GLM 4.7 Flash vs GPT-4
Updated October 21, 2026. 30 benchmarks have a result for both. Coding is ranked on the Vibe Coding Index; every other board is shown as published.
Decision reading
Neither model has a coding score yet, so the two cannot be ranked on coding.
GLM 4.7 Flash has the edge where output tokens cost 150.0x less, and where the context window is larger, 200K against 8K tokens.
- Shared results
- 30
- GLM 4.7 Flash only
- 13
- GPT-4 only
- 15
- Like-for-like categories
- 6 of 9
Shared results can support a head-to-head reading. Results only one model has describe coverage, not superiority.
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 6.7.
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
GLM 4.7 Flash
GLM 4.7 Flash takes 200K tokens against 8K.
Listed by OpenRouter
Everyday cost
1K input + 500 output tokens
GLM 4.7 Flash
GLM 4.7 Flash $0.0003, GPT-4 $0.060 per turn.
List prices
At a glance
- Coding index
- GLM 4.7 Flash not benchmarked, GPT-4 not benchmarked.
- Widest category
- Knowledge, 30.9 points apart on average over 4 shared benchmarks.
- Widest single gap
- LMArena Text: Chinese: GLM 4.7 Flash 1403 against GPT-4 1184, 36.5 points apart on a 0 to 100 scale.
- Output price
- GLM 4.7 Flash $0.40 against GPT-4 $60.00 per million output tokens, 150.0x apart.
- Context window
- GLM 4.7 Flash takes 200K tokens, GPT-4 8K.
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 | GPT-4 | Ties |
|---|---|---|---|---|
| Coding | 2 | 0 | ||
| Intelligence | 1 | 0 | ||
| Reasoning | 4 | 0 | ||
| Math | 2 | 0 | ||
| Knowledge | 4 | 0 | ||
| General | 17 | 0 | ||
| All shared boards | 30 | 0 |
CodingLike-for-likeSix of these boards feed the Vibe Coding Index20
| Benchmark | GLM 4.7 Flash | GPT-4 | Leader |
|---|---|---|---|
| SWE-bench Verified | Not measured | 22.4% | Only GPT-4 measured |
| SWE-bench Lite | Not measured | 39.7% | Only GPT-4 measured |
| SWE-bench (full test set) | Not measured | 12.5% | Only GPT-4 measured |
| AA Coding Index | Not measured | 13.1 | Only GPT-4 measured |
| Terminal-Bench Hard (Artificial Analysis run) | 22.0% | Not measured | Only GLM 4.7 Flash measured |
| LMArena Text: coding | 1382 | 1209 | GLM 4.7 Flash |
| LMArena Text: software and IT | 1389 | 1195 | GLM 4.7 Flash |
| Neither model has a result on the other 31 boards here | |||
IntelligenceLike-for-likePublic boards, shown as published, kept out of the index10
| Benchmark | GLM 4.7 Flash | GPT-4 | Leader |
|---|---|---|---|
| AA Intelligence Index | 14.9 | 6.7 | GLM 4.7 Flash |
| Epoch Capabilities Index | Not measured | 125.7 | Only GPT-4 measured |
Agentic workDirectional onlyPublic boards, shown as published, kept out of the index1 board, none shared
| Benchmark | GLM 4.7 Flash | GPT-4 | Leader |
|---|---|---|---|
| τ²-Bench Telecom | 98.8% | Not measured | Only GLM 4.7 Flash measured |
| Neither model has a result on the other 38 boards here | |||
ReasoningLike-for-likePublic boards, shown as published, kept out of the index40
| Benchmark | GLM 4.7 Flash | GPT-4 | Leader |
|---|---|---|---|
| GPQA Diamond (Artificial Analysis run) | 58.1% | 34.9% | GLM 4.7 Flash |
| 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 |
| BIG-Bench Hard | Not measured | 75.1% | Only GPT-4 measured |
| LMArena Text: hard prompts | 1356 | 1200 | GLM 4.7 Flash |
| LMArena Text: expert | 1357 | 1149 | GLM 4.7 Flash |
| LMArena Text: hard prompts in English | 1369 | 1209 | GLM 4.7 Flash |
| Neither model has a result on the other 14 boards here | |||
MathLike-for-likePublic boards, shown as published, kept out of the index20
| Benchmark | GLM 4.7 Flash | GPT-4 | Leader |
|---|---|---|---|
| OTIS Mock AIME | 58.3% | Not measured | Only GLM 4.7 Flash measured |
| GSM8K | Not measured | 92.0% | Only GPT-4 measured |
| LMArena Text: math | 1355 | 1230 | GLM 4.7 Flash |
| LMArena Text: mathematical work | 1369 | 1228 | GLM 4.7 Flash |
| Neither model has a result on the other 9 boards here | |||
KnowledgeLike-for-likePublic boards, shown as published, kept out of the index40
| Benchmark | GLM 4.7 Flash | GPT-4 | Leader |
|---|---|---|---|
