GLM 5.1 vs MiniMax M3
GLM 5.1 leads MiniMax M3 on coding, 49.2 to 39.2 on the Vibe Coding Index, and MiniMax M3 costs 3.7x less per output token ($1.20 vs $4.40 per million). Updated October 22, 2026; 81 benchmarks have a result for both.
GLM 5.1 or MiniMax M3: which to pick
Pick GLM 5.1 for coding: it scores 49.2 on the Vibe Coding Index against 39.2 for MiniMax M3, though on 3 benchmarks against 6.
Pick MiniMax M3 for output tokens 3.7x cheaper ($1.20 vs $4.40 per million) and a lower cost per solved task ($0.56 against $0.60).
- Shared results
- 81
- GLM 5.1 only
- 8
- MiniMax M3 only
- 35
- 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
GLM 5.1
GLM 5.1 scores 49.2 against 39.2.
3 and 6 benchmarks behind the scores
Intelligence
AA Intelligence Index
MiniMax M3
MiniMax M3 leads, 29.2 against 26.1.
Same board, both measured
Agentic work
AA Agentic Index
MiniMax M3
MiniMax M3 leads, 29.5 against 23.9.
Same board, both measured
Reasoning
GPQA Diamond
MiniMax M3
MiniMax M3 leads, 92.7% against 84.5%.
Same board, both measured
Math
AIME
No call
Only GLM 5.1 has a AIME result, 91.9%.
Directional only
Knowledge
MMLU Pro
GLM 5.1
GLM 5.1 leads, 86.9% against 84.2%.
Same board, both measured
Web design
Design Arena: websites
GLM 5.1
GLM 5.1 leads, 1290 against 1257.
Same board, both measured
Long documents
Listed context window
MiniMax M3
MiniMax M3 takes 1.05M tokens against 205K.
Listed by OpenRouter
Everyday cost
1K input + 500 output tokens
MiniMax M3
GLM 5.1 $0.0036, MiniMax M3 $0.0009 per turn.
List prices
Cost per solved task
One SWE-bench Verified task, solved
MiniMax M3
GLM 5.1 $0.60, MiniMax M3 $0.56.
Published attempt cost and pass rate
At a glance
- Coding index
- GLM 5.1 49.2 from 3 benchmarks, MiniMax M3 39.2 from 6 benchmarks.
- Widest category
- Math, 9.3 points apart on average over 4 shared benchmarks.
- Widest single gap
- Design Arena: spreadsheets: GLM 5.1 1315 against MiniMax M3 1039, 34.5 points apart on a 0 to 100 scale.
- Output price
- GLM 5.1 $4.40 against MiniMax M3 $1.20 per million output tokens, 3.7x apart.
- Context window
- GLM 5.1 takes 205K tokens, MiniMax M3 1.05M.
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 5.1 | Bars | MiniMax M3 | Ties |
|---|---|---|---|---|
| Coding | 10 | 4 | ||
| Intelligence | 1 | 1 | ||
| Agentic work | 6 | 8 | ||
| Reasoning | 5 | 4 | ||
| Math | 4 | 0 | ||
| Knowledge | 5 | 4 | ||
| Web design | 7 | 1 | ||
| General | 17 | 4 | ||
| All shared boards | 55 | 26 |
CodingLike-for-likeSix of these boards feed the Vibe Coding Index104
| Benchmark | GLM 5.1 | MiniMax M3 | Leader |
|---|---|---|---|
| LiveBench coding | Not measured | 70.9% | Only MiniMax M3 measured |
| LMArena WebDev | 1508 | 1482 | GLM 5.1 |
| AA Coding Index | 55.8 | 58.6 | MiniMax M3 |
| LiveCodeBench (Vals AI) | 81.4% | 82.2% | MiniMax M3 |
| SWE-bench Verified (Vals AI) | 76.4% | 75.0% | GLM 5.1 |
| Terminal-Bench 4 (Vals AI) | Not measured | 1.0% | Only MiniMax M3 measured |
| Vibe Code Bench (Vals AI) | 31.5% | 47.6% | MiniMax M3 |
| Code migration (Vals AI) | 25.8% | 19.9% | GLM 5.1 |
| ProgramBench (Vals AI) | 0.0% | Not measured | Only GLM 5.1 measured |
| Terminal-Bench 2.1 (Vals AI) | 56.9% | 53.6% | GLM 5.1 |
| SWE-bench Multimodal (Vals AI) | Not measured | 33.3% | Only MiniMax M3 measured |
