Model comparison
GLM-5.3-Flash vs Mistral Small 3.1
GLM-5.3-Flash is the stronger model overall, scoring 51.8 to 31.7 on the Noometry Index.
Last verified . 22 shared benchmarks.
Mistral AISummary
- They share 22 benchmarks with published results for both. GLM-5.3-Flash scores higher in 9 categories and Mistral Small 3.1 in 0 categories; 9 gaps are clear of the uncertainty.
- The widest gap is in math, where GLM-5.3-Flash leads 53.3 to 14.7.
- The biggest single-benchmark swing is OTIS Mock AIME 2024-2025: 93.9% for GLM-5.3-Flash and 3.9% for Mistral Small 3.1.
- GLM-5.3-Flash is cheaper at $0.15 / $0.50 per million input/output tokens, against $0.35 / $0.56 for Mistral Small 3.1.
- GLM-5.3-Flash accepts more context: 1M tokens versus 128K.
Side by side
| GLM-5.3-Flash | Mistral Small 3.1 | |
|---|---|---|
| Provider | Z.ai (Zhipu) | Mistral AI |
| Noometry Index | 51.8 | 31.7 |
| Released | 2026-08-20 | 2025-03-17 |
| Weights | Open | Open |
| Context window | 1M | 128K |
| Max output | 131K | 102K |
| Input $ / M tokens | $0.15 | $0.35 |
| Output $ / M tokens | $0.50 | $0.56 |
| Results tracked | 40 | 28 |
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Category by category
Coding GLM-5.3-Flash leads
GLM-5.3-Flash: 53.1 (#31), Mistral Small 3.1: 38.3 (#179)
| Benchmark | GLM-5.3-Flash | Mistral Small 3.1 |
|---|---|---|
| LMArena Coding | 1508 | 1309 |
| DeepSWE | 63.4% | — |
| FrontierCode | 31.8% | — |
| CursorBench | 36.8% | — |
| LMArena WebDev | 1609 | — |
| FrontierSWE | 18.1% | — |
| SciCode | 51.6% | — |
| ALE-Bench | 303.55 | — |
Agentic & Tool Use Not comparable
GLM-5.3-Flash: 34.2 (#47), Mistral Small 3.1: —
| Benchmark | GLM-5.3-Flash | Mistral Small 3.1 |
|---|---|---|
| APEX-Agents | 52.8% | — |
| GDP.pdf | 14% | — |
Reasoning GLM-5.3-Flash leads
GLM-5.3-Flash: 48.0 (#42), Mistral Small 3.1: 19.7 (#254)
| Benchmark | GLM-5.3-Flash | Mistral Small 3.1 |
|---|---|---|
| Chess Puzzles | 14% | 1% |
| LMArena Hard Prompts | 1491 | 1278 |
| Epoch Capabilities Index | 151.88 | 127.48 |
| ARC-AGI-2 | 65.8% | — |
| ARC-AGI-1 | 91% | — |
| CritPt | 15.4% | — |
| Mystery Game Puzzles | 8% | — |
| Surface Evolver Bench | 52.5% | — |
| Bench to the Future 3 | 0.15 | — |
Math GLM-5.3-Flash leads
GLM-5.3-Flash: 53.3 (#47), Mistral Small 3.1: 14.7 (#301)
| Benchmark | GLM-5.3-Flash | Mistral Small 3.1 |
|---|---|---|
| OTIS Mock AIME 2024-2025 | 93.9% | 3.9% |
| LMArena Math | 1500 | 1262 |
| FrontierMath (Tiers 1-3) | 55.8% | — |
| FrontierMath Tier 4 | 17.1% | — |
| ProofBench | 21% | — |
| Omni-MATH | — | 24.8% |
Knowledge GLM-5.3-Flash leads
GLM-5.3-Flash: 58.4 (#36), Mistral Small 3.1: 22.6 (#271)
| Benchmark | GLM-5.3-Flash | Mistral Small 3.1 |
|---|---|---|
| GPQA Diamond | 90.2% | 41.9% |
| LMArena Expert | 1513 | 1257 |
| MMLU-Pro | — | 61% |
| GPQA (HELM) | — | 39.2% |
Multimodal GLM-5.3-Flash leads
GLM-5.3-Flash: 42.8 (#27), Mistral Small 3.1: 33.2 (#99)
| Benchmark | GLM-5.3-Flash | Mistral Small 3.1 |
|---|---|---|
| LMArena Vision | 1296 | 1136 |
Multilingual GLM-5.3-Flash leads
GLM-5.3-Flash: 56.0 (#25), Mistral Small 3.1: 41.2 (#209)
| Benchmark | GLM-5.3-Flash | Mistral Small 3.1 |
|---|---|---|
| LMArena Non-English | 1462 | 1255 |
| LMArena Chinese | 1527 | 1253 |
| LMArena French | 1496 | 1273 |
| LMArena German | 1470 | 1266 |
| LMArena Japanese | 1429 | 1208 |
| LMArena Korean | 1446 | 1206 |
| LMArena Russian | 1469 | 1263 |
| LMArena Spanish | 1471 | 1283 |
Instruction Following GLM-5.3-Flash leads
GLM-5.3-Flash: 77.5 (#20), Mistral Small 3.1: 63.6 (#230)
| Benchmark | GLM-5.3-Flash | Mistral Small 3.1 |
|---|---|---|
| LMArena Instruction Following | 1478 | 1264 |
| IFEval | — | 75% |
Long Context GLM-5.3-Flash leads
GLM-5.3-Flash: 45.4 (#39), Mistral Small 3.1: 39.5 (#178)
| Benchmark | GLM-5.3-Flash | Mistral Small 3.1 |
|---|---|---|
| LMArena Longer Query | 1482 | 1299 |
Writing & Preference GLM-5.3-Flash leads
GLM-5.3-Flash: 65.3 (#50), Mistral Small 3.1: 37.0 (#259)
| Benchmark | GLM-5.3-Flash | Mistral Small 3.1 |
|---|---|---|
| LMArena Text | 1471 | 1277 |
| LMArena Creative Writing | 1442 | 1253 |
| LMArena Multi-Turn | 1467 | 1270 |
| EQ-Bench Creative Writing | — | 761 |
| WildBench | — | 78.8% |
Frequently asked questions
Is GLM-5.3-Flash better than Mistral Small 3.1?
GLM-5.3-Flash is the stronger model overall, scoring 51.8 to 31.7 on the Noometry Index.
Which is cheaper, GLM-5.3-Flash or Mistral Small 3.1?
GLM-5.3-Flash is cheaper. It lists at $0.15 per million input tokens and $0.50 per million output tokens; Mistral Small 3.1 lists at $0.35 and $0.56.
Is GLM-5.3-Flash or Mistral Small 3.1 better for coding?
GLM-5.3-Flash scores higher on coding benchmarks: 53.1 versus 38.3 in the Noometry coding category.
Which has the bigger context window?
GLM-5.3-Flash does, with 1M tokens against 128K.
How many benchmarks do GLM-5.3-Flash and Mistral Small 3.1 share?
22 benchmarks have published results for both models. GLM-5.3-Flash has 40 scored results on Noometry and Mistral Small 3.1 has 28.