GLM 4.7 vs GPT-4o
Updated October 21, 2026. 16 benchmarks have a result for both. Coding is ranked on the Vibe Coding Index; every other board is shown as published.
Decision reading
Pick GPT-4o for coding: it scores 38.8 on the Vibe Coding Index against 33.6 for GLM 4.7.
Pick GLM 4.7 when output tokens cost 5.1x less, or when the context window is larger, 205K against 128K tokens.
- Shared results
- 16
- GLM 4.7 only
- 64
- GPT-4o only
- 10
- Like-for-like categories
- 7 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, four public coding benchmarks
GPT-4o
GPT-4o scores 38.8 against 33.6.
One benchmark behind each score
Intelligence
AA Intelligence Index
No call
Only GLM 4.7 has a AA Intelligence Index result, 22.2.
Directional only
Reasoning
GPQA Diamond
No call
Only GLM 4.7 has a GPQA Diamond result, 80.0%.
Directional only
Math
AIME
No call
Only GLM 4.7 has a AIME result, 93.3%.
Directional only
Knowledge
MMLU Pro
No call
Only GLM 4.7 has a MMLU Pro result, 82.7%.
Directional only
Web design
Design Arena: websites
GLM 4.7
GLM 4.7 leads, 1226 against 831.
Same board, both measured
Long documents
Listed context window
GLM 4.7
GLM 4.7 takes 205K tokens against 128K.
Listed by OpenRouter
Everyday cost
1K input + 500 output tokens
GLM 4.7
GLM 4.7 $0.0015, GPT-4o $0.0075 per turn.
List prices
Cost per solved task
One SWE-bench Verified task, solved
No call
Only GLM 4.7 has the number, $0.29.
Directional only
At a glance
- Coding index
- GLM 4.7 33.6 from 1 benchmark, GPT-4o 38.8 from 1 benchmark.
- Widest category
- Math, 89.0 points apart on average over 2 shared benchmarks.
- Widest single gap
- AA Math Index: GLM 4.7 95.0 against GPT-4o 6.0, 89.0 points apart on a 0 to 100 scale.
- Output price
- GLM 4.7 $1.98 against GPT-4o $10.00 per million output tokens, 5.1x apart.
- Context window
- GLM 4.7 takes 205K tokens, GPT-4o 128K.
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 | Bars | GPT-4o | Ties |
|---|---|---|---|---|
| Coding | 2 | 0 | ||
| Agentic work | 1 | 0 | ||
| Reasoning | 2 | 0 | ||
| Math | 2 | 0 | ||
| Knowledge | 1 | 0 | ||
| Web design | 6 | 0 | ||
| General | 2 | 0 | ||
| All shared boards | 16 | 0 |
CodingLike-for-likeFour of these boards feed the Vibe Coding Index20
| Benchmark | GLM 4.7 | GPT-4o | Leader |
|---|---|---|---|
| SWE-bench Verified | Not measured | 38.8% | Only GPT-4o measured |
| SWE-bench Lite | Not measured | 38.0% | Only GPT-4o measured |
| SWE-bench (full test set) | Not measured | 18.8% | Only GPT-4o measured |
| SWE-bench Multimodal | Not measured | 30.4% | Only GPT-4o measured |
| LMArena WebDev | 1435 | Not measured | Only GLM 4.7 measured |
| AA Coding Index | 45.3 | Not measured | Only GLM 4.7 measured |
| LiveCodeBench (Vals AI) | 82.2% | Not measured | Only GLM 4.7 measured |
| SWE-bench Verified (Vals AI) | 69.4% | Not measured | Only GLM 4.7 measured |
| LiveCodeBench (Artificial Analysis run) | 89.4% | 30.9% | GLM 4.7 |
| Terminal-Bench Hard (Artificial Analysis run) | 31.8% | 8.3% | GLM 4.7 |
| Terminal-Bench | 33.4% | Not measured | Only GLM 4.7 measured |
| ALE-Bench | 399.5 | Not measured | Only GLM 4.7 measured |
| LMArena Text: coding | 1454 | Not measured | Only GLM 4.7 measured |
| LMArena Text: software and IT | 1453 | Not measured | Only GLM 4.7 measured |
| LMArena WebDev: React | 1420 | Not measured | Only GLM 4.7 measured |
| LMArena WebDev: HTML | 1447 | Not measured | Only GLM 4.7 measured |
| Neither model has a result on the other 22 boards here | |||
IntelligenceDirectional onlyPublic boards, shown as published, kept out of the index2 boards, none shared
| Benchmark | GLM 4.7 | GPT-4o | Leader |
|---|---|---|---|
| AA Intelligence Index | 22.2 | Not measured | Only GLM 4.7 measured |
| Epoch Capabilities Index | 143.6 | Not measured | Only GLM 4.7 measured |
Agentic workLike-for-likePublic boards, shown as published, kept out of the index10
