Model comparison
GLM-5.2 vs Mistral Small 3.1
GLM-5.2 is the stronger model overall, scoring 51.1 to 31.7 on the Noometry Index. Mistral Small 3.1 costs 5.3× less per token, which makes it the better buy when GLM-5.2's lead doesn't matter for your workload.
Last verified . 22 shared benchmarks.
Mistral AISummary
- They share 22 benchmarks with published results for both. GLM-5.2 scores higher in 8 categories and Mistral Small 3.1 in 0 categories; 8 gaps are clear of the uncertainty.
- The widest gap is in math, where GLM-5.2 leads 55.7 to 14.7.
- The biggest single-benchmark swing is OTIS Mock AIME 2024-2025: 86.4% for GLM-5.2 and 3.9% for Mistral Small 3.1.
- Mistral Small 3.1 is cheaper at $0.35 / $0.56 per million input/output tokens, against $1.40 / $4.40 for GLM-5.2.
- GLM-5.2 accepts more context: 1M tokens versus 128K.
Side by side
| GLM-5.2 | Mistral Small 3.1 | |
|---|---|---|
| Provider | Z.ai (Zhipu) | Mistral AI |
| Noometry Index | 51.1 | 31.7 |
| Released | 2026-06-13 | 2025-03-17 |
| Weights | Open | Open |
| Context window | 1M | 128K |
| Max output | 131K | 102K |
| Input $ / M tokens | $1.40 | $0.35 |
| Output $ / M tokens | $4.40 | $0.56 |
| Results tracked | 51 | 28 |
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Category by category
Coding GLM-5.2 leads
GLM-5.2: 51.3 (#41), Mistral Small 3.1: 38.3 (#179)
| Benchmark | GLM-5.2 | Mistral Small 3.1 |
|---|---|---|
| LMArena Coding | 1485 | 1309 |
| SWE-bench Verified | 78.7% | — |
| DeepSWE | 43.8% | — |
| FrontierCode | 24.5% | — |
| LMArena WebDev | 1603 | — |
| SciCode | 50.5% | — |
| WeirdML | 70.1% | — |
| ALE-Bench | 1,047 | — |
Agentic & Tool Use Not comparable
GLM-5.2: 32.4 (#63), Mistral Small 3.1: —
| Benchmark | GLM-5.2 | Mistral Small 3.1 |
|---|---|---|
| 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), Mistral Small 3.1: 19.7 (#254)
| Benchmark | GLM-5.2 | Mistral Small 3.1 |
|---|---|---|
| Chess Puzzles | 21% | 1% |
| LMArena Hard Prompts | 1480 | 1278 |
| Epoch Capabilities Index | 151.78 | 127.48 |
| 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% | — |
| EBR-Bench | 9.5% | — |
| Mystery Game Puzzles | 19% | — |
| DTBench | 93.6% | — |
| LMCA | 45.8% | — |
| Surface Evolver Bench | 55.6% | — |
Math GLM-5.2 leads
GLM-5.2: 55.7 (#43), Mistral Small 3.1: 14.7 (#301)
| Benchmark | GLM-5.2 | Mistral Small 3.1 |
|---|---|---|
| OTIS Mock AIME 2024-2025 | 86.4% | 3.9% |
| LMArena Math | 1482 | 1262 |
| FrontierMath (Tiers 1-3) | 59.2% | — |
| FrontierMath Tier 4 | 29.3% | — |
| MathArena Final-Answer Competitions | 67.6% | — |
| ProofBench | 35% | — |
| Omni-MATH | — | 24.8% |
Knowledge GLM-5.2 leads
GLM-5.2: 57.1 (#40), Mistral Small 3.1: 22.6 (#271)
| Benchmark | GLM-5.2 | Mistral Small 3.1 |
|---|---|---|
| GPQA Diamond | 91.9% | 41.9% |
| LMArena Expert | 1486 | 1257 |
| SimpleQA Verified | 34.2% | — |
| MMLU-Pro | — | 61% |
| GPQA (HELM) | — | 39.2% |
Multimodal Not comparable
GLM-5.2: —, Mistral Small 3.1: 33.2 (#99)
| Benchmark | GLM-5.2 | Mistral Small 3.1 |
|---|---|---|
| LMArena Vision | — | 1136 |
Multilingual GLM-5.2 leads
GLM-5.2: 55.8 (#26), Mistral Small 3.1: 41.2 (#209)
| Benchmark | GLM-5.2 | Mistral Small 3.1 |
|---|---|---|
| LMArena Non-English | 1459 | 1255 |
| LMArena Chinese | 1519 | 1253 |
| LMArena French | 1479 | 1273 |
| LMArena German | 1468 | 1266 |
| LMArena Japanese | 1451 | 1208 |
| LMArena Korean | 1445 | 1206 |
| LMArena Russian | 1466 | 1263 |
| LMArena Spanish | 1477 | 1283 |
Instruction Following GLM-5.2 leads
GLM-5.2: 76.9 (#34), Mistral Small 3.1: 63.6 (#230)
| Benchmark | GLM-5.2 | Mistral Small 3.1 |
|---|---|---|
| LMArena Instruction Following | 1465 | 1264 |
| IFEval | — | 75% |
Long Context GLM-5.2 leads
GLM-5.2: 45.3 (#43), Mistral Small 3.1: 39.5 (#178)
| Benchmark | GLM-5.2 | Mistral Small 3.1 |
|---|---|---|
| LMArena Longer Query | 1479 | 1299 |
Writing & Preference GLM-5.2 leads
GLM-5.2: 70.4 (#21), Mistral Small 3.1: 37.0 (#259)
| Benchmark | GLM-5.2 | Mistral Small 3.1 |
|---|---|---|
| LMArena Text | 1470 | 1277 |
| LMArena Creative Writing | 1462 | 1253 |
| EQ-Bench Creative Writing | 1757 | 761 |
| LMArena Multi-Turn | 1469 | 1270 |
| WildBench | — | 78.8% |
| EQ-Bench 4 | 1222 | — |
Frequently asked questions
Is GLM-5.2 better than Mistral Small 3.1?
GLM-5.2 is the stronger model overall, scoring 51.1 to 31.7 on the Noometry Index. Mistral Small 3.1 costs 5.3× less per token, which makes it the better buy when GLM-5.2's lead doesn't matter for your workload.
Which is cheaper, GLM-5.2 or Mistral Small 3.1?
Mistral Small 3.1 is cheaper. It lists at $0.35 per million input tokens and $0.56 per million output tokens; GLM-5.2 lists at $1.40 and $4.40.
Is GLM-5.2 or Mistral Small 3.1 better for coding?
GLM-5.2 scores higher on coding benchmarks: 51.3 versus 38.3 in the Noometry coding category.
Which has the bigger context window?
GLM-5.2 does, with 1M tokens against 128K.
How many benchmarks do GLM-5.2 and Mistral Small 3.1 share?
22 benchmarks have published results for both models. GLM-5.2 has 51 scored results on Noometry and Mistral Small 3.1 has 28.