GLM 4.7 Flash vs MiniMax M1
GLM 4.7 Flash leads MiniMax M1 on the Artificial Analysis Intelligence Index, 14.9 to 11.7, and GLM 4.7 Flash costs 5.5x less per output token ($0.40 vs $2.20 per million). Updated October 22, 2026; 28 benchmarks have a result for both.
GLM 4.7 Flash or MiniMax M1: 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 5.5x cheaper ($0.40 vs $2.20 per million) and work measured by LMArena Text: math or LMArena Text: medicine and healthcare, where it leads.
- Shared results
- 28
- GLM 4.7 Flash only
- 15
- MiniMax M1 only
- 1
- Like-for-like categories
- 6 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
GLM 4.7 Flash
GLM 4.7 Flash leads, 14.9 against 11.7.
Same board, both measured
Web design
Design Arena: websites
No call
Only GLM 4.7 Flash has a Design Arena: websites result, 1194.
Directional only
Long documents
Listed context window
MiniMax M1
MiniMax M1 takes 1M tokens against 200K.
Listed by OpenRouter
Everyday cost
1K input + 500 output tokens
GLM 4.7 Flash
GLM 4.7 Flash $0.0003, MiniMax M1 $0.0015 per turn.
List prices
At a glance
- Coding index
- GLM 4.7 Flash not benchmarked, MiniMax M1 not benchmarked.
- Widest category
- Coding, 4.5 points apart on average over 2 shared benchmarks.
- Widest single gap
- LMArena Text: Chinese: GLM 4.7 Flash 1403 against MiniMax M1 1360, 7.2 points apart on a 0 to 100 scale.
- Output price
- GLM 4.7 Flash $0.40 against MiniMax M1 $2.20 per million output tokens, 5.5x apart.
- Context window
- GLM 4.7 Flash takes 200K tokens, MiniMax M1 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 4.7 Flash | Bars | MiniMax M1 | Ties |
|---|---|---|---|---|
| Coding | 2 | 0 | ||
| Intelligence | 1 | 0 | ||
| Reasoning | 3 | 0 | ||
| Math | 1 | 1 | ||
| Knowledge | 3 | 1 | ||
| General | 11 | 5 | ||
| All shared boards | 21 | 7 |
CodingLike-for-likeSix of these boards feed the Vibe Coding Index20
| Benchmark | GLM 4.7 Flash | MiniMax M1 | Leader |
|---|---|---|---|
| Terminal-Bench Hard (Artificial Analysis run) | 22.0% | Not measured | Only GLM 4.7 Flash measured |
| LMArena Text: coding | 1382 | 1359 | GLM 4.7 Flash |
| LMArena Text: software and IT | 1389 | 1359 | GLM 4.7 Flash |
| Neither model has a result on the other 36 boards here | |||
IntelligenceLike-for-likePublic boards, shown as published, kept out of the index10
| Benchmark | GLM 4.7 Flash | MiniMax M1 | Leader |
|---|---|---|---|
| AA Intelligence Index | 14.9 | 11.7 | GLM 4.7 Flash |
| Neither model has a result on the other 1 board here | |||
Agentic workDirectional onlyPublic boards, shown as published, kept out of the index1 board, none shared
| Benchmark | GLM 4.7 Flash | MiniMax M1 | Leader |
|---|---|---|---|
| τ²-Bench Telecom | 98.8% | Not measured | Only GLM 4.7 Flash measured |
| Neither model has a result on the other 40 boards here | |||
ReasoningLike-for-likePublic boards, shown as published, kept out of the index30
| Benchmark | GLM 4.7 Flash | MiniMax M1 | Leader |
|---|---|---|---|
| GPQA Diamond (Artificial Analysis run) | 58.1% | Not measured | Only GLM 4.7 Flash measured |
| Humanity's Last Exam (Artificial Analysis run) | 7.6% | Not measured | Only GLM 4.7 Flash measured |
| GPQA Diamond (Epoch AI run) | 60.5% | Not measured | Only GLM 4.7 Flash measured |
| LMArena Text: hard prompts | 1356 | 1339 | GLM 4.7 Flash |
| LMArena Text: expert | 1357 | 1317 | GLM 4.7 Flash |
| LMArena Text: hard prompts in English | 1369 | 1353 | GLM 4.7 Flash |
| Neither model has a result on the other 16 boards here | |||
MathLike-for-likePublic boards, shown as published, kept out of the index11
| Benchmark | GLM 4.7 Flash | MiniMax M1 | Leader |
|---|---|---|---|
| OTIS Mock AIME | 58.3% | Not measured | Only GLM 4.7 Flash measured |
| LMArena Text: math | 1355 | 1361 | MiniMax M1 |
| LMArena Text: mathematical work | 1369 | 1363 | GLM 4.7 Flash |
| Neither model has a result on the other 10 boards here | |||
KnowledgeLike-for-likePublic boards, shown as published, kept out of the index31
