Model comparison
GLM-4.7 vs GLM-5
Head-to-head evidence from 21 shared benchmark results across 7 categories. Overall scores shown here use the public BenchAlign v5 ranking lane.
Public leaderboard positions: GLM-4.7 #42 (Supported); GLM-5 #28 (Supported). Intervals and evidence labels describe ranking uncertainty, not a guarantee for a specific workload.
Evidence parity. GLM-4.7 and GLM-5 share 21 comparable benchmark results. 4 of 8 categories are comparable. 9 results are unique to GLM-4.7; 28 to GLM-5.
Updated July 20, 2026- Shared results
- 21
- GLM-4.7 only
- 9
- GLM-5 only
- 28
- Comparable categories
- 4 / 8
Pick GLM-5 if you want the stronger benchmark profile. GLM-4.7 only becomes the better choice if coding is the priority or you want the cheaper token bill.
Confidence note. This is a partial-evidence comparison with 21 shared benchmark results across 7 evidence categories; 4 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, 66.06 to 61.16. 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 1.8. The single biggest benchmark swing on the page is HLE, 24.8% to 50.4%. GLM-4.7 does hit back in coding, so the answer changes if that is the part of the workload you care about most.
GLM-5 is also the more expensive model on tokens at $1.00 input / $3.20 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 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.
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 | GLM-5 |
|---|---|---|---|
| Math | GLM-4.71.8 | Margin→ 54.5 | GLM-556.3 |
| Knowledge | GLM-4.751.8 | Margin→ 14.6 | GLM-566.4 |
| Agentic | GLM-4.745.7 | Margin→ 10.5 | GLM-556.2 |
| Coding | GLM-4.775.4 | Margin← 9.1 | GLM-566.3 |
| Reasoning | GLM-4.7Not measured | MarginNo overlap | GLM-560.8 |
| Multilingual | GLM-4.7Not measured | MarginNo overlap | GLM-583.1 |
| Inst. Following | GLM-4.7Not measured | MarginNo overlap | GLM-592.6 |
Decisive benchmark drivers
The largest measured benchmark gaps in this matchup, with exact reported values.
More
- Source ↗
HLE
KnowledgeA 24.8%B 50.4%Winner: GLM-5Δ 25.6HLE: GLM-4.7 scored 24.8%; GLM-5 scored 50.4%. GLM-5 wins this benchmark. - Source ↗
Terminal-Bench 2.0
AgenticA 41%B 56.2%Winner: GLM-5Δ 15.2Terminal-Bench 2.0: GLM-4.7 scored 41%; GLM-5 scored 56.2%. GLM-5 wins this benchmark. - Source ↗
FrontierMath v2 (Tiers 1-3)
MathA 2.439%B 16.434%Winner: GLM-5Δ 14FrontierMath v2 (Tiers 1-3): GLM-4.7 scored 2.439%; GLM-5 scored 16.434%. GLM-5 wins this benchmark. - Source ↗
SWE-Rebench
CodingA 58.7%B 62.8%Winner: GLM-5Δ 4.1SWE-Rebench: GLM-4.7 scored 58.7%; GLM-5 scored 62.8%. GLM-5 wins this benchmark. - Source ↗
SWE-bench Verified
CodingA 73.8%B 77.8%Winner: GLM-5Δ 4SWE-bench Verified: GLM-4.7 scored 73.8%; GLM-5 scored 77.8%. GLM-5 wins this benchmark.
Operational comparison
Runtime and commercial metrics are compared only when both models have a complete sourced value.
