Claude Opus 4.7 vs GLM 5.2
Updated October 21, 2026. 76 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 5.2 for coding: it scores 57.7 on the Vibe Coding Index against 51.1 for Claude Opus 4.7.
Pick Claude Opus 4.7 when what matters is AA Coding Index or LiveCodeBench (Vals AI), where it leads.
- Shared results
- 76
- Claude Opus 4.7 only
- 25
- GLM 5.2 only
- 21
- 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
GLM 5.2
GLM 5.2 scores 57.7 against 51.1.
One benchmark behind each score
Intelligence
AA Intelligence Index
Claude Opus 4.7
Claude Opus 4.7 leads, 40.7 against 33.7.
Same board, both measured
Agentic work
AA Agentic Index
Claude Opus 4.7
Claude Opus 4.7 leads, 38.6 against 38.4.
Same board, both measured
Reasoning
GPQA Diamond
Claude Opus 4.7
Claude Opus 4.7 leads, 90.2% against 85.6%.
Same board, both measured
Math
AIME
No call
Only Claude Opus 4.7 has a AIME result, 96.3%.
Directional only
Knowledge
MMLU Pro
Claude Opus 4.7
Claude Opus 4.7 leads, 89.9% against 86.7%.
Same board, both measured
Web design
Design Arena: websites
No call
Only GLM 5.2 has a Design Arena: websites result, 1285.
Directional only
Long documents
Listed context window
GLM 5.2
GLM 5.2 takes 1.05M tokens against 1M.
Listed by OpenRouter
Everyday cost
1K input + 500 output tokens
GLM 5.2
Claude Opus 4.7 $0.018, GLM 5.2 $0.0035 per turn.
List prices
Cost per solved task
One SWE-bench Verified task, solved
GLM 5.2
Claude Opus 4.7 $2.95, GLM 5.2 $0.86.
Published attempt cost and pass rate
At a glance
- Coding index
- Claude Opus 4.7 51.1 from 1 benchmark, GLM 5.2 57.7 from 1 benchmark.
- Widest category
- Reasoning, 10.3 points apart on average over 13 shared benchmarks.
- Widest single gap
- ARC-AGI-2: Claude Opus 4.7 75.8% against GLM 5.2 22.8%, 53.1 points apart on a 0 to 100 scale.
- Output price
- Claude Opus 4.7 $25.00 against GLM 5.2 $7.00 per million output tokens, 3.6x apart.
- Context window
- Claude Opus 4.7 takes 1M tokens, GLM 5.2 1.05M.
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 | Claude Opus 4.7 | Bars | GLM 5.2 | Ties |
|---|---|---|---|---|
| Coding | 10 | 5 | ||
| Intelligence | 2 | 0 | ||
| Agentic work | 6 | 2 | ||
| Reasoning | 12 | 1 | ||
| Math | 6 | 0 | ||
| Knowledge | 9 | 1 | ||
| General | 20 | 2 | ||
| All shared boards | 65 | 11 |
CodingLike-for-likeFour of these boards feed the Vibe Coding Index105
| Benchmark | Claude Opus 4.7 | GLM 5.2 | Leader |
|---|---|---|---|
| LMArena WebDev | 1558 | 1604 | GLM 5.2 |
| AA Coding Index | 73.6 | 68.8 | Claude Opus 4.7 |
| LiveCodeBench (Vals AI) | 85.1% | 69.5% | Claude Opus 4.7 |
| SWE-bench Verified (Vals AI) | 82.0% | 82.8% | GLM 5.2 |
| Vibe Code Bench (Vals AI) | 71.0% | 64.0% | Claude Opus 4.7 |
| Code migration (Vals AI) | 43.9% | 37.9% | Claude Opus 4.7 |
| ProgramBench (Vals AI) | 0.0% | 0.5% | GLM 5.2 |
| Terminal-Bench 2.1 (Vals AI) | 68.5% | 67.8% | Claude Opus 4.7 |
| SkillsBench (Vals AI) | Not measured | 45.1% | Only GLM 5.2 measured |
| SciCode | Not measured | 51.2% | Only GLM 5.2 measured |
| Terminal-Bench Hard (Artificial Analysis run) | 54.5% | 50.8% | Claude Opus 4.7 |
| Terminal-Bench | 80.2% | Not measured | Only Claude Opus 4.7 measured |
| FrontierCode | 38.5% | 24.5% | Claude Opus 4.7 |
| GSO-Bench | 44.1% | Not measured | Only Claude Opus 4.7 measured |
| WeirdML | 76.4% | 70.1% | Claude Opus 4.7 |
| MirrorCode | 31.1% | Not measured | Only Claude Opus 4.7 measured |
