Model comparison
GLM-5.3 vs MiniMax-M2.5
GLM-5.3 is the stronger model overall, scoring 54.8 to 38.3 on the Noometry Index. MiniMax-M2.5 costs 4.1× less per token, which makes it the better buy when GLM-5.3's lead doesn't matter for your workload.
Last verified . 24 shared benchmarks.
MiniMaxSummary
- They share 24 benchmarks with published results for both. GLM-5.3 scores higher in 9 categories and MiniMax-M2.5 in 0 categories; 9 gaps are clear of the uncertainty.
- The widest gap is in math, where GLM-5.3 leads 62.3 to 26.9.
- The biggest single-benchmark swing is NYT Connections (extended): 74.2% for GLM-5.3 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-5.3.
- GLM-5.3 accepts more context: 1M tokens versus 205K.
Side by side
| GLM-5.3 | MiniMax-M2.5 | |
|---|---|---|
| Provider | Z.ai (Zhipu) | MiniMax |
| Noometry Index | 54.8 | 38.3 |
| Released | 2026-08-14 | 2026-02-12 |
| Weights | Open | Open |
| Context window | 1M | 205K |
| Max output | 131K | 131K |
| Input $ / M tokens | $1.40 | $0.30 |
| Output $ / M tokens | $4.40 | $1.20 |
| Results tracked | 42 | 33 |
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Category by category
Coding GLM-5.3 leads
GLM-5.3: 59.5 (#14), MiniMax-M2.5: 48.1 (#58)
| Benchmark | GLM-5.3 | MiniMax-M2.5 |
|---|---|---|
| LMArena WebDev | 1622 | 1387 |
| LMArena Coding | 1496 | 1381 |
| ALE-Bench | 1,317 | 618.17 |
| DeepSWE | 69% | — |
| FrontierCode | 40.1% | — |
| SWE-bench Verified (bash only) | — | 75.8% |
| CursorBench | 42.6% | — |
| SWE-bench Multilingual | — | 68.3% |
| FrontierSWE | 30.2% | — |
| SciCode | 59% | — |
| WeirdML | 75.4% | — |
Agentic & Tool Use GLM-5.3 leads
GLM-5.3: 36.4 (#38), MiniMax-M2.5: 30.4 (#77)
| Benchmark | GLM-5.3 | MiniMax-M2.5 |
|---|---|---|
| Vending-Bench 2 | 8,164 | -23.16 |
| Terminal-Bench | — | 42.7% |
| APEX-Agents | 56.6% | — |
Reasoning GLM-5.3 leads
GLM-5.3: 46.1 (#46), MiniMax-M2.5: 17.5 (#292)
| Benchmark | GLM-5.3 | MiniMax-M2.5 |
|---|---|---|
| NYT Connections (extended) | 74.2% | 16.8% |
| LMArena Hard Prompts | 1489 | 1372 |
| Epoch Capabilities Index | 155.61 | 146.68 |
| ARC-AGI-2 | — | 4.9% |
| Kagi LLM Benchmark | — | 55.2% |
| ARC-AGI-1 | — | 63.7% |
| CritPt | 19.1% | — |
| Chess Puzzles | 21% | — |
| Mystery Game Puzzles | 33% | — |
| DTBench | 87.7% | — |
| LMCA | 55.5% | — |
| Bench to the Future 3 | 0.15 | — |
Math GLM-5.3 leads
GLM-5.3: 62.3 (#33), MiniMax-M2.5: 26.9 (#253)
| Benchmark | GLM-5.3 | MiniMax-M2.5 |
|---|---|---|
| ProofBench | 49% | 4% |
| LMArena Math | 1489 | 1378 |
| FrontierMath (Tiers 1-3) | 68.8% | — |
| FrontierMath Tier 4 | 29.3% | — |
| OTIS Mock AIME 2024-2025 | 91.1% | — |
Knowledge GLM-5.3 leads
GLM-5.3: 58.3 (#37), MiniMax-M2.5: 39.2 (#135)
| Benchmark | GLM-5.3 | MiniMax-M2.5 |
|---|---|---|
| LMArena Expert | 1516 | 1379 |
| GPQA Diamond | 90.9% | — |
| SimpleQA Verified | 41% | — |
| Vectara Hallucination Rate | — | 9.1% |
Multilingual GLM-5.3 leads
GLM-5.3: 55.7 (#28), MiniMax-M2.5: 47.1 (#152)
| Benchmark | GLM-5.3 | MiniMax-M2.5 |
|---|---|---|
| LMArena Non-English | 1457 | 1338 |
| LMArena Chinese | 1528 | 1393 |
| LMArena French | 1499 | 1362 |
| LMArena German | 1499 | 1362 |
| LMArena Japanese | 1453 | 1171 |
| LMArena Korean | 1472 | 1232 |
| LMArena Russian | 1463 | 1358 |
| LMArena Spanish | 1460 | 1354 |
Instruction Following GLM-5.3 leads
GLM-5.3: 77.5 (#23), MiniMax-M2.5: 71.5 (#148)
| Benchmark | GLM-5.3 | MiniMax-M2.5 |
|---|---|---|
| LMArena Instruction Following | 1477 | 1353 |
Long Context GLM-5.3 leads
GLM-5.3: 45.4 (#41), MiniMax-M2.5: 37.5 (#216)
| Benchmark | GLM-5.3 | MiniMax-M2.5 |
|---|---|---|
| LMArena Longer Query | 1482 | 1366 |
| CL-bench | — | 11.4% |
| CL-bench Life | — | 6.3% |
Writing & Preference GLM-5.3 leads
GLM-5.3: 75.7 (#6), MiniMax-M2.5: 53.9 (#153)
| Benchmark | GLM-5.3 | MiniMax-M2.5 |
|---|---|---|
| LMArena Text | 1471 | 1359 |
| LMArena Creative Writing | 1457 | 1331 |
| EQ-Bench Creative Writing | 2075 | 1361 |
| LMArena Multi-Turn | 1472 | 1364 |
Frequently asked questions
Is GLM-5.3 better than MiniMax-M2.5?
GLM-5.3 is the stronger model overall, scoring 54.8 to 38.3 on the Noometry Index. MiniMax-M2.5 costs 4.1× less per token, which makes it the better buy when GLM-5.3's lead doesn't matter for your workload.
Which is cheaper, GLM-5.3 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-5.3 lists at $1.40 and $4.40.
Is GLM-5.3 or MiniMax-M2.5 better for coding?
GLM-5.3 scores higher on coding benchmarks: 59.5 versus 48.1 in the Noometry coding category.
Which has the bigger context window?
GLM-5.3 does, with 1M tokens against 205K.
How many benchmarks do GLM-5.3 and MiniMax-M2.5 share?
24 benchmarks have published results for both models. GLM-5.3 has 42 scored results on Noometry and MiniMax-M2.5 has 33.