GLM 4.7 vs Grok 4.3
Updated October 21, 2026. 68 benchmarks have a result for both. Coding is ranked on the Vibe Coding Index; every other board is shown as published.
Decision reading
Pick GLM 4.7 for coding: it scores 33.6 on the Vibe Coding Index against 22.5 for Grok 4.3.
Pick Grok 4.3 when the context window is larger, 1M against 205K tokens, or when what matters is LiveCodeBench (Vals AI) or SWE-bench Verified (Vals AI), where it leads.
- Shared results
- 68
- GLM 4.7 only
- 12
- Grok 4.3 only
- 36
- Like-for-like categories
- 8 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
GLM 4.7
GLM 4.7 scores 33.6 against 22.5.
One benchmark behind each score
Intelligence
AA Intelligence Index
Grok 4.3
Grok 4.3 leads, 24.9 against 22.2.
Same board, both measured
Agentic work
AA Agentic Index
No call
Only Grok 4.3 has a AA Agentic Index result, 15.5.
Directional only
Reasoning
GPQA Diamond
Grok 4.3
Grok 4.3 leads, 91.4% against 80.0%.
Same board, both measured
Math
AIME
No call
Only GLM 4.7 has a AIME result, 93.3%.
Directional only
Knowledge
MMLU Pro
Grok 4.3
Grok 4.3 leads, 85.8% against 82.7%.
Same board, both measured
Web design
Design Arena: websites
GLM 4.7
GLM 4.7 leads, 1226 against 1196.
Same board, both measured
Long documents
Listed context window
Grok 4.3
Grok 4.3 takes 1M tokens against 205K.
Listed by OpenRouter
Everyday cost
1K input + 500 output tokens
GLM 4.7
GLM 4.7 $0.0015, Grok 4.3 $0.0025 per turn.
List prices
Cost per solved task
One SWE-bench Verified task, solved
GLM 4.7
GLM 4.7 $0.29, Grok 4.3 $0.31.
Published attempt cost and pass rate
At a glance
- Coding index
- GLM 4.7 33.6 from 1 benchmark, Grok 4.3 22.5 from 1 benchmark.
- Widest category
- Agentic work, 10.8 points apart on average over 6 shared benchmarks.
- Widest single gap
- CaseLaw (Vals AI): GLM 4.7 54.9% against Grok 4.3 79.3%, 24.4 points apart on a 0 to 100 scale.
- Output price
- GLM 4.7 $1.98 against Grok 4.3 $2.50 per million output tokens, 1.3x apart.
- Context window
- GLM 4.7 takes 205K tokens, Grok 4.3 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 | Bars | Grok 4.3 | Ties |
|---|---|---|---|---|
| Coding | 6 | 4 | ||
| Intelligence | 0 | 1 | ||
| Agentic work | 5 | 1 | ||
| Reasoning | 4 | 4 | ||
| Math | 2 | 2 | ||
| Knowledge | 4 | 7 | ||
| Web design | 8 | 0 | ||
| General | 17 | 3 | ||
| All shared boards | 46 | 22 |
CodingLike-for-likeFour of these boards feed the Vibe Coding Index64
| Benchmark | GLM 4.7 | Grok 4.3 | Leader |
|---|---|---|---|
| LMArena WebDev | 1435 | 1358 | GLM 4.7 |
| AA Coding Index | 45.3 | 42.2 | GLM 4.7 |
| LiveCodeBench (Vals AI) | 82.2% | 84.5% | Grok 4.3 |
| SWE-bench Verified (Vals AI) | 69.4% | 71.4% | Grok 4.3 |
| Vibe Code Bench (Vals AI) | Not measured | 19.4% | Only Grok 4.3 measured |
| Code migration (Vals AI) | Not measured | 6.8% | Only Grok 4.3 measured |
| ProgramBench (Vals AI) | Not measured | 0.0% | Only Grok 4.3 measured |
| Terminal-Bench 2.1 (Vals AI) | Not measured | 42.0% | Only Grok 4.3 measured |
| SkillsBench (Vals AI) | Not measured | 40.6% | Only Grok 4.3 measured |