| ALE-Bench | Not measured | 1047.0 | Only GLM 5.2 measured |
| LMArena Text: coding | 1519 | 1487 | Claude Opus 4.7 |
| LMArena Text: software and IT | 1516 | 1486 | Claude Opus 4.7 |
| LMArena WebDev: React | 1560 | 1617 | GLM 5.2 |
| LMArena WebDev: HTML | 1554 | 1565 | GLM 5.2 |
| LMArena WebDev: from an image | 1577 | Not measured | Only Claude Opus 4.7 measured |
| Neither model has a result on the other 16 boards here | |||
IntelligenceLike-for-likePublic boards, shown as published, kept out of the index20
| Benchmark | Claude Opus 4.7 | GLM 5.2 | Leader |
|---|---|---|---|
| AA Intelligence Index | 40.7 | 33.7 | Claude Opus 4.7 |
| Epoch Capabilities Index | 156.8 | 151.9 | Claude Opus 4.7 |
Agentic workLike-for-likePublic boards, shown as published, kept out of the index62
| Benchmark | Claude Opus 4.7 | GLM 5.2 | Leader |
|---|---|---|---|
| AA Agentic Index | 38.6 | 38.4 | Claude Opus 4.7 |
| Design Arena: agents building web apps | Not measured | 1222 | Only GLM 5.2 measured |
| Design Arena: agents building full-stack apps | Not measured | 1241 | Only GLM 5.2 measured |
| Design Arena: agents building mobile apps | Not measured | 1160 | Only GLM 5.2 measured |
| Design Arena: agents building Android apps | Not measured | 1184 | Only GLM 5.2 measured |
| Design Arena: agents building Godot games | Not measured | 1142 | Only GLM 5.2 measured |
| Design Arena: agents building games | Not measured | 1206 | Only GLM 5.2 measured |
| Design Arena: HTML slides | Not measured | 1175 | Only GLM 5.2 measured |
| Design Arena: PowerPoint slides | Not measured | 1195 | Only GLM 5.2 measured |
| SREBench (Vals AI) | Not measured | 0.0% | Only GLM 5.2 measured |
| Finance Agent (Vals AI) | 51.5% | 49.7% | Claude Opus 4.7 |
| Tax Agent Bench (Vals AI) | 23.7% | Not measured | Only Claude Opus 4.7 measured |
| Legal Research Bench (Vals AI) | 38.5% | 31.3% | Claude Opus 4.7 |
| Excel Modeling Benchmark (Vals AI) | 63.7% | 61.5% | Claude Opus 4.7 |
| τ²-Bench Telecom | 88.6% | 99.1% | GLM 5.2 |
| APEX-Agents | 49.2% | 45.2% | Claude Opus 4.7 |
| OSWorld 2.0 | 18.2% | Not measured | Only Claude Opus 4.7 measured |
| PostTrainBench | 28.6% | 31.7% | GLM 5.2 |
| Vending-Bench 2 | $10,937 | $8,314 | Claude Opus 4.7 |
| LMArena Search | 1230 | Not measured | Only Claude Opus 4.7 measured |
| Neither model has a result on the other 19 boards here | |||
ReasoningLike-for-likePublic boards, shown as published, kept out of the index121
| Benchmark | Claude Opus 4.7 | GLM 5.2 | Leader |
|---|---|---|---|
| GPQA Diamond | 90.2% | 85.6% | Claude Opus 4.7 |
| MMMU Pro (Vals AI) | 85.5% | Not measured | Only Claude Opus 4.7 measured |
| GPQA Diamond (Artificial Analysis run) | 91.4% | 89.5% | Claude Opus 4.7 |
| Humanity's Last Exam (Artificial Analysis run) | 42.3% | 41.1% | Claude Opus 4.7 |
| GPQA Diamond (Epoch AI run) | 90.2% | 91.9% | GLM 5.2 |
| ARC-AGI | 93.5% | 77.0% | Claude Opus 4.7 |
| ARC-AGI-2 | 75.8% | 22.8% | Claude Opus 4.7 |
| SimpleBench | 61.7% | 58.8% | Claude Opus 4.7 |
| Chess puzzles | 41.0% | 22.0% | Claude Opus 4.7 |
| Mystery game puzzles | 28.0% | 19.0% | Claude Opus 4.7 |
| EBR-bench | 19.0% | 9.5% | Claude Opus 4.7 |
| LMArena Text: hard prompts | 1506 | 1481 | Claude Opus 4.7 |
| LMArena Text: expert | 1521 | 1487 | Claude Opus 4.7 |
| LMArena Text: hard prompts in English | 1508 | 1481 | Claude Opus 4.7 |
| LMArena Vision: diagrams | 1336 | Not measured | Only Claude Opus 4.7 measured |
