GLM 5.2 vs MiniMax M3.1 Flash
GLM 5.2 leads MiniMax M3.1 Flash on coding, 61.1 to 59.0 on the Vibe Coding Index, and MiniMax M3.1 Flash costs 7.3x less per output token ($0.60 vs $4.40 per million). Updated October 22, 2026; 9 benchmarks have a result for both.
GLM 5.2 or MiniMax M3.1 Flash: which to pick
Pick GLM 5.2 for coding: it scores 61.1 on the Vibe Coding Index against 59.0 for MiniMax M3.1 Flash.
Pick MiniMax M3.1 Flash for output tokens 7.3x cheaper ($0.60 vs $4.40 per million) and work measured by Vibe Code Bench (Vals AI) or SciCode, where it leads.
- Shared results
- 9
- GLM 5.2 only
- 95
- MiniMax M3.1 Flash only
- 11
- Like-for-like categories
- 5 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 5.2
GLM 5.2 scores 61.1 against 59.0.
4 benchmarks behind each score
Intelligence
AA Intelligence Index
MiniMax M3.1 Flash
MiniMax M3.1 Flash leads, 42.7 against 33.7.
Same board, both measured
Agentic work
AA Agentic Index
No call
Only GLM 5.2 has a AA Agentic Index result, 38.4.
Directional only
Reasoning
GPQA Diamond
No call
Only GLM 5.2 has a GPQA Diamond result, 85.6%.
Directional only
Knowledge
MMLU Pro
No call
Only GLM 5.2 has a MMLU Pro result, 86.7%.
Directional only
Web design
Design Arena: websites
No call
Only GLM 5.2 has a Design Arena: websites result, 1285.
Directional only
General
Vals Index
No call
Only MiniMax M3.1 Flash has a Vals Index result, 53.8%.
Directional only
Long documents
Listed context window
GLM 5.2
GLM 5.2 takes 1.05M tokens against 1M.
Listed by OpenRouter
Everyday cost
1K input + 500 output tokens
MiniMax M3.1 Flash
GLM 5.2 $0.0036, MiniMax M3.1 Flash $0.0004 per turn.
List prices
Cost per solved task
One SWE-bench Verified task, solved
No call
Only GLM 5.2 has the number, $0.86.
Directional only
At a glance
- Coding index
- GLM 5.2 61.1 from 4 benchmarks, MiniMax M3.1 Flash 59.0 from 4 benchmarks.
- Widest category
- Intelligence, 9.0 points apart on average over 1 shared benchmark.
- Widest single gap
- Vibe Code Bench (Vals AI): GLM 5.2 64.0% against MiniMax M3.1 Flash 73.9%, 10.0 points apart on a 0 to 100 scale.
- Output price
- GLM 5.2 $4.40 against MiniMax M3.1 Flash $0.60 per million output tokens, 7.3x apart.
- Context window
- GLM 5.2 takes 1.05M tokens, MiniMax M3.1 Flash 1M.
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 5.2 | Bars | MiniMax M3.1 Flash | Ties |
|---|---|---|---|---|
| Coding | 3 | 2 | ||
| Intelligence | 0 | 1 | ||
| Agentic work | 0 | 1 | ||
| Reasoning | 0 | 1 | ||
| General | 0 | 1 | ||
| All shared boards | 3 | 6 |
CodingLike-for-likeSix of these boards feed the Vibe Coding Index32
| Benchmark | GLM 5.2 | MiniMax M3.1 Flash | Leader |
|---|---|---|---|
| LiveBench coding | Not measured | 83.9% | Only MiniMax M3.1 Flash measured |
| LMArena WebDev | 1604 | 1587 | GLM 5.2 |
| AA Coding Index | 68.8 | Not measured | Only GLM 5.2 measured |
| LiveCodeBench (Vals AI) | 69.5% | Not measured | Only GLM 5.2 measured |
| SWE-bench Verified (Vals AI) | 82.8% | Not measured | Only GLM 5.2 measured |
| Terminal-Bench 4 (Vals AI) | Not measured | 25.3% | Only MiniMax M3.1 Flash measured |
| Vibe Code Bench (Vals AI) | 64.0% | 73.9% | MiniMax M3.1 Flash |
| Code migration (Vals AI) | 37.9% | Not measured | Only GLM 5.2 measured |
| ProgramBench (Vals AI) | 0.5% | Not measured | Only GLM 5.2 measured |
| Terminal-Bench 2.1 (Vals AI) | 67.8% | Not measured | Only GLM 5.2 measured |
| SkillsBench (Vals AI) | 45.1% | Not measured | Only GLM 5.2 measured |
| SciCode | 51.2% | 53.4% | MiniMax M3.1 Flash |
| Terminal-Bench Hard (Artificial Analysis run) | 50.8% | Not measured | Only GLM 5.2 measured |
