Model comparison
GLM-5.2 vs MiMo-V2-Pro
GLM-5.2 is the stronger model overall, scoring 50.9 to 42.4 on the Noometry Index. MiMo-V2-Pro costs 4.0× less per token, which makes it the better buy when GLM-5.2's lead doesn't matter for your workload.
Last verified . 20 shared benchmarks.
Summary
- They share 20 benchmarks with published results for both. GLM-5.2 scores higher in 8 categories and MiMo-V2-Pro in 0 categories; 7 gaps are clear of the uncertainty.
- The widest gap is in reasoning, where GLM-5.2 leads 42.1 to 21.3.
- The biggest single-benchmark swing is NYT Connections (extended): 74.3% for GLM-5.2 and 25.8% for MiMo-V2-Pro.
- MiMo-V2-Pro is cheaper at $0.43 / $0.87 per million input/output tokens, against $1.40 / $4.40 for GLM-5.2.
- MiMo-V2-Pro accepts more context: 1.05M tokens versus 1M.
- GLM-5.2 has downloadable open weights; the other is API-only.
Side by side
| GLM-5.2 | MiMo-V2-Pro | |
|---|---|---|
| Provider | Z.ai (Zhipu) | Xiaomi |
| Noometry Index | 50.9 | 42.4 |
| Released | 2026-06-13 | 2026-03-18 |
| Weights | Open | Proprietary |
| Context window | 1M | 1.05M |
| Max output | 131K | 131K |
| Input $ / M tokens | $1.40 | $0.43 |
| Output $ / M tokens | $4.40 | $0.87 |
| Results tracked | 50 | 23 |
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Category by category
Coding GLM-5.2 leads
GLM-5.2: 50.9 (#37), MiMo-V2-Pro: 43.4 (#76)
| Benchmark | GLM-5.2 | MiMo-V2-Pro |
|---|---|---|
| LMArena WebDev | 1604 | 1433 |
| LMArena Coding | 1484 | 1476 |
| ALE-Bench | 1,047 | 785.17 |
| SWE-bench Verified | 78.7% | — |
| DeepSWE | 43.8% | — |
| FrontierCode | 24.5% | — |
| SciCode | 50.5% | — |
| WeirdML | 70.1% | — |
Agentic & Tool Use Not comparable
GLM-5.2: 31.6 (#63), MiMo-V2-Pro: —
| Benchmark | GLM-5.2 | MiMo-V2-Pro |
|---|---|---|
| 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), MiMo-V2-Pro: 21.3 (#200)
| Benchmark | GLM-5.2 | MiMo-V2-Pro |
|---|---|---|
| NYT Connections (extended) | 74.3% | 25.8% |
| LMArena Hard Prompts | 1480 | 1457 |
| 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 | — | 45.9% |
| EBR-Bench | 9.5% | — |
| Mystery Game Puzzles | 19% | — |
| DTBench | 93.6% | — |
| LMCA | 45.8% | — |
| Surface Evolver Bench | 55.6% | — |
| Epoch Capabilities Index | 151.76 | — |
Math GLM-5.2 leads
GLM-5.2: 55.4 (#35), MiMo-V2-Pro: 36.7 (#104)
| Benchmark | GLM-5.2 | MiMo-V2-Pro |
|---|---|---|
| LMArena Math | 1482 | 1447 |
| 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% | — |
Knowledge GLM-5.2 leads
GLM-5.2: 57.1 (#36), MiMo-V2-Pro: 40.8 (#106)
| Benchmark | GLM-5.2 | MiMo-V2-Pro |
|---|---|---|
| LMArena Expert | 1484 | 1479 |
| GPQA Diamond | 91.9% | — |
| SimpleQA Verified | 34.2% | — |
Multilingual GLM-5.2 leads
GLM-5.2: 56.0 (#25), MiMo-V2-Pro: 52.9 (#77)
| Benchmark | GLM-5.2 | MiMo-V2-Pro |
|---|---|---|
| LMArena Non-English | 1459 | 1416 |
| LMArena Chinese | 1518 | 1455 |
| LMArena French | 1476 | 1470 |
| LMArena German | 1470 | 1417 |
| LMArena Japanese | 1451 | 1366 |
| LMArena Korean | 1445 | 1401 |
| LMArena Russian | 1467 | 1427 |
| LMArena Spanish | 1477 | 1458 |
Instruction Following Too close to call
GLM-5.2: 77.1 (#32), MiMo-V2-Pro: 76.2 (#46)
| Benchmark | GLM-5.2 | MiMo-V2-Pro |
|---|---|---|
| LMArena Instruction Following | 1465 | 1444 |
Long Context GLM-5.2 leads
GLM-5.2: 45.3 (#43), MiMo-V2-Pro: 41.5 (#133)
| Benchmark | GLM-5.2 | MiMo-V2-Pro |
|---|---|---|
| LMArena Longer Query | 1479 | 1455 |
| CL-bench | — | 15.7% |
| CL-bench Life | — | 6.9% |
Writing & Preference GLM-5.2 leads
GLM-5.2: 70.7 (#20), MiMo-V2-Pro: 62.9 (#67)
| Benchmark | GLM-5.2 | MiMo-V2-Pro |
|---|---|---|
| LMArena Text | 1470 | 1436 |
| LMArena Creative Writing | 1465 | 1415 |
| LMArena Multi-Turn | 1468 | 1456 |
| EQ-Bench Creative Writing | 1757 | — |
| EQ-Bench 4 | 1222 | — |
Frequently asked questions
Is GLM-5.2 better than MiMo-V2-Pro?
GLM-5.2 is the stronger model overall, scoring 50.9 to 42.4 on the Noometry Index. MiMo-V2-Pro costs 4.0× 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 MiMo-V2-Pro?
MiMo-V2-Pro is cheaper. It lists at $0.43 per million input tokens and $0.87 per million output tokens; GLM-5.2 lists at $1.40 and $4.40.
Is GLM-5.2 or MiMo-V2-Pro better for coding?
GLM-5.2 scores higher on coding benchmarks: 50.9 versus 43.4 in the Noometry coding category.
Which has the bigger context window?
MiMo-V2-Pro does, with 1.05M tokens against 1M.
How many benchmarks do GLM-5.2 and MiMo-V2-Pro share?
20 benchmarks have published results for both models. GLM-5.2 has 50 scored results on Noometry and MiMo-V2-Pro has 23.