| Vibe Code Bench 1-100 (Vals AI) | Not measured | 9.2% | Only MiniMax M3 measured |
| SkillsBench (Vals AI) | Not measured | 51.5% | Only MiniMax M3 measured |
| CyberBench (Vals AI) | Not measured | 65.0% | Only MiniMax M3 measured |
| SciCode | 44.8% | 47.1% | MiniMax M3 |
| Terminal-Bench Hard (Artificial Analysis run) | 43.2% | 42.4% | GLM 5.1 |
| FrontierCode | Not measured | 14.7% | Only MiniMax M3 measured |
| WeirdML | 57.1% | Not measured | Only GLM 5.1 measured |
| ALE-Bench | 887.1 | 640.0 | GLM 5.1 |
| LMArena Text: coding | 1485 | 1470 | GLM 5.1 |
| LMArena Text: software and IT | 1484 | 1466 | GLM 5.1 |
| LMArena WebDev: React | 1505 | 1478 | GLM 5.1 |
| LMArena WebDev: HTML | 1508 | 1455 | GLM 5.1 |
| LMArena WebDev: from an image | Not measured | 1470 | Only MiniMax M3 measured |
| Neither model has a result on the other 15 boards here | |||
IntelligenceLike-for-likePublic boards, shown as published, kept out of the index11
| Benchmark | GLM 5.1 | MiniMax M3 | Leader |
|---|---|---|---|
| AA Intelligence Index | 26.1 | 29.2 | MiniMax M3 |
| Epoch Capabilities Index | 149.8 | 146.9 | GLM 5.1 |
Agentic workLike-for-likePublic boards, shown as published, kept out of the index68
| Benchmark | GLM 5.1 | MiniMax M3 | Leader |
|---|---|---|---|
| AA Agentic Index | 23.9 | 29.5 | MiniMax M3 |
| Design Arena: agents building web apps | 1175 | 1200 | MiniMax M3 |
| Design Arena: agents building full-stack apps | 1167 | 1197 | MiniMax M3 |
| Design Arena: agents building mobile apps | 1161 | 1169 | MiniMax M3 |
| Design Arena: agents building Android apps | 1182 | 1159 | GLM 5.1 |
| Design Arena: agents building Godot games | 1053 | Not measured | Only GLM 5.1 measured |
| Design Arena: agents building games | 1164 | 1159 | GLM 5.1 |
| Design Arena: HTML slides | 1120 | 1130 | MiniMax M3 |
| Design Arena: PowerPoint slides | 1165 | 1181 | MiniMax M3 |
| Design Arena: spreadsheets | 1315 | 1039 | GLM 5.1 |
| LiveBench research | Not measured | 68.0% | Only MiniMax M3 measured |
| LiveBench browser | Not measured | 8.0% | Only MiniMax M3 measured |
| LiveBench long agentic | Not measured | 12.4% | Only MiniMax M3 measured |
| Finance Agent (Vals AI) | 44.8% | 48.3% | MiniMax M3 |
| Tax Agent Bench (Vals AI) | Not measured | 20.6% | Only MiniMax M3 measured |
| Legal Research Bench (Vals AI) | 27.9% | 29.8% | MiniMax M3 |
| Excel Modeling Benchmark (Vals AI) | Not measured | 47.8% | Only MiniMax M3 measured |
| τ²-Bench Telecom | 97.7% | 88.9% | GLM 5.1 |
| APEX-Agents | 40.9% | 37.7% | GLM 5.1 |
| OSWorld 2.0 | Not measured | 4.6% | Only MiniMax M3 measured |
| Vending-Bench 2 | $5,634 | $2,158 | GLM 5.1 |
| Neither model has a result on the other 20 boards here | |||
ReasoningLike-for-likePublic boards, shown as published, kept out of the index54
| Benchmark | GLM 5.1 | MiniMax M3 | Leader |
|---|---|---|---|
| GPQA Diamond | 84.5% | 92.7% | MiniMax M3 |
| LiveBench reasoning | Not measured | 74.5% | Only MiniMax M3 measured |
| MMMU Pro (Vals AI) | Not measured | 81.2% | Only MiniMax M3 measured |
| GPQA Diamond (Artificial Analysis run) | 86.8% | 92.9% | MiniMax M3 |
| Humanity's Last Exam (Artificial Analysis run) | 30.1% | 39.0% | MiniMax M3 |
| GPQA Diamond (Epoch AI run) | 89.9% | 90.9% | MiniMax M3 |
| SimpleBench | 55.1% | 45.8% | GLM 5.1 |
| Chess puzzles | 20.0% | 13.0% | GLM 5.1 |
