Model comparison
GLM-5 vs MiniCPM5-1B
Head-to-head evidence from 6 shared benchmark results across 3 categories. Overall scores shown here use the public BenchAlign v5 ranking lane.
Public leaderboard positions: GLM-5 #26 (Supported); MiniCPM5-1B unranked (Not scored). Intervals and evidence labels describe ranking uncertainty, not a guarantee for a specific workload.
Evidence parity. GLM-5 and MiniCPM5-1B share 6 comparable benchmark results. 3 of 8 categories are comparable. 44 results are unique to GLM-5; 8 to MiniCPM5-1B.
Updated July 17, 2026- Shared results
- 6
- GLM-5 only
- 44
- MiniCPM5-1B only
- 8
- Comparable categories
- 3 / 8
Pick GLM-5 if you want the stronger benchmark profile. MiniCPM5-1B only becomes the better choice if you want the stronger reasoning-first profile.
Confidence note. This is a partial-evidence comparison with 6 shared benchmark results across 3 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, 65.98 to 36. 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 instruction following, where it averages 92.6 against 58.5. The single biggest benchmark swing on the page is HMMT Feb 2026, 86.4% to 25.8%.
MiniCPM5-1B is the reasoning model in the pair, while GLM-5 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. GLM-5 gives you the larger context window at 200K, compared with 131K for MiniCPM5-1B.
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 | MiniCPM5-1B |
|---|---|---|---|
| Inst. Following | GLM-592.6 | Margin← 34.1 | MiniCPM5-1B58.5 |
| Math | GLM-556.3 | Margin← 23.2 | MiniCPM5-1B33.1 |
| Knowledge | GLM-566.6 | Margin← 22.5 | MiniCPM5-1B44.1 |
| Agentic | GLM-556.2 | MarginNo overlap | MiniCPM5-1BNot measured |
| Coding | GLM-566.3 | MarginNo overlap | MiniCPM5-1BNot measured |
| Reasoning | GLM-560.8 | MarginNo overlap | MiniCPM5-1BNot measured |
| Multilingual | GLM-583.1 | MarginNo overlap | MiniCPM5-1BNot measured |
Decisive benchmark drivers
The largest measured benchmark gaps in this matchup, with exact reported values.
More
- Source ↗
HMMT Feb 2026
MathA 86.4%B 25.8%Winner: GLM-5Δ 60.6HMMT Feb 2026: GLM-5 scored 86.4%; MiniCPM5-1B scored 25.8%. GLM-5 wins this benchmark. - Source ↗
AIME26
MathA 95.8%B 40.4%Winner: GLM-5Δ 55.4AIME26: GLM-5 scored 95.8%; MiniCPM5-1B scored 40.4%. GLM-5 wins this benchmark. - Source ↗
SuperGPQA
KnowledgeA 66.8%B 23.1%Winner: GLM-5Δ 43.7SuperGPQA: GLM-5 scored 66.8%; MiniCPM5-1B scored 23.1%. GLM-5 wins this benchmark. - Source ↗
MMLU-Pro
KnowledgeA 85.7%B 48.9%Winner: GLM-5Δ 36.9MMLU-Pro: GLM-5 scored 85.7%; MiniCPM5-1B scored 48.9%. GLM-5 wins this benchmark. - Source ↗
IFEval
Inst. FollowingA 92.6%B 80.4%Winner: GLM-5Δ 12.2IFEval: GLM-5 scored 92.6%; MiniCPM5-1B scored 80.4%. 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 | MiniCPM5-1B | Comparison |
|---|---|---|---|
| Input / output priceUSD per 1M tokens | GLM-5$1 input / $3.2 output | MiniCPM5-1BNot available | A complete price comparison is not available. |
| Generation speedtokens per second | GLM-574 tok/s | MiniCPM5-1BNot available | A complete speed comparison is not available. |
| First-answer latencyseconds to first token | GLM-51.64 s | MiniCPM5-1BNot available | A complete latency comparison is not available. |
