Model comparison
GLM-5.2 vs Pixtral Large
GLM-5.2 is the stronger model overall, scoring 51.1 to 32.2 on the Noometry Index.
Last verified . 1 shared benchmarks.
Mistral AISummary
- They share 1 benchmark with published results for both. GLM-5.2 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-5.2 leads 70.4 to 32.9.
- GLM-5.2 is cheaper at $1.40 / $4.40 per million input/output tokens, against $2 / $6 for Pixtral Large.
- GLM-5.2 accepts more context: 1M tokens versus 128K.
Side by side
| GLM-5.2 | Pixtral Large | |
|---|---|---|
| Provider | Z.ai (Zhipu) | Mistral AI |
| Noometry Index | 51.1 | 32.2 |
| Released | 2026-06-13 | 2024-11-01 |
| Weights | Open | Open |
| Context window | 1M | 128K |
| Max output | 131K | 128K |
| Input $ / M tokens | $1.40 | $2 |
| Output $ / M tokens | $4.40 | $6 |
| Results tracked | 51 | 3 |
Sponsored placements are available on pages like this one. Advertise on Noometry
Category by category
Coding Not comparable
GLM-5.2: 51.3 (#41), Pixtral Large: —
| Benchmark | GLM-5.2 | Pixtral Large |
|---|---|---|
| SWE-bench Verified | 78.7% | — |
| DeepSWE | 43.8% | — |
| FrontierCode | 24.5% | — |
| LMArena WebDev | 1603 | — |
| SciCode | 50.5% | — |
| WeirdML | 70.1% | — |
| LMArena Coding | 1485 | — |
| ALE-Bench | 1,047 | — |
Agentic & Tool Use Not comparable
GLM-5.2: 32.4 (#63), Pixtral Large: —
| Benchmark | GLM-5.2 | Pixtral Large |
|---|---|---|
| APEX-Agents | 45.2% | — |
| τ²-bench Banking | 37.1% | — |
| PostTrainBench | 31.7% | — |
| GBAEval | 0% | — |
| Vending-Bench 2 | 8,314 | — |
Reasoning GLM-5.2 leads
GLM-5.2: 42.3 (#52), Pixtral Large: 21.7 (#218)
| Benchmark | GLM-5.2 | Pixtral Large |
|---|---|---|
| ARC-AGI-2 | 22.8% | — |
| SimpleBench | 58.8% | — |
| Kagi LLM Benchmark | 62.6% | — |
| NYT Connections (extended) | 74.3% | — |
| ARC-AGI-1 | 77% | — |
| CritPt | 20.9% | — |
| Chess Puzzles | 21% | — |
| EnigmaEval | — | 0.8% |
| EBR-Bench | 9.5% | — |
| LMArena Hard Prompts | 1480 | — |
| Mystery Game Puzzles | 19% | — |
| DTBench | 93.6% | — |
| LMCA | 45.8% | — |
| Surface Evolver Bench | 55.6% | — |
| Epoch Capabilities Index | 151.78 | — |
Math Not comparable
GLM-5.2: 55.7 (#43), Pixtral Large: —
| Benchmark | GLM-5.2 | Pixtral Large |
|---|---|---|
| FrontierMath (Tiers 1-3) | 59.2% | — |
| FrontierMath Tier 4 | 29.3% | — |
| MathArena Final-Answer Competitions | 67.6% | — |
| OTIS Mock AIME 2024-2025 | 86.4% | — |
| ProofBench | 35% | — |
| LMArena Math | 1482 | — |
Knowledge Not comparable
GLM-5.2: 57.1 (#40), Pixtral Large: —
| Benchmark | GLM-5.2 | Pixtral Large |
|---|---|---|
| GPQA Diamond | 91.9% | — |
| SimpleQA Verified | 34.2% | — |
| LMArena Expert | 1486 | — |
Multimodal Not comparable
GLM-5.2: —, Pixtral Large: 30.6 (#111)
| Benchmark | GLM-5.2 | Pixtral Large |
|---|---|---|
| LMArena Vision | — | 1089 |
Multilingual Not comparable
GLM-5.2: 55.8 (#26), Pixtral Large: —
| Benchmark | GLM-5.2 | Pixtral Large |
|---|---|---|
| LMArena Non-English | 1459 | — |
| LMArena Chinese | 1519 | — |
| LMArena French | 1479 | — |
| LMArena German | 1468 | — |
| LMArena Japanese | 1451 | — |
| LMArena Korean | 1445 | — |
| LMArena Russian | 1466 | — |
| LMArena Spanish | 1477 | — |
Instruction Following Not comparable
GLM-5.2: 76.9 (#34), Pixtral Large: —
| Benchmark | GLM-5.2 | Pixtral Large |
|---|---|---|
| LMArena Instruction Following | 1465 | — |
Long Context Not comparable
GLM-5.2: 45.3 (#43), Pixtral Large: —
| Benchmark | GLM-5.2 | Pixtral Large |
|---|---|---|
| LMArena Longer Query | 1479 | — |
Writing & Preference GLM-5.2 leads
GLM-5.2: 70.4 (#21), Pixtral Large: 32.9 (#278)
| Benchmark | GLM-5.2 | Pixtral Large |
|---|---|---|
| EQ-Bench Creative Writing | 1757 | 988 |
| LMArena Text | 1470 | — |
| LMArena Creative Writing | 1462 | — |
| EQ-Bench 4 | 1222 | — |
| LMArena Multi-Turn | 1469 | — |
Frequently asked questions
Is GLM-5.2 better than Pixtral Large?
GLM-5.2 is the stronger model overall, scoring 51.1 to 32.2 on the Noometry Index.
Which is cheaper, GLM-5.2 or Pixtral Large?
GLM-5.2 is cheaper. It lists at $1.40 per million input tokens and $4.40 per million output tokens; Pixtral Large lists at $2 and $6.
Which has the bigger context window?
GLM-5.2 does, with 1M tokens against 128K.
How many benchmarks do GLM-5.2 and Pixtral Large share?
1 benchmark has published results for both models. GLM-5.2 has 51 scored results on Noometry and Pixtral Large has 3.