| Benchmark | GLM 4.7 Flash | MiniMax M1 | Leader |
|---|---|---|---|
| LMArena Text: science | 1358 | 1354 | GLM 4.7 Flash |
| LMArena Text: medicine and healthcare | 1343 | 1355 | MiniMax M1 |
| LMArena Text: legal and government | 1357 | 1337 | GLM 4.7 Flash |
| LMArena Text: business and finance | 1343 | 1319 | GLM 4.7 Flash |
| Neither model has a result on the other 14 boards here | |||
Web designDirectional onlyPublic boards, shown as published, kept out of the index7 boards, none shared
| Benchmark | GLM 4.7 Flash | MiniMax M1 | Leader |
|---|---|---|---|
| Design Arena: websites | 1194 | Not measured | Only GLM 4.7 Flash measured |
| Design Arena: UI components | 1205 | Not measured | Only GLM 4.7 Flash measured |
| Design Arena: game dev | 1134 | Not measured | Only GLM 4.7 Flash measured |
| Design Arena: data viz | 1123 | Not measured | Only GLM 4.7 Flash measured |
| Design Arena: 3D | 1120 | Not measured | Only GLM 4.7 Flash measured |
| Design Arena: all code categories | 1177 | Not measured | Only GLM 4.7 Flash measured |
| Design Arena: SVG | 1037 | Not measured | Only GLM 4.7 Flash measured |
| Neither model has a result on the other 2 boards here | |||
GeneralLike-for-likePublic boards, shown as published, kept out of the index115
| Benchmark | GLM 4.7 Flash | MiniMax M1 | Leader |
|---|---|---|---|
| IFBench | 60.8% | Not measured | Only GLM 4.7 Flash measured |
| Long context reasoning (AA-LCR) | 41.7% | Not measured | Only GLM 4.7 Flash measured |
| LMArena Text | 1351 | 1343 | GLM 4.7 Flash |
| LMArena Text: instruction following | 1327 | 1312 | GLM 4.7 Flash |
| LMArena Text: creative writing | 1296 | 1297 | MiniMax M1 |
| LMArena Text: English | 1373 | 1363 | GLM 4.7 Flash |
| LMArena Text: non-English | 1330 | 1319 | GLM 4.7 Flash |
| LMArena Text: Chinese | 1403 | 1360 | GLM 4.7 Flash |
| LMArena Text: German | 1336 | 1350 | MiniMax M1 |
| LMArena Text: Japanese | Not measured | 1217 | Only MiniMax M1 measured |
| LMArena Text: Korean | 1283 | 1266 | GLM 4.7 Flash |
| LMArena Text: French | 1332 | 1370 | MiniMax M1 |
| LMArena Text: Spanish | 1350 | 1353 | MiniMax M1 |
| LMArena Text: Russian | 1333 | 1329 | GLM 4.7 Flash |
| LMArena Text: Polish | 1291 | 1329 | MiniMax M1 |
| LMArena Text: multi-turn | 1342 | 1335 | GLM 4.7 Flash |
| LMArena Text: longer queries | 1345 | 1326 | GLM 4.7 Flash |
| LMArena Text: writing and language | 1320 | 1311 | GLM 4.7 Flash |
| LMArena Text: entertainment and media | 1304 | 1303 | GLM 4.7 Flash |
| Neither model has a result on the other 27 boards here | |||
Image generationNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
| Benchmark | GLM 4.7 Flash | MiniMax M1 | 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 M1 |
|---|---|---|
| Maker | Z.ai | MiniMax |
| Weights | Open weights | No public weights listed |
| Released | January 19, 2026 | June 17, 2025 |
| Context window | 200K | 1M |
| Input $/Mtok | $0.0653 | $0.40 |
| Output $/Mtok | $0.40 | $2.20 |
| Coding index | Not benchmarked | Not benchmarked |
| Independent measurements behind it | 0 | 0 |
| Cost per solved task | Not computable | Not computable |
| Source | OpenRouter listing | OpenRouter listing |
Questions
Is GLM 4.7 Flash better than MiniMax M1 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 5.5x cheaper ($0.40 vs $2.20 per million) and work measured by LMArena Text: math or LMArena Text: medicine and healthcare, where it leads.
Which is cheaper, GLM 4.7 Flash or MiniMax M1?
GLM 4.7 Flash costs $0.0653 per million input tokens and $0.40 per million output tokens; MiniMax M1 costs $0.40 and $2.20. Cost per solved task is known for neither of them.
Which has the bigger context window, GLM 4.7 Flash or MiniMax M1?
MiniMax M1, with 1M tokens against 200K.
How many benchmarks is the GLM 4.7 Flash vs MiniMax M1 comparison based on?
28 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 M1's on 0. Where only one model has a result, the page says so instead of naming a winner.