| Context windowmaximum listed tokens | GLM-5200K | MiniCPM5-1B131K | GLM-5 lists the larger context window. |
Benchmark Deep Dive
Agentic14 benchmarks
| Benchmark | GLM-5 | MiniCPM5-1B | 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% | — | Not comparable |
| CyberGymSource | 43.2% | — | Not comparable |
| APEX-Agents-AASource | 14.5% | — | Not comparable |
| Gert LabsSource | 50.99% | — | Not comparable |
| BFCL v4Source | — | 25.1% | Not comparable |
Coding10 benchmarks
| Benchmark | GLM-5 | MiniCPM5-1B | 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 |
| Terminal-Bench HardSource | 43.2% | — | Not comparable |
| AA-SciCodeSource | 46.2% | — | Not comparable |
| LiveCodeBench ProSource | — | 22.7% | Not comparable |
| LiveCodeBench v6Source | — | 33.5% | Not comparable |
Reasoning5 benchmarks
KnowledgeGLM-5 wins13 benchmarks
| Benchmark | GLM-5 | MiniCPM5-1B | Result |
|---|---|---|---|
| GPQASource | 86% | — | Not comparable |
| GPQA-DSource | 86.0% | 26.3% | GLM-5 leads |
| SuperGPQASource | 66.8% | 23.1% | GLM-5 leads |
| MMLU-ProSource | 85.7% | 48.9% | GLM-5 leads |
| MMLU-Pro (Arcee)Source | 85.8% | — | Not comparable |
| HLESource | 50.4% | — | Not comparable |
| Artificial Analysis Intelligence IndexSource | 39.5% | — | Not comparable |
| AA-GPQA DiamondSource | 82.0% | — | Not comparable |
| AA-HLESource | 27.2% | — | Not comparable |
| AA-Omniscience IndexSource | 2.0% | — | Not comparable |
| AA-Omniscience AccuracySource | 26.9% | — | Not comparable |
| AA-Omniscience Hallucination RateSource | 34.0% | — | Not comparable |
| MMLU-ReduxSource | — | 70.1% | Not comparable |
MathGLM-5 wins10 benchmarks
| Benchmark | GLM-5 | MiniCPM5-1B | Result |
|---|---|---|---|
| AIME26Source | 95.8% | 40.4% | GLM-5 leads |
| 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% | 25.8% | GLM-5 leads |
| MMAnswerBenchSource | 82.5% | — | Not comparable |
| FrontierMath v2 (Tiers 1-3)Source | 16.434% | — | Not comparable |
| FrontierMath v2 (Tier 4)Source | 2.100% | — | Not comparable |
| AIME 2025Source | — | 40.4% | Not comparable |
| MATH-500Source | — | 91.6% | Not comparable |
Multilingual2 benchmarks
Multimodal1 benchmarks
| Benchmark | GLM-5 | MiniCPM5-1B | Result |
|---|---|---|---|
| Design Arena WebsiteSource | 1282 | — | Not comparable |
Frequently Asked Questions (4)
Which is better, GLM-5 or MiniCPM5-1B?
GLM-5 is ahead on BenchLM's BenchAlign leaderboard, 65.98 to 36. The biggest single separator in this matchup is HMMT Feb 2026, where the scores are 86.4% and 25.8%.
Which is better for knowledge tasks, GLM-5 or MiniCPM5-1B?
GLM-5 has the edge for knowledge tasks in this comparison, averaging 66.6 versus 44.1. Inside this category, GPQA-D is the benchmark that creates the most daylight between them.
Which is better for math, GLM-5 or MiniCPM5-1B?
GLM-5 has the edge for math in this comparison, averaging 56.3 versus 33.1. Inside this category, HMMT Feb 2026 is the benchmark that creates the most daylight between them.
Which is better for instruction following, GLM-5 or MiniCPM5-1B?
GLM-5 has the edge for instruction following in this comparison, averaging 92.6 versus 58.5. Inside this category, IFEval is the benchmark that creates the most daylight between them.
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