| FrontierCode | 24.5% | Not measured | Only GLM 5.2 measured |
| WeirdML | 70.1% | Not measured | Only GLM 5.2 measured |
| ALE-Bench | 1047.0 | Not measured | Only GLM 5.2 measured |
| LMArena Text: coding | 1487 | Not measured | Only GLM 5.2 measured |
| LMArena Text: software and IT | 1486 | Not measured | Only GLM 5.2 measured |
| LMArena WebDev: React | 1617 | 1609 | GLM 5.2 |
| LMArena WebDev: HTML | 1565 | 1529 | GLM 5.2 |
| SWE Atlas: Codebase QnA | 48.1% | Not measured | Only GLM 5.2 measured |
| SWE Atlas: Refactoring | 42.4% | Not measured | Only GLM 5.2 measured |
| SWE Atlas: Test Writing | 41.5% | Not measured | Only GLM 5.2 measured |
| Neither model has a result on the other 20 boards here | |||
IntelligenceLike-for-likePublic boards, shown as published, kept out of the index01
| Benchmark | GLM 5.2 | MiniMax M3.1 Flash | Leader |
|---|---|---|---|
| AA Intelligence Index | 33.7 | 42.7 | MiniMax M3.1 Flash |
| Epoch Capabilities Index | 151.9 | Not measured | Only GLM 5.2 measured |
Agentic workLike-for-likePublic boards, shown as published, kept out of the index01
| Benchmark | GLM 5.2 | MiniMax M3.1 Flash | Leader |
|---|---|---|---|
| AA Agentic Index | 38.4 | Not measured | Only GLM 5.2 measured |
| Design Arena: agents building web apps | 1222 | Not measured | Only GLM 5.2 measured |
| Design Arena: agents building full-stack apps | 1241 | Not measured | Only GLM 5.2 measured |
| Design Arena: agents building mobile apps | 1159 | Not measured | Only GLM 5.2 measured |
| Design Arena: agents building Android apps | 1177 | Not measured | Only GLM 5.2 measured |
| Design Arena: agents building Godot games | 1142 | Not measured | Only GLM 5.2 measured |
| Design Arena: agents building games | 1206 | Not measured | Only GLM 5.2 measured |
| Design Arena: HTML slides | 1114 | Not measured | Only GLM 5.2 measured |
| Design Arena: PowerPoint slides | 1167 | Not measured | Only GLM 5.2 measured |
| LiveBench research | Not measured | 72.7% | Only MiniMax M3.1 Flash measured |
| LiveBench browser | Not measured | 92.0% | Only MiniMax M3.1 Flash measured |
| LiveBench long agentic | Not measured | 34.7% | Only MiniMax M3.1 Flash measured |
| SREBench (Vals AI) | 0.0% | Not measured | Only GLM 5.2 measured |
| Finance Agent (Vals AI) | 49.7% | Not measured | Only GLM 5.2 measured |
| Tax Agent Bench (Vals AI) | Not measured | 32.1% | Only MiniMax M3.1 Flash measured |
| Legal Research Bench (Vals AI) | 31.3% | 34.1% | MiniMax M3.1 Flash |
| Harvey's Legal Agent Benchmark (Vals AI) | Not measured | 7.5% | Only MiniMax M3.1 Flash measured |
| Excel Modeling Benchmark (Vals AI) | 61.5% | Not measured | Only GLM 5.2 measured |
| τ²-Bench Telecom | 99.1% | Not measured | Only GLM 5.2 measured |
| APEX-Agents | 45.2% | Not measured | Only GLM 5.2 measured |
| PostTrainBench | 31.7% | Not measured | Only GLM 5.2 measured |
| Vending-Bench 2 | $8,314 | Not measured | Only GLM 5.2 measured |
| MCP Atlas | 77.8% | Not measured | Only GLM 5.2 measured |
| Neither model has a result on the other 19 boards here | |||
ReasoningLike-for-likePublic boards, shown as published, kept out of the index01
| Benchmark | GLM 5.2 | MiniMax M3.1 Flash | Leader |
|---|---|---|---|
| GPQA Diamond | 85.6% | Not measured | Only GLM 5.2 measured |
| LiveBench reasoning | Not measured | 82.9% | Only MiniMax M3.1 Flash measured |
| GPQA Diamond (Artificial Analysis run) | 89.5% | Not measured | Only GLM 5.2 measured |
| Humanity's Last Exam (Artificial Analysis run) | 41.1% | 48.0% | MiniMax M3.1 Flash |
| GPQA Diamond (Epoch AI run) | 91.9% | Not measured | Only GLM 5.2 measured |
