Model comparison
GLM-4.7 vs Llama 3.1 405B
Head-to-head evidence from 12 shared benchmark results across 5 categories. Overall scores shown here use BenchLM's provisional ranking lane.
Verified leaderboard positions: GLM-4.7 #32; Llama 3.1 405B unranked
Evidence parity. GLM-4.7 and Llama 3.1 405B share 12 comparable benchmark results. 0 of 8 categories are comparable. 19 results are unique to GLM-4.7; 0 to Llama 3.1 405B.
Updated July 14, 2026- Shared results
- 12
- GLM-4.7 only
- 19
- Llama 3.1 405B only
- 0
- Comparable categories
- 0 / 8
Benchmark data for GLM-4.7 and Llama 3.1 405B is coming soon on BenchLM.
Confidence note. This is a partial-evidence comparison with 12 shared benchmark results across 5 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 has partial data for these models, but not enough overlapping benchmark coverage to produce a fair score-level comparison yet.
GLM-4.7 has the larger context window at 200K, compared with 128K for Llama 3.1 405B.
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 | Llama 3.1 405B |
|---|---|---|---|
| Agentic | GLM-4.745.7 | MarginNo overlap | Llama 3.1 405BNot measured |
| Coding | GLM-4.773.8 | MarginNo overlap | Llama 3.1 405BNot measured |
| Knowledge | GLM-4.752.1 | MarginNo overlap | Llama 3.1 405BNot measured |
| Math | GLM-4.71.8 | MarginNo overlap | Llama 3.1 405BNot measured |
Operational comparison
Runtime and commercial metrics are compared only when both models have a complete sourced value.
| Metric | GLM-4.7 | Llama 3.1 405B | Comparison |
|---|---|---|---|
| Input / output priceUSD per 1M tokens | GLM-4.7$0 input / $0 output | Llama 3.1 405B$0 input / $0 output | Listed prices are equal. |
| Generation speedtokens per second | GLM-4.782 tok/s | Llama 3.1 405B29 tok/s | GLM-4.7 has the higher measured throughput. |
| First-answer latencyseconds to first token | GLM-4.71.10 s | Llama 3.1 405B2.19 s | GLM-4.7 reaches the first token sooner. |
| Context windowmaximum listed tokens | GLM-4.7200K | Llama 3.1 405B128K | GLM-4.7 lists the larger context window. |
Benchmark Deep Dive
Agentic8 benchmarks
| Benchmark | GLM-4.7 | Llama 3.1 405B | 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 |
| Tau2-TelecomSource | 95.9% | 19% | GLM-4.7 leads |
| Gert LabsSource | 39.95% | — | Not comparable |
| GDPval-AASource | 33.3% | — | Not comparable |
| GDPval-AASource | 1165 | — | Not comparable |
Coding7 benchmarks
| Benchmark | GLM-4.7 | Llama 3.1 405B | Result |
|---|---|---|---|
| SWE-bench VerifiedSource | 73.8% | — | Not comparable |
| LiveCodeBenchSource | 84.9% | — | Not comparable |
| SWE-RebenchSource | 58.7% | — | Not comparable |
| AA Coding IndexSource | 45.3% | — | Not comparable |
| Terminal-Bench HardSource | 31.8% | 6.8% | GLM-4.7 leads |
| AA-SciCodeSource | 45.1% | 29.9% | GLM-4.7 leads |
| AA LiveCodeBenchSource | 89.4% | — | Not comparable |
Reasoning2 benchmarks
Knowledge9 benchmarks
| Benchmark | GLM-4.7 | Llama 3.1 405B | Result |
|---|---|---|---|
| GPQASource | 85.7% | — | Not comparable |
| MMLU-ProSource | 84.3% | — | Not comparable |
| HLESource | 24.8% | — | Not comparable |
| Artificial Analysis Intelligence IndexSource | 33.7% | 8.5% | GLM-4.7 leads |
| AA-GPQA DiamondSource | 85.9% | 51.5% | GLM-4.7 leads |
| AA-HLESource | 25.1% | 4.2% | GLM-4.7 leads |
| AA-Omniscience IndexSource | -34.6% | -17.3% | Llama 3.1 405B leads |
| AA-Omniscience AccuracySource | 29.3% | 22.3% | GLM-4.7 leads |
| AA-Omniscience Hallucination RateSource | 90.3% | 51.0% | Llama 3.1 405B leads |
Math3 benchmarks
Multimodal1 benchmarks
| Benchmark | GLM-4.7 | Llama 3.1 405B | Result |
|---|---|---|---|
| Design Arena WebsiteSource | 1260 | — | Not comparable |
Inst. Following1 benchmarks
| Benchmark | GLM-4.7 | Llama 3.1 405B | Result |
|---|---|---|---|
| AA-IFBenchSource | 67.9% | 39.0% | GLM-4.7 leads |
Frequently Asked Questions (3)
Can I compare GLM-4.7 and Llama 3.1 405B 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 3.1 405B today?
GLM-4.7: $0.00 input / $0.00 output per 1M tokens Llama 3.1 405B: $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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