores higher in 1 category and Qwen3-Next 80B-A3B Instruct in 7 categories; 8 gaps are clear of the uncertainty.
- The widest gap is in multilingual, where Qwen3-Next 80B-A3B Instruct leads 52.1 to 44.6.
- The biggest single-benchmark swing is Kagi LLM Benchmark: 59.8% for GLM-4.5V and 66.7% for Qwen3-Next 80B-A3B Instruct.
- Both cost about the same: $0.60 input and $1.80 output per million tokens.
- Qwen3-Next 80B-A3B Instruct accepts more context: 131K tokens versus 64K.
Side by side
| GLM-4.5V | Qwen3-Next 80B-A3B Instruct | |
|---|---|---|
| Provider | Z.ai (Zhipu) | Alibaba (Qwen) |
| Noometry Index | 39.8 | 43.0 |
| Released | 2025-08-11 | 2025-09 |
| Weights | Open | Open |
| Context window | 64K | 131K |
| Max output | 16K | 33K |
| Input $ / M tokens | $0.60 | $0.50 |
| Output $ / M tokens | $1.80 | $2 |
| Results tracked | 15 | 25 |
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Category by category
Coding Qwen3-Next 80B-A3B Instruct leads
GLM-4.5V: 39.5 (#155), Qwen3-Next 80B-A3B Instruct: 42.5 (#98)
| Benchmark | GLM-4.5V | Qwen3-Next 80B-A3B Instruct |
|---|---|---|
| LMArena Coding | 1347 | 1440 |
Reasoning Qwen3-Next 80B-A3B Instruct leads
GLM-4.5V: 27.4 (#119), Qwen3-Next 80B-A3B Instruct: 31.1 (#81)
| Benchmark | GLM-4.5V | Qwen3-Next 80B-A3B Instruct |
|---|---|---|
| Kagi LLM Benchmark | 59.8% | 66.7% |
| LMArena Hard Prompts | 1334 | 1428 |
Math Qwen3-Next 80B-A3B Instruct leads
GLM-4.5V: 37.4 (#159), Qwen3-Next 80B-A3B Instruct: 38.8 (#126)
| Benchmark | GLM-4.5V | Qwen3-Next 80B-A3B Instruct |
|---|---|---|
| LMArena Math | 1354 | 1440 |
| Omni-MATH | — | 46.7% |
Knowledge Qwen3-Next 80B-A3B Instruct leads
GLM-4.5V: 37.5 (#156), Qwen3-Next 80B-A3B Instruct: 41.8 (#106)
| Benchmark | GLM-4.5V | Qwen3-Next 80B-A3B Instruct |
|---|---|---|
| LMArena Expert | 1353 | 1417 |
| MMLU-Pro | — | 78.6% |
| Vectara Hallucination Rate | — | 9.3% |
| GPQA (HELM) | — | 63% |
Multimodal Not comparable
GLM-4.5V: 34.3 (#92), Qwen3-Next 80B-A3B Instruct: —
| Benchmark | GLM-4.5V | Qwen3-Next 80B-A3B Instruct |
|---|---|---|
| LMArena Vision | 1154 | — |
Multilingual Qwen3-Next 80B-A3B Instruct leads
GLM-4.5V: 44.6 (#177), Qwen3-Next 80B-A3B Instruct: 52.1 (#93)
| Benchmark | GLM-4.5V | Qwen3-Next 80B-A3B Instruct |
|---|---|---|
| LMArena Non-English | 1303 | 1407 |
| LMArena Chinese | 1337 | 1460 |
| LMArena Russian | 1298 | 1404 |
| LMArena Spanish | 1336 | 1435 |
| LMArena French | — | 1413 |
| LMArena German | — | 1417 |
| LMArena Japanese | — | 1395 |
| LMArena Korean | — | 1364 |
Instruction Following Qwen3-Next 80B-A3B Instruct leads
GLM-4.5V: 69.2 (#175), Qwen3-Next 80B-A3B Instruct: 70.8 (#159)
| Benchmark | GLM-4.5V | Qwen3-Next 80B-A3B Instruct |
|---|---|---|
| LMArena Instruction Following | 1311 | 1389 |
| IFEval | — | 81% |
Long Context GLM-4.5V leads
GLM-4.5V: 39.6 (#171), Qwen3-Next 80B-A3B Instruct: 37.0 (#223)
| Benchmark | GLM-4.5V | Qwen3-Next 80B-A3B Instruct |
|---|---|---|
| LMArena Longer Query | 1304 | 1403 |
| Fiction.LiveBench | — | 55.6% |
Writing & Preference Qwen3-Next 80B-A3B Instruct leads
GLM-4.5V: 52.5 (#170), Qwen3-Next 80B-A3B Instruct: 58.0 (#121)
| Benchmark | GLM-4.5V | Qwen3-Next 80B-A3B Instruct |
|---|---|---|
| LMArena Text | 1333 | 1417 |
| LMArena Creative Writing | 1295 | 1334 |
| LMArena Multi-Turn | 1332 | 1416 |
| WildBench | — | 80.7% |
Frequently asked questions
Is GLM-4.5V better than Qwen3-Next 80B-A3B Instruct?
Qwen3-Next 80B-A3B Instruct is the stronger model overall, scoring 43.0 to 39.8 on the Noometry Index.
Which is cheaper, GLM-4.5V 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-4.5V lists at $0.60 and $1.80.
Is GLM-4.5V or Qwen3-Next 80B-A3B Instruct better for coding?
Qwen3-Next 80B-A3B Instruct scores higher on coding benchmarks: 42.5 versus 39.5 in the Noometry coding category.
Which has the bigger context window?
Qwen3-Next 80B-A3B Instruct does, with 131K tokens against 64K.
How many benchmarks do GLM-4.5V and Qwen3-Next 80B-A3B Instruct share?
14 benchmarks have published results for both models. GLM-4.5V has 15 scored results on Noometry and Qwen3-Next 80B-A3B Instruct has 25.