GLM 4.7 Flash vs MiniMax M2
MiniMax M2 leads GLM 4.7 Flash on the Artificial Analysis Intelligence Index, 18.6 to 14.9, and GLM 4.7 Flash costs 2.5x less per output token ($0.40 vs $1.02 per million). Updated October 22, 2026; 40 benchmarks have a result for both.
GLM 4.7 Flash or MiniMax M2: which to pick
Neither model has a coding score yet, so the two cannot be ranked on coding.
Pick GLM 4.7 Flash for output tokens 2.5x cheaper ($0.40 vs $1.02 per million) and work measured by Terminal-Bench Hard (Artificial Analysis run) or AA Intelligence Index, where it leads.
- Shared results
- 40
- GLM 4.7 Flash only
- 3
- MiniMax M2 only
- 10
- 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 14.9.
Same board, both measured
Web design
Design Arena: websites
GLM 4.7 Flash
GLM 4.7 Flash leads, 1194 against 1143.
Same board, both measured
Long documents
Listed context window
MiniMax M2
MiniMax M2 takes 205K tokens against 200K.
Listed by OpenRouter
Everyday cost
1K input + 500 output tokens
GLM 4.7 Flash
GLM 4.7 Flash $0.0003, 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 4.7 Flash not benchmarked, MiniMax M2 not benchmarked.
- Widest category
- Agentic work, 12.0 points apart on average over 1 shared benchmark.
- Widest single gap
- Long context reasoning (AA-LCR): GLM 4.7 Flash 41.7% against MiniMax M2 64.3%, 22.7 points apart on a 0 to 100 scale.
- Output price
- GLM 4.7 Flash $0.40 against MiniMax M2 $1.02 per million output tokens, 2.5x apart.
- Context window
- GLM 4.7 Flash takes 200K 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 4.7 Flash | Bars | MiniMax M2 | Ties |
|---|---|---|---|---|
| Coding | 2 | 1 | ||
| Intelligence | 0 | 1 | ||
| Agentic work | 1 | 0 | ||
| Reasoning | 1 | 4 | ||
| Math | 2 | 0 | ||
| Knowledge | 3 | 1 | ||
| Web design | 5 | 2 | ||
| General | 10 | 7 | ||
| All shared boards | 24 | 16 |
CodingLike-for-likeSix of these boards feed the Vibe Coding Index21
| Benchmark | GLM 4.7 Flash | 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) | Not measured | 82.6% | Only MiniMax M2 measured |
| Terminal-Bench Hard (Artificial Analysis run) | 22.0% | 25.8% | MiniMax M2 |
| Terminal-Bench | Not measured | 30.0% | Only MiniMax M2 measured |
| LMArena Text: coding | 1382 | 1371 | GLM 4.7 Flash |
| LMArena Text: software and IT | 1389 | 1373 | GLM 4.7 Flash |
| LMArena WebDev: HTML | Not measured | 1306 | Only MiniMax M2 measured |
| Neither model has a result on the other 31 boards here | |||
IntelligenceLike-for-likePublic boards, shown as published, kept out of the index01
| Benchmark | GLM 4.7 Flash | MiniMax M2 | Leader |
|---|---|---|---|
| AA Intelligence Index | 14.9 | 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 index10
| Benchmark | GLM 4.7 Flash | MiniMax M2 | Leader |
|---|---|---|---|
| τ²-Bench Telecom | 98.8% | 86.8% | GLM 4.7 Flash |
| Vending-Bench 2 | Not measured | $161 | Only MiniMax M2 measured |
| Neither model has a result on the other 39 boards here | |||
ReasoningLike-for-likePublic boards, shown as published, kept out of the index14
| Benchmark | GLM 4.7 Flash | MiniMax M2 | Leader |
|---|---|---|---|
| GPQA Diamond (Artificial Analysis run) | 58.1% | 77.7% | MiniMax M2 |
| Humanity's Last Exam (Artificial Analysis run) | 7.6% | 13.7% | MiniMax M2 |
| GPQA Diamond (Epoch AI run) | 60.5% | Not measured | Only GLM 4.7 Flash measured |
| LMArena Text: hard prompts | 1356 | 1357 | MiniMax M2 |
| LMArena Text: expert | 1357 | 1338 | GLM 4.7 Flash |
| LMArena Text: hard prompts in English | 1369 | 1376 | MiniMax M2 |
| Kagi LLM Benchmark | Not measured | 57.8% | Only MiniMax M2 measured |
| Neither model has a result on the other 15 boards here | |||
MathLike-for-likePublic boards, shown as published, kept out of the index20
| Benchmark | GLM 4.7 Flash | 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 | 58.3% | Not measured | Only GLM 4.7 Flash measured |
