GLM 5.3 vs MiniMax M2
Updated October 21, 2026. 40 benchmarks have a result for both. Coding is ranked on the Vibe Coding Index; every other board is shown as published.
Decision reading
Pick GLM 5.3 for coding: it scores 73.1 on the Vibe Coding Index against 45.3 for MiniMax M2.
Pick MiniMax M2 when output tokens cost 1.4x less.
- Shared results
- 40
- GLM 5.3 only
- 60
- MiniMax M2 only
- 9
- Like-for-like categories
- 8 of 9
Shared results can support a head-to-head reading. Results only one model has describe coverage, not superiority.
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, four public coding benchmarks
GLM 5.3
GLM 5.3 scores 73.1 against 45.3.
2 benchmarks behind each score
Intelligence
AA Intelligence Index
GLM 5.3
GLM 5.3 leads, 44.8 against 18.6.
Same board, both measured
Agentic work
AA Agentic Index
No call
Only GLM 5.3 has a AA Agentic Index result, 53.1.
Directional only
Reasoning
GPQA Diamond
No call
Only GLM 5.3 has a GPQA Diamond result, 88.1%.
Directional only
Knowledge
MMLU Pro
No call
Only GLM 5.3 has a MMLU Pro result, 86.8%.
Directional only
Web design
Design Arena: websites
GLM 5.3
GLM 5.3 leads, 1295 against 1144.
Same board, both measured
General
Vals Index
No call
Only GLM 5.3 has a Vals Index result, 59.7%.
Directional only
Long documents
Listed context window
GLM 5.3
GLM 5.3 takes 1.05M tokens against 205K.
Listed by OpenRouter
Everyday cost
1K input + 500 output tokens
MiniMax M2
GLM 5.3 $0.0008, MiniMax M2 $0.0008 per turn.
List prices
Cost per solved task
One SWE-bench Verified task, solved
GLM 5.3
GLM 5.3 $0.36, MiniMax M2 $0.702.
Published attempt cost and pass rate
At a glance
- Coding index
- GLM 5.3 73.1 from 2 benchmarks, MiniMax M2 45.3 from 2 benchmarks.
- Widest category
- Agentic work, 50.0 points apart on average over 1 shared benchmark.
- Widest single gap
- Vending-Bench 2: GLM 5.3 $8,164 against MiniMax M2 $161, 50.0 points apart on a 0 to 100 scale.
- Output price
- GLM 5.3 $1.40 against MiniMax M2 $1.02 per million output tokens, 1.4x apart.
- Context window
- GLM 5.3 takes 1.05M 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 5.3 | Bars | MiniMax M2 | Ties |
|---|---|---|---|---|
| Coding | 4 | 0 | ||
| Intelligence | 1 | 0 | ||
| Agentic work | 1 | 0 | ||
| Reasoning | 5 | 0 | ||
| Math | 2 | 0 | ||
| Knowledge | 4 | 0 | ||
| Web design | 7 | 0 | ||
| General | 16 | 0 | ||
| All shared boards | 40 | 0 |
CodingLike-for-likeFour of these boards feed the Vibe Coding Index40
| Benchmark | GLM 5.3 | MiniMax M2 | Leader |
|---|---|---|---|
| SWE-bench Verified | Not measured | 61.0% | Only MiniMax M2 measured |
| LiveBench coding | 82.8% | Not measured | Only GLM 5.3 measured |
| LMArena WebDev | 1621 | 1297 | GLM 5.3 |
| AA Coding Index | 74.8 | Not measured | Only GLM 5.3 measured |
| LiveCodeBench (Vals AI) | 80.5% | Not measured | Only GLM 5.3 measured |
| SWE-bench Verified (Vals AI) | 95.4% | Not measured | Only GLM 5.3 measured |
| Terminal-Bench 4 (Vals AI) | 38.9% | Not measured | Only GLM 5.3 measured |
| Vibe Code Bench (Vals AI) | 78.1% | Not measured | Only GLM 5.3 measured |
| Code migration (Vals AI) | 44.2% | Not measured | Only GLM 5.3 measured |
