Model comparison
GLM-4.6 vs Pixtral Large
GLM-4.6 is the stronger model overall, scoring 41.4 to 32.2 on the Noometry Index.
Last verified . 1 shared benchmarks.
Mistral AISummary
- They share 1 benchmark with published results for both. GLM-4.6 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-4.6 leads 61.1 to 32.9.
- GLM-4.6 is cheaper at $0.60 / $2.20 per million input/output tokens, against $2 / $6 for Pixtral Large.
- GLM-4.6 accepts more context: 205K tokens versus 128K.
Side by side
| GLM-4.6 | Pixtral Large | |
|---|---|---|
| Provider | Z.ai (Zhipu) | Mistral AI |
| Noometry Index | 41.4 | 32.2 |
| Released | 2025-09-30 | 2024-11-01 |
| Weights | Open | Open |
| Context window | 205K | 128K |
| Max output | 131K | 128K |
| Input $ / M tokens | $0.60 | $2 |
| Output $ / M tokens | $2.20 | $6 |
| Results tracked | 29 | 3 |
Sponsored placements are available on pages like this one. Advertise on Noometry
Category by category
Coding Not comparable
GLM-4.6: 40.1 (#148), Pixtral Large: —
| Benchmark | GLM-4.6 | Pixtral Large |
|---|---|---|
| SWE-bench Verified (bash only) | 55.4% | — |
| LMArena WebDev | 1340 | — |
| SciCode | 38.4% | — |
| LMArena Coding | 1449 | — |
| ALE-Bench | 340.82 | — |
Agentic & Tool Use Not comparable
GLM-4.6: 32.3 (#66), Pixtral Large: —
| Benchmark | GLM-4.6 | Pixtral Large |
|---|---|---|
| Terminal-Bench | 24.5% | — |
| Berkeley Function Calling Leaderboard | 72.4% | — |
Reasoning GLM-4.6 leads
GLM-4.6: 23.7 (#172), Pixtral Large: 21.7 (#218)
| Benchmark | GLM-4.6 | Pixtral Large |
|---|---|---|
| Kagi LLM Benchmark | 47.4% | — |
| CritPt | 1.1% | — |
| EnigmaEval | — | 0.8% |
| LMArena Hard Prompts | 1440 | — |
Math Not comparable
GLM-4.6: 39.1 (#111), Pixtral Large: —
| Benchmark | GLM-4.6 | Pixtral Large |
|---|---|---|
| LMArena Math | 1432 | — |
| FrontierMath (Feb 2025 set) | 3.8% | — |
| FrontierMath Tier 4 (v1) | 2.1% | — |
Knowledge Not comparable
GLM-4.6: 40.2 (#124), Pixtral Large: —
| Benchmark | GLM-4.6 | Pixtral Large |
|---|---|---|
| Vectara Hallucination Rate | 9.5% | — |
| LMArena Expert | 1431 | — |
Multimodal Not comparable
GLM-4.6: —, Pixtral Large: 30.6 (#111)
| Benchmark | GLM-4.6 | Pixtral Large |
|---|---|---|
| LMArena Vision | — | 1089 |
Multilingual Not comparable
GLM-4.6: 53.5 (#66), Pixtral Large: —
| Benchmark | GLM-4.6 | Pixtral Large |
|---|---|---|
| LMArena Non-English | 1426 | — |
| LMArena Chinese | 1499 | — |
| LMArena French | 1459 | — |
| LMArena German | 1447 | — |
| LMArena Japanese | 1393 | — |
| LMArena Korean | 1400 | — |
| LMArena Russian | 1419 | — |
| LMArena Spanish | 1436 | — |
Instruction Following Not comparable
GLM-4.6: 74.3 (#98), Pixtral Large: —
| Benchmark | GLM-4.6 | Pixtral Large |
|---|---|---|
| LMArena Instruction Following | 1410 | — |
Long Context Not comparable
GLM-4.6: 43.4 (#94), Pixtral Large: —
| Benchmark | GLM-4.6 | Pixtral Large |
|---|---|---|
| LMArena Longer Query | 1422 | — |
Writing & Preference GLM-4.6 leads
GLM-4.6: 61.1 (#90), Pixtral Large: 32.9 (#278)
| Benchmark | GLM-4.6 | Pixtral Large |
|---|---|---|
| EQ-Bench Creative Writing | 1411 | 988 |
| LMArena Text | 1440 | — |
| LMArena Creative Writing | 1411 | — |
| LMArena Multi-Turn | 1427 | — |
Frequently asked questions
Is GLM-4.6 better than Pixtral Large?
GLM-4.6 is the stronger model overall, scoring 41.4 to 32.2 on the Noometry Index.
Which is cheaper, GLM-4.6 or Pixtral Large?
GLM-4.6 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-4.6 does, with 205K tokens against 128K.
How many benchmarks do GLM-4.6 and Pixtral Large share?
1 benchmark has published results for both models. GLM-4.6 has 29 scored results on Noometry and Pixtral Large has 3.