GLM 4.5V vs GPT-4
Updated October 21, 2026. 28 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 4.5V has the edge where output tokens cost 33.3x less, and where the context window is larger, 66K against 8K tokens.
- Shared results
- 28
- GLM 4.5V only
- 12
- GPT-4 only
- 17
- 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 4.5V
GLM 4.5V leads, 7.6 against 6.7.
Same board, both measured
Long documents
Listed context window
GLM 4.5V
GLM 4.5V takes 66K tokens against 8K.
Listed by OpenRouter
Everyday cost
1K input + 500 output tokens
GLM 4.5V
GLM 4.5V $0.0015, GPT-4 $0.060 per turn.
List prices
At a glance
- Coding index
- GLM 4.5V not benchmarked, GPT-4 not benchmarked.
- Widest category
- Reasoning, 28.7 points apart on average over 4 shared benchmarks.
- Widest single gap
- LMArena Text: expert: GLM 4.5V 1353 against GPT-4 1149, 34.0 points apart on a 0 to 100 scale.
- Output price
- GLM 4.5V $1.80 against GPT-4 $60.00 per million output tokens, 33.3x apart.
- Context window
- GLM 4.5V takes 66K 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 4.5V | Bars | GPT-4 | Ties |
|---|---|---|---|---|
| Coding | 2 | 0 | ||
| Intelligence | 1 | 0 | ||
| Reasoning | 4 | 0 | ||
| Math | 2 | 0 | ||
| Knowledge | 5 | 0 | ||
| General | 14 | 0 | ||
| All shared boards | 28 | 0 |
CodingLike-for-likeSix of these boards feed the Vibe Coding Index20
| Benchmark | GLM 4.5V | 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 |
| LiveCodeBench (Artificial Analysis run) | 60.4% | Not measured | Only GLM 4.5V measured |
| Terminal-Bench Hard (Artificial Analysis run) | 6.8% | Not measured | Only GLM 4.5V measured |
| LMArena Text: coding | 1347 | 1209 | GLM 4.5V |
| LMArena Text: software and IT | 1353 | 1195 | GLM 4.5V |
| Neither model has a result on the other 30 boards here | |||
IntelligenceLike-for-likePublic boards, shown as published, kept out of the index10
| Benchmark | GLM 4.5V | GPT-4 | Leader |
|---|---|---|---|
| AA Intelligence Index | 7.6 | 6.7 | GLM 4.5V |
| 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 4.5V | GPT-4 | Leader |
|---|---|---|---|
| τ²-Bench Telecom | 22.5% | Not measured | Only GLM 4.5V 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 4.5V | GPT-4 | Leader |
|---|---|---|---|
| GPQA Diamond (Artificial Analysis run) | 68.4% | 34.9% | GLM 4.5V |
| Humanity's Last Exam (Artificial Analysis run) | 6.3% | Not measured | Only GLM 4.5V measured |
| BIG-Bench Hard | Not measured | 75.1% | Only GPT-4 measured |
| LMArena Text: hard prompts | 1335 | 1200 | GLM 4.5V |
| LMArena Text: expert | 1353 | 1149 | GLM 4.5V |
| LMArena Text: hard prompts in English | 1357 | 1209 | GLM 4.5V |
| LMArena Vision: diagrams | 1151 | Not measured | Only GLM 4.5V measured |
| LMArena Vision: homework | 1163 | Not measured | Only GLM 4.5V measured |
| Neither model has a result on the other 13 boards here | |||
MathLike-for-likePublic boards, shown as published, kept out of the index20
| Benchmark | GLM 4.5V | GPT-4 | Leader |
|---|---|---|---|
| AA Math Index | 73.0 | Not measured | Only GLM 4.5V measured |
| AIME 2025 (Artificial Analysis run) | 73.0% | Not measured | Only GLM 4.5V measured |
| GSM8K | Not measured | 92.0% | Only GPT-4 measured |
| LMArena Text: math | 1354 | 1230 | GLM 4.5V |
| LMArena Text: mathematical work | 1336 | 1228 | GLM 4.5V |
| Neither model has a result on the other 8 boards here | |||
KnowledgeLike-for-likePublic boards, shown as published, kept out of the index50
| Benchmark | GLM 4.5V | GPT-4 | Leader |
|---|---|---|---|
