Model comparison
Claude Opus 4.8 vs GLM-5V-Turbo
Claude Opus 4.8 is the stronger model overall, scoring 60.7 to 43.1 on the Noometry Index. GLM-5V-Turbo costs 5.3× less per token, which makes it the better buy when Claude Opus 4.8's lead doesn't matter for your workload.
Last verified . 19 shared benchmarks.
Summary
- They share 19 benchmarks with published results for both. Claude Opus 4.8 scores higher in 9 categories and GLM-5V-Turbo in 0 categories; 9 gaps are clear of the uncertainty.
- The widest gap is in math, where Claude Opus 4.8 leads 78.2 to 36.6.
- GLM-5V-Turbo is cheaper at $1.20 / $4 per million input/output tokens, against $5 / $25 for Claude Opus 4.8.
- Claude Opus 4.8 accepts more context: 1M tokens versus 200K.
Side by side
| Claude Opus 4.8 | GLM-5V-Turbo | |
|---|---|---|
| Provider | Anthropic | Z.ai (Zhipu) |
| Noometry Index | 60.7 | 43.1 |
| Released | 2026-05-28 | 2026-04-01 |
| Weights | Proprietary | Proprietary |
| Context window | 1M | 200K |
| Max output | 128K | 131K |
| Input $ / M tokens | $5 | $1.20 |
| Output $ / M tokens | $25 | $4 |
| Results tracked | 65 | 19 |
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Category by category
Coding Claude Opus 4.8 leads
Claude Opus 4.8: 60.2 (#9), GLM-5V-Turbo: 41.8 (#105)
| Benchmark | Claude Opus 4.8 | GLM-5V-Turbo |
|---|---|---|
| LMArena WebDev | 1556 | 1400 |
| LMArena Coding | 1491 | 1467 |
| DeepSWE | 59% | — |
| FrontierCode | 46.5% | — |
| SciCode | 53.5% | — |
| GSO | 47.1% | — |
| WeirdML | 82.9% | — |
| ALE-Bench | 1,564 | — |
Agentic & Tool Use Not comparable
Claude Opus 4.8: 47.6 (#10), GLM-5V-Turbo: —
| Benchmark | Claude Opus 4.8 | GLM-5V-Turbo |
|---|---|---|
| APEX-Agents | 48.9% | — |
| OSWorld 2.0 | 20.6% | — |
| Remote Labor Index | 8.3% | — |
| τ²-bench Banking | 39.7% | — |
| DeepResearch Bench | 50.2% | — |
| PostTrainBench | 33.8% | — |
| GBAEval | 70.9% | — |
| GDP.pdf | 24% | — |
| LMArena Search | 1204 | — |
| Vending-Bench 2 | 5,787 | — |
Reasoning Claude Opus 4.8 leads
Claude Opus 4.8: 64.6 (#14), GLM-5V-Turbo: 28.5 (#87)
| Benchmark | Claude Opus 4.8 | GLM-5V-Turbo |
|---|---|---|
| LMArena Hard Prompts | 1481 | 1443 |
| ARC-AGI-2 | 72.1% | — |
| SimpleBench | 64.8% | — |
| Kagi LLM Benchmark | 88.8% | — |
| NYT Connections (extended) | 91.1% | — |
| ARC-AGI-1 | 92.5% | — |
| CritPt | 20.9% | — |
| Chess Puzzles | 34% | — |
| EnigmaEval | 23.5% | — |
| EBR-Bench | 28.6% | — |
| Mystery Game Puzzles | 36% | — |
| DTBench | 94.9% | — |
| LMCA | 57.5% | — |
| Surface Evolver Bench | 87.5% | — |
| Bench to the Future 3 | 0.14 | — |
| Epoch Capabilities Index | 158.3 | — |
| ForecastBench | 59.9 | — |
Math Claude Opus 4.8 leads
Claude Opus 4.8: 78.2 (#11), GLM-5V-Turbo: 36.6 (#109)
| Benchmark | Claude Opus 4.8 | GLM-5V-Turbo |
|---|---|---|
| LMArena Math | 1487 | 1440 |
| FrontierMath (Tiers 1-3) | 80% | — |