| Mystery game puzzles | Not measured | 8.0% | Only MiniMax M3 measured |
| LMArena Text: hard prompts | 1473 | 1447 | GLM 5.1 |
| LMArena Text: expert | 1478 | 1461 | GLM 5.1 |
| LMArena Text: hard prompts in English | 1482 | 1455 | GLM 5.1 |
| LMArena Vision: diagrams | Not measured | 1266 | Only MiniMax M3 measured |
| LMArena Vision: homework | Not measured | 1268 | Only MiniMax M3 measured |
| Neither model has a result on the other 8 boards here | |||
MathLike-for-likePublic boards, shown as published, kept out of the index40
| Benchmark | GLM 5.1 | MiniMax M3 | Leader |
|---|---|---|---|
| AIME | 91.9% | Not measured | Only GLM 5.1 measured |
| OTIS Mock AIME | 93.3% | 71.1% | GLM 5.1 |
| FrontierMath, tiers 1 to 3 | 36.8% | Not measured | Only GLM 5.1 measured |
| ProofBench | 22.2% | 18.0% | GLM 5.1 |
| LMArena Text: math | 1473 | 1429 | GLM 5.1 |
| LMArena Text: mathematical work | 1470 | 1448 | GLM 5.1 |
| Neither model has a result on the other 7 boards here | |||
KnowledgeLike-for-likePublic boards, shown as published, kept out of the index54
| Benchmark | GLM 5.1 | MiniMax M3 | Leader |
|---|---|---|---|
| MMLU Pro | 86.9% | 84.2% | GLM 5.1 |
| LegalBench (Vals AI) | 84.4% | 85.4% | MiniMax M3 |
| CaseLaw (Vals AI) | 51.5% | Not measured | Only GLM 5.1 measured |
| CorpFin (Vals AI) | 64.5% | 68.1% | MiniMax M3 |
| TaxEval (Vals AI) | 71.2% | 72.7% | MiniMax M3 |
| MortgageTax (Vals AI) | Not measured | 68.4% | Only MiniMax M3 measured |
| MedCode (Vals AI) | 41.6% | 46.3% | MiniMax M3 |
| SimpleQA Verified | 34.0% | Not measured | Only GLM 5.1 measured |
| LMArena Text: science | 1478 | 1452 | GLM 5.1 |
| LMArena Text: medicine and healthcare | 1474 | 1441 | GLM 5.1 |
| LMArena Text: legal and government | 1465 | 1437 | GLM 5.1 |
| LMArena Text: business and finance | 1452 | 1434 | GLM 5.1 |
| Neither model has a result on the other 6 boards here | |||
Web designLike-for-likePublic boards, shown as published, kept out of the index71
| Benchmark | GLM 5.1 | MiniMax M3 | Leader |
|---|---|---|---|
| Design Arena: websites | 1290 | 1257 | GLM 5.1 |
| Design Arena: UI components | 1292 | 1243 | GLM 5.1 |
| Design Arena: game dev | 1264 | 1211 | GLM 5.1 |
| Design Arena: data viz | 1366 | 1236 | GLM 5.1 |
| Design Arena: 3D | 1336 | 1208 | GLM 5.1 |
| Design Arena: all code categories | 1268 | 1246 | GLM 5.1 |
| Design Arena: SVG | 1217 | 1160 | GLM 5.1 |
| Design Arena: ASCII art | 1141 | 1161 | MiniMax M3 |
| LiveBench design | Not measured | 66.2% | Only MiniMax M3 measured |
GeneralLike-for-likePublic boards, shown as published, kept out of the index174
| Benchmark | GLM 5.1 | MiniMax M3 | Leader |
|---|---|---|---|
| LiveBench data analysis | Not measured | 76.2% | Only MiniMax M3 measured |
| Public Benefits Bench (Vals AI) | 61.8% | 64.1% | MiniMax M3 |
| SAGE (Vals AI) | Not measured | 50.6% | Only MiniMax M3 measured |
| MedScribe (Vals AI) | 72.3% | 87.3% | MiniMax M3 |
| Vals Multimodal Index | Not measured | 60.0% | Only MiniMax M3 measured |
| IFBench | 76.3% | 82.9% | MiniMax M3 |
| Long context reasoning (AA-LCR) | 73.7% | 83.0% | MiniMax M3 |
| DTBench | Not measured | 78.9% | Only MiniMax M3 measured |
| LMCA | Not measured | 33.7% | Only MiniMax M3 measured |
| Surface Evolver Bench | Not measured | 55.0% | Only MiniMax M3 measured |
| ExploitBench | 18.1% | Not measured | Only GLM 5.1 measured |
