Model comparison
GLM-5.3 vs MiMo-V2.5
GLM-5.3 is the stronger model overall, scoring 54.8 to 43.4 on the Noometry Index. MiMo-V2.5 costs 12× less per token, which makes it the better buy when GLM-5.3's lead doesn't matter for your workload.
Last verified . 22 shared benchmarks.
Summary
- They share 22 benchmarks with published results for both. GLM-5.3 scores higher in 8 categories and MiMo-V2.5 in 0 categories; 8 gaps are clear of the uncertainty.
- The widest gap is in math, where GLM-5.3 leads 62.3 to 36.8.
- The biggest single-benchmark swing is ProofBench: 49% for GLM-5.3 and 16% for MiMo-V2.5.
- MiMo-V2.5 is cheaper at $0.14 / $0.28 per million input/output tokens, against $1.40 / $4.40 for GLM-5.3.
- MiMo-V2.5 accepts more context: 1.05M tokens versus 1M.
Side by side
| GLM-5.3 | MiMo-V2.5 | |
|---|---|---|
| Provider | Z.ai (Zhipu) | Xiaomi |
| Noometry Index | 54.8 | 43.4 |
| Released | 2026-08-14 | 2026-04-22 |
| Weights | Open | Open |
| Context window | 1M | 1.05M |
| Max output | 131K | 131K |
| Input $ / M tokens | $1.40 | $0.14 |
| Output $ / M tokens | $4.40 | $0.28 |
| Results tracked | 42 | 23 |
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Category by category
Coding GLM-5.3 leads
GLM-5.3: 59.5 (#14), MiMo-V2.5: 43.9 (#81)
| Benchmark | GLM-5.3 | MiMo-V2.5 |
|---|---|---|
| LMArena WebDev | 1622 | 1438 |
| SciCode | 59% | 43.1% |
| LMArena Coding | 1496 | 1469 |
| ALE-Bench | 1,317 | 513.95 |
| DeepSWE | 69% | — |
| FrontierCode | 40.1% | — |
| CursorBench | 42.6% | — |
| FrontierSWE | 30.2% | — |
| WeirdML | 75.4% | — |
Agentic & Tool Use Not comparable
GLM-5.3: 36.4 (#38), MiMo-V2.5: —
| Benchmark | GLM-5.3 | MiMo-V2.5 |
|---|---|---|
| APEX-Agents | 56.6% | — |
| Vending-Bench 2 | 8,164 | — |
Reasoning GLM-5.3 leads
GLM-5.3: 46.1 (#46), MiMo-V2.5: 28.6 (#101)
| Benchmark | GLM-5.3 | MiMo-V2.5 |
|---|---|---|
| CritPt | 19.1% | 3.7% |
| LMArena Hard Prompts | 1489 | 1450 |
| NYT Connections (extended) | 74.2% | — |
| Chess Puzzles | 21% | — |
| Mystery Game Puzzles | 33% | — |
| DTBench | 87.7% | — |
| LMCA | 55.5% | — |
| Bench to the Future 3 | 0.15 | — |
| Epoch Capabilities Index | 155.61 | — |
Math GLM-5.3 leads
GLM-5.3: 62.3 (#33), MiMo-V2.5: 36.8 (#163)
| Benchmark | GLM-5.3 | MiMo-V2.5 |
|---|---|---|
| ProofBench | 49% | 16% |
| LMArena Math | 1489 | 1436 |
| FrontierMath (Tiers 1-3) | 68.8% | — |
| FrontierMath Tier 4 | 29.3% | — |
| OTIS Mock AIME 2024-2025 | 91.1% | — |
Knowledge GLM-5.3 leads
GLM-5.3: 58.3 (#37), MiMo-V2.5: 40.8 (#115)
| Benchmark | GLM-5.3 | MiMo-V2.5 |
|---|---|---|
| LMArena Expert | 1516 | 1460 |
| GPQA Diamond | 90.9% | — |
| SimpleQA Verified | 41% | — |
Multimodal Not comparable
GLM-5.3: —, MiMo-V2.5: 39.8 (#54)
| Benchmark | GLM-5.3 | MiMo-V2.5 |
|---|---|---|
| LMArena Vision | — | 1247 |
Multilingual GLM-5.3 leads
GLM-5.3: 55.7 (#28), MiMo-V2.5: 51.9 (#99)
| Benchmark | GLM-5.3 | MiMo-V2.5 |
|---|---|---|
| LMArena Non-English | 1457 | 1404 |
| LMArena Chinese | 1528 | 1468 |
| LMArena French | 1499 | 1447 |
| LMArena German | 1499 | 1421 |
| LMArena Japanese | 1453 | 1306 |
| LMArena Korean | 1472 | 1363 |
| LMArena Russian | 1463 | 1395 |
| LMArena Spanish | 1460 | 1416 |
Instruction Following GLM-5.3 leads
GLM-5.3: 77.5 (#23), MiMo-V2.5: 75.5 (#60)
| Benchmark | GLM-5.3 | MiMo-V2.5 |
|---|---|---|
| LMArena Instruction Following | 1477 | 1434 |
Long Context GLM-5.3 leads
GLM-5.3: 45.4 (#41), MiMo-V2.5: 44.2 (#73)
| Benchmark | GLM-5.3 | MiMo-V2.5 |
|---|---|---|
| LMArena Longer Query | 1482 | 1445 |
Writing & Preference GLM-5.3 leads
GLM-5.3: 75.7 (#6), MiMo-V2.5: 61.6 (#86)
| Benchmark | GLM-5.3 | MiMo-V2.5 |
|---|---|---|
| LMArena Text | 1471 | 1428 |
| LMArena Creative Writing | 1457 | 1393 |
| LMArena Multi-Turn | 1472 | 1445 |
| EQ-Bench Creative Writing | 2075 | — |
Frequently asked questions
Is GLM-5.3 better than MiMo-V2.5?
GLM-5.3 is the stronger model overall, scoring 54.8 to 43.4 on the Noometry Index. MiMo-V2.5 costs 12× less per token, which makes it the better buy when GLM-5.3's lead doesn't matter for your workload.
Which is cheaper, GLM-5.3 or MiMo-V2.5?
MiMo-V2.5 is cheaper. It lists at $0.14 per million input tokens and $0.28 per million output tokens; GLM-5.3 lists at $1.40 and $4.40.
Is GLM-5.3 or MiMo-V2.5 better for coding?
GLM-5.3 scores higher on coding benchmarks: 59.5 versus 43.9 in the Noometry coding category.
Which has the bigger context window?
MiMo-V2.5 does, with 1.05M tokens against 1M.
How many benchmarks do GLM-5.3 and MiMo-V2.5 share?
22 benchmarks have published results for both models. GLM-5.3 has 42 scored results on Noometry and MiMo-V2.5 has 23.