Model comparison
GLM-5 vs MiMo-V2.5-Pro
GLM-5 and MiMo-V2.5-Pro score almost the same on the Noometry Index (46.1 vs 45.2), so choose on price, context window or the category you care about most.
Last verified . 21 shared benchmarks.
Summary
- They share 21 benchmarks with published results for both. GLM-5 scores higher in 5 categories and MiMo-V2.5-Pro in 3 categories; 5 gaps are clear of the uncertainty.
- The widest gap is in knowledge, where GLM-5 leads 52.3 to 42.2.
- The biggest single-benchmark swing is NYT Connections (extended): 74.8% for GLM-5 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 / $3.20 for GLM-5.
- MiMo-V2.5-Pro accepts more context: 1.05M tokens versus 205K.
Side by side
| GLM-5 | MiMo-V2.5-Pro | |
|---|---|---|
| Provider | Z.ai (Zhipu) | Xiaomi |
| Noometry Index | 46.1 | 45.2 |
| Released | 2026-02-11 | 2026-04-22 |
| Weights | Open | Open |
| Context window | 205K | 1.05M |
| Max output | 131K | 131K |
| Input $ / M tokens | $1 | $0.43 |
| Output $ / M tokens | $3.20 | $0.87 |
| Results tracked | 45 | 27 |
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Category by category
Coding GLM-5 leads
GLM-5: 49.0 (#52), MiMo-V2.5-Pro: 47.4 (#60)
| Benchmark | GLM-5 | MiMo-V2.5-Pro |
|---|---|---|
| LMArena WebDev | 1434 | 1479 |
| LMArena Coding | 1461 | 1503 |
| ALE-Bench | 765.62 | 899.8 |
| SWE-bench Verified | 72.1% | — |
| SWE-bench Verified (bash only) | 72.8% | — |
| SWE-bench Multilingual | 69.7% | — |
| SciCode | — | 50.2% |
| WeirdML | 48.2% | — |
Agentic & Tool Use Not comparable
GLM-5: 31.1 (#71), MiMo-V2.5-Pro: —
| Benchmark | GLM-5 | MiMo-V2.5-Pro |
|---|---|---|
| Terminal-Bench | 52.4% | — |
| τ²-bench Airline | 82.5% | — |
| τ²-bench Banking | 9.8% | — |
| τ²-bench Retail | 73.7% | — |
| τ²-bench Telecom | 86.8% | — |
| Vending-Bench 2 | 4,432 | — |
Reasoning Too close to call
GLM-5: 27.6 (#116), MiMo-V2.5-Pro: 26.8 (#130)
| Benchmark | GLM-5 | MiMo-V2.5-Pro |
|---|---|---|
| NYT Connections (extended) | 74.8% | 34.4% |
| LMArena Hard Prompts | 1452 | 1488 |
| ARC-AGI-2 | 4.9% | — |
| SimpleBench | 53.2% | — |
| Kagi LLM Benchmark | 75% | — |
| ARC-AGI-1 | 44.7% | — |
| CritPt | — | 4% |
| Chess Puzzles | 10% | — |
| DTBench | — | 84.5% |
| LMCA | — | 29.5% |
| Epoch Capabilities Index | 145.83 | — |
| ForecastBench | 61 | — |
Math GLM-5 leads
GLM-5: 46.4 (#71), MiMo-V2.5-Pro: 40.0 (#96)
| Benchmark | GLM-5 | MiMo-V2.5-Pro |
|---|---|---|
| LMArena Math | 1440 | 1481 |
| MathArena Final-Answer Competitions | 65.7% | — |
| OTIS Mock AIME 2024-2025 | 80% | — |
| ProofBench | — | 22% |
| FrontierMath (Feb 2025 set) | 16.4% | — |
| FrontierMath Tier 4 (v1) | 2.1% | — |
Knowledge GLM-5 leads
GLM-5: 52.3 (#64), MiMo-V2.5-Pro: 42.2 (#98)
| Benchmark | GLM-5 | MiMo-V2.5-Pro |
|---|---|---|
| LMArena Expert | 1454 | 1503 |
| GPQA Diamond | 87.8% | — |
| Vectara Hallucination Rate | 10.1% | — |
Multilingual MiMo-V2.5-Pro leads
GLM-5: 53.7 (#58), MiMo-V2.5-Pro: 55.1 (#34)
| Benchmark | GLM-5 | MiMo-V2.5-Pro |
|---|---|---|
| LMArena Non-English | 1430 | 1449 |
| LMArena Chinese | 1511 | 1507 |
| LMArena French | 1455 | 1488 |
| LMArena German | 1445 | 1458 |
| LMArena Japanese | 1416 | 1412 |
| LMArena Korean | 1423 | 1437 |
| LMArena Russian | 1436 | 1450 |
| LMArena Spanish | 1454 | 1471 |
Instruction Following MiMo-V2.5-Pro leads
GLM-5: 75.2 (#67), MiMo-V2.5-Pro: 77.5 (#21)
| Benchmark | GLM-5 | MiMo-V2.5-Pro |
|---|---|---|
| LMArena Instruction Following | 1428 | 1477 |
Long Context Too close to call
GLM-5: 44.7 (#60), MiMo-V2.5-Pro: 45.4 (#37)
| Benchmark | GLM-5 | MiMo-V2.5-Pro |
|---|---|---|
| LMArena Longer Query | 1446 | 1483 |
| CL-bench | 18.7% | — |
Writing & Preference Too close to call
GLM-5: 66.0 (#38), MiMo-V2.5-Pro: 65.3 (#49)
| Benchmark | GLM-5 | MiMo-V2.5-Pro |
|---|---|---|
| LMArena Text | 1446 | 1465 |
| LMArena Creative Writing | 1439 | 1440 |
| EQ-Bench Creative Writing | 1601 | 1493 |
| LMArena Multi-Turn | 1456 | 1477 |
| EQ-Bench 4 | — | 1208 |
Frequently asked questions
Is GLM-5 better than MiMo-V2.5-Pro?
GLM-5 and MiMo-V2.5-Pro score almost the same on the Noometry Index (46.1 vs 45.2), so choose on price, context window or the category you care about most.
Which is cheaper, GLM-5 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 lists at $1 and $3.20.
Is GLM-5 or MiMo-V2.5-Pro better for coding?
GLM-5 scores higher on coding benchmarks: 49.0 versus 47.4 in the Noometry coding category.
Which has the bigger context window?
MiMo-V2.5-Pro does, with 1.05M tokens against 205K.
How many benchmarks do GLM-5 and MiMo-V2.5-Pro share?
21 benchmarks have published results for both models. GLM-5 has 45 scored results on Noometry and MiMo-V2.5-Pro has 27.