| ProgramBench (Vals AI) | 1.5% | Not measured | Only GLM 5.3 measured |
| IOI (Vals AI) | 68.4% | Not measured | Only GLM 5.3 measured |
| Terminal-Bench 2.1 (Vals AI) | 71.5% | Not measured | Only GLM 5.3 measured |
| Vibe Code Bench 1-100 (Vals AI) | 20.0% | Not measured | Only GLM 5.3 measured |
| SkillsBench (Vals AI) | 47.5% | Not measured | Only GLM 5.3 measured |
| CyberBench (Vals AI) | 72.1% | Not measured | Only GLM 5.3 measured |
| LiveCodeBench (Artificial Analysis run) | Not measured | 82.6% | Only MiniMax M2 measured |
| SciCode | 59.0% | Not measured | Only GLM 5.3 measured |
| Terminal-Bench Hard (Artificial Analysis run) | Not measured | 25.8% | Only MiniMax M2 measured |
| Terminal-Bench | Not measured | 30.0% | Only MiniMax M2 measured |
| FrontierCode | 40.1% | Not measured | Only GLM 5.3 measured |
| FrontierSWE | 30.2% | Not measured | Only GLM 5.3 measured |
| WeirdML | 75.4% | Not measured | Only GLM 5.3 measured |
| ALE-Bench | 1317.4 | Not measured | Only GLM 5.3 measured |
| LMArena Text: coding | 1497 | 1371 | GLM 5.3 |
| LMArena Text: software and IT | 1493 | 1373 | GLM 5.3 |
| LMArena WebDev: React | 1641 | Not measured | Only GLM 5.3 measured |
| LMArena WebDev: HTML | 1595 | 1306 | GLM 5.3 |
| Neither model has a result on the other 11 boards here | |||
IntelligenceLike-for-likePublic boards, shown as published, kept out of the index10
| Benchmark | GLM 5.3 | MiniMax M2 | Leader |
|---|---|---|---|
| AA Intelligence Index | 44.8 | 18.6 | GLM 5.3 |
| Epoch Capabilities Index | 156.0 | Not measured | Only GLM 5.3 measured |
Agentic workLike-for-likePublic boards, shown as published, kept out of the index10
| Benchmark | GLM 5.3 | MiniMax M2 | Leader |
|---|---|---|---|
| AA Agentic Index | 53.1 | Not measured | Only GLM 5.3 measured |
| Design Arena: agents building mobile apps | 1194 | Not measured | Only GLM 5.3 measured |
| Design Arena: HTML slides | 1189 | Not measured | Only GLM 5.3 measured |
| Design Arena: PowerPoint slides | 1255 | Not measured | Only GLM 5.3 measured |
| LiveBench research | 73.2% | Not measured | Only GLM 5.3 measured |
| LiveBench browser | 0.0% | Not measured | Only GLM 5.3 measured |
| LiveBench long agentic | 35.9% | Not measured | Only GLM 5.3 measured |
| SREBench (Vals AI) | 9.2% | Not measured | Only GLM 5.3 measured |
| Finance Agent (Vals AI) | 55.8% | Not measured | Only GLM 5.3 measured |
| Tax Agent Bench (Vals AI) | 39.7% | Not measured | Only GLM 5.3 measured |
| Legal Research Bench (Vals AI) | 49.0% | Not measured | Only GLM 5.3 measured |
| Harvey's Legal Agent Benchmark (Vals AI) | 14.6% | Not measured | Only GLM 5.3 measured |
| Excel Modeling Benchmark (Vals AI) | 56.3% | Not measured | Only GLM 5.3 measured |
| Terminal-Bench-Science (Vals AI) | 5.7% | Not measured | Only GLM 5.3 measured |
| τ²-Bench Telecom | Not measured | 86.8% | Only MiniMax M2 measured |
| APEX-Agents | 56.6% | Not measured | Only GLM 5.3 measured |
| Vending-Bench 2 | $8,164 | $161 | GLM 5.3 |
| Neither model has a result on the other 22 boards here | |||
ReasoningLike-for-likePublic boards, shown as published, kept out of the index50
| Benchmark | GLM 5.3 | MiniMax M2 | Leader |
