Model comparison
GLM-5.2 vs MiMo-V2.5-Pro
GLM-5.2 is the stronger model overall, scoring 51.1 to 45.2 on the Noometry Index. MiMo-V2.5-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 . 27 shared benchmarks.
Summary
- They share 27 benchmarks with published results for both. GLM-5.2 scores higher in 6 categories and MiMo-V2.5-Pro in 2 categories; 5 gaps are clear of the uncertainty.
- The widest gap is in math, where GLM-5.2 leads 55.7 to 40.0.
- The biggest single-benchmark swing is NYT Connections (extended): 74.3% for GLM-5.2 and 34.4% for MiMo-V2.5-Pro.
- MiMo-V2.5-Pro is cheaper at $0.43 / $0.87 per million input/output tokens, against $1.40 / $4.40 for GLM-5.2.
- MiMo-V2.5-Pro accepts more context: 1.05M tokens versus 1M.
Side by side
| GLM-5.2 | MiMo-V2.5-Pro | |
|---|---|---|
| Provider | Z.ai (Zhipu) | Xiaomi |
| Noometry Index | 51.1 | 45.2 |
| Released | 2026-06-13 | 2026-04-22 |
| Weights | Open | Open |
| 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 | 51 | 27 |
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Category by category
Coding GLM-5.2 leads
GLM-5.2: 51.3 (#41), MiMo-V2.5-Pro: 47.4 (#60)
| Benchmark | GLM-5.2 | MiMo-V2.5-Pro |
|---|---|---|
| LMArena WebDev | 1603 | 1479 |
| SciCode | 50.5% | 50.2% |
| LMArena Coding | 1485 | 1503 |
| ALE-Bench | 1,047 | 899.8 |
| SWE-bench Verified | 78.7% | — |
| DeepSWE | 43.8% | — |
| FrontierCode | 24.5% | — |
| WeirdML | 70.1% | — |
Agentic & Tool Use Not comparable
GLM-5.2: 32.4 (#63), MiMo-V2.5-Pro: —
| Benchmark | GLM-5.2 | MiMo-V2.5-Pro |
|---|---|---|
| APEX-Agents | 45.2% | — |
| τ²-bench Banking | 37.1% | — |
| PostTrainBench | 31.7% | — |
| GBAEval | 0% | — |
| Vending-Bench 2 | 8,314 | — |
Reasoning GLM-5.2 leads
GLM-5.2: 42.3 (#52), MiMo-V2.5-Pro: 26.8 (#130)
| Benchmark | GLM-5.2 | MiMo-V2.5-Pro |
|---|---|---|
| NYT Connections (extended) | 74.3% | 34.4% |
| CritPt | 20.9% | 4% |
| LMArena Hard Prompts | 1480 | 1488 |
| DTBench | 93.6% | 84.5% |
| LMCA | 45.8% | 29.5% |
| ARC-AGI-2 | 22.8% | — |
| SimpleBench | 58.8% | — |
| Kagi LLM Benchmark | 62.6% | — |
| ARC-AGI-1 | 77% | — |
| Chess Puzzles | 21% | — |
| EBR-Bench | 9.5% | — |
| Mystery Game Puzzles | 19% | — |
| Surface Evolver Bench | 55.6% | — |
| Epoch Capabilities Index | 151.78 | — |
Math GLM-5.2 leads
GLM-5.2: 55.7 (#43), MiMo-V2.5-Pro: 40.0 (#96)
| Benchmark | GLM-5.2 | MiMo-V2.5-Pro |
|---|---|---|
| ProofBench | 35% | 22% |
| LMArena Math | 1482 | 1481 |
| FrontierMath (Tiers 1-3) | 59.2% | — |
| FrontierMath Tier 4 | 29.3% | — |
| MathArena Final-Answer Competitions | 67.6% | — |
| OTIS Mock AIME 2024-2025 | 86.4% | — |
Knowledge GLM-5.2 leads
GLM-5.2: 57.1 (#40), MiMo-V2.5-Pro: 42.2 (#98)
| Benchmark | GLM-5.2 | MiMo-V2.5-Pro |
|---|---|---|
| LMArena Expert | 1486 | 1503 |
| GPQA Diamond | 91.9% | — |
| SimpleQA Verified | 34.2% | — |
Multilingual Too close to call
GLM-5.2: 55.8 (#26), MiMo-V2.5-Pro: 55.1 (#34)
| Benchmark | GLM-5.2 | MiMo-V2.5-Pro |
|---|---|---|
| LMArena Non-English | 1459 | 1449 |
| LMArena Chinese | 1519 | 1507 |
| LMArena French | 1479 | 1488 |
| LMArena German | 1468 | 1458 |
| LMArena Japanese | 1451 | 1412 |
| LMArena Korean | 1445 | 1437 |
| LMArena Russian | 1466 | 1450 |
| LMArena Spanish | 1477 | 1471 |
Instruction Following Too close to call
GLM-5.2: 76.9 (#34), MiMo-V2.5-Pro: 77.5 (#21)
| Benchmark | GLM-5.2 | MiMo-V2.5-Pro |
|---|---|---|
| LMArena Instruction Following | 1465 | 1477 |
Long Context Too close to call
GLM-5.2: 45.3 (#43), MiMo-V2.5-Pro: 45.4 (#37)
| Benchmark | GLM-5.2 | MiMo-V2.5-Pro |
|---|---|---|
| LMArena Longer Query | 1479 | 1483 |
Writing & Preference GLM-5.2 leads
GLM-5.2: 70.4 (#21), MiMo-V2.5-Pro: 65.3 (#49)
| Benchmark | GLM-5.2 | MiMo-V2.5-Pro |
|---|---|---|
| LMArena Text | 1470 | 1465 |
| LMArena Creative Writing | 1462 | 1440 |
| EQ-Bench Creative Writing | 1757 | 1493 |
| EQ-Bench 4 | 1222 | 1208 |
| LMArena Multi-Turn | 1469 | 1477 |
Frequently asked questions
Is GLM-5.2 better than MiMo-V2.5-Pro?
GLM-5.2 is the stronger model overall, scoring 51.1 to 45.2 on the Noometry Index. MiMo-V2.5-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-V2.5-Pro?
MiMo-V2.5-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-V2.5-Pro better for coding?
GLM-5.2 scores higher on coding benchmarks: 51.3 versus 47.4 in the Noometry coding category.
Which has the bigger context window?
MiMo-V2.5-Pro does, with 1.05M tokens against 1M.
How many benchmarks do GLM-5.2 and MiMo-V2.5-Pro share?
27 benchmarks have published results for both models. GLM-5.2 has 51 scored results on Noometry and MiMo-V2.5-Pro has 27.