GLM 4.7 Flash vs GLM 5 Turbo
GLM 5 Turbo leads GLM 4.7 Flash on the Artificial Analysis Intelligence Index, 26.6 to 14.9, and GLM 4.7 Flash costs 10.0x less per output token ($0.40 vs $4.00 per million). Updated October 22, 2026; 14 benchmarks have a result for both.
GLM 4.7 Flash or GLM 5 Turbo: 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 10.0x cheaper ($0.40 vs $4.00 per million) and work measured by Terminal-Bench Hard (Artificial Analysis run) or AA Intelligence Index, where it leads.
- Shared results
- 14
- GLM 4.7 Flash only
- 29
- GLM 5 Turbo only
- 2
- 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 5 Turbo
GLM 5 Turbo leads, 26.6 against 14.9.
Same board, both measured
Web design
Design Arena: websites
GLM 5 Turbo
GLM 5 Turbo leads, 1262 against 1194.
Same board, both measured
Long documents
Listed context window
GLM 5 Turbo
GLM 5 Turbo takes 203K tokens against 200K.
Listed by OpenRouter
Everyday cost
1K input + 500 output tokens
GLM 4.7 Flash
GLM 4.7 Flash $0.0003, GLM 5 Turbo $0.0032 per turn.
List prices
At a glance
- Coding index
- GLM 4.7 Flash not benchmarked, GLM 5 Turbo not benchmarked.
- Widest category
- Reasoning, 23.4 points apart on average over 2 shared benchmarks.
- Widest single gap
- Long context reasoning (AA-LCR): GLM 4.7 Flash 41.7% against GLM 5 Turbo 71.7%, 30.0 points apart on a 0 to 100 scale.
- Output price
- GLM 4.7 Flash $0.40 against GLM 5 Turbo $4.00 per million output tokens, 10.0x apart.
- Context window
- GLM 4.7 Flash takes 200K tokens, GLM 5 Turbo 203K.
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 | GLM 5 Turbo | Ties |
|---|---|---|---|---|
| Coding | 0 | 1 | ||
| Intelligence | 0 | 1 | ||
| Agentic work | 1 | 0 | ||
| Reasoning | 0 | 2 | ||
| Web design | 0 | 7 | ||
| General | 0 | 2 | ||
| All shared boards | 1 | 13 |
CodingLike-for-likeSix of these boards feed the Vibe Coding Index01
| Benchmark | GLM 4.7 Flash | GLM 5 Turbo | Leader |
|---|---|---|---|
| Terminal-Bench Hard (Artificial Analysis run) | 22.0% | 33.3% | GLM 5 Turbo |
| ALE-Bench | Not measured | 634.0 | Only GLM 5 Turbo measured |
| LMArena Text: coding | 1382 | Not measured | Only GLM 4.7 Flash measured |
| LMArena Text: software and IT | 1389 | Not measured | Only GLM 4.7 Flash measured |
| Neither model has a result on the other 35 boards here | |||
IntelligenceLike-for-likePublic boards, shown as published, kept out of the index01
| Benchmark | GLM 4.7 Flash | GLM 5 Turbo | Leader |
|---|---|---|---|
| AA Intelligence Index | 14.9 | 26.6 | GLM 5 Turbo |
| 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 | GLM 5 Turbo | Leader |
|---|---|---|---|
| τ²-Bench Telecom | 98.8% | 98.5% | GLM 4.7 Flash |
| Neither model has a result on the other 40 boards here | |||
ReasoningLike-for-likePublic boards, shown as published, kept out of the index02
| Benchmark | GLM 4.7 Flash | GLM 5 Turbo | Leader |
|---|---|---|---|
| GPQA Diamond (Artificial Analysis run) | 58.1% | 84.7% | GLM 5 Turbo |
| Humanity's Last Exam (Artificial Analysis run) | 7.6% | 27.8% | GLM 5 Turbo |
| GPQA Diamond (Epoch AI run) | 60.5% | Not measured | Only GLM 4.7 Flash measured |
| LMArena Text: hard prompts | 1356 | Not measured | Only GLM 4.7 Flash measured |
| LMArena Text: expert | 1357 | Not measured | Only GLM 4.7 Flash measured |
| LMArena Text: hard prompts in English | 1369 | Not measured | Only GLM 4.7 Flash measured |
| Neither model has a result on the other 16 boards here | |||
MathDirectional onlyPublic boards, shown as published, kept out of the index3 boards, none shared
| Benchmark | GLM 4.7 Flash | GLM 5 Turbo | Leader |
|---|---|---|---|
| OTIS Mock AIME | 58.3% | Not measured | Only GLM 4.7 Flash measured |
| LMArena Text: math | 1355 | Not measured | Only GLM 4.7 Flash measured |
| LMArena Text: mathematical work | 1369 | Not measured | Only GLM 4.7 Flash measured |
| Neither model has a result on the other 10 boards here | |||