| LiveCodeBench (Artificial Analysis run) | 89.4% | Not measured | Only GLM 4.7 measured |
| SciCode | Not measured | 48.3% | Only Grok 4.3 measured |
| Terminal-Bench Hard (Artificial Analysis run) | 31.8% | 37.9% | Grok 4.3 |
| Terminal-Bench | 33.4% | Not measured | Only GLM 4.7 measured |
| WeirdML | Not measured | 49.9% | Only Grok 4.3 measured |
| ALE-Bench | 399.5 | 944.2 | Grok 4.3 |
| LMArena Text: coding | 1454 | 1415 | GLM 4.7 |
| LMArena Text: software and IT | 1453 | 1416 | GLM 4.7 |
| LMArena WebDev: React | 1420 | 1353 | GLM 4.7 |
| LMArena WebDev: HTML | 1447 | 1367 | GLM 4.7 |
| LMArena WebDev: from an image | Not measured | 1375 | Only Grok 4.3 measured |
| Neither model has a result on the other 18 boards here | |||
IntelligenceLike-for-likePublic boards, shown as published, kept out of the index01
| Benchmark | GLM 4.7 | Grok 4.3 | Leader |
|---|---|---|---|
| AA Intelligence Index | 22.2 | 24.9 | Grok 4.3 |
| Epoch Capabilities Index | 143.6 | Not measured | Only GLM 4.7 measured |
Agentic workLike-for-likePublic boards, shown as published, kept out of the index51
| Benchmark | GLM 4.7 | Grok 4.3 | Leader |
|---|---|---|---|
| AA Agentic Index | Not measured | 15.5 | Only Grok 4.3 measured |
| Design Arena: agents building web apps | Not measured | 1126 | Only Grok 4.3 measured |
| Design Arena: agents building full-stack apps | 1050 | 1006 | GLM 4.7 |
| Design Arena: agents building mobile apps | 1105 | 1059 | GLM 4.7 |
| Design Arena: agents building Android apps | 1118 | 967 | GLM 4.7 |
| Design Arena: agents building Godot games | 1058 | 914 | GLM 4.7 |
| Design Arena: agents building games | Not measured | 1007 | Only Grok 4.3 measured |
| Design Arena: HTML slides | Not measured | 1008 | Only Grok 4.3 measured |
| Design Arena: PowerPoint slides | Not measured | 1012 | Only Grok 4.3 measured |
| Finance Agent (Vals AI) | Not measured | 37.7% | Only Grok 4.3 measured |
| Tax Agent Bench (Vals AI) | Not measured | 6.2% | Only Grok 4.3 measured |
| Legal Research Bench (Vals AI) | Not measured | 15.4% | Only Grok 4.3 measured |
| Excel Modeling Benchmark (Vals AI) | Not measured | 18.4% | Only Grok 4.3 measured |
| τ²-Bench Telecom | 95.9% | 97.7% | Grok 4.3 |
| Vending-Bench 2 | $2,377 | $35 | GLM 4.7 |
| LMArena Search | Not measured | 1166 | Only Grok 4.3 measured |
| Neither model has a result on the other 23 boards here | |||
ReasoningLike-for-likePublic boards, shown as published, kept out of the index44
| Benchmark | GLM 4.7 | Grok 4.3 | Leader |
|---|---|---|---|
| GPQA Diamond | 80.0% | 91.4% | Grok 4.3 |
| MMMU Pro (Vals AI) | Not measured | 83.1% | Only Grok 4.3 measured |
| GPQA Diamond (Artificial Analysis run) | 85.9% | 90.1% | Grok 4.3 |
| Humanity's Last Exam (Artificial Analysis run) | 27.4% | 37.2% | Grok 4.3 |
| GPQA Diamond (Epoch AI run) | 83.3% | 88.8% | Grok 4.3 |
| SimpleBench | 47.7% | Not measured | Only GLM 4.7 measured |
| Chess puzzles | 21.0% | 18.0% | GLM 4.7 |
| LMArena Text: hard prompts | 1443 | 1396 | GLM 4.7 |
| LMArena Text: expert | 1424 | 1384 | GLM 4.7 |
| LMArena Text: hard prompts in English | 1452 | 1402 | GLM 4.7 |
