Model comparison
GLM-5 vs MiMo-V2.6-Pro
GLM-5 and MiMo-V2.6-Pro score almost the same on the Noometry Index (45.9 vs 46.7), so choose on price, context window or the category you care about most.
Last verified . 13 shared benchmarks.
Summary
- They share 13 benchmarks with published results for both. GLM-5 scores higher in 2 categories and MiMo-V2.6-Pro in 6 categories; 7 gaps are clear of the uncertainty.
- The widest gap is in knowledge, where GLM-5 leads 52.2 to 43.0.
- MiMo-V2.6-Pro is cheaper at $0.43 / $0.87 per million input/output tokens, against $1 / $3.20 for GLM-5.
- MiMo-V2.6-Pro accepts more context: 1.05M tokens versus 205K.
Side by side
| GLM-5 | MiMo-V2.6-Pro | |
|---|---|---|
| Provider | Z.ai (Zhipu) | Xiaomi |
| Noometry Index | 45.9 | 46.7 |
| Released | 2026-02-11 | 2026-09-22 |
| Weights | Open | Open |
| Context window | 205K | 1.05M |
| Max output | 131K | 131K |
| Input $ / M tokens | $1 | $0.43 |
| Output $ / M tokens | $3.20 | $0.87 |
| Results tracked | 45 | 14 |
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Category by category
Coding MiMo-V2.6-Pro leads
GLM-5: 49.0 (#47), MiMo-V2.6-Pro: 53.2 (#27)
| Benchmark | GLM-5 | MiMo-V2.6-Pro |
|---|---|---|
| LMArena WebDev | 1434 | 1618 |
| LMArena Coding | 1461 | 1538 |
| SWE-bench Verified | 72.1% | — |
| SWE-bench Verified (bash only) | 72.8% | — |
| SWE-bench Multilingual | 69.7% | — |
| WeirdML | 48.2% | — |
| ALE-Bench | 765.62 | — |
Agentic & Tool Use Not comparable
GLM-5: 30.9 (#65), MiMo-V2.6-Pro: —
| Benchmark | GLM-5 | MiMo-V2.6-Pro |
|---|---|---|
| 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-Pro leads
GLM-5: 27.6 (#100), MiMo-V2.6-Pro: 30.4 (#76)
| Benchmark | GLM-5 | MiMo-V2.6-Pro |
|---|---|---|
| LMArena Hard Prompts | 1451 | 1511 |
| 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-V2.6-Pro: 37.9 (#91)
| Benchmark | GLM-5 | MiMo-V2.6-Pro |
|---|---|---|
| LMArena Math | 1440 | 1497 |
| 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-V2.6-Pro: 43.0 (#85)
| Benchmark | GLM-5 | MiMo-V2.6-Pro |
|---|---|---|
| LMArena Expert | 1454 | 1548 |
| GPQA Diamond | 87.8% | — |
| Vectara Hallucination Rate | 10.1% | — |
Multimodal Not comparable
GLM-5: —, MiMo-V2.6-Pro: 40.2 (#40)
| Benchmark | GLM-5 | MiMo-V2.6-Pro |
|---|---|---|
| LMArena Vision | — | 1265 |
Multilingual MiMo-V2.6-Pro leads
GLM-5: 53.9 (#57), MiMo-V2.6-Pro: 57.0 (#15)
| Benchmark | GLM-5 | MiMo-V2.6-Pro |
|---|---|---|
| LMArena Non-English | 1430 | 1474 |
| LMArena Chinese | 1511 | 1532 |
| LMArena Russian | 1436 | 1481 |
| LMArena French | 1455 | — |
| LMArena German | 1445 | — |
| LMArena Japanese | 1416 | — |
| LMArena Korean | 1421 | — |
| LMArena Spanish | 1452 | — |
Instruction Following MiMo-V2.6-Pro leads
GLM-5: 75.4 (#64), MiMo-V2.6-Pro: 78.2 (#10)
| Benchmark | GLM-5 | MiMo-V2.6-Pro |
|---|---|---|
| LMArena Instruction Following | 1427 | 1490 |
Long Context MiMo-V2.6-Pro leads
GLM-5: 44.6 (#58), MiMo-V2.6-Pro: 46.0 (#26)
| Benchmark | GLM-5 | MiMo-V2.6-Pro |
|---|---|---|
| LMArena Longer Query | 1446 | 1500 |
| CL-bench | 18.7% | — |
Writing & Preference Too close to call
GLM-5: 66.2 (#38), MiMo-V2.6-Pro: 66.9 (#32)
| Benchmark | GLM-5 | MiMo-V2.6-Pro |
|---|---|---|
| LMArena Text | 1446 | 1491 |
| LMArena Creative Writing | 1438 | 1465 |
| LMArena Multi-Turn | 1456 | 1463 |
| EQ-Bench Creative Writing | 1601 | — |
Frequently asked questions
Is GLM-5 better than MiMo-V2.6-Pro?
GLM-5 and MiMo-V2.6-Pro score almost the same on the Noometry Index (45.9 vs 46.7), so choose on price, context window or the category you care about most.
Which is cheaper, GLM-5 or MiMo-V2.6-Pro?
MiMo-V2.6-Pro is cheaper. It lists at $0.43 per million input tokens and $0.87 per million output tokens; GLM-5 lists at $1 and $3.20.
Is GLM-5 or MiMo-V2.6-Pro better for coding?
MiMo-V2.6-Pro scores higher on coding benchmarks: 53.2 versus 49.0 in the Noometry coding category.
Which has the bigger context window?
MiMo-V2.6-Pro does, with 1.05M tokens against 205K.
How many benchmarks do GLM-5 and MiMo-V2.6-Pro share?
13 benchmarks have published results for both models. GLM-5 has 45 scored results on Noometry and MiMo-V2.6-Pro has 14.