GLM 4.5 Air vs MiniMax M2
MiniMax M2 leads GLM 4.5 Air on the Artificial Analysis Intelligence Index, 18.6 to 11.1, at about the same output price ($1.02 vs $1.10 per million). Updated October 22, 2026; 45 benchmarks have a result for both.
GLM 4.5 Air or MiniMax M2: which to pick
Neither model has a coding score yet, so the two cannot be ranked on coding.
Pick GLM 4.5 Air for work measured by LiveCodeBench (Artificial Analysis run) or Terminal-Bench Hard (Artificial Analysis run), where it leads.
- Shared results
- 45
- GLM 4.5 Air only
- 4
- MiniMax M2 only
- 5
- Like-for-like categories
- 8 of 9
Only benchmarks that tested both models count toward a pick; a result only one model has is shown but not compared.
Which one for your work
Each card is decided by one number both models carry. Where only one has it, the card says so and makes no call.
Coding
Vibe Coding Index, six public coding benchmarks
No call
Neither has a coding score yet.
One side not measured
Intelligence
AA Intelligence Index
MiniMax M2
MiniMax M2 leads, 18.6 against 11.1.
Same board, both measured
Web design
Design Arena: websites
GLM 4.5 Air
GLM 4.5 Air leads, 1146 against 1143.
Same board, both measured
Long documents
Listed context window
MiniMax M2
MiniMax M2 takes 205K tokens against 131K.
Listed by OpenRouter
Everyday cost
1K input + 500 output tokens
GLM 4.5 Air
GLM 4.5 Air $0.0008, MiniMax M2 $0.0008 per turn.
List prices
Cost per solved task
One SWE-bench Verified task, solved
No call
Only MiniMax M2 has the number, $0.702.
Directional only
At a glance
- Coding index
- GLM 4.5 Air not benchmarked, MiniMax M2 not benchmarked.
- Widest category
- Agentic work, 40.4 points apart on average over 1 shared benchmark.
- Widest single gap
- τ²-Bench Telecom: GLM 4.5 Air 46.5% against MiniMax M2 86.8%, 40.4 points apart on a 0 to 100 scale.
- Output price
- GLM 4.5 Air $1.10 against MiniMax M2 $1.02 per million output tokens, 1.1x apart.
- Context window
- GLM 4.5 Air takes 131K tokens, MiniMax M2 205K.
Benchmark by benchmark
First the score: boards each model leads, per category, where both have a result. Then every board, a category to a fold; bars are each result on a 0 to 100 scale, so compare the two in a row, not one row with another.
| Category | GLM 4.5 Air | Bars | MiniMax M2 | Ties |
|---|---|---|---|---|
| Coding | 2 | 2 | ||
| Intelligence | 0 | 1 | ||
| Agentic work | 0 | 1 | ||
| Reasoning | 3 | 3 | ||
| Math | 4 | 0 | ||
| Knowledge | 4 | 1 | ||
| Web design | 4 | 3 | ||
| General | 15 | 2 | ||
| All shared boards | 32 | 13 |
CodingLike-for-likeSix of these boards feed the Vibe Coding Index22
| Benchmark | GLM 4.5 Air | MiniMax M2 | Leader |
|---|---|---|---|
| SWE-bench Verified | Not measured | 61.0% | Only MiniMax M2 measured |
| LMArena WebDev | Not measured | 1297 | Only MiniMax M2 measured |
| LiveCodeBench (Artificial Analysis run) | 68.4% | 82.6% | MiniMax M2 |
| Terminal-Bench Hard (Artificial Analysis run) | 20.5% | 25.8% | MiniMax M2 |
| Terminal-Bench | Not measured | 30.0% | Only MiniMax M2 measured |
| GSO-Bench | 2.9% | Not measured | Only GLM 4.5 Air measured |
| LMArena Text: coding | 1397 | 1371 | GLM 4.5 Air |
| LMArena Text: software and IT | 1401 | 1373 | GLM 4.5 Air |