| LMArena Vision: homework | 1337 | Not measured | Only Claude Opus 4.7 measured |
| Neither model has a result on the other 5 boards here | |||
MathLike-for-likePublic boards, shown as published, kept out of the index60
| Benchmark | Claude Opus 4.7 | GLM 5.2 | Leader |
|---|---|---|---|
| AIME | 96.3% | Not measured | Only Claude Opus 4.7 measured |
| OTIS Mock AIME | 97.8% | 86.4% | Claude Opus 4.7 |
| FrontierMath, tiers 1 to 3 | 70.2% | 59.2% | Claude Opus 4.7 |
| FrontierMath, tier 4 | 31.7% | 29.3% | Claude Opus 4.7 |
| ProofBench | 54.0% | 35.0% | Claude Opus 4.7 |
| LMArena Text: math | 1498 | 1483 | Claude Opus 4.7 |
| LMArena Text: mathematical work | 1509 | 1489 | Claude Opus 4.7 |
| Neither model has a result on the other 6 boards here | |||
KnowledgeLike-for-likePublic boards, shown as published, kept out of the index91
| Benchmark | Claude Opus 4.7 | GLM 5.2 | Leader |
|---|---|---|---|
| MMLU Pro | 89.9% | 86.7% | Claude Opus 4.7 |
| LegalBench (Vals AI) | 85.3% | 84.1% | Claude Opus 4.7 |
| CaseLaw (Vals AI) | 68.4% | Not measured | Only Claude Opus 4.7 measured |
| CorpFin (Vals AI) | 66.1% | 66.1% | GLM 5.2 |
| TaxEval (Vals AI) | 75.3% | 73.3% | Claude Opus 4.7 |
| MortgageTax (Vals AI) | 70.3% | Not measured | Only Claude Opus 4.7 measured |
| MedCode (Vals AI) | 54.9% | 40.8% | Claude Opus 4.7 |
| SimpleQA Verified | 51.7% | 34.2% | Claude Opus 4.7 |
| LMArena Text: science | 1508 | 1489 | Claude Opus 4.7 |
| LMArena Text: medicine and healthcare | 1495 | 1475 | Claude Opus 4.7 |
| LMArena Text: legal and government | 1504 | 1486 | Claude Opus 4.7 |
| LMArena Text: business and finance | 1490 | 1451 | Claude Opus 4.7 |
| Neither model has a result on the other 5 boards here | |||
Web designDirectional onlyPublic boards, shown as published, kept out of the index8 boards, none shared
| Benchmark | Claude Opus 4.7 | GLM 5.2 | Leader |
|---|---|---|---|
| Design Arena: websites | Not measured | 1285 | Only GLM 5.2 measured |
| Design Arena: UI components | Not measured | 1296 | Only GLM 5.2 measured |
| Design Arena: game dev | Not measured | 1268 | Only GLM 5.2 measured |
| Design Arena: data viz | Not measured | 1285 | Only GLM 5.2 measured |
| Design Arena: 3D | Not measured | 1288 | Only GLM 5.2 measured |
| Design Arena: all code categories | Not measured | 1288 | Only GLM 5.2 measured |
| Design Arena: SVG | Not measured | 1214 | Only GLM 5.2 measured |
| Design Arena: ASCII art | Not measured | 1190 | Only GLM 5.2 measured |
| Neither model has a result on the other 1 board here | |||
GeneralLike-for-likePublic boards, shown as published, kept out of the index202
| Benchmark | Claude Opus 4.7 | GLM 5.2 | Leader |
|---|---|---|---|
| SAGE (Vals AI) | 56.1% | Not measured | Only Claude Opus 4.7 measured |
| MedScribe (Vals AI) | 83.0% | 83.5% | GLM 5.2 |
| Vals Multimodal Index | 67.4% | Not measured | Only Claude Opus 4.7 measured |
| IFBench | 58.6% | 73.3% | GLM 5.2 |
| Long context reasoning (AA-LCR) | 78.7% | 78.3% | Claude Opus 4.7 |
| DTBench | 94.7% | 93.6% | Claude Opus 4.7 |
| LMCA | 52.2% | 45.8% | Claude Opus 4.7 |
| Surface Evolver Bench | Not measured | 55.6% | Only GLM 5.2 measured |
| Furniture assembly | 33.3% | Not measured | Only Claude Opus 4.7 measured |
| ExploitBench | 26.5% | Not measured | Only Claude Opus 4.7 measured |
| LMArena Text | 1490 | 1470 | Claude Opus 4.7 |
