Model comparison
GLM-5.1 vs MiniMax-M2.5
GLM-5.1 is the stronger model overall, scoring 47.6 to 38.1 on the Noometry Index. MiniMax-M2.5 costs 4.1× less per token, which makes it the better buy when GLM-5.1's lead doesn't matter for your workload.
Last verified . 24 shared benchmarks.
MiniMaxSummary
- They share 24 benchmarks with published results for both. GLM-5.1 scores higher in 8 categories and MiniMax-M2.5 in 1 category; 8 gaps are clear of the uncertainty.
- The widest gap is in math, where GLM-5.1 leads 49.0 to 25.8.
- The biggest single-benchmark swing is NYT Connections (extended): 77.7% for GLM-5.1 and 16.8% for MiniMax-M2.5.
- MiniMax-M2.5 is cheaper at $0.30 / $1.20 per million input/output tokens, against $1.40 / $4.40 for GLM-5.1.
- MiniMax-M2.5 accepts more context: 205K tokens versus 200K.
Side by side
| GLM-5.1 | MiniMax-M2.5 | |
|---|---|---|
| Provider | Z.ai (Zhipu) | MiniMax |
| Noometry Index | 47.6 | 38.1 |
| Released | 2026-04-07 | 2026-02-12 |
| Weights | Open | Open |
| Context window | 200K | 205K |
| Max output | 131K | 131K |
| Input $ / M tokens | $1.40 | $0.30 |
| Output $ / M tokens | $4.40 | $1.20 |
| Results tracked | 41 | 33 |
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Category by category
Coding Too close to call
GLM-5.1: 48.5 (#52), MiniMax-M2.5: 47.9 (#53)
| Benchmark | GLM-5.1 | MiniMax-M2.5 |
|---|---|---|
| LMArena WebDev | 1509 | 1386 |
| LMArena Coding | 1486 | 1380 |
| ALE-Bench | 887.1 | 618.17 |
| SWE-bench Verified | 74.2% | — |
| SWE-bench Verified (bash only) | — | 75.8% |
| SWE-bench Multilingual | — | 68.3% |
| SciCode | 43.8% | — |
| WeirdML | 57.1% | — |
Agentic & Tool Use MiniMax-M2.5 leads
GLM-5.1: 24.8 (#106), MiniMax-M2.5: 30.2 (#70)
| Benchmark | GLM-5.1 | MiniMax-M2.5 |
|---|---|---|
| Vending-Bench 2 | 5,634 | -23.16 |
| Terminal-Bench | — | 42.7% |
| APEX-Agents | 40.9% | — |
| ExploitBench | 18.1% | — |
| GBAEval | 0% | — |
Reasoning GLM-5.1 leads
GLM-5.1: 38.2 (#57), MiniMax-M2.5: 17.6 (#281)
| Benchmark | GLM-5.1 | MiniMax-M2.5 |
|---|---|---|
| NYT Connections (extended) | 77.7% | 16.8% |
| LMArena Hard Prompts | 1472 | 1372 |
| Epoch Capabilities Index | 149.86 | 146.68 |
| ARC-AGI-2 | — | 4.9% |
| SimpleBench | 55.1% | — |
| Kagi LLM Benchmark | — | 55.2% |
| ARC-AGI-1 | — | 63.7% |
| CritPt | 4.6% | — |
| Chess Puzzles | 19% | — |
| Thematic Generalization | 69.8% | — |
Math GLM-5.1 leads
GLM-5.1: 49.0 (#48), MiniMax-M2.5: 25.8 (#242)
| Benchmark | GLM-5.1 | MiniMax-M2.5 |
|---|---|---|
| ProofBench | 22.2% | 4% |
| LMArena Math | 1472 | 1378 |
| FrontierMath (Tiers 1-3) | 36.8% | — |
| MathArena Final-Answer Competitions | 67.1% | — |
| OTIS Mock AIME 2024-2025 | 93.3% | — |
| FrontierMath (Feb 2025 set) | 33.4% | — |
| FrontierMath Tier 4 (v1) | 12.5% | — |
Knowledge GLM-5.1 leads
GLM-5.1: 55.0 (#45), MiniMax-M2.5: 38.6 (#132)
| Benchmark | GLM-5.1 | MiniMax-M2.5 |
|---|---|---|
| LMArena Expert | 1477 | 1378 |
| GPQA Diamond | 89.9% | — |
| SimpleQA Verified | 34% | — |
| Vectara Hallucination Rate | — | 9.1% |
Multilingual GLM-5.1 leads
GLM-5.1: 55.2 (#34), MiniMax-M2.5: 47.3 (#147)
| Benchmark | GLM-5.1 | MiniMax-M2.5 |
|---|---|---|
| LMArena Non-English | 1448 | 1338 |
| LMArena Chinese | 1516 | 1392 |
| LMArena French | 1478 | 1363 |
| LMArena German | 1466 | 1361 |
| LMArena Japanese | 1436 | 1169 |
| LMArena Korean | 1423 | 1232 |
| LMArena Russian | 1453 | 1359 |
| LMArena Spanish | 1467 | 1354 |
Instruction Following GLM-5.1 leads
GLM-5.1: 76.5 (#41), MiniMax-M2.5: 71.7 (#143)
| Benchmark | GLM-5.1 | MiniMax-M2.5 |
|---|---|---|
| LMArena Instruction Following | 1451 | 1353 |
Long Context GLM-5.1 leads
GLM-5.1: 44.9 (#51), MiniMax-M2.5: 37.4 (#211)
| Benchmark | GLM-5.1 | MiniMax-M2.5 |
|---|---|---|
| LMArena Longer Query | 1467 | 1367 |
| CL-bench | — | 11.4% |
| CL-bench Life | — | 6.3% |
Writing & Preference GLM-5.1 leads
GLM-5.1: 67.2 (#29), MiniMax-M2.5: 54.1 (#149)
| Benchmark | GLM-5.1 | MiniMax-M2.5 |
|---|---|---|
| LMArena Text | 1462 | 1359 |
| LMArena Creative Writing | 1454 | 1331 |
| EQ-Bench Creative Writing | 1592 | 1361 |
| LMArena Multi-Turn | 1473 | 1364 |
Frequently asked questions
Is GLM-5.1 better than MiniMax-M2.5?
GLM-5.1 is the stronger model overall, scoring 47.6 to 38.1 on the Noometry Index. MiniMax-M2.5 costs 4.1× less per token, which makes it the better buy when GLM-5.1's lead doesn't matter for your workload.
Which is cheaper, GLM-5.1 or MiniMax-M2.5?
MiniMax-M2.5 is cheaper. It lists at $0.30 per million input tokens and $1.20 per million output tokens; GLM-5.1 lists at $1.40 and $4.40.
Is GLM-5.1 or MiniMax-M2.5 better for coding?
They score almost the same on coding (48.5 vs 47.9); test both on your own repository before choosing.
Which has the bigger context window?
MiniMax-M2.5 does, with 205K tokens against 200K.
How many benchmarks do GLM-5.1 and MiniMax-M2.5 share?
24 benchmarks have published results for both models. GLM-5.1 has 41 scored results on Noometry and MiniMax-M2.5 has 33.