Model comparison
GLM-5 vs GPT-4.1 nano
Head-to-head evidence from 15 shared benchmark results across 7 categories. Overall scores shown here use the public BenchAlign v5 ranking lane.
Public leaderboard positions: GLM-5 #30 (Supported); GPT-4.1 nano #170 (Estimated). Intervals and evidence labels describe ranking uncertainty, not a guarantee for a specific workload.
Evidence parity. GLM-5 and GPT-4.1 nano share 15 comparable benchmark results. 3 of 8 categories are comparable. 34 results are unique to GLM-5; 6 to GPT-4.1 nano.
Updated July 24, 2026- Shared results
- 15
- GLM-5 only
- 34
- GPT-4.1 nano only
- 6
- Comparable categories
- 3 / 8
Pick GLM-5 if you want the stronger benchmark profile. GPT-4.1 nano only becomes the better choice if you want the cheaper token bill or you need the larger 1M context window.
Confidence note. This is a partial-evidence comparison with 15 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-5 is clearly ahead on the BenchAlign aggregate, 65.29 to 41.14. The gap is large enough that you do not need to squint at the spreadsheet to see the difference.
GLM-5's sharpest advantage is in mathematics, where it averages 56.3 against 1. The single biggest benchmark swing on the page is GPQA, 86% to 50.3%.
GLM-5 is also the more expensive model on tokens at $1.00 input / $3.20 output per 1M tokens, versus $0.10 input / $0.40 output per 1M tokens for GPT-4.1 nano. That is roughly 8.0x on output cost alone. GPT-4.1 nano gives you the larger context window at 1M, compared with 200K for GLM-5.
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-5 | Δ | GPT-4.1 nano |
|---|---|---|---|
| Math | GLM-556.3 | Margin← 55.3 | GPT-4.1 nano1.0 |
| Knowledge | GLM-566.4 | Margin← 16.1 | GPT-4.1 nano50.3 |
| Inst. Following | GLM-592.6 | Margin← 9.4 | GPT-4.1 nano83.2 |
| Agentic | GLM-556.2 | MarginNo overlap | GPT-4.1 nanoNot measured |
| Coding | GLM-566.3 | MarginNo overlap | GPT-4.1 nanoNot measured |
| Reasoning | GLM-560.8 | MarginNo overlap | GPT-4.1 nanoNot measured |
| Multilingual | GLM-583.1 | MarginNo overlap | GPT-4.1 nanoNot measured |
Decisive benchmark drivers
The largest measured benchmark gaps in this matchup, with exact reported values.
More
- Source ↗
GPQA
KnowledgeA 86%B 50.3%Winner: GLM-5Δ 35.7GPQA: GLM-5 scored 86%; GPT-4.1 nano scored 50.3%. GLM-5 wins this benchmark. - Source ↗
FrontierMath v2 (Tiers 1-3)
MathA 16.434%B 1.034%Winner: GLM-5Δ 15.4FrontierMath v2 (Tiers 1-3): GLM-5 scored 16.434%; GPT-4.1 nano scored 1.034%. GLM-5 wins this benchmark. - Source ↗
IFEval
Inst. FollowingA 92.6%B 83.2%Winner: GLM-5Δ 9.4IFEval: GLM-5 scored 92.6%; GPT-4.1 nano scored 83.2%. GLM-5 wins this benchmark.
Operational comparison
Runtime and commercial metrics are compared only when both models have a complete sourced value.