| LMArena Text: instruction following | 1498 | 1466 | Claude Opus 4.7 |
| LMArena Text: creative writing | 1486 | 1463 | Claude Opus 4.7 |
| LMArena Vision | 1317 | Not measured | Only Claude Opus 4.7 measured |
| LMArena Text: English | 1496 | 1475 | Claude Opus 4.7 |
| LMArena Text: non-English | 1480 | 1459 | Claude Opus 4.7 |
| LMArena Text: Chinese | 1531 | 1522 | Claude Opus 4.7 |
| LMArena Text: German | 1495 | 1470 | Claude Opus 4.7 |
| LMArena Text: Japanese | 1474 | 1450 | Claude Opus 4.7 |
| LMArena Text: Korean | 1464 | 1440 | Claude Opus 4.7 |
| LMArena Text: French | 1503 | 1479 | Claude Opus 4.7 |
| LMArena Text: Spanish | 1494 | 1478 | Claude Opus 4.7 |
| LMArena Text: Russian | 1494 | 1468 | Claude Opus 4.7 |
| LMArena Text: Polish | 1489 | 1456 | Claude Opus 4.7 |
| LMArena Text: multi-turn | 1505 | 1469 | Claude Opus 4.7 |
| LMArena Text: longer queries | 1505 | 1480 | Claude Opus 4.7 |
| LMArena Text: writing and language | 1492 | 1463 | Claude Opus 4.7 |
| LMArena Text: entertainment and media | 1474 | 1450 | Claude Opus 4.7 |
| LMArena Vision: English | 1322 | Not measured | Only Claude Opus 4.7 measured |
| LMArena Vision: Chinese | 1371 | Not measured | Only Claude Opus 4.7 measured |
| LMArena Vision: reading text | 1327 | Not measured | Only Claude Opus 4.7 measured |
| LMArena Vision: humor | 1309 | Not measured | Only Claude Opus 4.7 measured |
| LMArena Vision: recognizing things | 1278 | Not measured | Only Claude Opus 4.7 measured |
| LMArena Vision: creative writing | 1326 | Not measured | Only Claude Opus 4.7 measured |
| LMArena Document | 1495 | Not measured | Only Claude Opus 4.7 measured |
| Neither model has a result on the other 11 boards here | |||
Image generationNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
| Benchmark | Claude Opus 4.7 | GLM 5.2 | 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 5.2 costs less
Repository review
50K input + 3K output tokens
GLM 5.2 costs less
Agent loop
200K input + 10K output tokens
GLM 5.2 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 | Anthropic Claude Opus 4.7 | Z.ai GLM 5.2 |
|---|---|---|
| Maker | Anthropic | Z.ai |
| Weights | No public weights listed | Open weights |
| Released | April 16, 2026 | June 16, 2026 |
| Context window | 1M | 1.05M |
| Input $/Mtok | $5.00 | $0.0341 |
| Output $/Mtok | $25.00 | $7.00 |
| Coding index | 51.1 | 57.7 |
| Independent measurements behind it | 1 | 1 |
| Cost per solved task | $2.95 | $0.86 |
| Source | OpenRouter listing | OpenRouter listing |
Questions
Is Claude Opus 4.7 better than GLM 5.2 for coding?
Pick GLM 5.2 for coding: it scores 57.7 on the Vibe Coding Index against 51.1 for Claude Opus 4.7. Pick Claude Opus 4.7 when what matters is AA Coding Index or LiveCodeBench (Vals AI), where it leads.
Which is cheaper, Claude Opus 4.7 or GLM 5.2?
Claude Opus 4.7 costs $5.00 per million input tokens and $25.00 per million output tokens; GLM 5.2 costs $0.0341 and $7.00. Per solved SWE-bench Verified task, Claude Opus 4.7 costs $2.95 and GLM 5.2 $0.86.
Which has the bigger context window, Claude Opus 4.7 or GLM 5.2?
GLM 5.2, with 1.05M tokens against 1M.
How many benchmarks is the Claude Opus 4.7 vs GLM 5.2 comparison based on?
76 benchmarks have a result for both models as of October 21, 2026. Claude Opus 4.7's coding index rests on 1 independent benchmark and GLM 5.2's on 1. Where only one model has a result, the page says so instead of naming a winner.