Claude Opus 4.7 vs GLM 4.5V
Claude Opus 4.7 leads GLM 4.5V on the Artificial Analysis Intelligence Index, 40.7 to 7.6, and GLM 4.5V costs 13.9x less per output token ($1.80 vs $25.00 per million). Updated October 21, 2026; 36 benchmarks have a result for both.
Claude Opus 4.7 or GLM 4.5V: which to pick
Only Claude Opus 4.7 has a coding score, 67.5 on the Vibe Coding Index; GLM 4.5V has none, so the two cannot be ranked on coding.
Pick GLM 4.5V for output tokens 13.9x cheaper ($1.80 vs $25.00 per million).
- Shared results
- 36
- Claude Opus 4.7 only
- 65
- GLM 4.5V only
- 4
- Like-for-like categories
- 7 of 9
Only benchmarks that tested both models count toward a pick; a result only one model has is shown but not compared.
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
Only Claude Opus 4.7 has a coding score, so no call is made.
One side not measured
Intelligence
AA Intelligence Index
Claude Opus 4.7
Claude Opus 4.7 leads, 40.7 against 7.6.
Same board, both measured
Agentic work
AA Agentic Index
No call
Only Claude Opus 4.7 has a AA Agentic Index result, 38.6.
Directional only
Reasoning
GPQA Diamond
No call
Only Claude Opus 4.7 has a GPQA Diamond result, 90.2%.
Directional only
Math
AIME
No call
Only Claude Opus 4.7 has a AIME result, 96.3%.
Directional only
Knowledge
MMLU Pro
No call
Only Claude Opus 4.7 has a MMLU Pro result, 89.9%.
Directional only
Long documents
Listed context window
Claude Opus 4.7
Claude Opus 4.7 takes 1M tokens against 66K.
Listed by OpenRouter
Everyday cost
1K input + 500 output tokens
GLM 4.5V
Claude Opus 4.7 $0.018, GLM 4.5V $0.0015 per turn.
List prices
Cost per solved task
One SWE-bench Verified task, solved
No call
Only Claude Opus 4.7 has the number, $2.95.
Directional only
At a glance
- Coding index
- Claude Opus 4.7 67.5 from 4 benchmarks, GLM 4.5V not benchmarked.
- Widest category
- Agentic work, 66.1 points apart on average over 1 shared benchmark.
- Widest single gap
- Long context reasoning (AA-LCR): Claude Opus 4.7 78.7% against GLM 4.5V 0.0%, 78.7 points apart on a 0 to 100 scale.
- Output price
- Claude Opus 4.7 $25.00 against GLM 4.5V $1.80 per million output tokens, 13.9x apart.
- Context window
- Claude Opus 4.7 takes 1M tokens, GLM 4.5V 66K.
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 4.5V | Ties |
|---|---|---|---|---|
| Coding | 3 | 0 | ||
| Intelligence | 1 | 0 | ||
| Agentic work | 1 | 0 | ||
| Reasoning | 7 | 0 | ||
| Math | 2 | 0 | ||
| Knowledge | 4 | 0 | ||
| General | 18 | 0 | ||
| All shared boards | 36 | 0 |
CodingLike-for-likeSix of these boards feed the Vibe Coding Index30
| Benchmark | Claude Opus 4.7 | GLM 4.5V | Leader |
|---|---|---|---|
| LMArena WebDev | 1558 | Not measured | Only Claude Opus 4.7 measured |
| AA Coding Index | 73.6 | Not measured | Only Claude Opus 4.7 measured |
| LiveCodeBench (Vals AI) | 85.1% | Not measured | Only Claude Opus 4.7 measured |
| SWE-bench Verified (Vals AI) | 82.0% | Not measured | Only Claude Opus 4.7 measured |
| Vibe Code Bench (Vals AI) | 71.0% | Not measured | Only Claude Opus 4.7 measured |
| Code migration (Vals AI) | 43.9% | Not measured | Only Claude Opus 4.7 measured |
| ProgramBench (Vals AI) | 0.0% | Not measured | Only Claude Opus 4.7 measured |
