Model comparison
GLM-4.5V vs Pixtral Large
GLM-4.5V is the stronger model overall, scoring 39.8 to 32.2 on the Noometry Index.
Last verified . 1 shared benchmarks.
Mistral AISummary
- They share 1 benchmark with published results for both. GLM-4.5V scores higher in 3 categories and Pixtral Large in 0 categories; 3 gaps are clear of the uncertainty.
- The widest gap is in writing & preference, where GLM-4.5V leads 52.5 to 32.9.
- GLM-4.5V is cheaper at $0.60 / $1.80 per million input/output tokens, against $2 / $6 for Pixtral Large.
- Pixtral Large accepts more context: 128K tokens versus 64K.
Side by side
| GLM-4.5V | Pixtral Large | |
|---|---|---|
| Provider | Z.ai (Zhipu) | Mistral AI |
| Noometry Index | 39.8 | 32.2 |
| Released | 2025-08-11 | 2024-11-01 |
| Weights | Open | Open |
| Context window | 64K | 128K |
| Max output | 16K | 128K |
| Input $ / M tokens | $0.60 | $2 |
| Output $ / M tokens | $1.80 | $6 |
| Results tracked | 15 | 3 |
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Category by category
Coding Not comparable
GLM-4.5V: 39.5 (#155), Pixtral Large: —
| Benchmark | GLM-4.5V | Pixtral Large |
|---|---|---|
| LMArena Coding | 1347 | — |
Reasoning GLM-4.5V leads
GLM-4.5V: 27.4 (#119), Pixtral Large: 21.7 (#218)
| Benchmark | GLM-4.5V | Pixtral Large |
|---|---|---|
| Kagi LLM Benchmark | 59.8% | — |
| EnigmaEval | — | 0.8% |
| LMArena Hard Prompts | 1334 | — |
Math Not comparable
GLM-4.5V: 37.4 (#159), Pixtral Large: —
| Benchmark | GLM-4.5V | Pixtral Large |
|---|---|---|
| LMArena Math | 1354 | — |
Knowledge Not comparable
GLM-4.5V: 37.5 (#156), Pixtral Large: —
| Benchmark | GLM-4.5V | Pixtral Large |
|---|---|---|
| LMArena Expert | 1353 | — |
Multimodal GLM-4.5V leads
GLM-4.5V: 34.3 (#92), Pixtral Large: 30.6 (#111)
| Benchmark | GLM-4.5V | Pixtral Large |
|---|---|---|
| LMArena Vision | 1154 | 1089 |
Multilingual Not comparable
GLM-4.5V: 44.6 (#177), Pixtral Large: —
| Benchmark | GLM-4.5V | Pixtral Large |
|---|---|---|
| LMArena Non-English | 1303 | — |
| LMArena Chinese | 1337 | — |
| LMArena Russian | 1298 | — |
| LMArena Spanish | 1336 | — |
Instruction Following Not comparable
GLM-4.5V: 69.2 (#175), Pixtral Large: —
| Benchmark | GLM-4.5V | Pixtral Large |
|---|---|---|
| LMArena Instruction Following | 1311 | — |
Long Context Not comparable
GLM-4.5V: 39.6 (#171), Pixtral Large: —
| Benchmark | GLM-4.5V | Pixtral Large |
|---|---|---|
| LMArena Longer Query | 1304 | — |
Writing & Preference GLM-4.5V leads
GLM-4.5V: 52.5 (#170), Pixtral Large: 32.9 (#278)
| Benchmark | GLM-4.5V | Pixtral Large |
|---|---|---|
| LMArena Text | 1333 | — |
| LMArena Creative Writing | 1295 | — |
| EQ-Bench Creative Writing | — | 988 |
| LMArena Multi-Turn | 1332 | — |
Frequently asked questions
Is GLM-4.5V better than Pixtral Large?
GLM-4.5V is the stronger model overall, scoring 39.8 to 32.2 on the Noometry Index.
Which is cheaper, GLM-4.5V or Pixtral Large?
GLM-4.5V is cheaper. It lists at $0.60 per million input tokens and $1.80 per million output tokens; Pixtral Large lists at $2 and $6.
Which has the bigger context window?
Pixtral Large does, with 128K tokens against 64K.
How many benchmarks do GLM-4.5V and Pixtral Large share?
1 benchmark has published results for both models. GLM-4.5V has 15 scored results on Noometry and Pixtral Large has 3.