|---|---|---|---|
| GPQA Diamond | 88.1% | Not measured | Only GLM 5.3 measured |
| LiveBench reasoning | 85.8% | Not measured | Only GLM 5.3 measured |
| MysteryMechanism (Vals AI) | 23.0% | Not measured | Only GLM 5.3 measured |
| GPQA Diamond (Artificial Analysis run) | 91.7% | 77.7% | GLM 5.3 |
| Humanity's Last Exam (Artificial Analysis run) | 42.3% | 13.7% | GLM 5.3 |
| GPQA Diamond (Epoch AI run) | 90.9% | Not measured | Only GLM 5.3 measured |
| Chess puzzles | 37.0% | Not measured | Only GLM 5.3 measured |
| Mystery game puzzles | 33.0% | Not measured | Only GLM 5.3 measured |
| LMArena Text: hard prompts | 1490 | 1357 | GLM 5.3 |
| LMArena Text: expert | 1514 | 1338 | GLM 5.3 |
| LMArena Text: hard prompts in English | 1496 | 1376 | GLM 5.3 |
| Neither model has a result on the other 10 boards here | |||
MathLike-for-likePublic boards, shown as published, kept out of the index20
| Benchmark | GLM 5.3 | MiniMax M2 | Leader |
|---|---|---|---|
| AA Math Index | Not measured | 78.3 | Only MiniMax M2 measured |
| AIME 2025 (Artificial Analysis run) | Not measured | 78.3% | Only MiniMax M2 measured |
| OTIS Mock AIME | 91.1% | Not measured | Only GLM 5.3 measured |
| FrontierMath, tiers 1 to 3 | 68.8% | Not measured | Only GLM 5.3 measured |
| FrontierMath, tier 4 | 29.3% | Not measured | Only GLM 5.3 measured |
| ProofBench | 49.0% | Not measured | Only GLM 5.3 measured |
| LMArena Text: math | 1495 | 1351 | GLM 5.3 |
| LMArena Text: mathematical work | 1497 | 1368 | GLM 5.3 |
| Neither model has a result on the other 5 boards here | |||
KnowledgeLike-for-likePublic boards, shown as published, kept out of the index40
| Benchmark | GLM 5.3 | MiniMax M2 | Leader |
|---|---|---|---|
| MMLU Pro | 86.8% | Not measured | Only GLM 5.3 measured |
| LegalBench (Vals AI) | 84.8% | Not measured | Only GLM 5.3 measured |
| TaxEval (Vals AI) | 72.4% | Not measured | Only GLM 5.3 measured |
| MedCode (Vals AI) | 42.9% | Not measured | Only GLM 5.3 measured |
| MMLU Pro (Artificial Analysis run) | Not measured | 82.0% | Only MiniMax M2 measured |
| SimpleQA Verified | 41.0% | Not measured | Only GLM 5.3 measured |
| LMArena Text: science | 1498 | 1348 | GLM 5.3 |
| LMArena Text: medicine and healthcare | 1481 | 1367 | GLM 5.3 |
| LMArena Text: legal and government | 1473 | 1349 | GLM 5.3 |
| LMArena Text: business and finance | 1459 | 1336 | GLM 5.3 |
| Neither model has a result on the other 7 boards here | |||
Web designLike-for-likePublic boards, shown as published, kept out of the index70
| Benchmark | GLM 5.3 | MiniMax M2 | Leader |
|---|---|---|---|
| Design Arena: websites | 1295 | 1144 | GLM 5.3 |
| Design Arena: UI components | 1314 | 1136 | GLM 5.3 |
| Design Arena: game dev | 1311 | 1119 | GLM 5.3 |
| Design Arena: data viz | 1260 | 1140 | GLM 5.3 |
| Design Arena: 3D | 1332 | 1098 | GLM 5.3 |
| Design Arena: all code categories | 1304 | 1135 | GLM 5.3 |
| Design Arena: SVG | 1292 | 1098 | GLM 5.3 |
| Design Arena: ASCII art | 1213 | Not measured | Only GLM 5.3 measured |
| LiveBench design | 77.3% | Not measured | Only GLM 5.3 measured |
GeneralLike-for-likePublic boards, shown as published, kept out of the index160
