Model comparison
Claude Opus 4.7 vs GLM-4.5V
Claude Opus 4.7 is the stronger model overall, scoring 58.3 to 39.8 on the Noometry Index. GLM-4.5V costs 11× less per token, which makes it the better buy when Claude Opus 4.7's lead doesn't matter for your workload.
Last verified . 15 shared benchmarks.
Summary
- They share 15 benchmarks with published results for both. Claude Opus 4.7 scores higher in 9 categories and GLM-4.5V in 0 categories; 9 gaps are clear of the uncertainty.
- The widest gap is in math, where Claude Opus 4.7 leads 66.7 to 37.4.
- The biggest single-benchmark swing is Kagi LLM Benchmark: 80.7% for Claude Opus 4.7 and 59.8% for GLM-4.5V.
- GLM-4.5V is cheaper at $0.60 / $1.80 per million input/output tokens, against $5 / $25 for Claude Opus 4.7.
- Claude Opus 4.7 accepts more context: 1M tokens versus 64K.
- GLM-4.5V has downloadable open weights; the other is API-only.
Side by side
| Claude Opus 4.7 | GLM-4.5V | |
|---|---|---|
| Provider | Anthropic | Z.ai (Zhipu) |
| Noometry Index | 58.3 | 39.8 |
| Released | 2026-04-14 | 2025-08-11 |
| Weights | Proprietary | Open |
| Context window | 1M | 64K |
| Max output | 128K | 16K |
| Input $ / M tokens | $5 | $0.60 |
| Output $ / M tokens | $25 | $1.80 |
| Results tracked | 66 | 15 |
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Category by category
Coding Claude Opus 4.7 leads
Claude Opus 4.7: 59.6 (#13), GLM-4.5V: 39.5 (#155)
| Benchmark | Claude Opus 4.7 | GLM-4.5V |
|---|---|---|
| LMArena Coding | 1518 | 1347 |
| SWE-bench Verified | 83.5% | — |
| FrontierCode | 38.5% | — |
| LMArena WebDev | 1558 | — |
| SciCode | 54.5% | — |
| GSO | 44.1% | — |
| WeirdML | 76.4% | — |
| MirrorCode | 31.1% | — |
| ALE-Bench | 1,323 | — |
Agentic & Tool Use Not comparable
Claude Opus 4.7: 47.9 (#10), GLM-4.5V: —
| Benchmark | Claude Opus 4.7 | GLM-4.5V |
|---|---|---|
| Terminal-Bench | 80.2% | — |
| APEX-Agents | 49.2% | — |
| OSWorld 2.0 | 18.2% | — |
| τ²-bench Banking | 40.2% | — |
| PostTrainBench | 28.6% | — |
| ExploitBench | 26.5% | — |
| GBAEval | 43.8% | — |
| GDP.pdf | 21% | — |
| LMArena Search | 1233 | — |
| Vending-Bench 2 | 10,937 | — |
Reasoning Claude Opus 4.7 leads
Claude Opus 4.7: 53.8 (#29), GLM-4.5V: 27.4 (#119)
| Benchmark | Claude Opus 4.7 | GLM-4.5V |
|---|---|---|
| Kagi LLM Benchmark | 80.7% | 59.8% |
| LMArena Hard Prompts | 1506 | 1334 |
| ARC-AGI-2 | 75.8% | — |
| SimpleBench | 61.7% | — |
| NYT Connections (extended) | 39% | — |
| ARC-AGI-1 | 93.5% | — |
| CritPt | 12% | — |
| Chess Puzzles | 30% | — |
| Thematic Generalization | 72.8% | — |
| EBR-Bench | 19% | — |
| Mystery Game Puzzles | 28% | — |
| DTBench | 94.7% | — |
| LMCA | 52.2% | — |
| Epoch Capabilities Index | 156.25 | — |
| ForecastBench | 60.3 | — |
Math Claude Opus 4.7 leads
Claude Opus 4.7: 66.7 (#26), GLM-4.5V: 37.4 (#159)
| Benchmark | Claude Opus 4.7 | GLM-4.5V |
|---|---|---|