| Terminal-Bench 2.1 (Vals AI) | 68.5% | Not measured | Only Claude Opus 4.7 measured |
| LiveCodeBench (Artificial Analysis run) | Not measured | 60.4% | Only GLM 4.5V measured |
| Terminal-Bench Hard (Artificial Analysis run) | 54.5% | 6.8% | Claude Opus 4.7 |
| Terminal-Bench | 80.2% | Not measured | Only Claude Opus 4.7 measured |
| FrontierCode | 38.5% | Not measured | Only Claude Opus 4.7 measured |
| GSO-Bench | 44.1% | Not measured | Only Claude Opus 4.7 measured |
| WeirdML | 76.4% | Not measured | Only Claude Opus 4.7 measured |
| MirrorCode | 31.1% | Not measured | Only Claude Opus 4.7 measured |
| LMArena Text: coding | 1519 | 1347 | Claude Opus 4.7 |
| LMArena Text: software and IT | 1516 | 1353 | Claude Opus 4.7 |
| LMArena WebDev: React | 1560 | Not measured | Only Claude Opus 4.7 measured |
| LMArena WebDev: HTML | 1554 | Not measured | Only Claude Opus 4.7 measured |
| LMArena WebDev: from an image | 1577 | Not measured | Only Claude Opus 4.7 measured |
| Neither model has a result on the other 18 boards here | |||
IntelligenceLike-for-likePublic boards, shown as published, kept out of the index10
| Benchmark | Claude Opus 4.7 | GLM 4.5V | Leader |
|---|---|---|---|
| AA Intelligence Index | 40.7 | 7.6 | Claude Opus 4.7 |
| Epoch Capabilities Index | 156.8 | Not measured | Only Claude Opus 4.7 measured |
Agentic workLike-for-likePublic boards, shown as published, kept out of the index10
| Benchmark | Claude Opus 4.7 | GLM 4.5V | Leader |
|---|---|---|---|
| AA Agentic Index | 38.6 | Not measured | Only Claude Opus 4.7 measured |
| Finance Agent (Vals AI) | 51.5% | Not measured | Only Claude Opus 4.7 measured |
| Tax Agent Bench (Vals AI) | 23.7% | Not measured | Only Claude Opus 4.7 measured |
| Legal Research Bench (Vals AI) | 38.5% | Not measured | Only Claude Opus 4.7 measured |
| Excel Modeling Benchmark (Vals AI) | 63.7% | Not measured | Only Claude Opus 4.7 measured |
| τ²-Bench Telecom | 88.6% | 22.5% | Claude Opus 4.7 |
| APEX-Agents | 49.2% | Not measured | Only Claude Opus 4.7 measured |
| OSWorld 2.0 | 18.2% | Not measured | Only Claude Opus 4.7 measured |
| PostTrainBench | 28.6% | Not measured | Only Claude Opus 4.7 measured |
| Vending-Bench 2 | $10,937 | Not measured | Only Claude Opus 4.7 measured |
| LMArena Search | 1230 | Not measured | Only Claude Opus 4.7 measured |
| Neither model has a result on the other 28 boards here | |||
ReasoningLike-for-likePublic boards, shown as published, kept out of the index70
| Benchmark | Claude Opus 4.7 | GLM 4.5V | Leader |
|---|---|---|---|
| GPQA Diamond | 90.2% | Not measured | Only Claude Opus 4.7 measured |
| MMMU Pro (Vals AI) | 85.5% | Not measured | Only Claude Opus 4.7 measured |
| GPQA Diamond (Artificial Analysis run) | 91.4% | 68.4% | Claude Opus 4.7 |
| Humanity's Last Exam (Artificial Analysis run) | 42.3% | 6.3% | Claude Opus 4.7 |
| GPQA Diamond (Epoch AI run) | 90.2% | Not measured | Only Claude Opus 4.7 measured |
| ARC-AGI | 93.5% | Not measured | Only Claude Opus 4.7 measured |
| ARC-AGI-2 | 75.8% | Not measured | Only Claude Opus 4.7 measured |
| SimpleBench | 61.7% | Not measured | Only Claude Opus 4.7 measured |
| Chess puzzles | 41.0% | Not measured | Only Claude Opus 4.7 measured |