| MMLU Pro (Artificial Analysis run) | 78.8% | 56.2% | GLM 4.5V |
| MMLU | Not measured | 86.4% | Only GPT-4 measured |
| AI2 Reasoning Challenge | Not measured | 96.3% | Only GPT-4 measured |
| LMArena Text: science | 1330 | 1177 | GLM 4.5V |
| LMArena Text: medicine and healthcare | 1349 | 1145 | GLM 4.5V |
| LMArena Text: legal and government | 1338 | 1191 | GLM 4.5V |
| LMArena Text: business and finance | 1309 | 1145 | GLM 4.5V |
| Neither model has a result on the other 10 boards here | |||
Web designNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
| Benchmark | GLM 4.5V | GPT-4 | Leader |
|---|---|---|---|
| Neither model has a result on 9 boards here | |||
GeneralLike-for-likePublic boards, shown as published, kept out of the index140
| Benchmark | GLM 4.5V | GPT-4 | Leader |
|---|---|---|---|
| IFBench | 34.2% | 33.2% | GLM 4.5V |
| Long context reasoning (AA-LCR) | 0.0% | Not measured | Only GLM 4.5V 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 | 1333 | 1206 | GLM 4.5V |
| LMArena Text: instruction following | 1311 | 1201 | GLM 4.5V |
| LMArena Text: creative writing | 1294 | 1191 | GLM 4.5V |
| LMArena Vision | 1154 | Not measured | Only GLM 4.5V measured |
| LMArena Text: English | 1362 | 1220 | GLM 4.5V |
| LMArena Text: non-English | 1303 | 1194 | GLM 4.5V |
| LMArena Text: Chinese | 1337 | 1184 | GLM 4.5V |
| LMArena Text: German | Not measured | 1198 | Only GPT-4 measured |
| LMArena Text: Japanese | Not measured | 1137 | Only GPT-4 measured |
| LMArena Text: Korean | Not measured | 1087 | Only GPT-4 measured |
| LMArena Text: French | Not measured | 1220 | Only GPT-4 measured |
| LMArena Text: Spanish | 1336 | 1197 | GLM 4.5V |
| LMArena Text: Russian | 1298 | 1195 | GLM 4.5V |
| LMArena Text: Polish | 1302 | 1265 | GLM 4.5V |
| LMArena Text: multi-turn | 1333 | 1206 | GLM 4.5V |
| LMArena Text: longer queries | 1305 | 1190 | GLM 4.5V |
| LMArena Text: writing and language | 1302 | 1219 | GLM 4.5V |
| LMArena Text: entertainment and media | 1303 | 1184 | GLM 4.5V |
| LMArena Vision: English | 1183 | Not measured | Only GLM 4.5V measured |
| LMArena Vision: reading text | 1151 | Not measured | Only GLM 4.5V measured |
| Neither model has a result on the other 20 boards here | |||
Image generationNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
| Benchmark | GLM 4.5V | 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 4.5V costs less
Repository review
50K input + 3K output tokens
GLM 4.5V costs less
Agent loop
200K input + 10K output tokens
GLM 4.5V 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 4.5V | OpenAI GPT-4 |
|---|---|---|
| Maker | Z.ai | OpenAI |
| Weights | Open weights | No public weights listed |
| Released | August 11, 2025 | May 28, 2023 |
| Context window | 66K | 8K |
| Input $/Mtok | $0.60 | $30.00 |
| Output $/Mtok | $1.80 | $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 4.5V better than GPT-4 for coding?
Neither model has a coding score yet, so the two cannot be ranked on coding. GLM 4.5V has the edge where output tokens cost 33.3x less, and where the context window is larger, 66K against 8K tokens.
Which is cheaper, GLM 4.5V or GPT-4?
GLM 4.5V costs $0.60 per million input tokens and $1.80 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 4.5V or GPT-4?
GLM 4.5V, with 66K tokens against 8K.
How many benchmarks is the GLM 4.5V vs GPT-4 comparison based on?
28 benchmarks have a result for both models as of October 21, 2026. GLM 4.5V'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.