KnowledgeDirectional onlyPublic boards, shown as published, kept out of the index4 boards, none shared
| Benchmark | GLM 4.7 Flash | GLM 5 Turbo | Leader |
|---|---|---|---|
| LMArena Text: science | 1358 | Not measured | Only GLM 4.7 Flash measured |
| LMArena Text: medicine and healthcare | 1343 | Not measured | Only GLM 4.7 Flash measured |
| LMArena Text: legal and government | 1357 | Not measured | Only GLM 4.7 Flash measured |
| LMArena Text: business and finance | 1343 | Not measured | Only GLM 4.7 Flash measured |
| Neither model has a result on the other 14 boards here | |||
Web designLike-for-likePublic boards, shown as published, kept out of the index07
| Benchmark | GLM 4.7 Flash | GLM 5 Turbo | Leader |
|---|---|---|---|
| Design Arena: websites | 1194 | 1262 | GLM 5 Turbo |
| Design Arena: UI components | 1205 | 1260 | GLM 5 Turbo |
| Design Arena: game dev | 1134 | 1248 | GLM 5 Turbo |
| Design Arena: data viz | 1123 | 1264 | GLM 5 Turbo |
| Design Arena: 3D | 1120 | 1247 | GLM 5 Turbo |
| Design Arena: all code categories | 1177 | 1261 | GLM 5 Turbo |
| Design Arena: SVG | 1037 | 1205 | GLM 5 Turbo |
| Design Arena: ASCII art | Not measured | 1160 | Only GLM 5 Turbo measured |
| Neither model has a result on the other 1 board here | |||
GeneralLike-for-likePublic boards, shown as published, kept out of the index02
| Benchmark | GLM 4.7 Flash | GLM 5 Turbo | Leader |
|---|---|---|---|
| IFBench | 60.8% | 73.2% | GLM 5 Turbo |
| Long context reasoning (AA-LCR) | 41.7% | 71.7% | GLM 5 Turbo |
| LMArena Text | 1351 | Not measured | Only GLM 4.7 Flash measured |
| LMArena Text: instruction following | 1327 | Not measured | Only GLM 4.7 Flash measured |
| LMArena Text: creative writing | 1296 | Not measured | Only GLM 4.7 Flash measured |
| LMArena Text: English | 1373 | Not measured | Only GLM 4.7 Flash measured |
| LMArena Text: non-English | 1330 | Not measured | Only GLM 4.7 Flash measured |
| LMArena Text: Chinese | 1403 | Not measured | Only GLM 4.7 Flash measured |
| LMArena Text: German | 1336 | Not measured | Only GLM 4.7 Flash measured |
| LMArena Text: Korean | 1283 | Not measured | Only GLM 4.7 Flash measured |
| LMArena Text: French | 1332 | Not measured | Only GLM 4.7 Flash measured |
| LMArena Text: Spanish | 1350 | Not measured | Only GLM 4.7 Flash measured |
| LMArena Text: Russian | 1333 | Not measured | Only GLM 4.7 Flash measured |
| LMArena Text: Polish | 1291 | Not measured | Only GLM 4.7 Flash measured |
| LMArena Text: multi-turn | 1342 | Not measured | Only GLM 4.7 Flash measured |
| LMArena Text: longer queries | 1345 | Not measured | Only GLM 4.7 Flash measured |
| LMArena Text: writing and language | 1320 | Not measured | Only GLM 4.7 Flash measured |
| LMArena Text: entertainment and media | 1304 | Not measured | Only GLM 4.7 Flash measured |
| 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 | GLM 5 Turbo | 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 | Z.ai GLM 5 Turbo |
|---|---|---|
| Maker | Z.ai | Z.ai |
| Weights | Open weights | No public weights listed |
| Released | January 19, 2026 | March 15, 2026 |
| Context window | 200K | 203K |
| Input $/Mtok | $0.0653 | $1.20 |
| Output $/Mtok | $0.40 | $4.00 |
| 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 GLM 5 Turbo 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 10.0x cheaper ($0.40 vs $4.00 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 GLM 5 Turbo?
GLM 4.7 Flash costs $0.0653 per million input tokens and $0.40 per million output tokens; GLM 5 Turbo costs $1.20 and $4.00. Cost per solved task is known for neither of them.
Which has the bigger context window, GLM 4.7 Flash or GLM 5 Turbo?
GLM 5 Turbo, with 203K tokens against 200K.
How many benchmarks is the GLM 4.7 Flash vs GLM 5 Turbo comparison based on?
14 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 GLM 5 Turbo's on 0. Where only one model has a result, the page says so instead of naming a winner.