Model comparison
GLM-4.5 vs Mistral Large
GLM-4.5 is the stronger model overall, scoring 42.0 to 31.9 on the Noometry Index.
Last verified . 21 shared benchmarks.
Mistral AISummary
- They share 21 benchmarks with published results for both. GLM-4.5 scores higher in 7 categories and Mistral Large in 1 category; 7 gaps are clear of the uncertainty.
- The widest gap is in math, where GLM-4.5 leads 39.0 to 18.2.
- The biggest single-benchmark swing is Confabulations: 11.3% for GLM-4.5 and 21.4% for Mistral Large.
- GLM-4.5 is cheaper at $0.60 / $2.20 per million input/output tokens, against $2 / $6 for Mistral Large.
Side by side
| GLM-4.5 | Mistral Large | |
|---|---|---|
| Provider | Z.ai (Zhipu) | Mistral AI |
| Noometry Index | 42.0 | 31.9 |
| Released | 2025-07-27 | 2024-02-26 |
| Weights | Open | Open |
| Context window | 131K | 131K |
| Max output | 98K | 16K |
| Input $ / M tokens | $0.60 | $2 |
| Output $ / M tokens | $2.20 | $6 |
| Results tracked | 27 | 51 |
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Category by category
Coding GLM-4.5 leads
GLM-4.5: 41.4 (#125), Mistral Large: 34.3 (#240)
| Benchmark | GLM-4.5 | Mistral Large |
|---|---|---|
| LMArena Coding | 1434 | 1277 |
| ALE-Bench | 344.82 | 264.7 |
| SWE-bench Verified (bash only) | 54.2% | — |
| SciCode | — | 36.2% |
| WeirdML | 40.6% | — |
| BigCodeBench Instruct | — | 30% |
| LiveBench Coding | — | 47.1% |
| BigCodeBench Complete | — | 38.3% |
| AlgoTune | 1.52 | — |
| HumanEval+ | — | 62.2% |
| MBPP+ | — | 59.5% |
Agentic & Tool Use Not comparable
GLM-4.5: —, Mistral Large: 28.6 (#89)
| Benchmark | GLM-4.5 | Mistral Large |
|---|---|---|
| Berkeley Function Calling Leaderboard | — | 38.4% |
Reasoning GLM-4.5 leads
GLM-4.5: 28.6 (#100), Mistral Large: 15.8 (#310)
| Benchmark | GLM-4.5 | Mistral Large |
|---|---|---|
| LMArena Hard Prompts | 1429 | 1257 |
| SimpleBench | — | 22.5% |
| Kagi LLM Benchmark | 57.9% | — |
| 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-4.5 leads
GLM-4.5: 39.0 (#116), Mistral Large: 18.2 (#291)
| Benchmark | GLM-4.5 | Mistral Large |
|---|---|---|
| LMArena Math | 1427 | 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-4.5 leads
GLM-4.5: 35.9 (#179), Mistral Large: 30.1 (#230)
| Benchmark | GLM-4.5 | Mistral Large |
|---|---|---|
| Confabulations | 11.3% | 21.4% |
| LMArena Expert | 1433 | 1232 |
| GPQA Diamond | — | 51.3% |
| Humanity's Last Exam | 8.3% | — |
| MMLU-Pro | — | 59.9% |
| Vectara Hallucination Rate | — | 4.5% |
| GPQA (HELM) | — | 43.5% |
| MMLU | — | 80% |
Multilingual GLM-4.5 leads
GLM-4.5: 52.8 (#77), Mistral Large: 40.0 (#219)
| Benchmark | GLM-4.5 | Mistral Large |
|---|---|---|
| LMArena Non-English | 1417 | 1237 |
| LMArena Chinese | 1465 | 1240 |
| LMArena French | 1418 | 1325 |
| LMArena German | 1407 | 1254 |
| LMArena Japanese | 1415 | 1188 |
| LMArena Korean | 1380 | 1202 |
| LMArena Russian | 1414 | 1257 |
| LMArena Spanish | 1454 | 1268 |
Instruction Following GLM-4.5 leads
GLM-4.5: 74.1 (#104), Mistral Large: 67.9 (#191)
| Benchmark | GLM-4.5 | Mistral Large |
|---|---|---|
| LMArena Instruction Following | 1404 | 1249 |
| LiveBench Instruction Following | — | 67.9% |
| IFEval | — | 87.7% |
Long Context Too close to call
GLM-4.5: 38.2 (#201), Mistral Large: 38.3 (#199)
| Benchmark | GLM-4.5 | Mistral Large |
|---|---|---|
| LMArena Longer Query | 1412 | 1261 |
| Fiction.LiveBench | 58.3% | — |
Writing & Preference GLM-4.5 leads
GLM-4.5: 57.5 (#127), Mistral Large: 40.7 (#242)
| Benchmark | GLM-4.5 | Mistral Large |
|---|---|---|
| LMArena Text | 1430 | 1266 |
| LMArena Creative Writing | 1395 | 1243 |
| Short-Story Creative Writing | 73.4% | 69% |
| EQ-Bench Creative Writing | 1343 | 985 |
| LMArena Multi-Turn | 1415 | 1260 |
| WildBench | — | 80.1% |
| LiveBench Language | — | 39.4% |
Frequently asked questions
Is GLM-4.5 better than Mistral Large?
GLM-4.5 is the stronger model overall, scoring 42.0 to 31.9 on the Noometry Index.
Which is cheaper, GLM-4.5 or Mistral Large?
GLM-4.5 is cheaper. It lists at $0.60 per million input tokens and $2.20 per million output tokens; Mistral Large lists at $2 and $6.
Is GLM-4.5 or Mistral Large better for coding?
GLM-4.5 scores higher on coding benchmarks: 41.4 versus 34.3 in the Noometry coding category.
Which has the bigger context window?
Both accept 131K tokens.
How many benchmarks do GLM-4.5 and Mistral Large share?
21 benchmarks have published results for both models. GLM-4.5 has 27 scored results on Noometry and Mistral Large has 51.