| LMArena Vision: diagrams | Not measured | 1241 | Only Grok 4.3 measured |
| LMArena Vision: homework | Not measured | 1241 | Only Grok 4.3 measured |
| Neither model has a result on the other 9 boards here | |||
MathLike-for-likePublic boards, shown as published, kept out of the index22
| Benchmark | GLM 4.7 | Grok 4.3 | 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 | Not measured | Only GLM 4.7 measured |
| AIME 2025 (Artificial Analysis run) | 95.0% | Not measured | Only GLM 4.7 measured |
| OTIS Mock AIME | 83.3% | 93.3% | Grok 4.3 |
| FrontierMath, tiers 1 to 3 | Not measured | 42.8% | Only Grok 4.3 measured |
| FrontierMath, tier 4 | Not measured | 14.6% | Only Grok 4.3 measured |
| ProofBench | 6.0% | 11.0% | Grok 4.3 |
| LMArena Text: math | 1424 | 1387 | GLM 4.7 |
| LMArena Text: mathematical work | 1411 | 1396 | GLM 4.7 |
| Neither model has a result on the other 3 boards here | |||
KnowledgeLike-for-likePublic boards, shown as published, kept out of the index47
| Benchmark | GLM 4.7 | Grok 4.3 | Leader |
|---|---|---|---|
| MMLU Pro | 82.7% | 85.8% | Grok 4.3 |
| LegalBench (Vals AI) | 83.4% | 84.5% | Grok 4.3 |
| CaseLaw (Vals AI) | 54.9% | 79.3% | Grok 4.3 |
| CorpFin (Vals AI) | 46.4% | 68.5% | Grok 4.3 |
| TaxEval (Vals AI) | 68.8% | 70.8% | Grok 4.3 |
| MortgageTax (Vals AI) | Not measured | 48.3% | Only Grok 4.3 measured |
| MedQA (Vals AI) | 93.7% | Not measured | Only GLM 4.7 measured |
| MedCode (Vals AI) | 32.8% | 38.1% | Grok 4.3 |
| MMLU Pro (Artificial Analysis run) | 85.6% | Not measured | Only GLM 4.7 measured |
| SimpleQA Verified | 32.2% | 33.2% | Grok 4.3 |
| LMArena Text: science | 1457 | 1399 | GLM 4.7 |
| LMArena Text: medicine and healthcare | 1469 | 1384 | GLM 4.7 |
| LMArena Text: legal and government | 1444 | 1386 | GLM 4.7 |
| LMArena Text: business and finance | 1429 | 1381 | GLM 4.7 |
| Neither model has a result on the other 3 boards here | |||
Web designLike-for-likePublic boards, shown as published, kept out of the index80
| Benchmark | GLM 4.7 | Grok 4.3 | Leader |
|---|---|---|---|
| Design Arena: websites | 1226 | 1196 | GLM 4.7 |
| Design Arena: UI components | 1199 | 1192 | GLM 4.7 |
| Design Arena: game dev | 1189 | 1174 | GLM 4.7 |
| Design Arena: data viz | 1195 | 1180 | GLM 4.7 |
| Design Arena: 3D | 1191 | 1125 | GLM 4.7 |
| Design Arena: all code categories | 1217 | 1187 | GLM 4.7 |
| Design Arena: SVG | 1142 | 1081 | GLM 4.7 |
| Design Arena: ASCII art | 1165 | 1125 | GLM 4.7 |
| Neither model has a result on the other 1 board here | |||
GeneralLike-for-likePublic boards, shown as published, kept out of the index173
| Benchmark | GLM 4.7 | Grok 4.3 | Leader |
|---|---|---|---|
| Public Benefits Bench (Vals AI) | Not measured | 51.7% | Only Grok 4.3 measured |
| SAGE (Vals AI) | Not measured | 19.7% | Only Grok 4.3 measured |
| MedScribe (Vals AI) | 68.6% | 74.4% | Grok 4.3 |
| Vals Multimodal Index | Not measured | 43.3% | Only Grok 4.3 measured |
| IFBench | 67.9% | 83.3% | Grok 4.3 |
| Long context reasoning (AA-LCR) | 71.0% | 75.0% | Grok 4.3 |
| DTBench | Not measured | 90.7% | Only Grok 4.3 measured |