| LMArena Text: math | 1355 | 1351 | GLM 4.7 Flash |
| LMArena Text: mathematical work | 1369 | 1368 | GLM 4.7 Flash |
| Neither model has a result on the other 8 boards here | |||
KnowledgeLike-for-likePublic boards, shown as published, kept out of the index31
| Benchmark | GLM 4.7 Flash | MiniMax M2 | Leader |
|---|---|---|---|
| MMLU Pro (Artificial Analysis run) | Not measured | 82.0% | Only MiniMax M2 measured |
| LMArena Text: science | 1358 | 1348 | GLM 4.7 Flash |
| LMArena Text: medicine and healthcare | 1343 | 1367 | MiniMax M2 |
| LMArena Text: legal and government | 1357 | 1349 | GLM 4.7 Flash |
| LMArena Text: business and finance | 1343 | 1336 | GLM 4.7 Flash |
| Neither model has a result on the other 13 boards here | |||
Web designLike-for-likePublic boards, shown as published, kept out of the index52
| Benchmark | GLM 4.7 Flash | MiniMax M2 | Leader |
|---|---|---|---|
| Design Arena: websites | 1194 | 1143 | GLM 4.7 Flash |
| Design Arena: UI components | 1205 | 1137 | GLM 4.7 Flash |
| Design Arena: game dev | 1134 | 1118 | GLM 4.7 Flash |
| Design Arena: data viz | 1123 | 1140 | MiniMax M2 |
| Design Arena: 3D | 1120 | 1098 | GLM 4.7 Flash |
| Design Arena: all code categories | 1177 | 1135 | GLM 4.7 Flash |
| Design Arena: SVG | 1037 | 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 index107
| Benchmark | GLM 4.7 Flash | MiniMax M2 | Leader |
|---|---|---|---|
| IFBench | 60.8% | 72.3% | MiniMax M2 |
| Long context reasoning (AA-LCR) | 41.7% | 64.3% | MiniMax M2 |
| LMArena Text | 1351 | 1340 | GLM 4.7 Flash |
| LMArena Text: instruction following | 1327 | 1329 | MiniMax M2 |
| LMArena Text: creative writing | 1296 | 1286 | GLM 4.7 Flash |
| LMArena Text: English | 1373 | 1364 | GLM 4.7 Flash |
| LMArena Text: non-English | 1330 | 1313 | GLM 4.7 Flash |
| LMArena Text: Chinese | 1403 | 1366 | GLM 4.7 Flash |
| LMArena Text: German | 1336 | 1357 | MiniMax M2 |
| LMArena Text: Korean | 1283 | Not measured | Only GLM 4.7 Flash measured |
| LMArena Text: French | 1332 | 1336 | MiniMax M2 |
| LMArena Text: Spanish | 1350 | 1326 | GLM 4.7 Flash |
| LMArena Text: Russian | 1333 | 1330 | GLM 4.7 Flash |
| LMArena Text: Polish | 1291 | 1312 | MiniMax M2 |
| LMArena Text: multi-turn | 1342 | 1361 | MiniMax M2 |
| LMArena Text: longer queries | 1345 | 1331 | GLM 4.7 Flash |
| LMArena Text: writing and language | 1320 | 1295 | GLM 4.7 Flash |
| LMArena Text: entertainment and media | 1304 | 1298 | GLM 4.7 Flash |
| Neither model has a result on the other 28 boards here | |||
Image generationNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
| Benchmark | GLM 4.7 Flash | 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 4.7 Flash costs less
Repository review
50K input + 3K output tokens
GLM 4.7 Flash costs less
Agent loop
200K input + 10K output tokens
GLM 4.7 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 4.7 Flash | MiniMax M2 |
|---|---|---|
| Maker | Z.ai | MiniMax |
| Weights | Open weights | Open weights |
| Released | January 19, 2026 | October 23, 2025 |
| Context window | 200K | 205K |
| Input $/Mtok | $0.0653 | $0.255 |
| Output $/Mtok | $0.40 | $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 4.7 Flash better than MiniMax M2 for coding?
Neither model has a coding score yet, so the two cannot be ranked on coding. Pick GLM 4.7 Flash for output tokens 2.5x cheaper ($0.40 vs $1.02 per million) and work measured by Terminal-Bench Hard (Artificial Analysis run) or AA Intelligence Index, where it leads.
Which is cheaper, GLM 4.7 Flash or MiniMax M2?
GLM 4.7 Flash costs $0.0653 per million input tokens and $0.40 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 4.7 Flash or MiniMax M2?
MiniMax M2, with 205K tokens against 200K.
How many benchmarks is the GLM 4.7 Flash vs MiniMax M2 comparison based on?
40 benchmarks have a result for both models as of October 22, 2026. GLM 4.7 Flash'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.