Model comparison
GLM-5.3-Flash vs Mistral Medium 3.5
GLM-5.3-Flash is the stronger model overall, scoring 51.8 to 40.2 on the Noometry Index.
Last verified . 19 shared benchmarks.
Mistral AISummary
- They share 19 benchmarks with published results for both. GLM-5.3-Flash scores higher in 9 categories and Mistral Medium 3.5 in 0 categories; 9 gaps are clear of the uncertainty.
- The widest gap is in reasoning, where GLM-5.3-Flash leads 48.0 to 17.3.
- GLM-5.3-Flash is cheaper at $0.15 / $0.50 per million input/output tokens, against $1.50 / $7.50 for Mistral Medium 3.5.
- GLM-5.3-Flash accepts more context: 1M tokens versus 262K.
Side by side
| GLM-5.3-Flash | Mistral Medium 3.5 | |
|---|---|---|
| Provider | Z.ai (Zhipu) | Mistral AI |
| Noometry Index | 51.8 | 40.2 |
| Released | 2026-08-20 | — |
| Weights | Open | Open |
| Context window | 1M | 262K |
| Max output | 131K | 210K |
| Input $ / M tokens | $0.15 | $1.50 |
| Output $ / M tokens | $0.50 | $7.50 |
| Results tracked | 40 | 22 |
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Category by category
Coding GLM-5.3-Flash leads
GLM-5.3-Flash: 53.1 (#31), Mistral Medium 3.5: 36.0 (#213)
| Benchmark | GLM-5.3-Flash | Mistral Medium 3.5 |
|---|---|---|
| LMArena WebDev | 1609 | 1264 |
| LMArena Coding | 1508 | 1461 |
| DeepSWE | 63.4% | — |
| FrontierCode | 31.8% | — |
| CursorBench | 36.8% | — |
| FrontierSWE | 18.1% | — |
| SciCode | 51.6% | — |
| ALE-Bench | 303.55 | — |
Agentic & Tool Use Not comparable
GLM-5.3-Flash: 34.2 (#47), Mistral Medium 3.5: —
| Benchmark | GLM-5.3-Flash | Mistral Medium 3.5 |
|---|---|---|
| APEX-Agents | 52.8% | — |
| GDP.pdf | 14% | — |
Reasoning GLM-5.3-Flash leads
GLM-5.3-Flash: 48.0 (#42), Mistral Medium 3.5: 17.3 (#295)
| Benchmark | GLM-5.3-Flash | Mistral Medium 3.5 |
|---|---|---|
| LMArena Hard Prompts | 1491 | 1436 |
| Epoch Capabilities Index | 151.88 | 141.35 |
| ARC-AGI-2 | 65.8% | — |
| Kagi LLM Benchmark | — | 41.4% |
| NYT Connections (extended) | — | 12.9% |
| ARC-AGI-1 | 91% | — |
| CritPt | 15.4% | — |
| Chess Puzzles | 14% | — |
| 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 Medium 3.5: 39.1 (#113)
| Benchmark | GLM-5.3-Flash | Mistral Medium 3.5 |
|---|---|---|
| LMArena Math | 1500 | 1431 |
| FrontierMath (Tiers 1-3) | 55.8% | — |
| FrontierMath Tier 4 | 17.1% | — |
| OTIS Mock AIME 2024-2025 | 93.9% | — |
| ProofBench | 21% | — |
Knowledge GLM-5.3-Flash leads
GLM-5.3-Flash: 58.4 (#36), Mistral Medium 3.5: 40.0 (#126)
| Benchmark | GLM-5.3-Flash | Mistral Medium 3.5 |
|---|---|---|
| LMArena Expert | 1513 | 1432 |
| GPQA Diamond | 90.2% | — |
Multimodal GLM-5.3-Flash leads
GLM-5.3-Flash: 42.8 (#27), Mistral Medium 3.5: 38.3 (#65)
| Benchmark | GLM-5.3-Flash | Mistral Medium 3.5 |
|---|---|---|
| LMArena Vision | 1296 | 1223 |
Multilingual GLM-5.3-Flash leads
GLM-5.3-Flash: 56.0 (#25), Mistral Medium 3.5: 51.9 (#100)
| Benchmark | GLM-5.3-Flash | Mistral Medium 3.5 |
|---|---|---|
| LMArena Non-English | 1462 | 1404 |
| LMArena Chinese | 1527 | 1442 |
| LMArena French | 1496 | 1448 |
| LMArena German | 1470 | 1451 |
| LMArena Korean | 1446 | 1385 |
| LMArena Russian | 1469 | 1395 |
| LMArena Spanish | 1471 | 1409 |
| LMArena Japanese | 1429 | — |
Instruction Following GLM-5.3-Flash leads
GLM-5.3-Flash: 77.5 (#20), Mistral Medium 3.5: 74.6 (#90)
| Benchmark | GLM-5.3-Flash | Mistral Medium 3.5 |
|---|---|---|
| LMArena Instruction Following | 1478 | 1415 |
Long Context GLM-5.3-Flash leads
GLM-5.3-Flash: 45.4 (#39), Mistral Medium 3.5: 43.2 (#103)
| Benchmark | GLM-5.3-Flash | Mistral Medium 3.5 |
|---|---|---|
| LMArena Longer Query | 1482 | 1415 |
Writing & Preference GLM-5.3-Flash leads
GLM-5.3-Flash: 65.3 (#50), Mistral Medium 3.5: 58.5 (#117)
| Benchmark | GLM-5.3-Flash | Mistral Medium 3.5 |
|---|---|---|
| LMArena Text | 1471 | 1421 |
| LMArena Creative Writing | 1442 | 1374 |
| LMArena Multi-Turn | 1467 | 1423 |
| EQ-Bench 4 | — | 993 |
Frequently asked questions
Is GLM-5.3-Flash better than Mistral Medium 3.5?
GLM-5.3-Flash is the stronger model overall, scoring 51.8 to 40.2 on the Noometry Index.
Which is cheaper, GLM-5.3-Flash or Mistral Medium 3.5?
GLM-5.3-Flash is cheaper. It lists at $0.15 per million input tokens and $0.50 per million output tokens; Mistral Medium 3.5 lists at $1.50 and $7.50.
Is GLM-5.3-Flash or Mistral Medium 3.5 better for coding?
GLM-5.3-Flash scores higher on coding benchmarks: 53.1 versus 36.0 in the Noometry coding category.
Which has the bigger context window?
GLM-5.3-Flash does, with 1M tokens against 262K.
How many benchmarks do GLM-5.3-Flash and Mistral Medium 3.5 share?
19 benchmarks have published results for both models. GLM-5.3-Flash has 40 scored results on Noometry and Mistral Medium 3.5 has 22.