| LMCA | Not measured | 38.3% | Only Grok 4.3 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 | 1397 | GLM 4.7 |
| LMArena Text: instruction following | 1412 | 1366 | GLM 4.7 |
| LMArena Text: creative writing | 1401 | 1380 | GLM 4.7 |
| LMArena Vision | Not measured | 1229 | Only Grok 4.3 measured |
| LMArena Text: English | 1453 | 1404 | GLM 4.7 |
| LMArena Text: non-English | 1417 | 1385 | GLM 4.7 |
| LMArena Text: Chinese | 1495 | 1422 | GLM 4.7 |
| LMArena Text: German | 1426 | 1395 | GLM 4.7 |
| LMArena Text: Japanese | 1439 | 1379 | GLM 4.7 |
| LMArena Text: Korean | 1399 | 1354 | GLM 4.7 |
| LMArena Text: French | 1432 | 1411 | GLM 4.7 |
| LMArena Text: Spanish | 1434 | 1399 | GLM 4.7 |
| LMArena Text: Russian | 1423 | 1399 | GLM 4.7 |
| LMArena Text: Polish | 1435 | 1398 | GLM 4.7 |
| LMArena Text: multi-turn | 1446 | 1406 | GLM 4.7 |
| LMArena Text: longer queries | 1432 | 1393 | GLM 4.7 |
| LMArena Text: writing and language | 1411 | 1385 | GLM 4.7 |
| LMArena Text: entertainment and media | 1400 | 1370 | GLM 4.7 |
| LMArena Vision: English | Not measured | 1235 | Only Grok 4.3 measured |
| LMArena Vision: Chinese | Not measured | 1257 | Only Grok 4.3 measured |
| LMArena Vision: reading text | Not measured | 1235 | Only Grok 4.3 measured |
| LMArena Vision: humor | Not measured | 1222 | Only Grok 4.3 measured |
| LMArena Vision: recognizing things | Not measured | 1217 | Only Grok 4.3 measured |
| LMArena Vision: creative writing | Not measured | 1237 | Only Grok 4.3 measured |
| Neither model has a result on the other 12 boards here | |||
Image generationNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
| Benchmark | GLM 4.7 | Grok 4.3 | 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 | SpaceXAI Grok 4.3 |
|---|---|---|
| Maker | Z.ai | xAI |
| Weights | Open weights | No public weights listed |
| Released | December 22, 2025 | April 30, 2026 |
| Context window | 205K | 1M |
| Input $/Mtok | $0.54 | $1.25 |
| Output $/Mtok | $1.98 | $2.50 |
| Coding index | 33.6 | 22.5 |
| Independent measurements behind it | 1 | 1 |
| Cost per solved task | $0.29 | $0.31 |
| Source | OpenRouter listing | OpenRouter listing |
Questions
Is GLM 4.7 better than Grok 4.3 for coding?
Pick GLM 4.7 for coding: it scores 33.6 on the Vibe Coding Index against 22.5 for Grok 4.3. Pick Grok 4.3 when the context window is larger, 1M against 205K tokens, or when what matters is LiveCodeBench (Vals AI) or SWE-bench Verified (Vals AI), where it leads.
Which is cheaper, GLM 4.7 or Grok 4.3?
GLM 4.7 costs $0.54 per million input tokens and $1.98 per million output tokens; Grok 4.3 costs $1.25 and $2.50. Per solved SWE-bench Verified task, GLM 4.7 costs $0.29 and Grok 4.3 $0.31.
Which has the bigger context window, GLM 4.7 or Grok 4.3?
Grok 4.3, with 1M tokens against 205K.
How many benchmarks is the GLM 4.7 vs Grok 4.3 comparison based on?
68 benchmarks have a result for both models as of October 21, 2026. GLM 4.7's coding index rests on 1 independent benchmark and Grok 4.3's on 1. Where only one model has a result, the page says so instead of naming a winner.