Model comparison
GLM-5 vs MiMo-V2-Flash
GLM-5 is the stronger model overall, scoring 45.9 to 40.8 on the Noometry Index. MiMo-V2-Flash 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 . 19 shared benchmarks.
Summary
- They share 19 benchmarks with published results for both. GLM-5 scores higher in 8 categories and MiMo-V2-Flash in 0 categories; 8 gaps are clear of the uncertainty.
- The widest gap is in coding, where GLM-5 leads 49.0 to 35.8.
- MiMo-V2-Flash is cheaper at $0.14 / $0.28 per million input/output tokens, against $1 / $3.20 for GLM-5.
- MiMo-V2-Flash accepts more context: 262K tokens versus 205K.
Side by side
| GLM-5 | MiMo-V2-Flash | |
|---|---|---|
| Provider | Z.ai (Zhipu) | Xiaomi |
| Noometry Index | 45.9 | 40.8 |
| Released | 2026-02-11 | 2025-12-16 |
| Weights | Open | Open |
| Context window | 205K | 262K |
| Max output | 131K | 66K |
| Input $ / M tokens | $1 | $0.14 |
| Output $ / M tokens | $3.20 | $0.28 |
| Results tracked | 45 | 21 |
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Category by category
Coding GLM-5 leads
GLM-5: 49.0 (#47), MiMo-V2-Flash: 35.8 (#205)
| Benchmark | GLM-5 | MiMo-V2-Flash |
|---|---|---|
| LMArena WebDev | 1434 | 1330 |
| LMArena Coding | 1461 | 1443 |
| ALE-Bench | 765.62 | 737.95 |
| SWE-bench Verified | 72.1% | — |
| SWE-bench Verified (bash only) | 72.8% | — |
| SWE-bench Multilingual | 69.7% | — |
| SciCode | — | 25.9% |
| WeirdML | 48.2% | — |
Agentic & Tool Use Not comparable
GLM-5: 30.9 (#65), MiMo-V2-Flash: —
| Benchmark | GLM-5 | MiMo-V2-Flash |
|---|---|---|
| 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 GLM-5 leads
GLM-5: 27.6 (#100), MiMo-V2-Flash: 24.1 (#155)
| Benchmark | GLM-5 | MiMo-V2-Flash |
|---|---|---|
| LMArena Hard Prompts | 1451 | 1420 |
| ARC-AGI-2 | 4.9% | — |
| SimpleBench | 53.2% | — |
| Kagi LLM Benchmark | 75% | — |
| NYT Connections (extended) | 74.8% | — |
| ARC-AGI-1 | 44.7% | — |
| CritPt | — | 0% |
| Chess Puzzles | 10% | — |
| Epoch Capabilities Index | 145.84 | — |
| ForecastBench | 61 | — |
Math GLM-5 leads
GLM-5: 44.8 (#66), MiMo-V2-Flash: 35.6 (#141)
| Benchmark | GLM-5 | MiMo-V2-Flash |
|---|---|---|
| LMArena Math | 1440 | 1396 |
| 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-Flash: 39.1 (#126)
| Benchmark | GLM-5 | MiMo-V2-Flash |
|---|---|---|
| LMArena Expert | 1454 | 1425 |
| GPQA Diamond | 87.8% | — |
| Vectara Hallucination Rate | 10.1% | — |
Multilingual GLM-5 leads
GLM-5: 53.9 (#57), MiMo-V2-Flash: 51.1 (#109)
| Benchmark | GLM-5 | MiMo-V2-Flash |
|---|---|---|
| LMArena Non-English | 1430 | 1391 |
| LMArena Chinese | 1511 | 1463 |
| LMArena French | 1455 | 1429 |
| LMArena German | 1445 | 1395 |
| LMArena Japanese | 1416 | 1324 |
| LMArena Korean | 1421 | 1358 |
| LMArena Russian | 1436 | 1387 |
| LMArena Spanish | 1452 | 1420 |
Instruction Following GLM-5 leads
GLM-5: 75.4 (#64), MiMo-V2-Flash: 73.7 (#115)
| Benchmark | GLM-5 | MiMo-V2-Flash |
|---|---|---|
| LMArena Instruction Following | 1427 | 1392 |
Long Context GLM-5 leads
GLM-5: 44.6 (#58), MiMo-V2-Flash: 43.0 (#106)
| Benchmark | GLM-5 | MiMo-V2-Flash |
|---|---|---|
| LMArena Longer Query | 1446 | 1409 |
| CL-bench | 18.7% | — |
Writing & Preference GLM-5 leads
GLM-5: 66.2 (#38), MiMo-V2-Flash: 59.9 (#101)
| Benchmark | GLM-5 | MiMo-V2-Flash |
|---|---|---|
| LMArena Text | 1446 | 1411 |
| LMArena Creative Writing | 1438 | 1375 |
| LMArena Multi-Turn | 1456 | 1404 |
| EQ-Bench Creative Writing | 1601 | — |
Frequently asked questions
Is GLM-5 better than MiMo-V2-Flash?
GLM-5 is the stronger model overall, scoring 45.9 to 40.8 on the Noometry Index. MiMo-V2-Flash 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-Flash?
MiMo-V2-Flash 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-Flash better for coding?
GLM-5 scores higher on coding benchmarks: 49.0 versus 35.8 in the Noometry coding category.
Which has the bigger context window?
MiMo-V2-Flash does, with 262K tokens against 205K.
How many benchmarks do GLM-5 and MiMo-V2-Flash share?
19 benchmarks have published results for both models. GLM-5 has 45 scored results on Noometry and MiMo-V2-Flash has 21.