Model comparison
GLM-4.7 vs GPT-4.1
Head-to-head evidence from 17 shared benchmark results across 7 categories. Overall scores shown here use the public BenchAlign v5 ranking lane.
Public leaderboard positions: GLM-4.7 #45 (Supported); GPT-4.1 #111 (Supported). Intervals and evidence labels describe ranking uncertainty, not a guarantee for a specific workload.
Evidence parity. GLM-4.7 and GPT-4.1 share 17 comparable benchmark results. 3 of 8 categories are comparable. 13 results are unique to GLM-4.7; 3 to GPT-4.1.
Updated July 24, 2026- Shared results
- 17
- GLM-4.7 only
- 13
- GPT-4.1 only
- 3
- Comparable categories
- 3 / 8
Pick GLM-4.7 if you want the stronger benchmark profile. GPT-4.1 only becomes the better choice if knowledge is the priority or you need the larger 1M context window.
Confidence note. This is a partial-evidence comparison with 17 shared benchmark results across 7 evidence categories; 3 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, 60.44 to 50.44. 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 54.6. The single biggest benchmark swing on the page is GPQA, 85.7% to 66.3%. GPT-4.1 does hit back in knowledge, so the answer changes if that is the part of the workload you care about most.
GPT-4.1 is also the more expensive model on tokens at $2.00 input / $8.00 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 GPT-4.1 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. GPT-4.1 gives you the larger context window at 1M, compared with 200K for GLM-4.7.
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 | GLM-4.7 | Δ | GPT-4.1 |
|---|---|---|---|
| Coding | GLM-4.775.4 | Margin← 20.8 | GPT-4.154.6 |
| Knowledge | GLM-4.751.8 | Margin→ 14.5 | GPT-4.166.3 |
| Math | GLM-4.71.8 | Margin→ 2.3 | GPT-4.14.1 |
| Agentic | GLM-4.745.7 | MarginNo overlap | GPT-4.1Not measured |
| Inst. Following | GLM-4.7Not measured | MarginNo overlap | GPT-4.187.4 |
Decisive benchmark drivers
The largest measured benchmark gaps in this matchup, with exact reported values.
More
- Source ↗
GPQA
KnowledgeA 85.7%B 66.3%Winner: GLM-4.7Δ 19.4GPQA: GLM-4.7 scored 85.7%; GPT-4.1 scored 66.3%. GLM-4.7 wins this benchmark. - Source ↗
SWE-bench Verified
CodingA 73.8%B 54.6%Winner: GLM-4.7Δ 19.2SWE-bench Verified: GLM-4.7 scored 73.8%; GPT-4.1 scored 54.6%. GLM-4.7 wins this benchmark. - Source ↗
FrontierMath v2 (Tiers 1-3)
MathA 2.439%B 5.517%Winner: GPT-4.1Δ 3.1FrontierMath v2 (Tiers 1-3): GLM-4.7 scored 2.439%; GPT-4.1 scored 5.517%. GPT-4.1 wins this benchmark.
Operational comparison
Runtime and commercial metrics are compared only when both models have a complete sourced value.
| Metric | GLM-4.7 | GPT-4.1 | Comparison |
|---|---|---|---|
| Input / output priceUSD per 1M tokens | GLM-4.7$0 input / $0 output | GPT-4.1$2 input / $8 output | GLM-4.7 has the lower combined listed price. |
| Generation speedtokens per second | GLM-4.782 tok/s | GPT-4.1108 tok/s | GPT-4.1 has the higher measured throughput. |
| First-answer latencyseconds to first token | GLM-4.71.10 s | GPT-4.11.02 s | GPT-4.1 reaches the first token sooner. |
| Context windowmaximum listed tokens | GLM-4.7200K | GPT-4.11M | GPT-4.1 lists the larger context window. |
Benchmark Deep Dive
Agentic8 benchmarks
| Benchmark | GLM-4.7 | GPT-4.1 | Result |
|---|---|---|---|
| Terminal-Bench 2.0Source | 41% | — | Not comparable |
| BrowseCompSource | 52% | — | Not comparable |
| VITA-BenchSource | 15.5% | — | Not comparable |
| AA Agentic IndexSource | 25.4% | — | Not comparable |
| τ²-bench resultsSource | 95.9% | 47.1% | GLM-4.7 leads |
| Gert LabsSource | 39.95% | 25.65% | GLM-4.7 leads |
| GDPval-AASource | 33.3% | — | Not comparable |
| GDPval-AASource | 1166 | — | Not comparable |
CodingGLM-4.7 wins6 benchmarks
Reasoning2 benchmarks
KnowledgeGPT-4.1 wins10 benchmarks
| Benchmark | GLM-4.7 | GPT-4.1 | Result |
|---|---|---|---|
| GPQASource | 85.7% | 66.3% | GLM-4.7 leads |
| MMLU-ProSource | 84.3% | — | Not comparable |
| HLESource | 24.8% | — | Not comparable |
| Artificial Analysis Intelligence IndexSource | 33.7% | 19.4% | GLM-4.7 leads |
| AA-GPQA DiamondSource | 85.9% | 66.6% | GLM-4.7 leads |
| AA-HLESource | 25.1% | 4.6% | GLM-4.7 leads |
| AA-Omniscience IndexSource | -34.6% | -36.2% | GLM-4.7 leads |
| AA-Omniscience AccuracySource | 29.3% | 24.2% | GLM-4.7 leads |
| AA-Omniscience Hallucination RateSource | 90.3% | 79.6% | GPT-4.1 leads |
| MMLUSource | — | 90.2% | Not comparable |
MathGPT-4.1 wins3 benchmarks
Multimodal2 benchmarks
Frequently Asked Questions (4)
Which is better, GLM-4.7 or GPT-4.1?
GLM-4.7 is ahead on BenchLM's BenchAlign leaderboard, 60.44 to 50.44. The biggest single separator in this matchup is GPQA, where the scores are 85.7% and 66.3%.
Which is better for knowledge tasks, GLM-4.7 or GPT-4.1?
GPT-4.1 has the edge for knowledge tasks in this comparison, averaging 66.3 versus 51.8. Inside this category, AA-HLE is the benchmark that creates the most daylight between them.
Which is better for coding, GLM-4.7 or GPT-4.1?
GLM-4.7 has the edge for coding in this comparison, averaging 75.4 versus 54.6. Inside this category, SWE-bench Verified is the benchmark that creates the most daylight between them.
Which is better for math, GLM-4.7 or GPT-4.1?
GPT-4.1 has the edge for math in this comparison, averaging 4.1 versus 1.8. Inside this category, FrontierMath v2 (Tiers 1-3) is the benchmark that creates the most daylight between them.