| Benchmark | GLM 5.3 | MiniMax M2 | Leader |
|---|---|---|---|
| Vals Index | 59.7% | Not measured | Only GLM 5.3 measured |
| LiveBench data analysis | 70.2% | Not measured | Only GLM 5.3 measured |
| Public Benefits Bench (Vals AI) | 68.5% | Not measured | Only GLM 5.3 measured |
| MedScribe (Vals AI) | 88.8% | Not measured | Only GLM 5.3 measured |
| IFBench | Not measured | 72.3% | Only MiniMax M2 measured |
| Long context reasoning (AA-LCR) | 79.7% | 64.3% | GLM 5.3 |
| DTBench | 87.7% | Not measured | Only GLM 5.3 measured |
| LMCA | 55.5% | Not measured | Only GLM 5.3 measured |
| LMArena Text | 1474 | 1340 | GLM 5.3 |
| LMArena Text: instruction following | 1479 | 1329 | GLM 5.3 |
| LMArena Text: creative writing | 1458 | 1286 | GLM 5.3 |
| LMArena Text: English | 1482 | 1364 | GLM 5.3 |
| LMArena Text: non-English | 1459 | 1313 | GLM 5.3 |
| LMArena Text: Chinese | 1532 | 1366 | GLM 5.3 |
| LMArena Text: German | 1488 | 1357 | GLM 5.3 |
| LMArena Text: Japanese | 1460 | Not measured | Only GLM 5.3 measured |
| LMArena Text: Korean | 1460 | Not measured | Only GLM 5.3 measured |
| LMArena Text: French | 1503 | 1336 | GLM 5.3 |
| LMArena Text: Spanish | 1462 | 1326 | GLM 5.3 |
| LMArena Text: Russian | 1469 | 1330 | GLM 5.3 |
| LMArena Text: Polish | 1466 | 1312 | GLM 5.3 |
| LMArena Text: multi-turn | 1472 | 1361 | GLM 5.3 |
| LMArena Text: longer queries | 1483 | 1331 | GLM 5.3 |
| LMArena Text: writing and language | 1469 | 1295 | GLM 5.3 |
| LMArena Text: entertainment and media | 1447 | 1298 | GLM 5.3 |
| Neither model has a result on the other 21 boards here | |||
Image generationNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
| Benchmark | GLM 5.3 | 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
MiniMax M2 costs less
Repository review
50K input + 3K output tokens
GLM 5.3 costs less
Agent loop
200K input + 10K output tokens
GLM 5.3 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 5.3 | MiniMax M2 |
|---|---|---|
| Maker | Z.ai | MiniMax |
| Weights | Open weights | Open weights |
| Released | August 18, 2026 | October 23, 2025 |
| Context window | 1.05M | 205K |
| Input $/Mtok | $0.079 | $0.255 |
| Output $/Mtok | $1.40 | $1.02 |
| Coding index | 73.1 | 45.3 |
| Independent measurements behind it | 2 | 2 |
| Cost per solved task | $0.36 | $0.702 |
| Source | OpenRouter listing | OpenRouter listing |
Questions
Is GLM 5.3 better than MiniMax M2 for coding?
Pick GLM 5.3 for coding: it scores 73.1 on the Vibe Coding Index against 45.3 for MiniMax M2. Pick MiniMax M2 when output tokens cost 1.4x less.
Which is cheaper, GLM 5.3 or MiniMax M2?
GLM 5.3 costs $0.079 per million input tokens and $1.40 per million output tokens; MiniMax M2 costs $0.255 and $1.02. Per solved SWE-bench Verified task, GLM 5.3 costs $0.36 and MiniMax M2 $0.702.
Which has the bigger context window, GLM 5.3 or MiniMax M2?
GLM 5.3, with 1.05M tokens against 205K.
How many benchmarks is the GLM 5.3 vs MiniMax M2 comparison based on?
40 benchmarks have a result for both models as of October 21, 2026. GLM 5.3's coding index rests on 2 independent benchmarks and MiniMax M2's on 2. Where only one model has a result, the page says so instead of naming a winner.