| LMArena WebDev: HTML | Not measured | 1306 | Only MiniMax M2 measured |
| Neither model has a result on the other 34 boards here | |||
IntelligenceLike-for-likePublic boards, shown as published, kept out of the index01
| Benchmark | GLM 4.5 Air | MiniMax M2 | Leader |
|---|---|---|---|
| AA Intelligence Index | 11.1 | 18.6 | MiniMax M2 |
| Neither model has a result on the other 1 board here | |||
Agentic workLike-for-likePublic boards, shown as published, kept out of the index01
| Benchmark | GLM 4.5 Air | MiniMax M2 | Leader |
|---|---|---|---|
| τ²-Bench Telecom | 46.5% | 86.8% | MiniMax M2 |
| Vending-Bench 2 | Not measured | $161 | Only MiniMax M2 measured |
| Neither model has a result on the other 40 boards here | |||
ReasoningLike-for-likePublic boards, shown as published, kept out of the index33
| Benchmark | GLM 4.5 Air | MiniMax M2 | Leader |
|---|---|---|---|
| GPQA Diamond (Artificial Analysis run) | 73.3% | 77.7% | MiniMax M2 |
| Humanity's Last Exam (Artificial Analysis run) | 7.0% | 13.7% | MiniMax M2 |
| LMArena Text: hard prompts | 1379 | 1357 | GLM 4.5 Air |
| LMArena Text: expert | 1370 | 1338 | GLM 4.5 Air |
| LMArena Text: hard prompts in English | 1388 | 1376 | GLM 4.5 Air |
| Kagi LLM Benchmark | 43.0% | 57.8% | MiniMax M2 |
| Neither model has a result on the other 17 boards here | |||
MathLike-for-likePublic boards, shown as published, kept out of the index40
| Benchmark | GLM 4.5 Air | MiniMax M2 | Leader |
|---|---|---|---|
| AA Math Index | 80.7 | 78.3 | GLM 4.5 Air |
| AIME 2025 (Artificial Analysis run) | 80.7% | 78.3% | GLM 4.5 Air |
| LMArena Text: math | 1396 | 1351 | GLM 4.5 Air |
| LMArena Text: mathematical work | 1396 | 1368 | GLM 4.5 Air |
| MathArena | 12.0% | Not measured | Only GLM 4.5 Air measured |
| Neither model has a result on the other 9 boards here | |||
KnowledgeLike-for-likePublic boards, shown as published, kept out of the index41
| Benchmark | GLM 4.5 Air | MiniMax M2 | Leader |
|---|---|---|---|
| MMLU Pro (Artificial Analysis run) | 81.5% | 82.0% | MiniMax M2 |
| LMArena Text: science | 1407 | 1348 | GLM 4.5 Air |
| LMArena Text: medicine and healthcare | 1402 | 1367 | GLM 4.5 Air |
| LMArena Text: legal and government | 1382 | 1349 | GLM 4.5 Air |
| LMArena Text: business and finance | 1371 | 1336 | GLM 4.5 Air |
| Neither model has a result on the other 15 boards here | |||
Web designLike-for-likePublic boards, shown as published, kept out of the index43
| Benchmark | GLM 4.5 Air | MiniMax M2 | Leader |
|---|---|---|---|
| Design Arena: websites | 1146 | 1143 | GLM 4.5 Air |
| Design Arena: UI components | 1121 | 1134 | MiniMax M2 |
| Design Arena: game dev | 1093 | 1118 | MiniMax M2 |
| Design Arena: data viz | 1186 | 1140 | GLM 4.5 Air |
| Design Arena: 3D | 1122 | 1098 | GLM 4.5 Air |
| Design Arena: all code categories | 1137 | 1135 | GLM 4.5 Air |
| Design Arena: SVG | 1070 | 1100 | MiniMax M2 |
| Neither model has a result on the other 2 boards here | |||
GeneralLike-for-likePublic boards, shown as published, kept out of the index152
| Benchmark | GLM 4.5 Air | MiniMax M2 | Leader |
|---|---|---|---|
| IFBench | 37.6% | 72.3% | MiniMax M2 |
| Long context reasoning (AA-LCR) | 46.7% | 64.3% | MiniMax M2 |