| LMArena Math | 1499 | 1354 |
| FrontierMath (Tiers 1-3) | 70.2% | — |
| FrontierMath Tier 4 | 31.7% | — |
| MathArena Final-Answer Competitions | 73.6% | — |
| OTIS Mock AIME 2024-2025 | 97.8% | — |
| ProofBench | 54% | — |
| FrontierMath (Feb 2025 set) | 43.8% | — |
| FrontierMath Tier 4 (v1) | 22.9% | — |
Knowledge Claude Opus 4.7 leads
Claude Opus 4.7: 62.6 (#23), GLM-4.5V: 37.5 (#156)
| Benchmark | Claude Opus 4.7 | GLM-4.5V |
|---|---|---|
| LMArena Expert | 1521 | 1353 |
| GPQA Diamond | 90.2% | — |
| Humanity's Last Exam | 36.2% | — |
| SimpleQA Verified | 51.7% | — |
| Vectara Hallucination Rate | 12% | — |
Multimodal Claude Opus 4.7 leads
Claude Opus 4.7: 41.2 (#38), GLM-4.5V: 34.3 (#92)
| Benchmark | Claude Opus 4.7 | GLM-4.5V |
|---|---|---|
| LMArena Vision | 1316 | 1154 |
| Blueprint-Bench 2 | 24.5% | — |
| Furniture Assembly | 33.3% | — |
| LMArena Document | 1495 | — |
Multilingual Claude Opus 4.7 leads
Claude Opus 4.7: 57.3 (#10), GLM-4.5V: 44.6 (#177)
| Benchmark | Claude Opus 4.7 | GLM-4.5V |
|---|---|---|
| LMArena Non-English | 1480 | 1303 |
| LMArena Chinese | 1531 | 1337 |
| LMArena Russian | 1494 | 1298 |
| LMArena Spanish | 1495 | 1336 |
| LMArena French | 1503 | — |
| LMArena German | 1495 | — |
| LMArena Japanese | 1472 | — |
| LMArena Korean | 1464 | — |
Instruction Following Claude Opus 4.7 leads
Claude Opus 4.7: 78.4 (#10), GLM-4.5V: 69.2 (#175)
| Benchmark | Claude Opus 4.7 | GLM-4.5V |
|---|---|---|
| LMArena Instruction Following | 1498 | 1311 |
Long Context Claude Opus 4.7 leads
Claude Opus 4.7: 46.2 (#25), GLM-4.5V: 39.6 (#171)
| Benchmark | Claude Opus 4.7 | GLM-4.5V |
|---|---|---|
| LMArena Longer Query | 1505 | 1304 |
Writing & Preference Claude Opus 4.7 leads
Claude Opus 4.7: 75.1 (#8), GLM-4.5V: 52.5 (#170)
| Benchmark | Claude Opus 4.7 | GLM-4.5V |
|---|---|---|
| LMArena Text | 1490 | 1333 |
| LMArena Creative Writing | 1486 | 1295 |
| LMArena Multi-Turn | 1505 | 1332 |
| EQ-Bench Creative Writing | 1914 | — |
| EQ-Bench 4 | 1311 | — |
Frequently asked questions
Is Claude Opus 4.7 better than GLM-4.5V?
Claude Opus 4.7 is the stronger model overall, scoring 58.3 to 39.8 on the Noometry Index. GLM-4.5V costs 11× less per token, which makes it the better buy when Claude Opus 4.7's lead doesn't matter for your workload.
Which is cheaper, Claude Opus 4.7 or GLM-4.5V?
GLM-4.5V is cheaper. It lists at $0.60 per million input tokens and $1.80 per million output tokens; Claude Opus 4.7 lists at $5 and $25.
Is Claude Opus 4.7 or GLM-4.5V better for coding?
Claude Opus 4.7 scores higher on coding benchmarks: 59.6 versus 39.5 in the Noometry coding category.
Which has the bigger context window?
Claude Opus 4.7 does, with 1M tokens against 64K.
How many benchmarks do Claude Opus 4.7 and GLM-4.5V share?
15 benchmarks have published results for both models. Claude Opus 4.7 has 66 scored results on Noometry and GLM-4.5V has 15.