Model comparison
GLM-5 vs MiMo-V2-Omni
GLM-5 is the stronger model overall, scoring 45.9 to 42.9 on the Noometry Index. MiMo-V2-Omni costs 8.9× less per token, which makes it the better buy when GLM-5'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 scores higher in 6 categories and MiMo-V2-Omni in 2 categories; 6 gaps are clear of the uncertainty.
- The widest gap is in knowledge, where GLM-5 leads 52.2 to 39.9.
- MiMo-V2-Omni is cheaper at $0.14 / $0.28 per million input/output tokens, against $1 / $3.20 for GLM-5.
- MiMo-V2-Omni accepts more context: 262K tokens versus 205K.
- GLM-5 has downloadable open weights; the other is API-only.
Side by side
| GLM-5 | MiMo-V2-Omni | |
|---|---|---|
| Provider | Z.ai (Zhipu) | Xiaomi |
| Noometry Index | 45.9 | 42.9 |
| Released | 2026-02-11 | 2026-03-18 |
| Weights | Open | Proprietary |
| Context window | 205K | 262K |
| Max output | 131K | 131K |
| Input $ / M tokens | $1 | $0.14 |
| Output $ / M tokens | $3.20 | $0.28 |
| Results tracked | 45 | 18 |
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Category by category
Coding GLM-5 leads
GLM-5: 49.0 (#47), MiMo-V2-Omni: 42.7 (#87)
| Benchmark | GLM-5 | MiMo-V2-Omni |
|---|---|---|
| LMArena Coding | 1461 | 1467 |
| SWE-bench Verified | 72.1% | — |
| SWE-bench Verified (bash only) | 72.8% | — |
| LMArena WebDev | 1434 | — |
| SWE-bench Multilingual | 69.7% | — |
| WeirdML | 48.2% | — |
| ALE-Bench | 765.62 | — |
Agentic & Tool Use Not comparable
GLM-5: 30.9 (#65), MiMo-V2-Omni: —
| Benchmark | GLM-5 | MiMo-V2-Omni |
|---|---|---|
| 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 MiMo-V2-Omni leads
GLM-5: 27.6 (#100), MiMo-V2-Omni: 28.6 (#86)
| Benchmark | GLM-5 | MiMo-V2-Omni |
|---|---|---|
| LMArena Hard Prompts | 1451 | 1445 |
| ARC-AGI-2 | 4.9% | — |
| SimpleBench | 53.2% | — |
| Kagi LLM Benchmark | 75% | — |
| NYT Connections (extended) | 74.8% | — |
| ARC-AGI-1 | 44.7% | — |
| Chess Puzzles | 10% | — |
| Epoch Capabilities Index | 145.84 | — |
| ForecastBench | 61 | — |
Math GLM-5 leads
GLM-5: 44.8 (#66), MiMo-V2-Omni: 36.4 (#112)
| Benchmark | GLM-5 | MiMo-V2-Omni |
|---|---|---|
| LMArena Math | 1440 | 1432 |
| MathArena Final-Answer Competitions | 65.7% | — |
| OTIS Mock AIME 2024-2025 | 80% | — |
| FrontierMath (Feb 2025 set) | 16.4% | — |
| FrontierMath Tier 4 (v1) | 2.1% | — |
Knowledge GLM-5 leads
GLM-5: 52.2 (#59), MiMo-V2-Omni: 39.9 (#114)
| Benchmark | GLM-5 | MiMo-V2-Omni |
|---|---|---|
| LMArena Expert | 1454 | 1449 |
| GPQA Diamond | 87.8% | — |
| Vectara Hallucination Rate | 10.1% | — |
Multimodal Not comparable
GLM-5: —, MiMo-V2-Omni: 37.9 (#62)
| Benchmark | GLM-5 | MiMo-V2-Omni |
|---|---|---|
| LMArena Vision | — | 1228 |
Multilingual GLM-5 leads
GLM-5: 53.9 (#57), MiMo-V2-Omni: 52.0 (#97)
| Benchmark | GLM-5 | MiMo-V2-Omni |
|---|---|---|
| LMArena Non-English | 1430 | 1404 |
| LMArena Chinese | 1511 | 1469 |
| LMArena French | 1455 | 1448 |
| LMArena German | 1445 | 1398 |
| LMArena Japanese | 1416 | 1318 |
| LMArena Korean | 1421 | 1355 |
| LMArena Russian | 1436 | 1412 |
| LMArena Spanish | 1452 | 1432 |
Instruction Following Too close to call
GLM-5: 75.4 (#64), MiMo-V2-Omni: 75.4 (#62)
| Benchmark | GLM-5 | MiMo-V2-Omni |
|---|---|---|
| LMArena Instruction Following | 1427 | 1428 |
Long Context Too close to call
GLM-5: 44.6 (#58), MiMo-V2-Omni: 44.1 (#72)
| Benchmark | GLM-5 | MiMo-V2-Omni |
|---|---|---|
| LMArena Longer Query | 1446 | 1442 |
| CL-bench | 18.7% | — |
Writing & Preference GLM-5 leads
GLM-5: 66.2 (#38), MiMo-V2-Omni: 61.6 (#83)
| Benchmark | GLM-5 | MiMo-V2-Omni |
|---|---|---|
| LMArena Text | 1446 | 1424 |
| LMArena Creative Writing | 1438 | 1393 |
| LMArena Multi-Turn | 1456 | 1446 |
| EQ-Bench Creative Writing | 1601 | — |
Frequently asked questions
Is GLM-5 better than MiMo-V2-Omni?
GLM-5 is the stronger model overall, scoring 45.9 to 42.9 on the Noometry Index. MiMo-V2-Omni costs 8.9× less per token, which makes it the better buy when GLM-5's lead doesn't matter for your workload.
Which is cheaper, GLM-5 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 lists at $1 and $3.20.
Is GLM-5 or MiMo-V2-Omni better for coding?
GLM-5 scores higher on coding benchmarks: 49.0 versus 42.7 in the Noometry coding category.
Which has the bigger context window?
MiMo-V2-Omni does, with 262K tokens against 205K.
How many benchmarks do GLM-5 and MiMo-V2-Omni share?
17 benchmarks have published results for both models. GLM-5 has 45 scored results on Noometry and MiMo-V2-Omni has 18.