Model comparison
Claude 4 Sonnet vs GLM-4.7
Head-to-head evidence from 14 shared benchmark results across 6 categories. Overall scores shown here use the public BenchAlign v5 ranking lane.
Public leaderboard positions: Claude 4 Sonnet #158 (Supported); GLM-4.7 #42 (Supported). Intervals and evidence labels describe ranking uncertainty, not a guarantee for a specific workload.
Evidence parity. Claude 4 Sonnet and GLM-4.7 share 14 comparable benchmark results. 1 of 8 categories are comparable. 2 results are unique to Claude 4 Sonnet; 16 to GLM-4.7.
Updated July 22, 2026- Shared results
- 14
- Claude 4 Sonnet only
- 2
- GLM-4.7 only
- 16
- Comparable categories
- 1 / 8
Pick GLM-4.7 if you want the stronger benchmark profile. Claude 4 Sonnet only becomes the better choice if you would rather avoid the extra latency and token burn of a reasoning model.
Confidence note. This is a partial-evidence comparison with 14 shared benchmark results across 6 evidence categories; 1 of 8 categories currently have scoreable aggregates for both models. Treat the verdict as directional until coverage is more balanced.
Why this result
GLM-4.7 is clearly ahead on the BenchAlign aggregate, 61.16 to 42.79. The gap is large enough that you do not need to squint at the spreadsheet to see the difference.
GLM-4.7's sharpest advantage is in coding, where it averages 75.4 against 72.7. The single biggest benchmark swing on the page is SWE-bench Verified, 72.7% to 73.8%.
Claude 4 Sonnet is also the more expensive model on tokens at $3.00 input / $15.00 output per 1M tokens, versus $0.00 input / $0.00 output per 1M tokens for GLM-4.7. That is roughly Infinityx on output cost alone. GLM-4.7 is the reasoning model in the pair, while Claude 4 Sonnet is not. That usually helps on harder chain-of-thought-heavy tests, but it can also mean more latency and more token spend in real use.
Category breakdown
Exact category averages are shown below. Not measured means BenchLM does not have enough sourced public coverage for that model and category.
| Category | Claude 4 Sonnet | Δ | GLM-4.7 |
|---|---|---|---|
| Coding | Claude 4 Sonnet72.7 | Margin→ 2.7 | GLM-4.775.4 |
| Agentic | Claude 4 SonnetNot measured | MarginNo overlap | GLM-4.745.7 |
| Knowledge | Claude 4 SonnetNot measured | MarginNo overlap | GLM-4.751.8 |
| Math | Claude 4 SonnetNot measured | MarginNo overlap | GLM-4.71.8 |
Decisive benchmark drivers
The largest measured benchmark gaps in this matchup, with exact reported values.
More
- Source ↗
SWE-bench Verified
CodingA 72.7%B 73.8%Winner: GLM-4.7Δ 1.1SWE-bench Verified: Claude 4 Sonnet scored 72.7%; GLM-4.7 scored 73.8%. GLM-4.7 wins this benchmark.
Operational comparison
Runtime and commercial metrics are compared only when both models have a complete sourced value.
| Metric | Claude 4 Sonnet | GLM-4.7 | Comparison |
|---|---|---|---|
| Input / output priceUSD per 1M tokens | Claude 4 Sonnet$3 input / $15 output | GLM-4.7$0 input / $0 output | GLM-4.7 has the lower combined listed price. |
| Generation speedtokens per second | Claude 4 Sonnet40 tok/s | GLM-4.782 tok/s | GLM-4.7 has the higher measured throughput. |
| First-answer latencyseconds to first token | Claude 4 Sonnet1.33 s | GLM-4.71.10 s | GLM-4.7 reaches the first token sooner. |
| Context windowmaximum listed tokens | Claude 4 Sonnet200K | GLM-4.7200K | Listed context windows are equal. |
Benchmark Deep Dive
Agentic9 benchmarks
| Benchmark | Claude 4 Sonnet | GLM-4.7 | Result |
|---|---|---|---|
| τ²-bench resultsSource | 52.3% | 95.9% | GLM-4.7 leads |
| Gert LabsSource | 39.66% | 39.95% | GLM-4.7 leads |
| JobBenchSource | 18.4% | — | Not comparable |
| Terminal-Bench 2.0Source | — | 41% | Not comparable |
| BrowseCompSource | — | 52% | Not comparable |
| VITA-BenchSource | — | 15.5% | Not comparable |
| AA Agentic IndexSource | — | 25.4% | Not comparable |
| GDPval-AASource | — | 33.3% | Not comparable |
| GDPval-AASource | — | 1165 | Not comparable |
CodingGLM-4.7 wins6 benchmarks
Reasoning2 benchmarks
Knowledge9 benchmarks
| Benchmark | Claude 4 Sonnet | GLM-4.7 | Result |
|---|---|---|---|
| Artificial Analysis Intelligence IndexSource | 25.5% | 33.7% | GLM-4.7 leads |
| AA-GPQA DiamondSource | 68.3% | 85.9% | GLM-4.7 leads |
| AA-HLESource | 4.0% | 25.1% | GLM-4.7 leads |
| AA-Omniscience IndexSource | -9.2% | -34.6% | Claude 4 Sonnet leads |
| AA-Omniscience AccuracySource | 22.4% | 29.3% | GLM-4.7 leads |
| AA-Omniscience Hallucination RateSource | 40.8% | 90.3% | Claude 4 Sonnet leads |
| GPQASource | — | 85.7% | Not comparable |
| MMLU-ProSource | — | 84.3% | Not comparable |
| HLESource | — | 24.8% | Not comparable |
Math3 benchmarks
Multimodal2 benchmarks
Inst. Following1 benchmarks
| Benchmark | Claude 4 Sonnet | GLM-4.7 | Result |
|---|---|---|---|
| AA-IFBenchSource | 45.4% | 67.9% | GLM-4.7 leads |
Frequently Asked Questions (2)
Which is better, Claude 4 Sonnet or GLM-4.7?
GLM-4.7 is ahead on BenchLM's BenchAlign leaderboard, 61.16 to 42.79. The biggest single separator in this matchup is SWE-bench Verified, where the scores are 72.7% and 73.8%.
Which is better for coding, Claude 4 Sonnet or GLM-4.7?
GLM-4.7 has the edge for coding in this comparison, averaging 75.4 versus 72.7. Inside this category, AA-SciCode is the benchmark that creates the most daylight between them.
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