| LMArena Text | 1463 | 1433 | GLM 5.1 |
| LMArena Text: instruction following | 1453 | 1433 | GLM 5.1 |
| LMArena Text: creative writing | 1453 | 1404 | GLM 5.1 |
| LMArena Vision | Not measured | 1251 | Only MiniMax M3 measured |
| LMArena Text: English | 1476 | 1443 | GLM 5.1 |
| LMArena Text: non-English | 1448 | 1420 | GLM 5.1 |
| LMArena Text: Chinese | 1515 | 1466 | GLM 5.1 |
| LMArena Text: German | 1467 | 1428 | GLM 5.1 |
| LMArena Text: Japanese | 1435 | 1380 | GLM 5.1 |
| LMArena Text: Korean | 1424 | 1373 | GLM 5.1 |
| LMArena Text: French | 1470 | 1444 | GLM 5.1 |
| LMArena Text: Spanish | 1468 | 1433 | GLM 5.1 |
| LMArena Text: Russian | 1454 | 1426 | GLM 5.1 |
| LMArena Text: Polish | 1449 | 1423 | GLM 5.1 |
| LMArena Text: multi-turn | 1473 | 1443 | GLM 5.1 |
| LMArena Text: longer queries | 1467 | 1446 | GLM 5.1 |
| LMArena Text: writing and language | 1454 | 1419 | GLM 5.1 |
| LMArena Text: entertainment and media | 1442 | 1403 | GLM 5.1 |
| LMArena Vision: English | Not measured | 1263 | Only MiniMax M3 measured |
| LMArena Vision: Chinese | Not measured | 1311 | Only MiniMax M3 measured |
| LMArena Vision: reading text | Not measured | 1259 | Only MiniMax M3 measured |
| LMArena Vision: humor | Not measured | 1235 | Only MiniMax M3 measured |
| LMArena Vision: recognizing things | Not measured | 1197 | Only MiniMax M3 measured |
| LMArena Vision: creative writing | Not measured | 1234 | Only MiniMax M3 measured |
| LMArena Document | Not measured | 1435 | Only MiniMax M3 measured |
| Neither model has a result on the other 10 boards here | |||
Image generationNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
| Benchmark | GLM 5.1 | MiniMax M3 | 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 M3 costs less
Repository review
50K input + 3K output tokens
MiniMax M3 costs less
Agent loop
200K input + 10K output tokens
MiniMax M3 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 5.1 | MiniMax M3 |
|---|---|---|
| Maker | Z.ai | MiniMax |
| Weights | Open weights | Open weights |
| Released | April 7, 2026 | May 31, 2026 |
| Context window | 205K | 1.05M |
| Input $/Mtok | $1.40 | $0.30 |
| Output $/Mtok | $4.40 | $1.20 |
| Coding index | 49.2 | 39.2 |
| Independent measurements behind it | 3 | 6 |
| Cost per solved task | $0.60 | $0.56 |
| Source | OpenRouter listing | OpenRouter listing |
Questions
Is GLM 5.1 better than MiniMax M3 for coding?
Pick GLM 5.1 for coding: it scores 49.2 on the Vibe Coding Index against 39.2 for MiniMax M3, though on 3 benchmarks against 6. Pick MiniMax M3 for output tokens 3.7x cheaper ($1.20 vs $4.40 per million) and a lower cost per solved task ($0.56 against $0.60).
Which is cheaper, GLM 5.1 or MiniMax M3?
GLM 5.1 costs $1.40 per million input tokens and $4.40 per million output tokens; MiniMax M3 costs $0.30 and $1.20. Per solved SWE-bench Verified task, GLM 5.1 costs $0.60 and MiniMax M3 $0.56.
Which has the bigger context window, GLM 5.1 or MiniMax M3?
MiniMax M3, with 1.05M tokens against 205K.
How many benchmarks is the GLM 5.1 vs MiniMax M3 comparison based on?
81 benchmarks have a result for both models as of October 22, 2026. GLM 5.1's coding index rests on 3 independent benchmarks and MiniMax M3's on 6. Where only one model has a result, the page says so instead of naming a winner.