Model comparison
GLM-5.3 vs Qwen2.5 7B Instruct
GLM-5.3 is the stronger model overall, scoring 54.8 to 29.0 on the Noometry Index. Qwen2.5 7B Instruct costs 7.0× less per token, which makes it the better buy when GLM-5.3's lead doesn't matter for your workload.
Last verified . 6 shared benchmarks.
Alibaba (Qwen)Summary
- They share 6 benchmarks with published results for both. GLM-5.3 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 math, where GLM-5.3 leads 62.3 to 12.6.
- The biggest single-benchmark swing is OTIS Mock AIME 2024-2025: 91.1% for GLM-5.3 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 $1.40 / $4.40 for GLM-5.3.
- GLM-5.3 accepts more context: 1M tokens versus 131K.
Side by side
| GLM-5.3 | Qwen2.5 7B Instruct | |
|---|---|---|
| Provider | Z.ai (Zhipu) | Alibaba (Qwen) |
| Noometry Index | 54.8 | 29.0 |
| Released | 2026-08-14 | 2024-09 |
| Weights | Open | Open |
| Context window | 1M | 131K |
| Max output | 131K | 8K |
| Input $ / M tokens | $1.40 | $0.17 |
| Output $ / M tokens | $4.40 | $0.70 |
| Results tracked | 42 | 15 |
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Category by category
Coding GLM-5.3 leads
GLM-5.3: 59.5 (#14), Qwen2.5 7B Instruct: 36.5 (#208)
| Benchmark | GLM-5.3 | Qwen2.5 7B Instruct |
|---|---|---|
| DeepSWE | 69% | — |
| FrontierCode | 40.1% | — |
| CursorBench | 42.6% | — |
| LMArena WebDev | 1622 | — |
| FrontierSWE | 30.2% | — |
| SciCode | 59% | — |
| WeirdML | 75.4% | — |
| BigCodeBench Instruct | — | 37.6% |
| LMArena Coding | 1496 | — |
| BigCodeBench Complete | — | 46.1% |
| ALE-Bench | 1,317 | — |
Agentic & Tool Use GLM-5.3 leads
GLM-5.3: 36.4 (#38), Qwen2.5 7B Instruct: 23.8 (#124)
| Benchmark | GLM-5.3 | Qwen2.5 7B Instruct |
|---|---|---|
| APEX-Agents | 56.6% | — |
| BALROG | — | 7.8% |
| Vending-Bench 2 | 8,164 | — |
Reasoning GLM-5.3 leads
GLM-5.3: 46.1 (#46), Qwen2.5 7B Instruct: 14.8 (#322)
| Benchmark | GLM-5.3 | Qwen2.5 7B Instruct |
|---|---|---|
| Chess Puzzles | 21% | 0% |
| DTBench | 87.7% | 47.7% |
| LMCA | 55.5% | 6.4% |
| Epoch Capabilities Index | 155.61 | 118.51 |
| NYT Connections (extended) | 74.2% | — |
| CritPt | 19.1% | — |
| LMArena Hard Prompts | 1489 | — |
| Mystery Game Puzzles | 33% | — |
| Bench to the Future 3 | 0.15 | — |
Math GLM-5.3 leads
GLM-5.3: 62.3 (#33), Qwen2.5 7B Instruct: 12.6 (#306)
| Benchmark | GLM-5.3 | Qwen2.5 7B Instruct |
|---|---|---|
| OTIS Mock AIME 2024-2025 | 91.1% | 2.5% |
| FrontierMath (Tiers 1-3) | 68.8% | — |
| FrontierMath Tier 4 | 29.3% | — |
| ProofBench | 49% | — |
| Omni-MATH | — | 29.4% |
| LMArena Math | 1489 | — |
Knowledge GLM-5.3 leads
GLM-5.3: 58.3 (#37), Qwen2.5 7B Instruct: 17.0 (#286)
| Benchmark | GLM-5.3 | Qwen2.5 7B Instruct |
|---|---|---|
| GPQA Diamond | 90.9% | 35.5% |
| SimpleQA Verified | 41% | — |
| MMLU-Pro | — | 53.9% |
| GPQA (HELM) | — | 34.1% |
| LMArena Expert | 1516 | — |
| MMLU | — | 72.9% |
Multilingual Not comparable
GLM-5.3: 55.7 (#28), Qwen2.5 7B Instruct: —
| Benchmark | GLM-5.3 | Qwen2.5 7B Instruct |
|---|---|---|
| LMArena Non-English | 1457 | — |
| LMArena Chinese | 1528 | — |
| LMArena French | 1499 | — |
| LMArena German | 1499 | — |
| LMArena Japanese | 1453 | — |
| LMArena Korean | 1472 | — |
| LMArena Russian | 1463 | — |
| LMArena Spanish | 1460 | — |
Instruction Following GLM-5.3 leads
GLM-5.3: 77.5 (#23), Qwen2.5 7B Instruct: 63.2 (#231)
| Benchmark | GLM-5.3 | Qwen2.5 7B Instruct |
|---|---|---|
| IFEval | — | 74.1% |
| LMArena Instruction Following | 1477 | — |
Long Context Not comparable
GLM-5.3: 45.4 (#41), Qwen2.5 7B Instruct: —
| Benchmark | GLM-5.3 | Qwen2.5 7B Instruct |
|---|---|---|
| LMArena Longer Query | 1482 | — |
Writing & Preference GLM-5.3 leads
GLM-5.3: 75.7 (#6), Qwen2.5 7B Instruct: 48.8 (#195)
| Benchmark | GLM-5.3 | Qwen2.5 7B Instruct |
|---|---|---|
| LMArena Text | 1471 | — |
| LMArena Creative Writing | 1457 | — |
| EQ-Bench Creative Writing | 2075 | — |
| WildBench | — | 73.1% |
| LMArena Multi-Turn | 1472 | — |
Frequently asked questions
Is GLM-5.3 better than Qwen2.5 7B Instruct?
GLM-5.3 is the stronger model overall, scoring 54.8 to 29.0 on the Noometry Index. Qwen2.5 7B Instruct costs 7.0× less per token, which makes it the better buy when GLM-5.3's lead doesn't matter for your workload.
Which is cheaper, GLM-5.3 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-5.3 lists at $1.40 and $4.40.
Is GLM-5.3 or Qwen2.5 7B Instruct better for coding?
GLM-5.3 scores higher on coding benchmarks: 59.5 versus 36.5 in the Noometry coding category.
Which has the bigger context window?
GLM-5.3 does, with 1M tokens against 131K.
How many benchmarks do GLM-5.3 and Qwen2.5 7B Instruct share?
6 benchmarks have published results for both models. GLM-5.3 has 42 scored results on Noometry and Qwen2.5 7B Instruct has 15.