Model comparison
GLM-5.2 vs Mistral Medium 3.1
GLM-5.2 is the stronger model overall, scoring 50.9 to 31.5 on the Noometry Index. Mistral Medium 3.1 costs 2.7× less per token, which makes it the better buy when GLM-5.2's lead doesn't matter for your workload.
Last verified . 2 shared benchmarks.
Mistral AISummary
- They share 2 benchmarks with published results for both. GLM-5.2 scores higher in 2 categories and Mistral Medium 3.1 in 0 categories; 2 gaps are clear of the uncertainty.
- The widest gap is in reasoning, where GLM-5.2 leads 42.1 to 10.1.
- The biggest single-benchmark swing is NYT Connections (extended): 74.3% for GLM-5.2 and 6.5% for Mistral Medium 3.1.
- Mistral Medium 3.1 is cheaper at $0.40 / $2 per million input/output tokens, against $1.40 / $4.40 for GLM-5.2.
- GLM-5.2 accepts more context: 1M tokens versus 131K.
- GLM-5.2 has downloadable open weights; the other is API-only.
Side by side
| GLM-5.2 | Mistral Medium 3.1 | |
|---|---|---|
| Provider | Z.ai (Zhipu) | Mistral AI |
| Noometry Index | 50.9 | 31.5 |
| Released | 2026-06-13 | — |
| Weights | Open | Proprietary |
| Context window | 1M | 131K |
| Max output | 131K | 105K |
| Input $ / M tokens | $1.40 | $0.40 |
| Output $ / M tokens | $4.40 | $2 |
| Results tracked | 50 | 3 |
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Category by category
Coding Not comparable
GLM-5.2: 50.9 (#37), Mistral Medium 3.1: —
| Benchmark | GLM-5.2 | Mistral Medium 3.1 |
|---|---|---|
| SWE-bench Verified | 78.7% | — |
| DeepSWE | 43.8% | — |
| FrontierCode | 24.5% | — |
| LMArena WebDev | 1604 | — |
| SciCode | 50.5% | — |
| WeirdML | 70.1% | — |
| LMArena Coding | 1484 | — |
| ALE-Bench | 1,047 | — |
Agentic & Tool Use Not comparable
GLM-5.2: 31.6 (#63), Mistral Medium 3.1: —
| Benchmark | GLM-5.2 | Mistral Medium 3.1 |
|---|---|---|
| τ²-bench Banking | 37.1% | — |
| PostTrainBench | 31.7% | — |
| GBAEval | 0% | — |
| Vending-Bench 2 | 8,314 | — |
Reasoning GLM-5.2 leads
GLM-5.2: 42.1 (#46), Mistral Medium 3.1: 10.1 (#336)
| Benchmark | GLM-5.2 | Mistral Medium 3.1 |
|---|---|---|
| NYT Connections (extended) | 74.3% | 6.5% |
| ARC-AGI-2 | 22.8% | — |
| SimpleBench | 58.8% | — |
| Kagi LLM Benchmark | 62.6% | — |
| ARC-AGI-1 | 77% | — |
| CritPt | 20.9% | — |
| Chess Puzzles | 21% | — |
| Thematic Generalization | — | 20.3% |
| EBR-Bench | 9.5% | — |
| LMArena Hard Prompts | 1480 | — |
| Mystery Game Puzzles | 19% | — |
| DTBench | 93.6% | — |
| LMCA | 45.8% | — |
| Surface Evolver Bench | 55.6% | — |
| Epoch Capabilities Index | 151.76 | — |
Math Not comparable
GLM-5.2: 55.4 (#35), Mistral Medium 3.1: —
| Benchmark | GLM-5.2 | Mistral Medium 3.1 |
|---|---|---|
| FrontierMath (Tiers 1-3) | 59.2% | — |
| FrontierMath Tier 4 | 29.3% | — |
| MathArena Final-Answer Competitions | 67.6% | — |
| OTIS Mock AIME 2024-2025 | 86.4% | — |
| ProofBench | 35% | — |
| LMArena Math | 1482 | — |
Knowledge Not comparable
GLM-5.2: 57.1 (#36), Mistral Medium 3.1: —
| Benchmark | GLM-5.2 | Mistral Medium 3.1 |
|---|---|---|
| GPQA Diamond | 91.9% | — |
| SimpleQA Verified | 34.2% | — |
| LMArena Expert | 1484 | — |
Multilingual Not comparable
GLM-5.2: 56.0 (#25), Mistral Medium 3.1: —
| Benchmark | GLM-5.2 | Mistral Medium 3.1 |
|---|---|---|
| LMArena Non-English | 1459 | — |
| LMArena Chinese | 1518 | — |
| LMArena French | 1476 | — |
| LMArena German | 1470 | — |
| LMArena Japanese | 1451 | — |
| LMArena Korean | 1445 | — |
| LMArena Russian | 1467 | — |
| LMArena Spanish | 1477 | — |
Instruction Following Not comparable
GLM-5.2: 77.1 (#32), Mistral Medium 3.1: —
| Benchmark | GLM-5.2 | Mistral Medium 3.1 |
|---|---|---|
| LMArena Instruction Following | 1465 | — |
Long Context Not comparable
GLM-5.2: 45.3 (#43), Mistral Medium 3.1: —
| Benchmark | GLM-5.2 | Mistral Medium 3.1 |
|---|---|---|
| LMArena Longer Query | 1479 | — |
Writing & Preference GLM-5.2 leads
GLM-5.2: 70.7 (#20), Mistral Medium 3.1: 55.6 (#140)
| Benchmark | GLM-5.2 | Mistral Medium 3.1 |
|---|---|---|
| EQ-Bench Creative Writing | 1757 | 1476 |
| LMArena Text | 1470 | — |
| LMArena Creative Writing | 1465 | — |
| EQ-Bench 4 | 1222 | — |
| LMArena Multi-Turn | 1468 | — |
Frequently asked questions
Is GLM-5.2 better than Mistral Medium 3.1?
GLM-5.2 is the stronger model overall, scoring 50.9 to 31.5 on the Noometry Index. Mistral Medium 3.1 costs 2.7× 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 Medium 3.1?
Mistral Medium 3.1 is cheaper. It lists at $0.40 per million input tokens and $2 per million output tokens; GLM-5.2 lists at $1.40 and $4.40.
Which has the bigger context window?
GLM-5.2 does, with 1M tokens against 131K.
How many benchmarks do GLM-5.2 and Mistral Medium 3.1 share?
2 benchmarks have published results for both models. GLM-5.2 has 50 scored results on Noometry and Mistral Medium 3.1 has 3.