Model comparison
GLM-5 vs Llama 4 Maverick
Head-to-head evidence from 13 shared benchmark results across 7 categories. Overall scores shown here use the public BenchAlign v5 ranking lane.
Public leaderboard positions: GLM-5 #28 (Supported); Llama 4 Maverick #191 (Supported). Intervals and evidence labels describe ranking uncertainty, not a guarantee for a specific workload.
Evidence parity. GLM-5 and Llama 4 Maverick share 13 comparable benchmark results. 1 of 8 categories are comparable. 36 results are unique to GLM-5; 5 to Llama 4 Maverick.
Updated July 22, 2026- Shared results
- 13
- GLM-5 only
- 36
- Llama 4 Maverick only
- 5
- Comparable categories
- 1 / 8
Pick GLM-5 if you want the stronger benchmark profile. Llama 4 Maverick 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 13 shared benchmark results across 7 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 BenchAlign aggregate, 66.06 to 23.49. 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 0.7. The single biggest benchmark swing on the page is FrontierMath v2 (Tiers 1-3), 16.434% to 0.690%.
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 Llama 4 Maverick. That is roughly Infinityx on output cost alone. Llama 4 Maverick 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 | Llama 4 Maverick |
|---|---|---|---|
| Math | GLM-556.3 | Margin← 55.6 | Llama 4 Maverick0.7 |
| Agentic | GLM-556.2 | MarginNo overlap | Llama 4 MaverickNot measured |
| Coding | GLM-566.3 | MarginNo overlap | Llama 4 MaverickNot measured |
| Reasoning | GLM-560.8 | MarginNo overlap | Llama 4 MaverickNot measured |
| Knowledge | GLM-566.4 | MarginNo overlap | Llama 4 MaverickNot measured |
| Multilingual | GLM-583.1 | MarginNo overlap | Llama 4 MaverickNot measured |
| Inst. Following | GLM-592.6 | MarginNo overlap | Llama 4 MaverickNot measured |
Decisive benchmark drivers
The largest measured benchmark gaps in this matchup, with exact reported values.
More
- Source ↗
FrontierMath v2 (Tiers 1-3)
MathA 16.434%B 0.690%Winner: GLM-5Δ 15.7FrontierMath v2 (Tiers 1-3): GLM-5 scored 16.434%; Llama 4 Maverick scored 0.690%. 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 | Llama 4 Maverick | Comparison |
|---|---|---|---|
| Input / output priceUSD per 1M tokens | GLM-5$1 input / $3.2 output | Llama 4 Maverick$0 input / $0 output | Llama 4 Maverick has the lower combined listed price. |
| Generation speedtokens per second | GLM-574 tok/s | Llama 4 Maverick121 tok/s | Llama 4 Maverick has the higher measured throughput. |
| First-answer latencyseconds to first token | GLM-51.64 s | Llama 4 Maverick0.95 s | Llama 4 Maverick reaches the first token sooner. |
| Context windowmaximum listed tokens | GLM-5200K | Llama 4 Maverick1M | Llama 4 Maverick lists the larger context window. |
Benchmark Deep Dive
Agentic16 benchmarks
| Benchmark | GLM-5 | Llama 4 Maverick | 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% | 17.8% | GLM-5 leads |
| CyberGymSource | 43.2% | — | Not comparable |
| APEX-Agents-AASource | 14.5% | — | Not comparable |
| Gert LabsSource | 50.99% | — | Not comparable |
| AA Agentic IndexSource | — | 1.3% | Not comparable |
| GDPval-AASource | — | 0.0% | Not comparable |
| GDPval-AASource | — | -16 | Not comparable |
Coding8 benchmarks
| Benchmark | GLM-5 | Llama 4 Maverick | 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% | 33.1% | GLM-5 leads |
| AA Coding IndexSource | — | 16.3% | Not comparable |
Reasoning4 benchmarks
Knowledge12 benchmarks
| Benchmark | GLM-5 | Llama 4 Maverick | Result |
|---|---|---|---|
| GPQASource | 86% | — | Not comparable |
| 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% | 14.3% | GLM-5 leads |
| AA-GPQA DiamondSource | 82.0% | 67.1% | GLM-5 leads |
| AA-HLESource | 27.2% | 4.8% | GLM-5 leads |
| AA-Omniscience IndexSource | 2.0% | -41.8% | GLM-5 leads |
| AA-Omniscience AccuracySource | 26.9% | 24.3% | GLM-5 leads |
| AA-Omniscience Hallucination RateSource | 34.0% | 87.3% | GLM-5 leads |
MathGLM-5 wins8 benchmarks
| Benchmark | GLM-5 | Llama 4 Maverick | 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% | 0.690% | GLM-5 leads |
| FrontierMath v2 (Tier 4)Source | 2.100% | — | Not comparable |
Multilingual2 benchmarks
Multimodal2 benchmarks
Frequently Asked Questions (2)
Which is better, GLM-5 or Llama 4 Maverick?
GLM-5 is ahead on BenchLM's BenchAlign leaderboard, 66.06 to 23.49. The biggest single separator in this matchup is FrontierMath v2 (Tiers 1-3), where the scores are 16.434% and 0.690%.
Which is better for math, GLM-5 or Llama 4 Maverick?
GLM-5 has the edge for math in this comparison, averaging 56.3 versus 0.7. Inside this category, FrontierMath v2 (Tiers 1-3) is the benchmark that creates the most daylight between them.
Self-host vs API cost
Estimates at 50,000 req/day · 1000 tokens/req average.
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