| FrontierMath Tier 4 | 56.1% | — |
| MathArena Final-Answer Competitions | 91.8% | — |
| OTIS Mock AIME 2024-2025 | 98.3% | — |
| ProofBench | 69% | — |
| FrontierMath (Feb 2025 set) | 47.2% | — |
| FrontierMath Tier 4 (v1) | 31.3% | — |
Knowledge Claude Opus 4.8 leads
Claude Opus 4.8: 61.3 (#26), GLM-5V-Turbo: 39.9 (#113)
| Benchmark | Claude Opus 4.8 | GLM-5V-Turbo |
|---|---|---|
| LMArena Expert | 1501 | 1451 |
| GPQA Diamond | 91% | — |
| SimpleQA Verified | 53% | — |
Multimodal Claude Opus 4.8 leads
Claude Opus 4.8: 43.2 (#20), GLM-5V-Turbo: 40.1 (#41)
| Benchmark | Claude Opus 4.8 | GLM-5V-Turbo |
|---|---|---|
| LMArena Vision | 1295 | 1264 |
| LMArena Document | 1475 | 1416 |
| Blueprint-Bench 2 | 14.5% | — |
| Furniture Assembly | 42.5% | — |
Multilingual Claude Opus 4.8 leads
Claude Opus 4.8: 55.4 (#31), GLM-5V-Turbo: 53.2 (#69)
| Benchmark | Claude Opus 4.8 | GLM-5V-Turbo |
|---|---|---|
| LMArena Non-English | 1450 | 1420 |
| LMArena Chinese | 1509 | 1488 |
| LMArena French | 1482 | 1443 |
| LMArena German | 1470 | 1422 |
| LMArena Korean | 1432 | 1399 |
| LMArena Russian | 1476 | 1432 |
| LMArena Spanish | 1470 | 1446 |
| LMArena Japanese | 1440 | — |
Instruction Following Claude Opus 4.8 leads
Claude Opus 4.8: 77.6 (#22), GLM-5V-Turbo: 75.1 (#75)
| Benchmark | Claude Opus 4.8 | GLM-5V-Turbo |
|---|---|---|
| LMArena Instruction Following | 1476 | 1422 |
Long Context Claude Opus 4.8 leads
Claude Opus 4.8: 45.4 (#37), GLM-5V-Turbo: 44.0 (#76)
| Benchmark | Claude Opus 4.8 | GLM-5V-Turbo |
|---|---|---|
| LMArena Longer Query | 1482 | 1439 |
Writing & Preference Claude Opus 4.8 leads
Claude Opus 4.8: 72.1 (#17), GLM-5V-Turbo: 62.6 (#69)
| Benchmark | Claude Opus 4.8 | GLM-5V-Turbo |
|---|---|---|
| LMArena Text | 1460 | 1437 |
| LMArena Creative Writing | 1453 | 1414 |
| LMArena Multi-Turn | 1476 | 1432 |
| EQ-Bench Creative Writing | 1840 | — |
| EQ-Bench 4 | 1281 | — |
Frequently asked questions
Is Claude Opus 4.8 better than GLM-5V-Turbo?
Claude Opus 4.8 is the stronger model overall, scoring 60.7 to 43.1 on the Noometry Index. GLM-5V-Turbo costs 5.3× less per token, which makes it the better buy when Claude Opus 4.8's lead doesn't matter for your workload.
Which is cheaper, Claude Opus 4.8 or GLM-5V-Turbo?
GLM-5V-Turbo is cheaper. It lists at $1.20 per million input tokens and $4 per million output tokens; Claude Opus 4.8 lists at $5 and $25.
Is Claude Opus 4.8 or GLM-5V-Turbo better for coding?
Claude Opus 4.8 scores higher on coding benchmarks: 60.2 versus 41.8 in the Noometry coding category.
Which has the bigger context window?
Claude Opus 4.8 does, with 1M tokens against 200K.
How many benchmarks do Claude Opus 4.8 and GLM-5V-Turbo share?
19 benchmarks have published results for both models. Claude Opus 4.8 has 65 scored results on Noometry and GLM-5V-Turbo has 19.