| ARC-AGI | 77.0% | Not measured | Only GLM 5.2 measured |
| ARC-AGI-2 | 22.8% | Not measured | Only GLM 5.2 measured |
| SimpleBench | 58.8% | Not measured | Only GLM 5.2 measured |
| Chess puzzles | 22.0% | Not measured | Only GLM 5.2 measured |
| Mystery game puzzles | 19.0% | Not measured | Only GLM 5.2 measured |
| EBR-bench | 9.5% | Not measured | Only GLM 5.2 measured |
| LMArena Text: hard prompts | 1481 | Not measured | Only GLM 5.2 measured |
| LMArena Text: expert | 1487 | Not measured | Only GLM 5.2 measured |
| LMArena Text: hard prompts in English | 1481 | Not measured | Only GLM 5.2 measured |
| Kagi LLM Benchmark | 62.6% | Not measured | Only GLM 5.2 measured |
| Neither model has a result on the other 8 boards here | |||
MathDirectional onlyPublic boards, shown as published, kept out of the index7 boards, none shared
| Benchmark | GLM 5.2 | MiniMax M3.1 Flash | Leader |
|---|---|---|---|
| OTIS Mock AIME | 86.4% | Not measured | Only GLM 5.2 measured |
| FrontierMath, tiers 1 to 3 | 59.2% | Not measured | Only GLM 5.2 measured |
| FrontierMath, tier 4 | 29.3% | Not measured | Only GLM 5.2 measured |
| ProofBench | 35.0% | Not measured | Only GLM 5.2 measured |
| LMArena Text: math | 1483 | Not measured | Only GLM 5.2 measured |
| LMArena Text: mathematical work | 1489 | Not measured | Only GLM 5.2 measured |
| MathArena | 22.7% | Not measured | Only GLM 5.2 measured |
| Neither model has a result on the other 7 boards here | |||
KnowledgeDirectional onlyPublic boards, shown as published, kept out of the index10 boards, none shared
| Benchmark | GLM 5.2 | MiniMax M3.1 Flash | Leader |
|---|---|---|---|
| MMLU Pro | 86.7% | Not measured | Only GLM 5.2 measured |
| LegalBench (Vals AI) | 84.1% | Not measured | Only GLM 5.2 measured |
| CorpFin (Vals AI) | 66.1% | Not measured | Only GLM 5.2 measured |
| TaxEval (Vals AI) | 73.3% | Not measured | Only GLM 5.2 measured |
| MedCode (Vals AI) | 40.8% | Not measured | Only GLM 5.2 measured |
| SimpleQA Verified | 34.2% | Not measured | Only GLM 5.2 measured |
| LMArena Text: science | 1489 | Not measured | Only GLM 5.2 measured |
| LMArena Text: medicine and healthcare | 1475 | Not measured | Only GLM 5.2 measured |
| LMArena Text: legal and government | 1486 | Not measured | Only GLM 5.2 measured |
| LMArena Text: business and finance | 1451 | Not measured | Only GLM 5.2 measured |
| Neither model has a result on the other 10 boards here | |||
Web designDirectional onlyPublic boards, shown as published, kept out of the index9 boards, none shared
| Benchmark | GLM 5.2 | MiniMax M3.1 Flash | Leader |
|---|---|---|---|
| Design Arena: websites | 1285 | Not measured | Only GLM 5.2 measured |
| Design Arena: UI components | 1294 | Not measured | Only GLM 5.2 measured |
| Design Arena: game dev | 1267 | Not measured | Only GLM 5.2 measured |
| Design Arena: data viz | 1286 | Not measured | Only GLM 5.2 measured |
| Design Arena: 3D | 1287 | Not measured | Only GLM 5.2 measured |
| Design Arena: all code categories | 1287 | Not measured | Only GLM 5.2 measured |
| Design Arena: SVG | 1213 | Not measured | Only GLM 5.2 measured |
| Design Arena: ASCII art | 1191 | Not measured | Only GLM 5.2 measured |
| LiveBench design | Not measured | 74.0% | Only MiniMax M3.1 Flash measured |
GeneralLike-for-likePublic boards, shown as published, kept out of the index01
| Benchmark | GLM 5.2 | MiniMax M3.1 Flash | Leader |
|---|---|---|---|
| Vals Index | Not measured | 53.8% | Only MiniMax M3.1 Flash measured |
| LiveBench data analysis | Not measured | 71.4% | Only MiniMax M3.1 Flash measured |
