Model comparison
GLM-4.7 vs GPT-4.1 mini
Head-to-head evidence from 19 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 mini #160 (Estimated). Intervals and evidence labels describe ranking uncertainty, not a guarantee for a specific workload.
Evidence parity. GLM-4.7 and GPT-4.1 mini share 19 comparable benchmark results. 3 of 8 categories are comparable. 11 results are unique to GLM-4.7; 3 to GPT-4.1 mini.
Updated July 24, 2026- Shared results
- 19
- GLM-4.7 only
- 11
- GPT-4.1 mini only
- 3
- Comparable categories
- 3 / 8
Pick GLM-4.7 if you want the stronger benchmark profile. GPT-4.1 mini 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 19 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 43.1. 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 23.6. The single biggest benchmark swing on the page is SWE-bench Verified, 73.8% to 23.6%. GPT-4.1 mini does hit back in knowledge, so the answer changes if that is the part of the workload you care about most.
GPT-4.1 mini is also the more expensive model on tokens at $0.40 input / $1.60 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 mini 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 mini 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 mini |
|---|---|---|---|
| Coding | GLM-4.775.4 | Margin← 51.8 | GPT-4.1 mini23.6 |
| Knowledge | GLM-4.751.8 | Margin→ 12.4 | GPT-4.1 mini64.2 |
| Math | GLM-4.71.8 | Margin→ 2.7 | GPT-4.1 mini4.5 |
| Agentic | GLM-4.745.7 | MarginNo overlap | GPT-4.1 miniNot measured |
| Inst. Following | GLM-4.7Not measured | MarginNo overlap | GPT-4.1 mini88.5 |
Decisive benchmark drivers
The largest measured benchmark gaps in this matchup, with exact reported values.
More
- Source ↗
SWE-bench Verified
CodingA 73.8%B 23.6%Winner: GLM-4.7Δ 50.2SWE-bench Verified: GLM-4.7 scored 73.8%; GPT-4.1 mini scored 23.6%. GLM-4.7 wins this benchmark. - Source ↗
GPQA
KnowledgeA 85.7%B 64.2%Winner: GLM-4.7Δ 21.5GPQA: GLM-4.7 scored 85.7%; GPT-4.1 mini scored 64.2%. GLM-4.7 wins this benchmark. - Source ↗
FrontierMath v2 (Tiers 1-3)
MathA 2.439%B 4.483%Winner: GPT-4.1 miniΔ 2FrontierMath v2 (Tiers 1-3): GLM-4.7 scored 2.439%; GPT-4.1 mini scored 4.483%. GPT-4.1 mini 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 mini | Comparison |
|---|---|---|---|
| Input / output priceUSD per 1M tokens | GLM-4.7$0 input / $0 output | GPT-4.1 mini$0.4 input / $1.6 output | GLM-4.7 has the lower combined listed price. |
| Generation speedtokens per second | GLM-4.782 tok/s | GPT-4.1 mini80 tok/s | GLM-4.7 has the higher measured throughput. |
| First-answer latencyseconds to first token | GLM-4.71.10 s | GPT-4.1 mini0.76 s | GPT-4.1 mini reaches the first token sooner. |
| Context windowmaximum listed tokens | GLM-4.7200K | GPT-4.1 mini1M | GPT-4.1 mini lists the larger context window. |
Benchmark Deep Dive
Agentic8 benchmarks
| Benchmark | GLM-4.7 | GPT-4.1 mini | Result |
|---|---|---|---|
| Terminal-Bench 2.0Source | 41% | — | Not comparable |
| BrowseCompSource | 52% | — | Not comparable |
| VITA-BenchSource | 15.5% | — | Not comparable |
| AA Agentic IndexSource | 25.4% | 1.7% | GLM-4.7 leads |
| τ²-bench resultsSource | 95.9% | 52.9% | GLM-4.7 leads |
| Gert LabsSource | 39.95% | — | Not comparable |
| GDPval-AASource | 33.3% | 0.4% | GLM-4.7 leads |
| GDPval-AASource | 1166 | 508 | GLM-4.7 leads |
CodingGLM-4.7 wins6 benchmarks
Reasoning2 benchmarks
KnowledgeGPT-4.1 mini wins10 benchmarks
| Benchmark | GLM-4.7 | GPT-4.1 mini | Result |
|---|---|---|---|
| GPQASource | 85.7% | 64.2% | GLM-4.7 leads |
| MMLU-ProSource | 84.3% | — | Not comparable |
| HLESource | 24.8% | — | Not comparable |
| Artificial Analysis Intelligence IndexSource | 33.7% | 14.8% | GLM-4.7 leads |
| AA-GPQA DiamondSource | 85.9% | 66.4% | GLM-4.7 leads |
| AA-HLESource | 25.1% | 4.6% | GLM-4.7 leads |
| AA-Omniscience IndexSource | -34.6% | -50.1% | GLM-4.7 leads |
| AA-Omniscience AccuracySource | 29.3% | 17.5% | GLM-4.7 leads |
| AA-Omniscience Hallucination RateSource | 90.3% | 82.0% | GPT-4.1 mini leads |
| MMLUSource | — | 87.5% | Not comparable |
MathGPT-4.1 mini wins3 benchmarks
Multimodal2 benchmarks
Frequently Asked Questions (4)
Which is better, GLM-4.7 or GPT-4.1 mini?
GLM-4.7 is ahead on BenchLM's BenchAlign leaderboard, 60.44 to 43.1. The biggest single separator in this matchup is SWE-bench Verified, where the scores are 73.8% and 23.6%.
Which is better for knowledge tasks, GLM-4.7 or GPT-4.1 mini?
GPT-4.1 mini has the edge for knowledge tasks in this comparison, averaging 64.2 versus 51.8. Inside this category, GPQA is the benchmark that creates the most daylight between them.
Which is better for coding, GLM-4.7 or GPT-4.1 mini?
GLM-4.7 has the edge for coding in this comparison, averaging 75.4 versus 23.6. 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 mini?
GPT-4.1 mini has the edge for math in this comparison, averaging 4.5 versus 1.8. Inside this category, FrontierMath v2 (Tiers 1-3) is the benchmark that creates the most daylight between them.