Model comparison
GLM-4.7 vs GPT-4.1 nano
Head-to-head evidence from 18 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 nano #170 (Estimated). Intervals and evidence labels describe ranking uncertainty, not a guarantee for a specific workload.
Evidence parity. GLM-4.7 and GPT-4.1 nano share 18 comparable benchmark results. 2 of 8 categories are comparable. 12 results are unique to GLM-4.7; 3 to GPT-4.1 nano.
Updated July 24, 2026- Shared results
- 18
- GLM-4.7 only
- 12
- GPT-4.1 nano only
- 3
- Comparable categories
- 2 / 8
Pick GLM-4.7 if you want the stronger benchmark profile. GPT-4.1 nano only becomes the better choice if you need the larger 1M context window or you would rather avoid the extra latency and token burn of a reasoning model.
Confidence note. This is a partial-evidence comparison with 18 shared benchmark results across 7 evidence categories; 2 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 41.14. 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 knowledge, where it averages 51.8 against 50.3. The single biggest benchmark swing on the page is GPQA, 85.7% to 50.3%.
GPT-4.1 nano is also the more expensive model on tokens at $0.10 input / $0.40 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 nano 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 nano 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 nano |
|---|---|---|---|
| Knowledge | GLM-4.751.8 | Margin← 1.5 | GPT-4.1 nano50.3 |
| Math | GLM-4.71.8 | Margin← 0.8 | GPT-4.1 nano1.0 |
| Agentic | GLM-4.745.7 | MarginNo overlap | GPT-4.1 nanoNot measured |
| Coding | GLM-4.775.4 | MarginNo overlap | GPT-4.1 nanoNot measured |
| Inst. Following | GLM-4.7Not measured | MarginNo overlap | GPT-4.1 nano83.2 |
Decisive benchmark drivers
The largest measured benchmark gaps in this matchup, with exact reported values.
More
- Source ↗
GPQA
KnowledgeA 85.7%B 50.3%Winner: GLM-4.7Δ 35.4GPQA: GLM-4.7 scored 85.7%; GPT-4.1 nano scored 50.3%. GLM-4.7 wins this benchmark. - Source ↗
FrontierMath v2 (Tiers 1-3)
MathA 2.439%B 1.034%Winner: GLM-4.7Δ 1.4FrontierMath v2 (Tiers 1-3): GLM-4.7 scored 2.439%; GPT-4.1 nano scored 1.034%. GLM-4.7 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 nano | Comparison |
|---|---|---|---|
| Input / output priceUSD per 1M tokens | GLM-4.7$0 input / $0 output | GPT-4.1 nano$0.1 input / $0.4 output | GLM-4.7 has the lower combined listed price. |
| Generation speedtokens per second | GLM-4.782 tok/s | GPT-4.1 nano181 tok/s | GPT-4.1 nano has the higher measured throughput. |
| First-answer latencyseconds to first token | GLM-4.71.10 s | GPT-4.1 nano0.63 s | GPT-4.1 nano reaches the first token sooner. |
| Context windowmaximum listed tokens | GLM-4.7200K | GPT-4.1 nano1M | GPT-4.1 nano lists the larger context window. |
Benchmark Deep Dive
Agentic8 benchmarks
| Benchmark | GLM-4.7 | GPT-4.1 nano | Result |
|---|---|---|---|
| Terminal-Bench 2.0Source | 41% | — | Not comparable |
| BrowseCompSource | 52% | — | Not comparable |
| VITA-BenchSource | 15.5% | — | Not comparable |
| AA Agentic IndexSource | 25.4% | 1.2% | GLM-4.7 leads |
| τ²-bench resultsSource | 95.9% | 17.3% | GLM-4.7 leads |
| Gert LabsSource | 39.95% | — | Not comparable |
| GDPval-AASource | 33.3% | 0.0% | GLM-4.7 leads |
| GDPval-AASource | 1166 | 63 | GLM-4.7 leads |
Coding6 benchmarks
Reasoning2 benchmarks
KnowledgeGLM-4.7 wins10 benchmarks
| Benchmark | GLM-4.7 | GPT-4.1 nano | Result |
|---|---|---|---|
| GPQASource | 85.7% | 50.3% | GLM-4.7 leads |
| MMLU-ProSource | 84.3% | — | Not comparable |
| HLESource | 24.8% | — | Not comparable |
| Artificial Analysis Intelligence IndexSource | 33.7% | 9.6% | GLM-4.7 leads |
| AA-GPQA DiamondSource | 85.9% | 51.2% | GLM-4.7 leads |
| AA-HLESource | 25.1% | 3.9% | GLM-4.7 leads |
| AA-Omniscience IndexSource | -34.6% | -56.4% | GLM-4.7 leads |
| AA-Omniscience AccuracySource | 29.3% | 13.3% | GLM-4.7 leads |
| AA-Omniscience Hallucination RateSource | 90.3% | 80.4% | GPT-4.1 nano leads |
| MMLUSource | — | 80.1% | Not comparable |
MathGLM-4.7 wins3 benchmarks
Multimodal2 benchmarks
Frequently Asked Questions (3)
Which is better, GLM-4.7 or GPT-4.1 nano?
GLM-4.7 is ahead on BenchLM's BenchAlign leaderboard, 60.44 to 41.14. The biggest single separator in this matchup is GPQA, where the scores are 85.7% and 50.3%.
Which is better for knowledge tasks, GLM-4.7 or GPT-4.1 nano?
GLM-4.7 has the edge for knowledge tasks in this comparison, averaging 51.8 versus 50.3. Inside this category, GPQA is the benchmark that creates the most daylight between them.
Which is better for math, GLM-4.7 or GPT-4.1 nano?
GLM-4.7 has the edge for math in this comparison, averaging 1.8 versus 1. Inside this category, FrontierMath v2 (Tiers 1-3) is the benchmark that creates the most daylight between them.