Model comparison
GLM-5.3 vs MiMo-V2-Flash
GLM-5.3 is the stronger model overall, scoring 54.8 to 41.3 on the Noometry Index. MiMo-V2-Flash 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 . 21 shared benchmarks.
Summary
- They share 21 benchmarks with published results for both. GLM-5.3 scores higher in 8 categories and MiMo-V2-Flash in 0 categories; 8 gaps are clear of the uncertainty.
- The widest gap is in math, where GLM-5.3 leads 62.3 to 38.3.
- The biggest single-benchmark swing is SciCode: 59% for GLM-5.3 and 25.9% for MiMo-V2-Flash.
- MiMo-V2-Flash 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.
Side by side
| GLM-5.3 | MiMo-V2-Flash | |
|---|---|---|
| Provider | Z.ai (Zhipu) | Xiaomi |
| Noometry Index | 54.8 | 41.3 |
| Released | 2026-08-14 | 2025-12-16 |
| Weights | Open | Open |
| Context window | 1M | 262K |
| Max output | 131K | 66K |
| Input $ / M tokens | $1.40 | $0.14 |
| Output $ / M tokens | $4.40 | $0.28 |
| Results tracked | 42 | 21 |
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Category by category
Coding GLM-5.3 leads
GLM-5.3: 59.5 (#14), MiMo-V2-Flash: 36.1 (#211)
| Benchmark | GLM-5.3 | MiMo-V2-Flash |
|---|---|---|
| LMArena WebDev | 1622 | 1330 |
| SciCode | 59% | 25.9% |
| LMArena Coding | 1496 | 1443 |
| ALE-Bench | 1,317 | 737.95 |
| 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-Flash: —
| Benchmark | GLM-5.3 | MiMo-V2-Flash |
|---|---|---|
| APEX-Agents | 56.6% | — |
| Vending-Bench 2 | 8,164 | — |
Reasoning GLM-5.3 leads
GLM-5.3: 46.1 (#46), MiMo-V2-Flash: 24.9 (#157)
| Benchmark | GLM-5.3 | MiMo-V2-Flash |
|---|---|---|
| CritPt | 19.1% | 0% |
| LMArena Hard Prompts | 1489 | 1420 |
| 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-Flash: 38.3 (#139)
| Benchmark | GLM-5.3 | MiMo-V2-Flash |
|---|---|---|
| LMArena Math | 1489 | 1396 |
| 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-Flash: 39.7 (#131)
| Benchmark | GLM-5.3 | MiMo-V2-Flash |
|---|---|---|
| LMArena Expert | 1516 | 1425 |
| GPQA Diamond | 90.9% | — |
| SimpleQA Verified | 41% | — |
Multilingual GLM-5.3 leads
GLM-5.3: 55.7 (#28), MiMo-V2-Flash: 51.0 (#113)
| Benchmark | GLM-5.3 | MiMo-V2-Flash |
|---|---|---|
| LMArena Non-English | 1457 | 1392 |
| LMArena Chinese | 1528 | 1462 |
| LMArena French | 1499 | 1429 |
| LMArena German | 1499 | 1395 |
| LMArena Japanese | 1453 | 1325 |
| LMArena Korean | 1472 | 1358 |
| LMArena Russian | 1463 | 1387 |
| LMArena Spanish | 1460 | 1420 |
Instruction Following GLM-5.3 leads
GLM-5.3: 77.5 (#23), MiMo-V2-Flash: 73.5 (#120)
| Benchmark | GLM-5.3 | MiMo-V2-Flash |
|---|---|---|
| LMArena Instruction Following | 1477 | 1392 |
Long Context GLM-5.3 leads
GLM-5.3: 45.4 (#41), MiMo-V2-Flash: 43.0 (#110)
| Benchmark | GLM-5.3 | MiMo-V2-Flash |
|---|---|---|
| LMArena Longer Query | 1482 | 1409 |
Writing & Preference GLM-5.3 leads
GLM-5.3: 75.7 (#6), MiMo-V2-Flash: 59.7 (#106)
| Benchmark | GLM-5.3 | MiMo-V2-Flash |
|---|---|---|
| LMArena Text | 1471 | 1411 |
| LMArena Creative Writing | 1457 | 1375 |
| LMArena Multi-Turn | 1472 | 1404 |
| EQ-Bench Creative Writing | 2075 | — |
Frequently asked questions
Is GLM-5.3 better than MiMo-V2-Flash?
GLM-5.3 is the stronger model overall, scoring 54.8 to 41.3 on the Noometry Index. MiMo-V2-Flash 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-Flash?
MiMo-V2-Flash 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-Flash better for coding?
GLM-5.3 scores higher on coding benchmarks: 59.5 versus 36.1 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-Flash share?
21 benchmarks have published results for both models. GLM-5.3 has 42 scored results on Noometry and MiMo-V2-Flash has 21.