Model comparison
Gemma 4 E4B vs GLM-5
Head-to-head evidence from 14 shared benchmark results across 5 categories. Overall scores shown here use BenchLM's provisional ranking lane.
Verified leaderboard positions: Gemma 4 E4B unranked; GLM-5 #16
Evidence parity. Gemma 4 E4B and GLM-5 share 14 comparable benchmark results. 1 of 8 categories are comparable. 1 result is unique to Gemma 4 E4B; 36 to GLM-5.
Updated July 14, 2026- Shared results
- 14
- Gemma 4 E4B only
- 1
- GLM-5 only
- 36
- Comparable categories
- 1 / 8
Pick GLM-5 if you want the stronger benchmark profile. Gemma 4 E4B only becomes the better choice if knowledge is the priority or you want the cheaper token bill.
Confidence note. This is a partial-evidence comparison with 14 shared benchmark results across 5 evidence categories; 1 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 provisional aggregate, 63 to 39. The gap is large enough that you do not need to squint at the spreadsheet to see the difference.
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 Gemma 4 E4B. That is roughly Infinityx on output cost alone. Gemma 4 E4B 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 128K for Gemma 4 E4B.
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 | Gemma 4 E4B | Δ | GLM-5 |
|---|---|---|---|
| Knowledge | Gemma 4 E4B67.4 | Margin← 0.8 | GLM-566.6 |
| Agentic | Gemma 4 E4BNot measured | MarginNo overlap | GLM-556.2 |
| Coding | Gemma 4 E4BNot measured | MarginNo overlap | GLM-563.3 |
| Reasoning | Gemma 4 E4BNot measured | MarginNo overlap | GLM-560.8 |
| Math | Gemma 4 E4BNot measured | MarginNo overlap | GLM-556.3 |
| Multilingual | Gemma 4 E4BNot measured | MarginNo overlap | GLM-583.1 |
| Inst. Following | Gemma 4 E4BNot measured | MarginNo overlap | GLM-592.6 |
Decisive benchmark drivers
The largest measured benchmark gaps in this matchup, with exact reported values.
More
Operational comparison
Runtime and commercial metrics are compared only when both models have a complete sourced value.
| Metric | Gemma 4 E4B | GLM-5 | Comparison |
|---|---|---|---|
| Input / output priceUSD per 1M tokens | Gemma 4 E4B$0 input / $0 output | GLM-5$1 input / $3.2 output | Gemma 4 E4B has the lower combined listed price. |
| Generation speedtokens per second | Gemma 4 E4BNot available | GLM-574 tok/s | A complete speed comparison is not available. |
| First-answer latencyseconds to first token | Gemma 4 E4BNot available | GLM-51.64 s | A complete latency comparison is not available. |
| Context windowmaximum listed tokens | Gemma 4 E4B128K | GLM-5200K | GLM-5 lists the larger context window. |
Benchmark Deep Dive
Agentic13 benchmarks
| Benchmark | Gemma 4 E4B | GLM-5 | Result |
|---|---|---|---|
| Tau2-TelecomSource | 20.8% | 98.2% | GLM-5 leads |
| Terminal-Bench 2.0Source | — | 56.2% | Not comparable |
| Claw-EvalSource | — | 57.7% | Not comparable |
| QwenClawBenchSource | — | 54.1% | Not comparable |
| TAU3-BenchSource | — | 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 |
| Gert LabsSource | — | 50.99% | Not comparable |
Coding8 benchmarks
| Benchmark | Gemma 4 E4B | GLM-5 | Result |
|---|---|---|---|
| Terminal-Bench HardSource | 8.3% | 43.2% | GLM-5 leads |
| AA-SciCodeSource | 24.4% | 46.2% | GLM-5 leads |
| 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 |
Reasoning4 benchmarks
KnowledgeGemma 4 E4B wins12 benchmarks
| Benchmark | Gemma 4 E4B | GLM-5 | Result |
|---|---|---|---|
| GPQASource | 58.6% | 86% | GLM-5 leads |
| MMLU-ProSource | 69.4% | 85.7% | GLM-5 leads |
| Artificial Analysis Intelligence IndexSource | 12.5% | 39.5% | GLM-5 leads |
| AA-GPQA DiamondSource | 57.6% | 82.0% | GLM-5 leads |
| AA-HLESource | 3.7% | 27.2% | GLM-5 leads |
| AA-Omniscience IndexSource | -20.0% | 2.0% | GLM-5 leads |
| AA-Omniscience AccuracySource | 8.6% | 26.9% | GLM-5 leads |
| AA-Omniscience Hallucination RateSource | 31.3% | 34.0% | Gemma 4 E4B leads |
| GPQA-DSource | — | 86.0% | Not comparable |
| SuperGPQASource | — | 66.8% | Not comparable |
| MMLU-Pro (Arcee)Source | — | 85.8% | Not comparable |
| HLESource | — | 50.4% | Not comparable |
Math8 benchmarks
| Benchmark | Gemma 4 E4B | GLM-5 | 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% | Not comparable |
| FrontierMath v2 (Tier 4)Source | — | 2.100% | Not comparable |
Multilingual2 benchmarks
Multimodal2 benchmarks
Frequently Asked Questions (2)
Which is better, Gemma 4 E4B or GLM-5?
GLM-5 is ahead on BenchLM's provisional leaderboard, 63 to 39. The biggest single separator in this matchup is GPQA, where the scores are 58.6% and 86%.
Which is better for knowledge tasks, Gemma 4 E4B or GLM-5?
Gemma 4 E4B has the edge for knowledge tasks in this comparison, averaging 67.4 versus 66.6. Inside this category, GPQA is the benchmark that creates the most daylight between them.
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