| Mystery game puzzles | 28.0% | Not measured | Only Claude Opus 4.7 measured |
| EBR-bench | 19.0% | Not measured | Only Claude Opus 4.7 measured |
| LMArena Text: hard prompts | 1506 | 1335 | Claude Opus 4.7 |
| LMArena Text: expert | 1521 | 1353 | Claude Opus 4.7 |
| LMArena Text: hard prompts in English | 1508 | 1357 | Claude Opus 4.7 |
| LMArena Vision: diagrams | 1336 | 1151 | Claude Opus 4.7 |
| LMArena Vision: homework | 1337 | 1163 | Claude Opus 4.7 |
| Neither model has a result on the other 5 boards here | |||
MathLike-for-likePublic boards, shown as published, kept out of the index20
| Benchmark | Claude Opus 4.7 | GLM 4.5V | Leader |
|---|---|---|---|
| AIME | 96.3% | Not measured | Only Claude Opus 4.7 measured |
| AA Math Index | Not measured | 73.0 | Only GLM 4.5V measured |
| AIME 2025 (Artificial Analysis run) | Not measured | 73.0% | Only GLM 4.5V measured |
| OTIS Mock AIME | 97.8% | Not measured | Only Claude Opus 4.7 measured |
| FrontierMath, tiers 1 to 3 | 70.2% | Not measured | Only Claude Opus 4.7 measured |
| FrontierMath, tier 4 | 31.7% | Not measured | Only Claude Opus 4.7 measured |
| ProofBench | 54.0% | Not measured | Only Claude Opus 4.7 measured |
| LMArena Text: math | 1498 | 1354 | Claude Opus 4.7 |
| LMArena Text: mathematical work | 1509 | 1336 | Claude Opus 4.7 |
| Neither model has a result on the other 4 boards here | |||
KnowledgeLike-for-likePublic boards, shown as published, kept out of the index40
| Benchmark | Claude Opus 4.7 | GLM 4.5V | Leader |
|---|---|---|---|
| MMLU Pro | 89.9% | Not measured | Only Claude Opus 4.7 measured |
| LegalBench (Vals AI) | 85.3% | Not measured | Only Claude Opus 4.7 measured |
| CaseLaw (Vals AI) | 68.4% | Not measured | Only Claude Opus 4.7 measured |
| CorpFin (Vals AI) | 66.1% | Not measured | Only Claude Opus 4.7 measured |
| TaxEval (Vals AI) | 75.3% | Not measured | Only Claude Opus 4.7 measured |
| MortgageTax (Vals AI) | 70.3% | Not measured | Only Claude Opus 4.7 measured |
| MedCode (Vals AI) | 54.9% | Not measured | Only Claude Opus 4.7 measured |
| MMLU Pro (Artificial Analysis run) | Not measured | 78.8% | Only GLM 4.5V measured |
| SimpleQA Verified | 51.7% | Not measured | Only Claude Opus 4.7 measured |
| LMArena Text: science | 1508 | 1330 | Claude Opus 4.7 |
| LMArena Text: medicine and healthcare | 1495 | 1349 | Claude Opus 4.7 |
| LMArena Text: legal and government | 1504 | 1338 | Claude Opus 4.7 |
| LMArena Text: business and finance | 1490 | 1309 | Claude Opus 4.7 |
| Neither model has a result on the other 4 boards here | |||
Web designNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
| Benchmark | Claude Opus 4.7 | GLM 4.5V | Leader |
|---|---|---|---|
| Neither model has a result on 9 boards here | |||
GeneralLike-for-likePublic boards, shown as published, kept out of the index180
| Benchmark | Claude Opus 4.7 | GLM 4.5V | Leader |
|---|---|---|---|
| SAGE (Vals AI) | 56.1% | Not measured | Only Claude Opus 4.7 measured |
| MedScribe (Vals AI) | 83.0% | Not measured | Only Claude Opus 4.7 measured |
| Vals Multimodal Index | 67.4% | Not measured | Only Claude Opus 4.7 measured |
| IFBench | 58.6% | 34.2% | Claude Opus 4.7 |
