Model comparison
GLM-5.3-Flash vs Mistral Small 3.2
GLM-5.3-Flash is the stronger model overall, scoring 51.8 to 31.2 on the Noometry Index. Mistral Small 3.2 costs 1.8× less per token, which makes it the better buy when GLM-5.3-Flash's lead doesn't matter for your workload.
Last verified . 4 shared benchmarks.
Mistral AISummary
- They share 4 benchmarks with published results for both. GLM-5.3-Flash scores higher in 4 categories and Mistral Small 3.2 in 0 categories; 4 gaps are clear of the uncertainty.
- The widest gap is in knowledge, where GLM-5.3-Flash leads 58.4 to 26.7.
- The biggest single-benchmark swing is OTIS Mock AIME 2024-2025: 93.9% for GLM-5.3-Flash and 30.3% for Mistral Small 3.2.
- Mistral Small 3.2 is cheaper at $0.0938 / $0.25 per million input/output tokens, against $0.15 / $0.50 for GLM-5.3-Flash.
- GLM-5.3-Flash accepts more context: 1M tokens versus 256K.
Side by side
| GLM-5.3-Flash | Mistral Small 3.2 | |
|---|---|---|
| Provider | Z.ai (Zhipu) | Mistral AI |
| Noometry Index | 51.8 | 31.2 |
| Released | 2026-08-20 | 2025-06-20 |
| Weights | Open | Open |
| Context window | 1M | 256K |
| Max output | 131K | 16K |
| Input $ / M tokens | $0.15 | $0.0938 |
| Output $ / M tokens | $0.50 | $0.25 |
| Results tracked | 40 | 6 |
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Category by category
Coding Not comparable
GLM-5.3-Flash: 53.1 (#31), Mistral Small 3.2: —
| Benchmark | GLM-5.3-Flash | Mistral Small 3.2 |
|---|---|---|
| DeepSWE | 63.4% | — |
| FrontierCode | 31.8% | — |
| CursorBench | 36.8% | — |
| LMArena WebDev | 1609 | — |
| FrontierSWE | 18.1% | — |
| SciCode | 51.6% | — |
| LMArena Coding | 1508 | — |
| ALE-Bench | 303.55 | — |
Agentic & Tool Use Not comparable
GLM-5.3-Flash: 34.2 (#47), Mistral Small 3.2: —
| Benchmark | GLM-5.3-Flash | Mistral Small 3.2 |
|---|---|---|
| APEX-Agents | 52.8% | — |
| GDP.pdf | 14% | — |
Reasoning GLM-5.3-Flash leads
GLM-5.3-Flash: 48.0 (#42), Mistral Small 3.2: 18.1 (#287)
| Benchmark | GLM-5.3-Flash | Mistral Small 3.2 |
|---|---|---|
| Chess Puzzles | 14% | 1% |
| Epoch Capabilities Index | 151.88 | 131.74 |
| ARC-AGI-2 | 65.8% | — |
| Kagi LLM Benchmark | — | 40.4% |
| ARC-AGI-1 | 91% | — |
| CritPt | 15.4% | — |
| LMArena Hard Prompts | 1491 | — |
| 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 3.2: 26.3 (#260)
| Benchmark | GLM-5.3-Flash | Mistral Small 3.2 |
|---|---|---|
| OTIS Mock AIME 2024-2025 | 93.9% | 30.3% |
| FrontierMath (Tiers 1-3) | 55.8% | — |
| FrontierMath Tier 4 | 17.1% | — |
| ProofBench | 21% | — |
| LMArena Math | 1500 | — |
Knowledge GLM-5.3-Flash leads
GLM-5.3-Flash: 58.4 (#36), Mistral Small 3.2: 26.7 (#256)
| Benchmark | GLM-5.3-Flash | Mistral Small 3.2 |
|---|---|---|
| GPQA Diamond | 90.2% | 49.1% |
| LMArena Expert | 1513 | — |
Multimodal Not comparable
GLM-5.3-Flash: 42.8 (#27), Mistral Small 3.2: —
| Benchmark | GLM-5.3-Flash | Mistral Small 3.2 |
|---|---|---|
| LMArena Vision | 1296 | — |
Multilingual Not comparable
GLM-5.3-Flash: 56.0 (#25), Mistral Small 3.2: —
| Benchmark | GLM-5.3-Flash | Mistral Small 3.2 |
|---|---|---|
| LMArena Non-English | 1462 | — |
| LMArena Chinese | 1527 | — |
| LMArena French | 1496 | — |
| LMArena German | 1470 | — |
| LMArena Japanese | 1429 | — |
| LMArena Korean | 1446 | — |
| LMArena Russian | 1469 | — |
| LMArena Spanish | 1471 | — |
Instruction Following Not comparable
GLM-5.3-Flash: 77.5 (#20), Mistral Small 3.2: —
| Benchmark | GLM-5.3-Flash | Mistral Small 3.2 |
|---|---|---|
| LMArena Instruction Following | 1478 | — |
Long Context Not comparable
GLM-5.3-Flash: 45.4 (#39), Mistral Small 3.2: —
| Benchmark | GLM-5.3-Flash | Mistral Small 3.2 |
|---|---|---|
| LMArena Longer Query | 1482 | — |
Writing & Preference GLM-5.3-Flash leads
GLM-5.3-Flash: 65.3 (#50), Mistral Small 3.2: 45.0 (#224)
| Benchmark | GLM-5.3-Flash | Mistral Small 3.2 |
|---|---|---|
| LMArena Text | 1471 | — |
| LMArena Creative Writing | 1442 | — |
| EQ-Bench Creative Writing | — | 1255 |
| LMArena Multi-Turn | 1467 | — |
Frequently asked questions
Is GLM-5.3-Flash better than Mistral Small 3.2?
GLM-5.3-Flash is the stronger model overall, scoring 51.8 to 31.2 on the Noometry Index. Mistral Small 3.2 costs 1.8× less per token, which makes it the better buy when GLM-5.3-Flash's lead doesn't matter for your workload.
Which is cheaper, GLM-5.3-Flash or Mistral Small 3.2?
Mistral Small 3.2 is cheaper. It lists at $0.0938 per million input tokens and $0.25 per million output tokens; GLM-5.3-Flash lists at $0.15 and $0.50.
Which has the bigger context window?
GLM-5.3-Flash does, with 1M tokens against 256K.
How many benchmarks do GLM-5.3-Flash and Mistral Small 3.2 share?
4 benchmarks have published results for both models. GLM-5.3-Flash has 40 scored results on Noometry and Mistral Small 3.2 has 6.