Model comparison
GLM-4.7 vs Llama 4 Behemoth
Head-to-head evidence from 0 shared benchmark results across 0 categories. Overall scores shown here use the public BenchAlign v5 ranking lane.
Public leaderboard positions: GLM-4.7 #42 (Supported); Llama 4 Behemoth #176 (Estimated). Intervals and evidence labels describe ranking uncertainty, not a guarantee for a specific workload.
Evidence parity. GLM-4.7 and Llama 4 Behemoth share 0 comparable benchmark results. 0 of 8 categories are comparable. 30 results are unique to GLM-4.7; 0 to Llama 4 Behemoth.
Updated July 22, 2026- Shared results
- 0
- GLM-4.7 only
- 30
- Llama 4 Behemoth only
- 0
- Comparable categories
- 0 / 8
Benchmark data for GLM-4.7 and Llama 4 Behemoth is coming soon on BenchLM.
Confidence note. This is a partial-evidence comparison with 0 shared benchmark results across 0 evidence categories; 0 of 8 categories currently have scoreable aggregates for both models. Treat the verdict as directional until coverage is more balanced.
Why this result
BenchLM does not have sourced benchmark coverage for Llama 4 Behemoth yet. This comparison is currently limited to metadata such as context window, reasoning mode, and pricing where available.
GLM-4.7 has the larger context window at 200K, compared with 32K for Llama 4 Behemoth.
Operational comparison
Runtime and commercial metrics are compared only when both models have a complete sourced value.
| Metric | GLM-4.7 | Llama 4 Behemoth | Comparison |
|---|---|---|---|
| Input / output priceUSD per 1M tokens | GLM-4.7$0 input / $0 output | Llama 4 Behemoth$0 input / $0 output | Listed prices are equal. |
| Generation speedtokens per second | GLM-4.782 tok/s | Llama 4 BehemothNot available | A complete speed comparison is not available. |
| First-answer latencyseconds to first token | GLM-4.71.10 s | Llama 4 BehemothNot available | A complete latency comparison is not available. |
| Context windowmaximum listed tokens | GLM-4.7200K | Llama 4 Behemoth32K | GLM-4.7 lists the larger context window. |
Benchmark Deep Dive
Agentic8 benchmarks
| Benchmark | GLM-4.7 | Llama 4 Behemoth | Result |
|---|---|---|---|
| Terminal-Bench 2.0Source | 41% | — | Not comparable |
| BrowseCompSource | 52% | — | Not comparable |
| VITA-BenchSource | 15.5% | — | Not comparable |
| AA Agentic IndexSource | 25.4% | — | Not comparable |
| τ²-bench resultsSource | 95.9% | — | Not comparable |
| Gert LabsSource | 39.95% | — | Not comparable |
| GDPval-AASource | 33.3% | — | Not comparable |
| GDPval-AASource | 1165 | — | Not comparable |
Coding6 benchmarks
Reasoning2 benchmarks
Knowledge9 benchmarks
| Benchmark | GLM-4.7 | Llama 4 Behemoth | Result |
|---|---|---|---|
| GPQASource | 85.7% | — | Not comparable |
| MMLU-ProSource | 84.3% | — | Not comparable |
| HLESource | 24.8% | — | Not comparable |
| Artificial Analysis Intelligence IndexSource | 33.7% | — | Not comparable |
| AA-GPQA DiamondSource | 85.9% | — | Not comparable |
| AA-HLESource | 25.1% | — | Not comparable |
| AA-Omniscience IndexSource | -34.6% | — | Not comparable |
| AA-Omniscience AccuracySource | 29.3% | — | Not comparable |
| AA-Omniscience Hallucination RateSource | 90.3% | — | Not comparable |
Math3 benchmarks
Multimodal1 benchmarks
| Benchmark | GLM-4.7 | Llama 4 Behemoth | Result |
|---|---|---|---|
| Design Arena WebsiteSource | 1255 | — | Not comparable |
Inst. Following1 benchmarks
| Benchmark | GLM-4.7 | Llama 4 Behemoth | Result |
|---|---|---|---|
| AA-IFBenchSource | 67.9% | — | Not comparable |
Frequently Asked Questions (3)
Can I compare GLM-4.7 and Llama 4 Behemoth on BenchLM yet?
Not fully yet. BenchLM is tracking both models, but the sourced benchmark breakdown for this comparison is still coming soon.
Why does this comparison show “coming soon”?
BenchLM only shows category winners and benchmark-level calls when we have sourced results that can be compared fairly. For these models, the public benchmark coverage is not complete enough yet.
What data is available for GLM-4.7 and Llama 4 Behemoth today?
GLM-4.7: $0.00 input / $0.00 output per 1M tokens Llama 4 Behemoth: $0.00 input / $0.00 output per 1M tokens Both model pages still include creator, context window, reasoning mode, and other metadata while benchmark coverage fills in.
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