Model comparison
GLM-4.7 vs Qwen3-Next 80B-A3B Instruct
GLM-4.7 and Qwen3-Next 80B-A3B Instruct score almost the same on the Noometry Index (41.7 vs 42.5), so choose on price, context window or the category you care about most.
Last verified . 18 shared benchmarks.
Alibaba (Qwen)Summary
- They share 18 benchmarks with published results for both. GLM-4.7 scores higher in 7 categories and Qwen3-Next 80B-A3B Instruct in 1 category; 7 gaps are clear of the uncertainty.
- The widest gap is in reasoning, where Qwen3-Next 80B-A3B Instruct leads 30.1 to 23.7.
- Qwen3-Next 80B-A3B Instruct is cheaper at $0.50 / $2 per million input/output tokens, against $0.60 / $2.20 for GLM-4.7.
- GLM-4.7 accepts more context: 205K tokens versus 131K.
Side by side
| GLM-4.7 | Qwen3-Next 80B-A3B Instruct | |
|---|---|---|
| Provider | Z.ai (Zhipu) | Alibaba (Qwen) |
| Noometry Index | 41.7 | 42.5 |
| Released | 2025-12-22 | 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 | 36 | 25 |
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Category by category
Coding GLM-4.7 leads
GLM-4.7: 43.6 (#74), Qwen3-Next 80B-A3B Instruct: 41.9 (#101)
| Benchmark | GLM-4.7 | Qwen3-Next 80B-A3B Instruct |
|---|---|---|
| LMArena Coding | 1454 | 1441 |
| LMArena WebDev | 1435 | — |
| SciCode | 45.1% | — |
| ALE-Bench | 399.48 | — |
Agentic & Tool Use Not comparable
GLM-4.7: 26.4 (#96), Qwen3-Next 80B-A3B Instruct: —
| Benchmark | GLM-4.7 | Qwen3-Next 80B-A3B Instruct |
|---|---|---|
| Terminal-Bench | 33.4% | — |
| Vending-Bench 2 | 2,377 | — |
Reasoning Qwen3-Next 80B-A3B Instruct leads
GLM-4.7: 23.7 (#161), Qwen3-Next 80B-A3B Instruct: 30.1 (#79)
| Benchmark | GLM-4.7 | Qwen3-Next 80B-A3B Instruct |
|---|---|---|
| LMArena Hard Prompts | 1443 | 1428 |
| SimpleBench | 47.7% | — |
| Kagi LLM Benchmark | — | 66.7% |
| CritPt | 1.7% | — |
| Chess Puzzles | 6% | — |
| Epoch Capabilities Index | 143.53 | — |
Math GLM-4.7 leads
GLM-4.7: 37.7 (#92), Qwen3-Next 80B-A3B Instruct: 36.3 (#118)
| Benchmark | GLM-4.7 | Qwen3-Next 80B-A3B Instruct |
|---|---|---|
| LMArena Math | 1423 | 1440 |
| OTIS Mock AIME 2024-2025 | 83.3% | — |
| ProofBench | 6% | — |
| Omni-MATH | — | 46.7% |
| FrontierMath (Feb 2025 set) | 2.4% | — |
| FrontierMath Tier 4 (v1) | 0% | — |
Knowledge GLM-4.7 leads
GLM-4.7: 47.0 (#74), Qwen3-Next 80B-A3B Instruct: 41.3 (#102)
| Benchmark | GLM-4.7 | Qwen3-Next 80B-A3B Instruct |
|---|---|---|
| Vectara Hallucination Rate | 11.7% | 9.3% |
| LMArena Expert | 1424 | 1416 |
| GPQA Diamond | 83.3% | — |
| SimpleQA Verified | 32.2% | — |
| MMLU-Pro | — | 78.6% |
| GPQA (HELM) | — | 63% |
Multilingual Too close to call
GLM-4.7: 53.0 (#75), Qwen3-Next 80B-A3B Instruct: 52.3 (#88)
| Benchmark | GLM-4.7 | Qwen3-Next 80B-A3B Instruct |
|---|---|---|
| LMArena Non-English | 1417 | 1407 |
| LMArena Chinese | 1495 | 1461 |
| LMArena French | 1432 | 1415 |
| LMArena German | 1426 | 1416 |
| LMArena Japanese | 1439 | 1398 |
| LMArena Korean | 1401 | 1366 |
| LMArena Russian | 1423 | 1403 |
| LMArena Spanish | 1433 | 1436 |
Instruction Following GLM-4.7 leads
GLM-4.7: 74.6 (#90), Qwen3-Next 80B-A3B Instruct: 71.0 (#154)
| Benchmark | GLM-4.7 | Qwen3-Next 80B-A3B Instruct |
|---|---|---|
| LMArena Instruction Following | 1411 | 1388 |
| IFEval | — | 81% |
Long Context GLM-4.7 leads
GLM-4.7: 42.8 (#111), Qwen3-Next 80B-A3B Instruct: 37.0 (#218)
| Benchmark | GLM-4.7 | Qwen3-Next 80B-A3B Instruct |
|---|---|---|
| LMArena Longer Query | 1432 | 1403 |
| Fiction.LiveBench | — | 55.6% |
| CL-bench | 15.9% | — |
| CL-bench Life | 10.9% | — |
Writing & Preference GLM-4.7 leads
GLM-4.7: 61.1 (#89), Qwen3-Next 80B-A3B Instruct: 58.2 (#116)
| Benchmark | GLM-4.7 | Qwen3-Next 80B-A3B Instruct |
|---|---|---|
| LMArena Text | 1436 | 1417 |
| LMArena Creative Writing | 1401 | 1333 |
| LMArena Multi-Turn | 1446 | 1415 |
| EQ-Bench Creative Writing | 1413 | — |
| WildBench | — | 80.7% |
Frequently asked questions
Is GLM-4.7 better than Qwen3-Next 80B-A3B Instruct?
GLM-4.7 and Qwen3-Next 80B-A3B Instruct score almost the same on the Noometry Index (41.7 vs 42.5), so choose on price, context window or the category you care about most.
Which is cheaper, GLM-4.7 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-4.7 lists at $0.60 and $2.20.
Is GLM-4.7 or Qwen3-Next 80B-A3B Instruct better for coding?
GLM-4.7 scores higher on coding benchmarks: 43.6 versus 41.9 in the Noometry coding category.
Which has the bigger context window?
GLM-4.7 does, with 205K tokens against 131K.
How many benchmarks do GLM-4.7 and Qwen3-Next 80B-A3B Instruct share?
18 benchmarks have published results for both models. GLM-4.7 has 36 scored results on Noometry and Qwen3-Next 80B-A3B Instruct has 25.