Model comparison
GLM-4.6V vs Mistral Large
GLM-4.6V is the stronger model overall, scoring 40.9 to 31.6 on the Noometry Index.
Last verified . 11 shared benchmarks.
Mistral AISummary
- They share 11 benchmarks with published results for both. GLM-4.6V scores higher in 7 categories and Mistral Large in 0 categories; 7 gaps are clear of the uncertainty.
- The widest gap is in writing & preference, where GLM-4.6V leads 56.8 to 40.9.
- GLM-4.6V is cheaper at $0.30 / $0.90 per million input/output tokens, against $2 / $6 for Mistral Large.
- Mistral Large accepts more context: 131K tokens versus 128K.
Side by side
| GLM-4.6V | Mistral Large | |
|---|---|---|
| Provider | Z.ai (Zhipu) | Mistral AI |
| Noometry Index | 40.9 | 31.6 |
| Released | 2025-12-08 | 2024-02-26 |
| Weights | Open | Open |
| Context window | 128K | 131K |
| Max output | 33K | 16K |
| Input $ / M tokens | $0.30 | $2 |
| Output $ / M tokens | $0.90 | $6 |
| Results tracked | 12 | 51 |
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Category by category
Coding GLM-4.6V leads
GLM-4.6V: 40.2 (#128), Mistral Large: 33.8 (#240)
| Benchmark | GLM-4.6V | Mistral Large |
|---|---|---|
| LMArena Coding | 1390 | 1277 |
| SciCode | — | 36.2% |
| BigCodeBench Instruct | — | 30% |
| LiveBench Coding | — | 47.1% |
| BigCodeBench Complete | — | 38.3% |
| ALE-Bench | — | 264.7 |
| HumanEval+ | — | 62.2% |
| MBPP+ | — | 59.5% |
Agentic & Tool Use Not comparable
GLM-4.6V: —, Mistral Large: 28.4 (#83)
| Benchmark | GLM-4.6V | Mistral Large |
|---|---|---|
| Berkeley Function Calling Leaderboard | — | 38.4% |
Reasoning GLM-4.6V leads
GLM-4.6V: 26.5 (#117), Mistral Large: 15.4 (#305)
| Benchmark | GLM-4.6V | Mistral Large |
|---|---|---|
| LMArena Hard Prompts | 1368 | 1257 |
| SimpleBench | — | 22.5% |
| CritPt | — | 0% |
| LiveBench Reasoning | — | 43.5% |
| DTBench | — | 65.1% |
| LiveBench Data Analysis | — | 50.1% |
| LMCA | — | 16.7% |
| Epoch Capabilities Index | — | 128.52 |
| ForecastBench | — | 57.1 |
| LiveBench | — | 48.4% |
Math Not comparable
GLM-4.6V: —, Mistral Large: 17.1 (#285)
| Benchmark | GLM-4.6V | Mistral Large |
|---|---|---|
| OTIS Mock AIME 2024-2025 | — | 8.5% |
| Omni-MATH | — | 28.1% |
| LiveBench Math | — | 42.5% |
| LMArena Math | — | 1262 |
| MATH Level 5 | — | 50.3% |
| FrontierMath (Feb 2025 set) | — | 0.3% |
Knowledge GLM-4.6V leads
GLM-4.6V: 37.4 (#148), Mistral Large: 30.0 (#224)
| Benchmark | GLM-4.6V | Mistral Large |
|---|---|---|
| LMArena Expert | 1372 | 1232 |
| GPQA Diamond | — | 51.3% |
| MMLU-Pro | — | 59.9% |
| Confabulations | — | 21.4% |
| Vectara Hallucination Rate | — | 4.5% |
| GPQA (HELM) | — | 43.5% |
| MMLU | — | 80% |
Multimodal Not comparable
GLM-4.6V: 34.2 (#83), Mistral Large: —
| Benchmark | GLM-4.6V | Mistral Large |
|---|---|---|
| LMArena Vision | 1164 | — |
Multilingual GLM-4.6V leads
GLM-4.6V: 48.8 (#135), Mistral Large: 40.1 (#214)
| Benchmark | GLM-4.6V | Mistral Large |
|---|---|---|
| LMArena Non-English | 1359 | 1237 |
| LMArena Chinese | 1426 | 1240 |
| LMArena Russian | 1341 | 1257 |
| LMArena French | — | 1324 |
| LMArena German | — | 1254 |
| LMArena Japanese | — | 1187 |
| LMArena Korean | — | 1202 |
| LMArena Spanish | — | 1268 |
Instruction Following GLM-4.6V leads
GLM-4.6V: 71.6 (#145), Mistral Large: 68.1 (#186)
| Benchmark | GLM-4.6V | Mistral Large |
|---|---|---|
| LMArena Instruction Following | 1351 | 1249 |
| LiveBench Instruction Following | — | 67.9% |
| IFEval | — | 87.7% |
Long Context GLM-4.6V leads
GLM-4.6V: 41.3 (#138), Mistral Large: 38.3 (#194)
| Benchmark | GLM-4.6V | Mistral Large |
|---|---|---|
| LMArena Longer Query | 1358 | 1262 |
Writing & Preference GLM-4.6V leads
GLM-4.6V: 56.8 (#132), Mistral Large: 40.9 (#237)
| Benchmark | GLM-4.6V | Mistral Large |
|---|---|---|
| LMArena Text | 1377 | 1266 |
| LMArena Creative Writing | 1347 | 1243 |
| LMArena Multi-Turn | 1359 | 1260 |
| Short-Story Creative Writing | — | 69% |
| EQ-Bench Creative Writing | — | 985 |
| WildBench | — | 80.1% |
| LiveBench Language | — | 39.4% |
Frequently asked questions
Is GLM-4.6V better than Mistral Large?
GLM-4.6V is the stronger model overall, scoring 40.9 to 31.6 on the Noometry Index.
Which is cheaper, GLM-4.6V or Mistral Large?
GLM-4.6V is cheaper. It lists at $0.30 per million input tokens and $0.90 per million output tokens; Mistral Large lists at $2 and $6.
Is GLM-4.6V or Mistral Large better for coding?
GLM-4.6V scores higher on coding benchmarks: 40.2 versus 33.8 in the Noometry coding category.
Which has the bigger context window?
Mistral Large does, with 131K tokens against 128K.
How many benchmarks do GLM-4.6V and Mistral Large share?
11 benchmarks have published results for both models. GLM-4.6V has 12 scored results on Noometry and Mistral Large has 51.