| LMArena Text | 1384 | 1340 | GLM 4.5 Air |
| LMArena Text: instruction following | 1354 | 1329 | GLM 4.5 Air |
| LMArena Text: creative writing | 1343 | 1286 | GLM 4.5 Air |
| LMArena Text: English | 1397 | 1364 | GLM 4.5 Air |
| LMArena Text: non-English | 1366 | 1313 | GLM 4.5 Air |
| LMArena Text: Chinese | 1426 | 1366 | GLM 4.5 Air |
| LMArena Text: German | 1377 | 1357 | GLM 4.5 Air |
| LMArena Text: Japanese | 1348 | Not measured | Only GLM 4.5 Air measured |
| LMArena Text: Korean | 1308 | Not measured | Only GLM 4.5 Air measured |
| LMArena Text: French | 1400 | 1336 | GLM 4.5 Air |
| LMArena Text: Spanish | 1385 | 1326 | GLM 4.5 Air |
| LMArena Text: Russian | 1373 | 1330 | GLM 4.5 Air |
| LMArena Text: Polish | 1368 | 1312 | GLM 4.5 Air |
| LMArena Text: multi-turn | 1371 | 1361 | GLM 4.5 Air |
| LMArena Text: longer queries | 1366 | 1331 | GLM 4.5 Air |
| LMArena Text: writing and language | 1354 | 1295 | GLM 4.5 Air |
| LMArena Text: entertainment and media | 1339 | 1298 | GLM 4.5 Air |
| Neither model has a result on the other 29 boards here | |||
Image generationNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
| Benchmark | GLM 4.5 Air | MiniMax M2 | Leader |
|---|---|---|---|
| Neither model has a result on 5 boards here | |||
What each workload costs
Three fixed token mixes at each model's listed price, so per-token rates become a bill.
Chat turn
1K input + 500 output tokens
GLM 4.5 Air costs less
Repository review
50K input + 3K output tokens
GLM 4.5 Air costs less
Agent loop
200K input + 10K output tokens
GLM 4.5 Air costs less
Listed OpenRouter rates, without cache or batch discounts. A workload that does not fit a model's context window would need splitting.
Specification differences
The leading value in a row is marked; a row with a missing value names no leader. Our two rows are marked in red.
| Specification | Z.ai GLM 4.5 Air | MiniMax M2 |
|---|---|---|
| Maker | Z.ai | MiniMax |
| Weights | Open weights | Open weights |
| Released | July 25, 2025 | October 23, 2025 |
| Context window | 131K | 205K |
| Input $/Mtok | $0.20 | $0.255 |
| Output $/Mtok | $1.10 | $1.02 |
| Coding index | Not benchmarked | Not benchmarked |
| Independent measurements behind it | 0 | 1 |
| Cost per solved task | Not computable | $0.702 |
| Source | OpenRouter listing | OpenRouter listing |
Questions
Is GLM 4.5 Air better than MiniMax M2 for coding?
Neither model has a coding score yet, so the two cannot be ranked on coding. Pick GLM 4.5 Air for work measured by LiveCodeBench (Artificial Analysis run) or Terminal-Bench Hard (Artificial Analysis run), where it leads.
Which is cheaper, GLM 4.5 Air or MiniMax M2?
GLM 4.5 Air costs $0.20 per million input tokens and $1.10 per million output tokens; MiniMax M2 costs $0.255 and $1.02. Cost per solved task is known for MiniMax M2 only.
Which has the bigger context window, GLM 4.5 Air or MiniMax M2?
MiniMax M2, with 205K tokens against 131K.
How many benchmarks is the GLM 4.5 Air vs MiniMax M2 comparison based on?
45 benchmarks have a result for both models as of October 22, 2026. GLM 4.5 Air's coding index rests on 0 independent benchmarks and MiniMax M2's on 1. Where only one model has a result, the page says so instead of naming a winner.