Model comparison
GLM-4.5-Air vs Pixtral Large
GLM-4.5-Air is the stronger model overall, scoring 38.7 to 31.7 on the Noometry Index.
Last verified . 0 shared benchmarks.
Mistral AISummary
- The widest gap is in writing & preference, where GLM-4.5-Air leads 56.1 to 32.9.
- GLM-4.5-Air is cheaper at $0.20 / $1.10 per million input/output tokens, against $2 / $6 for Pixtral Large.
- GLM-4.5-Air accepts more context: 131K tokens versus 128K.
Side by side
| GLM-4.5-Air | Pixtral Large | |
|---|---|---|
| Provider | Z.ai (Zhipu) | Mistral AI |
| Noometry Index | 38.7 | 31.7 |
| Released | 2025-07-20 | 2024-11-01 |
| Weights | Open | Open |
| Context window | 131K | 128K |
| Max output | 98K | 128K |
| Input $ / M tokens | $0.20 | $2 |
| Output $ / M tokens | $1.10 | $6 |
| Results tracked | 27 | 3 |
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Category by category
Coding Not comparable
GLM-4.5-Air: 34.5 (#225), Pixtral Large: —
| Benchmark | GLM-4.5-Air | Pixtral Large |
|---|---|---|
| GSO | 2.9% | — |
| LMArena Coding | 1397 | — |
Reasoning GLM-4.5-Air leads
GLM-4.5-Air: 23.3 (#166), Pixtral Large: 20.9 (#208)
| Benchmark | GLM-4.5-Air | Pixtral Large |
|---|---|---|
| Kagi LLM Benchmark | 43% | — |
| EnigmaEval | — | 0.8% |
| LMArena Hard Prompts | 1379 | — |
| ForecastBench | 59.2 | — |
Math Not comparable
GLM-4.5-Air: 33.8 (#166), Pixtral Large: —
| Benchmark | GLM-4.5-Air | Pixtral Large |
|---|---|---|
| Omni-MATH | 39.1% | — |
| LMArena Math | 1396 | — |
Knowledge Not comparable
GLM-4.5-Air: 34.6 (#184), Pixtral Large: —
| Benchmark | GLM-4.5-Air | Pixtral Large |
|---|---|---|
| Humanity's Last Exam | 8.1% | — |
| MMLU-Pro | 76.2% | — |
| Vectara Hallucination Rate | 9.3% | — |
| GPQA (HELM) | 59.4% | — |
| LMArena Expert | 1370 | — |
Multimodal Not comparable
GLM-4.5-Air: —, Pixtral Large: 30.0 (#105)
| Benchmark | GLM-4.5-Air | Pixtral Large |
|---|---|---|
| LMArena Vision | — | 1089 |
Multilingual Not comparable
GLM-4.5-Air: 49.4 (#130), Pixtral Large: —
| Benchmark | GLM-4.5-Air | Pixtral Large |
|---|---|---|
| LMArena Non-English | 1366 | — |
| LMArena Chinese | 1427 | — |
| LMArena French | 1399 | — |
| LMArena German | 1376 | — |
| LMArena Japanese | 1348 | — |
| LMArena Korean | 1306 | — |
| LMArena Russian | 1373 | — |
| LMArena Spanish | 1385 | — |
Instruction Following Not comparable
GLM-4.5-Air: 69.8 (#165), Pixtral Large: —
| Benchmark | GLM-4.5-Air | Pixtral Large |
|---|---|---|
| IFEval | 81.2% | — |
| LMArena Instruction Following | 1353 | — |
Long Context Not comparable
GLM-4.5-Air: 41.6 (#130), Pixtral Large: —
| Benchmark | GLM-4.5-Air | Pixtral Large |
|---|---|---|
| LMArena Longer Query | 1367 | — |
Writing & Preference GLM-4.5-Air leads
GLM-4.5-Air: 56.1 (#134), Pixtral Large: 32.9 (#273)
| Benchmark | GLM-4.5-Air | Pixtral Large |
|---|---|---|
| LMArena Text | 1384 | — |
| LMArena Creative Writing | 1343 | — |
| EQ-Bench Creative Writing | — | 988 |
| WildBench | 78.9% | — |
| LMArena Multi-Turn | 1371 | — |
Frequently asked questions
Is GLM-4.5-Air better than Pixtral Large?
GLM-4.5-Air is the stronger model overall, scoring 38.7 to 31.7 on the Noometry Index.
Which is cheaper, GLM-4.5-Air or Pixtral Large?
GLM-4.5-Air is cheaper. It lists at $0.20 per million input tokens and $1.10 per million output tokens; Pixtral Large lists at $2 and $6.
Which has the bigger context window?
GLM-4.5-Air does, with 131K tokens against 128K.
How many benchmarks do GLM-4.5-Air and Pixtral Large share?
0 benchmarks have published results for both models. GLM-4.5-Air has 27 scored results on Noometry and Pixtral Large has 3.