Model comparison
GLM-4.5V vs Mistral Large
GLM-4.5V is the stronger model overall, scoring 39.8 to 31.9 on the Noometry Index.
Last verified . 13 shared benchmarks.
Mistral AISummary
- They share 13 benchmarks with published results for both. GLM-4.5V scores higher in 8 categories and Mistral Large in 0 categories; 8 gaps are clear of the uncertainty.
- The widest gap is in math, where GLM-4.5V leads 37.4 to 18.2.
- GLM-4.5V is cheaper at $0.60 / $1.80 per million input/output tokens, against $2 / $6 for Mistral Large.
- Mistral Large accepts more context: 131K tokens versus 64K.
Side by side
| GLM-4.5V | Mistral Large | |
|---|---|---|
| Provider | Z.ai (Zhipu) | Mistral AI |
| Noometry Index | 39.8 | 31.9 |
| Released | 2025-08-11 | 2024-02-26 |
| Weights | Open | Open |
| Context window | 64K | 131K |
| Max output | 16K | 16K |
| Input $ / M tokens | $0.60 | $2 |
| Output $ / M tokens | $1.80 | $6 |
| Results tracked | 15 | 51 |
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Category by category
Coding GLM-4.5V leads
GLM-4.5V: 39.5 (#155), Mistral Large: 34.3 (#240)
| Benchmark | GLM-4.5V | Mistral Large |
|---|---|---|
| LMArena Coding | 1347 | 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-4.5V: —, Mistral Large: 28.6 (#89)
| Benchmark | GLM-4.5V | Mistral Large |
|---|---|---|
| Berkeley Function Calling Leaderboard | — | 38.4% |
Reasoning GLM-4.5V leads
GLM-4.5V: 27.4 (#119), Mistral Large: 15.8 (#310)
| Benchmark | GLM-4.5V | Mistral Large |
|---|---|---|
| LMArena Hard Prompts | 1334 | 1257 |
| SimpleBench | — | 22.5% |
| Kagi LLM Benchmark | 59.8% | — |
| 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 GLM-4.5V leads
GLM-4.5V: 37.4 (#159), Mistral Large: 18.2 (#291)
| Benchmark | GLM-4.5V | Mistral Large |
|---|---|---|
| LMArena Math | 1354 | 1262 |
| OTIS Mock AIME 2024-2025 | — | 8.5% |
| Omni-MATH | — | 28.1% |
| LiveBench Math | — | 42.5% |
| MATH Level 5 | — | 50.3% |
| FrontierMath (Feb 2025 set) | — | 0.3% |
Knowledge GLM-4.5V leads
GLM-4.5V: 37.5 (#156), Mistral Large: 30.1 (#230)
| Benchmark | GLM-4.5V | Mistral Large |
|---|---|---|
| LMArena Expert | 1353 | 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-4.5V: 34.3 (#92), Mistral Large: —
| Benchmark | GLM-4.5V | Mistral Large |
|---|---|---|
| LMArena Vision | 1154 | — |
Multilingual GLM-4.5V leads
GLM-4.5V: 44.6 (#177), Mistral Large: 40.0 (#219)
| Benchmark | GLM-4.5V | Mistral Large |
|---|---|---|
| LMArena Non-English | 1303 | 1237 |
| LMArena Chinese | 1337 | 1240 |
| LMArena Russian | 1298 | 1257 |
| LMArena Spanish | 1336 | 1268 |
| LMArena French | — | 1325 |
| LMArena German | — | 1254 |
| LMArena Japanese | — | 1188 |
| LMArena Korean | — | 1202 |
Instruction Following GLM-4.5V leads
GLM-4.5V: 69.2 (#175), Mistral Large: 67.9 (#191)
| Benchmark | GLM-4.5V | Mistral Large |
|---|---|---|
| LMArena Instruction Following | 1311 | 1249 |
| LiveBench Instruction Following | — | 67.9% |
| IFEval | — | 87.7% |
Long Context GLM-4.5V leads
GLM-4.5V: 39.6 (#171), Mistral Large: 38.3 (#199)
| Benchmark | GLM-4.5V | Mistral Large |
|---|---|---|
| LMArena Longer Query | 1304 | 1261 |
Writing & Preference GLM-4.5V leads
GLM-4.5V: 52.5 (#170), Mistral Large: 40.7 (#242)
| Benchmark | GLM-4.5V | Mistral Large |
|---|---|---|
| LMArena Text | 1333 | 1266 |
| LMArena Creative Writing | 1295 | 1243 |
| LMArena Multi-Turn | 1332 | 1260 |
| Short-Story Creative Writing | — | 69% |
| EQ-Bench Creative Writing | — | 985 |
| WildBench | — | 80.1% |
| LiveBench Language | — | 39.4% |
Frequently asked questions
Is GLM-4.5V better than Mistral Large?
GLM-4.5V is the stronger model overall, scoring 39.8 to 31.9 on the Noometry Index.
Which is cheaper, GLM-4.5V or Mistral Large?
GLM-4.5V is cheaper. It lists at $0.60 per million input tokens and $1.80 per million output tokens; Mistral Large lists at $2 and $6.
Is GLM-4.5V or Mistral Large better for coding?
GLM-4.5V scores higher on coding benchmarks: 39.5 versus 34.3 in the Noometry coding category.
Which has the bigger context window?
Mistral Large does, with 131K tokens against 64K.
How many benchmarks do GLM-4.5V and Mistral Large share?
13 benchmarks have published results for both models. GLM-4.5V has 15 scored results on Noometry and Mistral Large has 51.