| MMLU Pro (Artificial Analysis run) | Not measured | 56.2% | Only GPT-4 measured |
| MMLU | Not measured | 86.4% | Only GPT-4 measured |
| AI2 Reasoning Challenge | Not measured | 96.3% | Only GPT-4 measured |
| LMArena Text: science | 1358 | 1177 | GLM 4.7 Flash |
| LMArena Text: medicine and healthcare | 1343 | 1145 | GLM 4.7 Flash |
| LMArena Text: legal and government | 1357 | 1191 | GLM 4.7 Flash |
| LMArena Text: business and finance | 1343 | 1145 | GLM 4.7 Flash |
| Neither model has a result on the other 10 boards here | |||
Web designDirectional onlyPublic boards, shown as published, kept out of the index7 boards, none shared
| Benchmark | GLM 4.7 Flash | GPT-4 | Leader |
|---|---|---|---|
| Design Arena: websites | 1194 | Not measured | Only GLM 4.7 Flash measured |
| Design Arena: UI components | 1208 | Not measured | Only GLM 4.7 Flash measured |
| Design Arena: game dev | 1135 | Not measured | Only GLM 4.7 Flash measured |
| Design Arena: data viz | 1123 | Not measured | Only GLM 4.7 Flash measured |
| Design Arena: 3D | 1121 | Not measured | Only GLM 4.7 Flash measured |
| Design Arena: all code categories | 1178 | Not measured | Only GLM 4.7 Flash measured |
| Design Arena: SVG | 1038 | 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 index170
| Benchmark | GLM 4.7 Flash | GPT-4 | Leader |
|---|---|---|---|
| IFBench | 60.8% | 33.2% | GLM 4.7 Flash |
| Long context reasoning (AA-LCR) | 41.7% | Not measured | Only GLM 4.7 Flash measured |
| DTBench | Not measured | 62.7% | Only GPT-4 measured |
| LMCA | Not measured | 17.1% | Only GPT-4 measured |
| HellaSwag | Not measured | 95.3% | Only GPT-4 measured |
| WinoGrande | Not measured | 87.5% | Only GPT-4 measured |
| LMArena Text | 1351 | 1206 | GLM 4.7 Flash |
| LMArena Text: instruction following | 1327 | 1201 | GLM 4.7 Flash |
| LMArena Text: creative writing | 1296 | 1191 | GLM 4.7 Flash |
| LMArena Text: English | 1373 | 1220 | GLM 4.7 Flash |
| LMArena Text: non-English | 1330 | 1194 | GLM 4.7 Flash |
| LMArena Text: Chinese | 1403 | 1184 | GLM 4.7 Flash |
| LMArena Text: German | 1336 | 1198 | GLM 4.7 Flash |
| LMArena Text: Japanese | Not measured | 1137 | Only GPT-4 measured |
| LMArena Text: Korean | 1283 | 1087 | GLM 4.7 Flash |
| LMArena Text: French | 1332 | 1220 | GLM 4.7 Flash |
| LMArena Text: Spanish | 1350 | 1197 | GLM 4.7 Flash |
| LMArena Text: Russian | 1333 | 1195 | GLM 4.7 Flash |
| LMArena Text: Polish | 1291 | 1265 | GLM 4.7 Flash |
| LMArena Text: multi-turn | 1342 | 1206 | GLM 4.7 Flash |
| LMArena Text: longer queries | 1345 | 1190 | GLM 4.7 Flash |
| LMArena Text: writing and language | 1320 | 1219 | GLM 4.7 Flash |
| LMArena Text: entertainment and media | 1304 | 1184 | GLM 4.7 Flash |
| Neither model has a result on the other 23 boards here | |||
Image generationNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
| Benchmark | GLM 4.7 Flash | GPT-4 | 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 | OpenAI GPT-4 |
|---|---|---|
| Maker | Z.ai | OpenAI |
| Weights | Open weights | No public weights listed |
| Released | January 19, 2026 | May 28, 2023 |
| Context window | 200K | 8K |
| Input $/Mtok | $0.0653 | $30.00 |
| Output $/Mtok | $0.40 | $60.00 |
| 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 GPT-4 for coding?
Neither model has a coding score yet, so the two cannot be ranked on coding. GLM 4.7 Flash has the edge where output tokens cost 150.0x less, and where the context window is larger, 200K against 8K tokens.
Which is cheaper, GLM 4.7 Flash or GPT-4?
GLM 4.7 Flash costs $0.0653 per million input tokens and $0.40 per million output tokens; GPT-4 costs $30.00 and $60.00. Cost per solved task is known for neither of them.
Which has the bigger context window, GLM 4.7 Flash or GPT-4?
GLM 4.7 Flash, with 200K tokens against 8K.
How many benchmarks is the GLM 4.7 Flash vs GPT-4 comparison based on?
30 benchmarks have a result for both models as of October 21, 2026. GLM 4.7 Flash's coding index rests on 0 independent benchmarks and GPT-4's on 0. Where only one model has a result, the page says so instead of naming a winner.