| Metric | GLM-5 | GPT-4.1 nano | Comparison |
|---|---|---|---|
| Input / output priceUSD per 1M tokens | GLM-5$1 input / $3.2 output | GPT-4.1 nano$0.1 input / $0.4 output | GPT-4.1 nano has the lower combined listed price. |
| Generation speedtokens per second | GLM-574 tok/s | GPT-4.1 nano181 tok/s | GPT-4.1 nano has the higher measured throughput. |
| First-answer latencyseconds to first token | GLM-51.64 s | GPT-4.1 nano0.63 s | GPT-4.1 nano reaches the first token sooner. |
| Context windowmaximum listed tokens | GLM-5200K | GPT-4.1 nano1M | GPT-4.1 nano lists the larger context window. |
Benchmark Deep Dive
Agentic16 benchmarks
| Benchmark | GLM-5 | GPT-4.1 nano | Result |
|---|---|---|---|
| Terminal-Bench 2.0Source | 56.2% | — | Not comparable |
| Claw-EvalSource | 57.7% | — | Not comparable |
| QwenClawBenchSource | 54.1% | — | Not comparable |
| τ³-bench resultsSource | 65.6% | — | Not comparable |
| DeepPlanningSource | 14.6% | — | Not comparable |
| ToolathlonSource | 38% | — | Not comparable |
| MCP AtlasSource | 31.1% | — | Not comparable |
| MCP-TasksSource | 60.8% | — | Not comparable |
| WideResearchSource | 69.8% | — | Not comparable |
| τ²-bench resultsSource | 98.2% | 17.3% | GLM-5 leads |
| CyberGymSource | 43.2% | — | Not comparable |
| APEX-Agents-AASource | 14.5% | — | Not comparable |
| Gert LabsSource | 50.99% | — | Not comparable |
| AA Agentic IndexSource | — | 1.2% | Not comparable |
| GDPval-AASource | — | 0.0% | Not comparable |
| GDPval-AASource | — | 63 | Not comparable |
Coding8 benchmarks
| Benchmark | GLM-5 | GPT-4.1 nano | Result |
|---|---|---|---|
| SWE-bench VerifiedSource | 77.8% | — | Not comparable |
| SWE-bench Verified*Source | 72.8% | — | Not comparable |
| SWE-bench ProSource | 55.1% | — | Not comparable |
| SWE MultilingualSource | 73.3% | — | Not comparable |
| SWE-RebenchSource | 62.8% | — | Not comparable |
| React Native EvalsSource | 74.8% | — | Not comparable |
| AA-SciCodeSource | 46.2% | 25.9% | GLM-5 leads |
| AA Coding IndexSource | — | 11.1% | Not comparable |
Reasoning4 benchmarks
KnowledgeGLM-5 wins13 benchmarks
| Benchmark | GLM-5 | GPT-4.1 nano | Result |
|---|---|---|---|
| GPQASource | 86% | 50.3% | GLM-5 leads |
| GPQA-DSource | 86.0% | — | Not comparable |
| SuperGPQASource | 66.8% | — | Not comparable |
| MMLU-ProSource | 85.7% | — | Not comparable |
| MMLU-Pro (Arcee)Source | 85.8% | — | Not comparable |
| HLESource | 50.4% | — | Not comparable |
| Artificial Analysis Intelligence IndexSource | 39.5% | 9.6% | GLM-5 leads |
| AA-GPQA DiamondSource | 82.0% | 51.2% | GLM-5 leads |
| AA-HLESource | 27.2% | 3.9% | GLM-5 leads |
| AA-Omniscience IndexSource | 2.0% | -56.4% | GLM-5 leads |
| AA-Omniscience AccuracySource | 26.9% | 13.3% | GLM-5 leads |
| AA-Omniscience Hallucination RateSource | 34.0% | 80.4% | GLM-5 leads |
| MMLUSource | — | 80.1% | Not comparable |
MathGLM-5 wins8 benchmarks
| Benchmark | GLM-5 | GPT-4.1 nano | Result |
|---|---|---|---|
| AIME26Source | 95.8% | — | Not comparable |
| AIME25 (Arcee)Source | 93.3% | — | Not comparable |
| HMMT Feb 2025Source | 97.5% | — | Not comparable |
| HMMT Nov 2025Source | 96.9% | — | Not comparable |
| HMMT Feb 2026Source | 86.4% | — | Not comparable |
| MMAnswerBenchSource | 82.5% | — | Not comparable |
| FrontierMath v2 (Tiers 1-3)Source | 16.434% | 1.034% | GLM-5 leads |
| FrontierMath v2 (Tier 4)Source | 2.100% | — | Not comparable |
Multilingual2 benchmarks
Multimodal2 benchmarks
Frequently Asked Questions (4)
Which is better, GLM-5 or GPT-4.1 nano?
GLM-5 is ahead on BenchLM's BenchAlign leaderboard, 65.29 to 41.14. The biggest single separator in this matchup is GPQA, where the scores are 86% and 50.3%.
Which is better for knowledge tasks, GLM-5 or GPT-4.1 nano?
GLM-5 has the edge for knowledge tasks in this comparison, averaging 66.4 versus 50.3. Inside this category, AA-Omniscience Index is the benchmark that creates the most daylight between them.
Which is better for math, GLM-5 or GPT-4.1 nano?
GLM-5 has the edge for math in this comparison, averaging 56.3 versus 1. Inside this category, FrontierMath v2 (Tiers 1-3) is the benchmark that creates the most daylight between them.
Which is better for instruction following, GLM-5 or GPT-4.1 nano?
GLM-5 has the edge for instruction following in this comparison, averaging 92.6 versus 83.2. Inside this category, AA-IFBench is the benchmark that creates the most daylight between them.