Model comparison
GLM-4.5 vs Pixtral Large
GLM-4.5 is the stronger model overall, scoring 41.4 to 31.7 on the Noometry Index.
Last verified . 1 shared benchmarks.
Mistral AISummary
- They share 1 benchmark with published results for both. GLM-4.5 scores higher in 2 categories and Pixtral Large in 0 categories; 2 gaps are clear of the uncertainty.
- The widest gap is in writing & preference, where GLM-4.5 leads 57.7 to 32.9.
- GLM-4.5 is cheaper at $0.60 / $2.20 per million input/output tokens, against $2 / $6 for Pixtral Large.
- GLM-4.5 accepts more context: 131K tokens versus 128K.
Side by side
| GLM-4.5 | Pixtral Large | |
|---|---|---|
| Provider | Z.ai (Zhipu) | Mistral AI |
| Noometry Index | 41.4 | 31.7 |
| Released | 2025-07-27 | 2024-11-01 |
| Weights | Open | Open |
| Context window | 131K | 128K |
| Max output | 98K | 128K |
| Input $ / M tokens | $0.60 | $2 |
| Output $ / M tokens | $2.20 | $6 |
| Results tracked | 27 | 3 |
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Category by category
Coding Not comparable
GLM-4.5: 41.1 (#121), Pixtral Large: —
| Benchmark | GLM-4.5 | Pixtral Large |
|---|---|---|
| SWE-bench Verified (bash only) | 54.2% | — |
| WeirdML | 40.6% | — |
| LMArena Coding | 1434 | — |
| ALE-Bench | 344.82 | — |
| AlgoTune | 1.52 | — |
Reasoning GLM-4.5 leads
GLM-4.5: 27.7 (#99), Pixtral Large: 20.9 (#208)
| Benchmark | GLM-4.5 | Pixtral Large |
|---|---|---|
| Kagi LLM Benchmark | 57.9% | — |
| EnigmaEval | — | 0.8% |
| LMArena Hard Prompts | 1430 | — |
Math Not comparable
GLM-4.5: 36.3 (#119), Pixtral Large: —
| Benchmark | GLM-4.5 | Pixtral Large |
|---|---|---|
| LMArena Math | 1427 | — |
Knowledge Not comparable
GLM-4.5: 35.5 (#172), Pixtral Large: —
| Benchmark | GLM-4.5 | Pixtral Large |
|---|---|---|
| Humanity's Last Exam | 8.3% | — |
| Confabulations | 11.3% | — |
| LMArena Expert | 1433 | — |
Multimodal Not comparable
GLM-4.5: —, Pixtral Large: 30.0 (#105)
| Benchmark | GLM-4.5 | Pixtral Large |
|---|---|---|
| LMArena Vision | — | 1089 |
Multilingual Not comparable
GLM-4.5: 53.0 (#74), Pixtral Large: —
| Benchmark | GLM-4.5 | Pixtral Large |
|---|---|---|
| LMArena Non-English | 1417 | — |
| LMArena Chinese | 1465 | — |
| LMArena French | 1423 | — |
| LMArena German | 1406 | — |
| LMArena Japanese | 1416 | — |
| LMArena Korean | 1382 | — |
| LMArena Russian | 1414 | — |
| LMArena Spanish | 1454 | — |
Instruction Following Not comparable
GLM-4.5: 74.3 (#100), Pixtral Large: —
| Benchmark | GLM-4.5 | Pixtral Large |
|---|---|---|
| LMArena Instruction Following | 1405 | — |
Long Context Not comparable
GLM-4.5: 38.2 (#196), Pixtral Large: —
| Benchmark | GLM-4.5 | Pixtral Large |
|---|---|---|
| Fiction.LiveBench | 58.3% | — |
| LMArena Longer Query | 1413 | — |
Writing & Preference GLM-4.5 leads
GLM-4.5: 57.7 (#121), Pixtral Large: 32.9 (#273)
| Benchmark | GLM-4.5 | Pixtral Large |
|---|---|---|
| EQ-Bench Creative Writing | 1343 | 988 |
| LMArena Text | 1430 | — |
| LMArena Creative Writing | 1395 | — |
| Short-Story Creative Writing | 73.4% | — |
| LMArena Multi-Turn | 1415 | — |
Frequently asked questions
Is GLM-4.5 better than Pixtral Large?
GLM-4.5 is the stronger model overall, scoring 41.4 to 31.7 on the Noometry Index.
Which is cheaper, GLM-4.5 or Pixtral Large?
GLM-4.5 is cheaper. It lists at $0.60 per million input tokens and $2.20 per million output tokens; Pixtral Large lists at $2 and $6.
Which has the bigger context window?
GLM-4.5 does, with 131K tokens against 128K.
How many benchmarks do GLM-4.5 and Pixtral Large share?
1 benchmark has published results for both models. GLM-4.5 has 27 scored results on Noometry and Pixtral Large has 3.