| Metric | GLM-4.7 | GLM-5 | Comparison |
|---|---|---|---|
| Input / output priceUSD per 1M tokens | GLM-4.7$0 input / $0 output | GLM-5$1 input / $3.2 output | GLM-4.7 has the lower combined listed price. |
| Generation speedtokens per second | GLM-4.782 tok/s | GLM-574 tok/s | GLM-4.7 has the higher measured throughput. |
| First-answer latencyseconds to first token | GLM-4.71.10 s | GLM-51.64 s | GLM-4.7 reaches the first token sooner. |
| Context windowmaximum listed tokens | GLM-4.7200K | GLM-5200K | Listed context windows are equal. |
Benchmark Deep Dive
AgenticGLM-5 wins18 benchmarks
| Benchmark | GLM-4.7 | GLM-5 | Result |
|---|---|---|---|
| Terminal-Bench 2.0Source | 41% | 56.2% | GLM-5 leads |
| BrowseCompSource | 52% | — | Not comparable |
| VITA-BenchSource | 15.5% | — | Not comparable |
| AA Agentic IndexSource | 25.4% | — | Not comparable |
| τ²-bench resultsSource | 95.9% | 98.2% | GLM-5 leads |
| Gert LabsSource | 39.95% | 50.99% | GLM-5 leads |
| GDPval-AASource | 33.3% | — | Not comparable |
| GDPval-AASource | 1165 | — | 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 |
| CyberGymSource | — | 43.2% | Not comparable |
| APEX-Agents-AASource | — | 14.5% | Not comparable |
CodingGLM-4.7 wins10 benchmarks
| Benchmark | GLM-4.7 | GLM-5 | Result |
|---|---|---|---|
| SWE-bench VerifiedSource | 73.8% | 77.8% | GLM-5 leads |
| LiveCodeBenchSource | 84.9% | — | Not comparable |
| SWE-RebenchSource | 58.7% | 62.8% | GLM-5 leads |
| AA Coding IndexSource | 45.3% | — | Not comparable |
| AA-SciCodeSource | 45.1% | 46.2% | GLM-5 leads |
| AA LiveCodeBenchSource | 89.4% | — | Not comparable |
| SWE-bench Verified*Source | — | 72.8% | Not comparable |
| SWE-bench ProSource | — | 55.1% | Not comparable |
| SWE MultilingualSource | — | 73.3% | Not comparable |
| React Native EvalsSource | — | 74.8% | Not comparable |
Reasoning4 benchmarks
KnowledgeGLM-5 wins12 benchmarks
| Benchmark | GLM-4.7 | GLM-5 | Result |
|---|---|---|---|
| GPQASource | 85.7% | 86% | GLM-5 leads |
| MMLU-ProSource | 84.3% | 85.7% | GLM-5 leads |
| HLESource | 24.8% | 50.4% | GLM-5 leads |
| Artificial Analysis Intelligence IndexSource | 33.7% | 39.5% | GLM-5 leads |
| AA-GPQA DiamondSource | 85.9% | 82.0% | GLM-4.7 leads |
| AA-HLESource | 25.1% | 27.2% | GLM-5 leads |
| AA-Omniscience IndexSource | -34.6% | 2.0% | GLM-5 leads |
| AA-Omniscience AccuracySource | 29.3% | 26.9% | GLM-4.7 leads |
| AA-Omniscience Hallucination RateSource | 90.3% | 34.0% | GLM-5 leads |
| GPQA-DSource | — | 86.0% | Not comparable |
| SuperGPQASource | — | 66.8% | Not comparable |
| MMLU-Pro (Arcee)Source | — | 85.8% | Not comparable |
MathGLM-5 wins9 benchmarks
| Benchmark | GLM-4.7 | GLM-5 | Result |
|---|---|---|---|
| AIME 2025Source | 95.7% | — | Not comparable |
| FrontierMath v2 (Tiers 1-3)Source | 2.439% | 16.434% | GLM-5 leads |
| FrontierMath v2 (Tier 4)Source | 0.000% | 2.100% | GLM-5 leads |
| 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 |
Multilingual2 benchmarks
Multimodal1 benchmarks
| Benchmark | GLM-4.7 | GLM-5 | Result |
|---|---|---|---|
| Design Arena WebsiteSource | 1258 | 1280 | GLM-5 leads |
Frequently Asked Questions (5)
Which is better, GLM-4.7 or GLM-5?
GLM-5 is ahead on BenchLM's BenchAlign leaderboard, 66.06 to 61.16. The biggest single separator in this matchup is HLE, where the scores are 24.8% and 50.4%.
Which is better for knowledge tasks, GLM-4.7 or GLM-5?
GLM-5 has the edge for knowledge tasks in this comparison, averaging 66.4 versus 51.8. Inside this category, AA-Omniscience Hallucination Rate is the benchmark that creates the most daylight between them.
Which is better for coding, GLM-4.7 or GLM-5?
GLM-4.7 has the edge for coding in this comparison, averaging 75.4 versus 66.3. Inside this category, SWE-Rebench is the benchmark that creates the most daylight between them.
Which is better for math, GLM-4.7 or GLM-5?
GLM-5 has the edge for math in this comparison, averaging 56.3 versus 1.8. Inside this category, FrontierMath v2 (Tiers 1-3) is the benchmark that creates the most daylight between them.
Which is better for agentic tasks, GLM-4.7 or GLM-5?
GLM-5 has the edge for agentic tasks in this comparison, averaging 56.2 versus 45.7. Inside this category, Terminal-Bench 2.0 is the benchmark that creates the most daylight between them.
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