| Benchmark | GLM 4.7 | GPT-4o | Leader |
|---|---|---|---|
| Design Arena: agents building full-stack apps | 1050 | Not measured | Only GLM 4.7 measured |
| Design Arena: agents building mobile apps | 1105 | Not measured | Only GLM 4.7 measured |
| Design Arena: agents building Android apps | 1118 | Not measured | Only GLM 4.7 measured |
| Design Arena: agents building Godot games | 1058 | Not measured | Only GLM 4.7 measured |
| τ²-Bench Telecom | 95.9% | 25.1% | GLM 4.7 |
| GDPval | Not measured | 9.9% | Only GPT-4o measured |
| Vending-Bench 2 | $2,377 | Not measured | Only GLM 4.7 measured |
| Neither model has a result on the other 32 boards here | |||
ReasoningLike-for-likePublic boards, shown as published, kept out of the index20
| Benchmark | GLM 4.7 | GPT-4o | Leader |
|---|---|---|---|
| GPQA Diamond | 80.0% | Not measured | Only GLM 4.7 measured |
| GPQA Diamond (Artificial Analysis run) | 85.9% | 54.3% | GLM 4.7 |
| Humanity's Last Exam (Artificial Analysis run) | 27.4% | 2.4% | GLM 4.7 |
| GPQA Diamond (Epoch AI run) | 83.3% | Not measured | Only GLM 4.7 measured |
| ARC-AGI | Not measured | 4.5% | Only GPT-4o measured |
| ARC-AGI-2 | Not measured | 0.0% | Only GPT-4o measured |
| SimpleBench | 47.7% | Not measured | Only GLM 4.7 measured |
| Chess puzzles | 21.0% | Not measured | Only GLM 4.7 measured |
| LMArena Text: hard prompts | 1443 | Not measured | Only GLM 4.7 measured |
| LMArena Text: expert | 1424 | Not measured | Only GLM 4.7 measured |
| LMArena Text: hard prompts in English | 1452 | Not measured | Only GLM 4.7 measured |
| Neither model has a result on the other 10 boards here | |||
MathLike-for-likePublic boards, shown as published, kept out of the index20
| Benchmark | GLM 4.7 | GPT-4o | Leader |
|---|---|---|---|
| AIME | 93.3% | Not measured | Only GLM 4.7 measured |
| MGSM (Vals AI) | 88.2% | Not measured | Only GLM 4.7 measured |
| AA Math Index | 95.0 | 6.0 | GLM 4.7 |
| AIME 2025 (Artificial Analysis run) | 95.0% | 6.0% | GLM 4.7 |
| OTIS Mock AIME | 83.3% | Not measured | Only GLM 4.7 measured |
| ProofBench | 6.0% | Not measured | Only GLM 4.7 measured |
| LMArena Text: math | 1424 | Not measured | Only GLM 4.7 measured |
| LMArena Text: mathematical work | 1411 | Not measured | Only GLM 4.7 measured |
| Neither model has a result on the other 5 boards here | |||
KnowledgeLike-for-likePublic boards, shown as published, kept out of the index10
| Benchmark | GLM 4.7 | GPT-4o | Leader |
|---|---|---|---|
| MMLU Pro | 82.7% | Not measured | Only GLM 4.7 measured |
| LegalBench (Vals AI) | 83.4% | Not measured | Only GLM 4.7 measured |
| CaseLaw (Vals AI) | 54.9% | Not measured | Only GLM 4.7 measured |
| CorpFin (Vals AI) | 46.4% | Not measured | Only GLM 4.7 measured |
| TaxEval (Vals AI) | 68.8% | Not measured | Only GLM 4.7 measured |
| MedQA (Vals AI) | 93.7% | Not measured | Only GLM 4.7 measured |
| MedCode (Vals AI) | 32.8% | Not measured | Only GLM 4.7 measured |
| MMLU Pro (Artificial Analysis run) | 85.6% | 74.8% | GLM 4.7 |
| SimpleQA Verified | 32.2% | Not measured | Only GLM 4.7 measured |
| MMLU | Not measured | 88.1% | Only GPT-4o measured |
| LMArena Text: science | 1457 | Not measured | Only GLM 4.7 measured |
| LMArena Text: medicine and healthcare | 1469 | Not measured | Only GLM 4.7 measured |
| LMArena Text: legal and government | 1444 | Not measured | Only GLM 4.7 measured |
| LMArena Text: business and finance | 1429 | Not measured | Only GLM 4.7 measured |
| Neither model has a result on the other 3 boards here | |||
Web designLike-for-likePublic boards, shown as published, kept out of the index60
| Benchmark | GLM 4.7 | GPT-4o | Leader |
|---|---|---|---|
| Design Arena: websites | 1226 | 831 | GLM 4.7 |
| Design Arena: UI components | 1199 | 885 | GLM 4.7 |
| Design Arena: game dev | 1189 | 906 | GLM 4.7 |