| Long context reasoning (AA-LCR) | 78.7% | 0.0% | Claude Opus 4.7 |
| DTBench | 94.7% | Not measured | Only Claude Opus 4.7 measured |
| LMCA | 52.2% | Not measured | Only Claude Opus 4.7 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 | 1333 | Claude Opus 4.7 |
| LMArena Text: instruction following | 1498 | 1311 | Claude Opus 4.7 |
| LMArena Text: creative writing | 1486 | 1294 | Claude Opus 4.7 |
| LMArena Vision | 1317 | 1154 | Claude Opus 4.7 |
| LMArena Text: English | 1496 | 1362 | Claude Opus 4.7 |
| LMArena Text: non-English | 1480 | 1303 | Claude Opus 4.7 |
| LMArena Text: Chinese | 1531 | 1337 | Claude Opus 4.7 |
| LMArena Text: German | 1495 | Not measured | Only Claude Opus 4.7 measured |
| LMArena Text: Japanese | 1474 | Not measured | Only Claude Opus 4.7 measured |
| LMArena Text: Korean | 1464 | Not measured | Only Claude Opus 4.7 measured |
| LMArena Text: French | 1503 | Not measured | Only Claude Opus 4.7 measured |
| LMArena Text: Spanish | 1494 | 1336 | Claude Opus 4.7 |
| LMArena Text: Russian | 1494 | 1298 | Claude Opus 4.7 |
| LMArena Text: Polish | 1489 | 1302 | Claude Opus 4.7 |
| LMArena Text: multi-turn | 1505 | 1333 | Claude Opus 4.7 |
| LMArena Text: longer queries | 1505 | 1305 | Claude Opus 4.7 |
| LMArena Text: writing and language | 1492 | 1302 | Claude Opus 4.7 |
| LMArena Text: entertainment and media | 1474 | 1303 | Claude Opus 4.7 |
| LMArena Vision: English | 1322 | 1183 | Claude Opus 4.7 |
| LMArena Vision: Chinese | 1371 | Not measured | Only Claude Opus 4.7 measured |
| LMArena Vision: reading text | 1327 | 1151 | Claude Opus 4.7 |
| 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 12 boards here | |||
Image generationNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
| Benchmark | Claude Opus 4.7 | GLM 4.5V | 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 | Anthropic Claude Opus 4.7 | Z.ai GLM 4.5V |
|---|---|---|
| Maker | Anthropic | Z.ai |
| Weights | No public weights listed | Open weights |
| Released | April 16, 2026 | August 11, 2025 |
| Context window | 1M | 66K |
| Input $/Mtok | $5.00 | $0.60 |
| Output $/Mtok | $25.00 | $1.80 |
| Coding index | 67.5 | Not benchmarked |
| Independent measurements behind it | 4 | 0 |
| Cost per solved task | $2.95 | Not computable |
| Source | OpenRouter listing | OpenRouter listing |
Questions
Is Claude Opus 4.7 better than GLM 4.5V for coding?
Only Claude Opus 4.7 has a coding score, 67.5 on the Vibe Coding Index; GLM 4.5V has none, so the two cannot be ranked on coding. Pick GLM 4.5V for output tokens 13.9x cheaper ($1.80 vs $25.00 per million).
Which is cheaper, Claude Opus 4.7 or GLM 4.5V?
Claude Opus 4.7 costs $5.00 per million input tokens and $25.00 per million output tokens; GLM 4.5V costs $0.60 and $1.80. Cost per solved task is known for Claude Opus 4.7 only.
Which has the bigger context window, Claude Opus 4.7 or GLM 4.5V?
Claude Opus 4.7, with 1M tokens against 66K.
How many benchmarks is the Claude Opus 4.7 vs GLM 4.5V comparison based on?
36 benchmarks have a result for both models as of October 21, 2026. Claude Opus 4.7's coding index rests on 4 independent benchmarks and GLM 4.5V's on 0. Where only one model has a result, the page says so instead of naming a winner.