Model comparison
GLM-5.3 vs MiMo-V2-Omni
GLM-5.3 is the stronger model overall, scoring 54.8 to 43.6 on the Noometry Index. MiMo-V2-Omni 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 . 17 shared benchmarks.
Summary
- They share 17 benchmarks with published results for both. GLM-5.3 scores higher in 8 categories and MiMo-V2-Omni in 0 categories; 8 gaps are clear of the uncertainty.
- The widest gap is in math, where GLM-5.3 leads 62.3 to 39.1.
- MiMo-V2-Omni is cheaper at $0.14 / $0.28 per million input/output tokens, against $1.40 / $4.40 for GLM-5.3.
- GLM-5.3 accepts more context: 1M tokens versus 262K.
- GLM-5.3 has downloadable open weights; the other is API-only.
Side by side
| GLM-5.3 | MiMo-V2-Omni | |
|---|---|---|
| Provider | Z.ai (Zhipu) | Xiaomi |
| Noometry Index | 54.8 | 43.6 |
| Released | 2026-08-14 | 2026-03-18 |
| Weights | Open | Proprietary |
| Context window | 1M | 262K |
| Max output | 131K | 131K |
| Input $ / M tokens | $1.40 | $0.14 |
| Output $ / M tokens | $4.40 | $0.28 |
| Results tracked | 42 | 18 |
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Category by category
Coding GLM-5.3 leads
GLM-5.3: 59.5 (#14), MiMo-V2-Omni: 43.3 (#89)
| Benchmark | GLM-5.3 | MiMo-V2-Omni |
|---|---|---|
| LMArena Coding | 1496 | 1466 |
| DeepSWE | 69% | — |
| FrontierCode | 40.1% | — |
| CursorBench | 42.6% | — |
| LMArena WebDev | 1622 | — |
| FrontierSWE | 30.2% | — |
| SciCode | 59% | — |
| WeirdML | 75.4% | — |
| ALE-Bench | 1,317 | — |
Agentic & Tool Use Not comparable
GLM-5.3: 36.4 (#38), MiMo-V2-Omni: —
| Benchmark | GLM-5.3 | MiMo-V2-Omni |
|---|---|---|
| APEX-Agents | 56.6% | — |
| Vending-Bench 2 | 8,164 | — |
Reasoning GLM-5.3 leads
GLM-5.3: 46.1 (#46), MiMo-V2-Omni: 29.7 (#88)
| Benchmark | GLM-5.3 | MiMo-V2-Omni |
|---|---|---|
| LMArena Hard Prompts | 1489 | 1445 |
| NYT Connections (extended) | 74.2% | — |
| CritPt | 19.1% | — |
| 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-Omni: 39.1 (#115)
| Benchmark | GLM-5.3 | MiMo-V2-Omni |
|---|---|---|
| LMArena Math | 1489 | 1430 |
| FrontierMath (Tiers 1-3) | 68.8% | — |
| FrontierMath Tier 4 | 29.3% | — |
| OTIS Mock AIME 2024-2025 | 91.1% | — |
| ProofBench | 49% | — |
Knowledge GLM-5.3 leads
GLM-5.3: 58.3 (#37), MiMo-V2-Omni: 40.5 (#118)
| Benchmark | GLM-5.3 | MiMo-V2-Omni |
|---|---|---|
| LMArena Expert | 1516 | 1449 |
| GPQA Diamond | 90.9% | — |
| SimpleQA Verified | 41% | — |
Multimodal Not comparable
GLM-5.3: —, MiMo-V2-Omni: 38.6 (#63)
| Benchmark | GLM-5.3 | MiMo-V2-Omni |
|---|---|---|
| LMArena Vision | — | 1228 |
Multilingual GLM-5.3 leads
GLM-5.3: 55.7 (#28), MiMo-V2-Omni: 51.8 (#102)
| Benchmark | GLM-5.3 | MiMo-V2-Omni |
|---|---|---|
| LMArena Non-English | 1457 | 1404 |
| LMArena Chinese | 1528 | 1465 |
| LMArena French | 1499 | 1447 |
| LMArena German | 1499 | 1399 |
| LMArena Japanese | 1453 | 1317 |
| LMArena Korean | 1472 | 1355 |
| LMArena Russian | 1463 | 1412 |
| LMArena Spanish | 1460 | 1434 |
Instruction Following GLM-5.3 leads
GLM-5.3: 77.5 (#23), MiMo-V2-Omni: 75.2 (#66)
| Benchmark | GLM-5.3 | MiMo-V2-Omni |
|---|---|---|
| LMArena Instruction Following | 1477 | 1428 |
Long Context GLM-5.3 leads
GLM-5.3: 45.4 (#41), MiMo-V2-Omni: 44.1 (#76)
| Benchmark | GLM-5.3 | MiMo-V2-Omni |
|---|---|---|
| LMArena Longer Query | 1482 | 1442 |
Writing & Preference GLM-5.3 leads
GLM-5.3: 75.7 (#6), MiMo-V2-Omni: 61.4 (#87)
| Benchmark | GLM-5.3 | MiMo-V2-Omni |
|---|---|---|
| LMArena Text | 1471 | 1423 |
| LMArena Creative Writing | 1457 | 1392 |
| LMArena Multi-Turn | 1472 | 1445 |
| EQ-Bench Creative Writing | 2075 | — |
Frequently asked questions
Is GLM-5.3 better than MiMo-V2-Omni?
GLM-5.3 is the stronger model overall, scoring 54.8 to 43.6 on the Noometry Index. MiMo-V2-Omni 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-Omni?
MiMo-V2-Omni 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-Omni better for coding?
GLM-5.3 scores higher on coding benchmarks: 59.5 versus 43.3 in the Noometry coding category.
Which has the bigger context window?
GLM-5.3 does, with 1M tokens against 262K.
How many benchmarks do GLM-5.3 and MiMo-V2-Omni share?
17 benchmarks have published results for both models. GLM-5.3 has 42 scored results on Noometry and MiMo-V2-Omni has 18.