| MedScribe (Vals AI) | 83.5% | Not measured | Only GLM 5.2 measured |
| IFBench | 73.3% | Not measured | Only GLM 5.2 measured |
| Long context reasoning (AA-LCR) | 78.3% | 79.7% | MiniMax M3.1 Flash |
| DTBench | 93.6% | Not measured | Only GLM 5.2 measured |
| LMCA | 45.8% | Not measured | Only GLM 5.2 measured |
| Surface Evolver Bench | 55.6% | Not measured | Only GLM 5.2 measured |
| LMArena Text | 1470 | Not measured | Only GLM 5.2 measured |
| LMArena Text: instruction following | 1466 | Not measured | Only GLM 5.2 measured |
| LMArena Text: creative writing | 1463 | Not measured | Only GLM 5.2 measured |
| LMArena Text: English | 1475 | Not measured | Only GLM 5.2 measured |
| LMArena Text: non-English | 1459 | Not measured | Only GLM 5.2 measured |
| LMArena Text: Chinese | 1522 | Not measured | Only GLM 5.2 measured |
| LMArena Text: German | 1470 | Not measured | Only GLM 5.2 measured |
| LMArena Text: Japanese | 1450 | Not measured | Only GLM 5.2 measured |
| LMArena Text: Korean | 1440 | Not measured | Only GLM 5.2 measured |
| LMArena Text: French | 1479 | Not measured | Only GLM 5.2 measured |
| LMArena Text: Spanish | 1478 | Not measured | Only GLM 5.2 measured |
| LMArena Text: Russian | 1468 | Not measured | Only GLM 5.2 measured |
| LMArena Text: Polish | 1456 | Not measured | Only GLM 5.2 measured |
| LMArena Text: multi-turn | 1469 | Not measured | Only GLM 5.2 measured |
| LMArena Text: longer queries | 1480 | Not measured | Only GLM 5.2 measured |
| LMArena Text: writing and language | 1463 | Not measured | Only GLM 5.2 measured |
| LMArena Text: entertainment and media | 1450 | Not measured | Only GLM 5.2 measured |
| EQ-Bench 4 | 1222 | Not measured | Only GLM 5.2 measured |
| Neither model has a result on the other 22 boards here | |||
Image generationNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
| Benchmark | GLM 5.2 | MiniMax M3.1 Flash | 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 M3.1 Flash costs less
Repository review
50K input + 3K output tokens
MiniMax M3.1 Flash costs less
Agent loop
200K input + 10K output tokens
MiniMax M3.1 Flash 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 5.2 | MiniMax M3.1 Flash |
|---|---|---|
| Maker | Z.ai | MiniMax |
| Weights | Open weights | No public weights listed |
| Released | June 16, 2026 | October 19, 2026 |
| Context window | 1.05M | 1M |
| Input $/Mtok | $1.40 | $0.15 |
| Output $/Mtok | $4.40 | $0.60 |
| Coding index | 61.1 | 59.0 |
| Independent measurements behind it | 4 | 4 |
| Cost per solved task | $0.86 | Not computable |
| Source | OpenRouter listing | OpenRouter listing |
Questions
Is GLM 5.2 better than MiniMax M3.1 Flash for coding?
Pick GLM 5.2 for coding: it scores 61.1 on the Vibe Coding Index against 59.0 for MiniMax M3.1 Flash. Pick MiniMax M3.1 Flash for output tokens 7.3x cheaper ($0.60 vs $4.40 per million) and work measured by Vibe Code Bench (Vals AI) or SciCode, where it leads.
Which is cheaper, GLM 5.2 or MiniMax M3.1 Flash?
GLM 5.2 costs $1.40 per million input tokens and $4.40 per million output tokens; MiniMax M3.1 Flash costs $0.15 and $0.60. Cost per solved task is known for GLM 5.2 only.
Which has the bigger context window, GLM 5.2 or MiniMax M3.1 Flash?
GLM 5.2, with 1.05M tokens against 1M.
How many benchmarks is the GLM 5.2 vs MiniMax M3.1 Flash comparison based on?
9 benchmarks have a result for both models as of October 22, 2026. GLM 5.2's coding index rests on 4 independent benchmarks and MiniMax M3.1 Flash's on 4. Where only one model has a result, the page says so instead of naming a winner.