| Design Arena: data viz | 1195 | 858 | GLM 4.7 |
| Design Arena: 3D | 1191 | 869 | GLM 4.7 |
| Design Arena: all code categories | 1217 | 861 | GLM 4.7 |
| Design Arena: SVG | 1142 | Not measured | Only GLM 4.7 measured |
| Design Arena: ASCII art | 1165 | Not measured | Only GLM 4.7 measured |
| Neither model has a result on the other 1 board here | |||
GeneralLike-for-likePublic boards, shown as published, kept out of the index20
| Benchmark | GLM 4.7 | GPT-4o | Leader |
|---|---|---|---|
| MedScribe (Vals AI) | 68.6% | Not measured | Only GLM 4.7 measured |
| IFBench | 67.9% | 34.3% | GLM 4.7 |
| Long context reasoning (AA-LCR) | 71.0% | 49.3% | GLM 4.7 |
| DTBench | Not measured | 64.5% | Only GPT-4o measured |
| LMCA | Not measured | 16.6% | Only GPT-4o measured |
| CL-bench | 15.9% | Not measured | Only GLM 4.7 measured |
| CL-bench Life | 10.9% | Not measured | Only GLM 4.7 measured |
| LMArena Text | 1435 | Not measured | Only GLM 4.7 measured |
| LMArena Text: instruction following | 1412 | Not measured | Only GLM 4.7 measured |
| LMArena Text: creative writing | 1401 | Not measured | Only GLM 4.7 measured |
| LMArena Text: English | 1453 | Not measured | Only GLM 4.7 measured |
| LMArena Text: non-English | 1417 | Not measured | Only GLM 4.7 measured |
| LMArena Text: Chinese | 1495 | Not measured | Only GLM 4.7 measured |
| LMArena Text: German | 1426 | Not measured | Only GLM 4.7 measured |
| LMArena Text: Japanese | 1439 | Not measured | Only GLM 4.7 measured |
| LMArena Text: Korean | 1399 | Not measured | Only GLM 4.7 measured |
| LMArena Text: French | 1432 | Not measured | Only GLM 4.7 measured |
| LMArena Text: Spanish | 1434 | Not measured | Only GLM 4.7 measured |
| LMArena Text: Russian | 1423 | Not measured | Only GLM 4.7 measured |
| LMArena Text: Polish | 1435 | Not measured | Only GLM 4.7 measured |
| LMArena Text: multi-turn | 1446 | Not measured | Only GLM 4.7 measured |
| LMArena Text: longer queries | 1432 | Not measured | Only GLM 4.7 measured |
| LMArena Text: writing and language | 1411 | Not measured | Only GLM 4.7 measured |
| LMArena Text: entertainment and media | 1400 | Not measured | Only GLM 4.7 measured |
| Neither model has a result on the other 22 boards here | |||
Image generationNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
| Benchmark | GLM 4.7 | GPT-4o | 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 costs less
Repository review
50K input + 3K output tokens
GLM 4.7 costs less
Agent loop
200K input + 10K output tokens
GLM 4.7 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 | OpenAI GPT-4o |
|---|---|---|
| Maker | Z.ai | OpenAI |
| Weights | Open weights | No public weights listed |
| Released | December 22, 2025 | May 13, 2024 |
| Context window | 205K | 128K |
| Input $/Mtok | $0.54 | $2.50 |
| Output $/Mtok | $1.98 | $10.00 |
| Coding index | 33.6 | 38.8 |
| Independent measurements behind it | 1 | 1 |
| Cost per solved task | $0.29 | Not computable |
| Source | OpenRouter listing | OpenRouter listing |
Questions
Is GLM 4.7 better than GPT-4o for coding?
Pick GPT-4o for coding: it scores 38.8 on the Vibe Coding Index against 33.6 for GLM 4.7. Pick GLM 4.7 when output tokens cost 5.1x less, or when the context window is larger, 205K against 128K tokens.
Which is cheaper, GLM 4.7 or GPT-4o?
GLM 4.7 costs $0.54 per million input tokens and $1.98 per million output tokens; GPT-4o costs $2.50 and $10.00. Cost per solved task is known for GLM 4.7 only.
Which has the bigger context window, GLM 4.7 or GPT-4o?
GLM 4.7, with 205K tokens against 128K.
How many benchmarks is the GLM 4.7 vs GPT-4o comparison based on?
16 benchmarks have a result for both models as of October 21, 2026. GLM 4.7's coding index rests on 1 independent benchmark and GPT-4o's on 1. Where only one model has a result, the page says so instead of naming a winner.