Model comparison
GLM-4.7 vs Qwen2.5 7B Instruct
GLM-4.7 is the stronger model overall, scoring 42.0 to 29.0 on the Noometry Index. Qwen2.5 7B Instruct costs 3.3× less per token, which makes it the better buy when GLM-4.7's lead doesn't matter for your workload.
Last verified . 4 shared benchmarks.
Alibaba (Qwen)Summary
- They share 4 benchmarks with published results for both. GLM-4.7 scores higher in 7 categories and Qwen2.5 7B Instruct in 0 categories; 7 gaps are clear of the uncertainty.
- The widest gap is in knowledge, where GLM-4.7 leads 47.0 to 17.0.
- The biggest single-benchmark swing is OTIS Mock AIME 2024-2025: 83.3% for GLM-4.7 and 2.5% for Qwen2.5 7B Instruct.
- Qwen2.5 7B Instruct is cheaper at $0.17 / $0.70 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 | Qwen2.5 7B Instruct | |
|---|---|---|
| Provider | Z.ai (Zhipu) | Alibaba (Qwen) |
| Noometry Index | 42.0 | 29.0 |
| Released | 2025-12-22 | 2024-09 |
| Weights | Open | Open |
| Context window | 205K | 131K |
| Max output | 131K | 8K |
| Input $ / M tokens | $0.60 | $0.17 |
| Output $ / M tokens | $2.20 | $0.70 |
| Results tracked | 36 | 15 |
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Category by category
Coding GLM-4.7 leads
GLM-4.7: 44.0 (#79), Qwen2.5 7B Instruct: 36.5 (#208)
| Benchmark | GLM-4.7 | Qwen2.5 7B Instruct |
|---|---|---|
| LMArena WebDev | 1435 | — |
| SciCode | 45.1% | — |
| BigCodeBench Instruct | — | 37.6% |
| LMArena Coding | 1454 | — |
| BigCodeBench Complete | — | 46.1% |
| ALE-Bench | 399.48 | — |
Agentic & Tool Use GLM-4.7 leads
GLM-4.7: 26.5 (#103), Qwen2.5 7B Instruct: 23.8 (#124)
| Benchmark | GLM-4.7 | Qwen2.5 7B Instruct |
|---|---|---|
| Terminal-Bench | 33.4% | — |
| BALROG | — | 7.8% |
| Vending-Bench 2 | 2,377 | — |
Reasoning GLM-4.7 leads
GLM-4.7: 24.3 (#164), Qwen2.5 7B Instruct: 14.8 (#322)
| Benchmark | GLM-4.7 | Qwen2.5 7B Instruct |
|---|---|---|
| Chess Puzzles | 6% | 0% |
| Epoch Capabilities Index | 143.51 | 118.51 |
| SimpleBench | 47.7% | — |
| CritPt | 1.7% | — |
| LMArena Hard Prompts | 1443 | — |
| DTBench | — | 47.7% |
| LMCA | — | 6.4% |
Math GLM-4.7 leads
GLM-4.7: 38.6 (#135), Qwen2.5 7B Instruct: 12.6 (#306)
| Benchmark | GLM-4.7 | Qwen2.5 7B Instruct |
|---|---|---|
| OTIS Mock AIME 2024-2025 | 83.3% | 2.5% |
| ProofBench | 6% | — |
| Omni-MATH | — | 29.4% |
| LMArena Math | 1423 | — |
| FrontierMath (Feb 2025 set) | 2.4% | — |
| FrontierMath Tier 4 (v1) | 0% | — |
Knowledge GLM-4.7 leads
GLM-4.7: 47.0 (#80), Qwen2.5 7B Instruct: 17.0 (#286)
| Benchmark | GLM-4.7 | Qwen2.5 7B Instruct |
|---|---|---|
| GPQA Diamond | 83.3% | 35.5% |
| SimpleQA Verified | 32.2% | — |
| MMLU-Pro | — | 53.9% |
| Vectara Hallucination Rate | 11.7% | — |
| GPQA (HELM) | — | 34.1% |
| LMArena Expert | 1424 | — |
| MMLU | — | 72.9% |
Multilingual Not comparable
GLM-4.7: 52.8 (#79), Qwen2.5 7B Instruct: —
| Benchmark | GLM-4.7 | Qwen2.5 7B Instruct |
|---|---|---|
| LMArena Non-English | 1417 | — |
| LMArena Chinese | 1495 | — |
| LMArena French | 1432 | — |
| LMArena German | 1424 | — |
| LMArena Japanese | 1439 | — |
| LMArena Korean | 1399 | — |
| LMArena Russian | 1423 | — |
| LMArena Spanish | 1434 | — |
Instruction Following GLM-4.7 leads
GLM-4.7: 74.4 (#95), Qwen2.5 7B Instruct: 63.2 (#231)
| Benchmark | GLM-4.7 | Qwen2.5 7B Instruct |
|---|---|---|
| IFEval | — | 74.1% |
| LMArena Instruction Following | 1411 | — |
Long Context Not comparable
GLM-4.7: 42.8 (#116), Qwen2.5 7B Instruct: —
| Benchmark | GLM-4.7 | Qwen2.5 7B Instruct |
|---|---|---|
| CL-bench | 15.9% | — |
| CL-bench Life | 10.9% | — |
| LMArena Longer Query | 1432 | — |
Writing & Preference GLM-4.7 leads
GLM-4.7: 60.9 (#93), Qwen2.5 7B Instruct: 48.8 (#195)
| Benchmark | GLM-4.7 | Qwen2.5 7B Instruct |
|---|---|---|
| LMArena Text | 1435 | — |
| LMArena Creative Writing | 1401 | — |
| EQ-Bench Creative Writing | 1413 | — |
| WildBench | — | 73.1% |
| LMArena Multi-Turn | 1446 | — |
Frequently asked questions
Is GLM-4.7 better than Qwen2.5 7B Instruct?
GLM-4.7 is the stronger model overall, scoring 42.0 to 29.0 on the Noometry Index. Qwen2.5 7B Instruct costs 3.3× less per token, which makes it the better buy when GLM-4.7's lead doesn't matter for your workload.
Which is cheaper, GLM-4.7 or Qwen2.5 7B Instruct?
Qwen2.5 7B Instruct is cheaper. It lists at $0.17 per million input tokens and $0.70 per million output tokens; GLM-4.7 lists at $0.60 and $2.20.
Is GLM-4.7 or Qwen2.5 7B Instruct better for coding?
GLM-4.7 scores higher on coding benchmarks: 44.0 versus 36.5 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 Qwen2.5 7B Instruct share?
4 benchmarks have published results for both models. GLM-4.7 has 36 scored results on Noometry and Qwen2.5 7B Instruct has 15.