Model comparison
GLM-5 vs MiMo-V2.6-Flash
MiMo-V2.6-Flash is the stronger model overall, scoring 48.5 to 46.1 on the Noometry Index.
Last verified . 15 shared benchmarks.
Summary
- They share 15 benchmarks with published results for both. GLM-5 scores higher in 2 categories and MiMo-V2.6-Flash in 6 categories; 6 gaps are clear of the uncertainty.
- The widest gap is in knowledge, where GLM-5 leads 52.3 to 42.2.
- MiMo-V2.6-Flash is cheaper at $0.14 / $0.28 per million input/output tokens, against $1 / $3.20 for GLM-5.
- MiMo-V2.6-Flash accepts more context: 1.05M tokens versus 205K.
Side by side
| GLM-5 | MiMo-V2.6-Flash | |
|---|---|---|
| Provider | Z.ai (Zhipu) | Xiaomi |
| Noometry Index | 46.1 | 48.5 |
| Released | 2026-02-11 | 2026-09-21 |
| Weights | Open | Open |
| Context window | 205K | 1.05M |
| Max output | 131K | 131K |
| Input $ / M tokens | $1 | $0.14 |
| Output $ / M tokens | $3.20 | $0.28 |
| Results tracked | 45 | 19 |
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Category by category
Coding MiMo-V2.6-Flash leads
GLM-5: 49.0 (#52), MiMo-V2.6-Flash: 53.4 (#30)
| Benchmark | GLM-5 | MiMo-V2.6-Flash |
|---|---|---|
| LMArena WebDev | 1434 | 1637 |
| LMArena Coding | 1461 | 1504 |
| SWE-bench Verified | 72.1% | — |
| SWE-bench Verified (bash only) | 72.8% | — |
| SWE-bench Multilingual | 69.7% | — |
| SciCode | — | 51.3% |
| WeirdML | 48.2% | — |
| ALE-Bench | 765.62 | — |
Agentic & Tool Use Not comparable
GLM-5: 31.1 (#71), MiMo-V2.6-Flash: —
| Benchmark | GLM-5 | MiMo-V2.6-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 MiMo-V2.6-Flash leads
GLM-5: 27.6 (#116), MiMo-V2.6-Flash: 36.5 (#66)
| Benchmark | GLM-5 | MiMo-V2.6-Flash |
|---|---|---|
| LMArena Hard Prompts | 1452 | 1482 |
| ARC-AGI-2 | 4.9% | — |
| SimpleBench | 53.2% | — |
| Kagi LLM Benchmark | 75% | — |
| NYT Connections (extended) | 74.8% | — |
| ARC-AGI-1 | 44.7% | — |
| CritPt | — | 12% |
| Chess Puzzles | 10% | — |
| Epoch Capabilities Index | 145.83 | — |
| ForecastBench | 61 | — |
Math MiMo-V2.6-Flash leads
GLM-5: 46.4 (#71), MiMo-V2.6-Flash: 51.9 (#52)
| Benchmark | GLM-5 | MiMo-V2.6-Flash |
|---|---|---|
| LMArena Math | 1440 | 1468 |
| MathArena Final-Answer Competitions | 65.7% | — |
| OTIS Mock AIME 2024-2025 | 80% | — |
| ProofBench | — | 63% |
| FrontierMath (Feb 2025 set) | 16.4% | — |
| FrontierMath Tier 4 (v1) | 2.1% | — |
Knowledge GLM-5 leads
GLM-5: 52.3 (#64), MiMo-V2.6-Flash: 42.2 (#99)
| Benchmark | GLM-5 | MiMo-V2.6-Flash |
|---|---|---|
| LMArena Expert | 1454 | 1501 |
| GPQA Diamond | 87.8% | — |
| Vectara Hallucination Rate | 10.1% | — |
Multimodal Not comparable
GLM-5: —, MiMo-V2.6-Flash: 40.5 (#47)
| Benchmark | GLM-5 | MiMo-V2.6-Flash |
|---|---|---|
| LMArena Vision | — | 1259 |
Multilingual Too close to call
GLM-5: 53.7 (#58), MiMo-V2.6-Flash: 54.0 (#51)
| Benchmark | GLM-5 | MiMo-V2.6-Flash |
|---|---|---|
| LMArena Non-English | 1430 | 1434 |
| LMArena Chinese | 1511 | 1511 |
| LMArena French | 1455 | 1475 |
| LMArena Russian | 1436 | 1409 |
| LMArena Spanish | 1454 | 1456 |
| LMArena German | 1445 | — |
| LMArena Japanese | 1416 | — |
| LMArena Korean | 1423 | — |
Instruction Following MiMo-V2.6-Flash leads
GLM-5: 75.2 (#67), MiMo-V2.6-Flash: 76.8 (#35)
| Benchmark | GLM-5 | MiMo-V2.6-Flash |
|---|---|---|
| LMArena Instruction Following | 1428 | 1463 |
Long Context Too close to call
GLM-5: 44.7 (#60), MiMo-V2.6-Flash: 44.8 (#57)
| Benchmark | GLM-5 | MiMo-V2.6-Flash |
|---|---|---|
| LMArena Longer Query | 1446 | 1463 |
| CL-bench | 18.7% | — |
Writing & Preference GLM-5 leads
GLM-5: 66.0 (#38), MiMo-V2.6-Flash: 63.1 (#67)
| Benchmark | GLM-5 | MiMo-V2.6-Flash |
|---|---|---|
| LMArena Text | 1446 | 1455 |
| LMArena Creative Writing | 1439 | 1400 |
| LMArena Multi-Turn | 1456 | 1451 |
| EQ-Bench Creative Writing | 1601 | — |
Frequently asked questions
Is GLM-5 better than MiMo-V2.6-Flash?
MiMo-V2.6-Flash is the stronger model overall, scoring 48.5 to 46.1 on the Noometry Index.
Which is cheaper, GLM-5 or MiMo-V2.6-Flash?
MiMo-V2.6-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-V2.6-Flash better for coding?
MiMo-V2.6-Flash scores higher on coding benchmarks: 53.4 versus 49.0 in the Noometry coding category.
Which has the bigger context window?
MiMo-V2.6-Flash does, with 1.05M tokens against 205K.
How many benchmarks do GLM-5 and MiMo-V2.6-Flash share?
15 benchmarks have published results for both models. GLM-5 has 45 scored results on Noometry and MiMo-V2.6-Flash has 19.