Model comparison
GLM-5.1 vs Llama 3.1-70B
GLM-5.1 is the stronger model overall, scoring 47.6 to 29.4 on the Noometry Index. Llama 3.1-70B costs 5.4× less per token, which makes it the better buy when GLM-5.1's lead doesn't matter for your workload.
Last verified . 22 shared benchmarks.
MetaSummary
- They share 22 benchmarks with published results for both. GLM-5.1 scores higher in 8 categories and Llama 3.1-70B in 1 category; 8 gaps are clear of the uncertainty.
- The widest gap is in math, where GLM-5.1 leads 49.0 to 12.7.
- The biggest single-benchmark swing is OTIS Mock AIME 2024-2025: 93.3% for GLM-5.1 and 3.6% for Llama 3.1-70B.
- Llama 3.1-70B is cheaper at $0.40 / $0.40 per million input/output tokens, against $1.40 / $4.40 for GLM-5.1.
- GLM-5.1 accepts more context: 200K tokens versus 128K.
Side by side
| GLM-5.1 | Llama 3.1-70B | |
|---|---|---|
| Provider | Z.ai (Zhipu) | Meta |
| Noometry Index | 47.6 | 29.4 |
| Released | 2026-04-07 | 2024-07-23 |
| Weights | Open | Open |
| Context window | 200K | 128K |
| Max output | 131K | 4K |
| Input $ / M tokens | $1.40 | $0.40 |
| Output $ / M tokens | $4.40 | $0.40 |
| Results tracked | 41 | 35 |
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Category by category
Coding GLM-5.1 leads
GLM-5.1: 48.5 (#52), Llama 3.1-70B: 29.8 (#292)
| Benchmark | GLM-5.1 | Llama 3.1-70B |
|---|---|---|
| WeirdML | 57.1% | 9% |
| LMArena Coding | 1486 | 1260 |
| SWE-bench Verified | 74.2% | — |
| LMArena WebDev | 1509 | — |
| SciCode | 43.8% | — |
| BigCodeBench Instruct | — | 46.1% |
| BigCodeBench Complete | — | 54.8% |
| ALE-Bench | 887.1 | — |
Agentic & Tool Use Too close to call
GLM-5.1: 24.8 (#106), Llama 3.1-70B: 24.9 (#104)
| Benchmark | GLM-5.1 | Llama 3.1-70B |
|---|---|---|
| APEX-Agents | 40.9% | — |
| TheAgentCompany | — | 6.9% |
| BALROG | — | 27.9% |
| ExploitBench | 18.1% | — |
| GBAEval | 0% | — |
| Vending-Bench 2 | 5,634 | — |
Reasoning GLM-5.1 leads
GLM-5.1: 38.2 (#57), Llama 3.1-70B: 20.8 (#215)
| Benchmark | GLM-5.1 | Llama 3.1-70B |
|---|---|---|
| LMArena Hard Prompts | 1472 | 1241 |
| Epoch Capabilities Index | 149.86 | 125.91 |
| SimpleBench | 55.1% | — |
| NYT Connections (extended) | 77.7% | — |
| CritPt | 4.6% | — |
| Chess Puzzles | 19% | — |
| Thematic Generalization | 69.8% | — |
| DTBench | — | 60% |
| LMCA | — | 14.8% |
Math GLM-5.1 leads
GLM-5.1: 49.0 (#48), Llama 3.1-70B: 12.7 (#298)
| Benchmark | GLM-5.1 | Llama 3.1-70B |
|---|---|---|
| OTIS Mock AIME 2024-2025 | 93.3% | 3.6% |
| LMArena Math | 1472 | 1252 |
| FrontierMath (Tiers 1-3) | 36.8% | — |
| MathArena Final-Answer Competitions | 67.1% | — |
| ProofBench | 22.2% | — |
| Omni-MATH | — | 21% |
| MATH Level 5 | — | 36.7% |
| FrontierMath (Feb 2025 set) | 33.4% | — |
| FrontierMath Tier 4 (v1) | 12.5% | — |
Knowledge GLM-5.1 leads
GLM-5.1: 55.0 (#45), Llama 3.1-70B: 24.1 (#264)
| Benchmark | GLM-5.1 | Llama 3.1-70B |
|---|---|---|
| GPQA Diamond | 89.9% | 44.2% |
| LMArena Expert | 1477 | 1209 |
| SimpleQA Verified | 34% | — |
| MMLU-Pro | — | 65.3% |
| GPQA (HELM) | — | 42.6% |
| MMLU | — | 80.1% |
Multilingual GLM-5.1 leads
GLM-5.1: 55.2 (#34), Llama 3.1-70B: 38.9 (#220)
| Benchmark | GLM-5.1 | Llama 3.1-70B |
|---|---|---|
| LMArena Non-English | 1448 | 1219 |
| LMArena Chinese | 1516 | 1215 |
| LMArena French | 1478 | 1260 |
| LMArena German | 1466 | 1222 |
| LMArena Japanese | 1436 | 1132 |
| LMArena Korean | 1423 | 1140 |
| LMArena Russian | 1453 | 1234 |
| LMArena Spanish | 1467 | 1253 |
Instruction Following GLM-5.1 leads
GLM-5.1: 76.5 (#41), Llama 3.1-70B: 65.5 (#218)
| Benchmark | GLM-5.1 | Llama 3.1-70B |
|---|---|---|
| LMArena Instruction Following | 1451 | 1231 |
| IFEval | — | 82.1% |
Long Context GLM-5.1 leads
GLM-5.1: 44.9 (#51), Llama 3.1-70B: 37.6 (#208)
| Benchmark | GLM-5.1 | Llama 3.1-70B |
|---|---|---|
| LMArena Longer Query | 1467 | 1241 |
Writing & Preference GLM-5.1 leads
GLM-5.1: 67.2 (#29), Llama 3.1-70B: 35.6 (#262)
| Benchmark | GLM-5.1 | Llama 3.1-70B |
|---|---|---|
| LMArena Text | 1462 | 1261 |
| LMArena Creative Writing | 1454 | 1232 |
| EQ-Bench Creative Writing | 1592 | 784 |
| LMArena Multi-Turn | 1473 | 1256 |
| WildBench | — | 75.8% |
Frequently asked questions
Is GLM-5.1 better than Llama 3.1-70B?
GLM-5.1 is the stronger model overall, scoring 47.6 to 29.4 on the Noometry Index. Llama 3.1-70B costs 5.4× less per token, which makes it the better buy when GLM-5.1's lead doesn't matter for your workload.
Which is cheaper, GLM-5.1 or Llama 3.1-70B?
Llama 3.1-70B is cheaper. It lists at $0.40 per million input tokens and $0.40 per million output tokens; GLM-5.1 lists at $1.40 and $4.40.
Is GLM-5.1 or Llama 3.1-70B better for coding?
GLM-5.1 scores higher on coding benchmarks: 48.5 versus 29.8 in the Noometry coding category.
Which has the bigger context window?
GLM-5.1 does, with 200K tokens against 128K.
How many benchmarks do GLM-5.1 and Llama 3.1-70B share?
22 benchmarks have published results for both models. GLM-5.1 has 41 scored results on Noometry and Llama 3.1-70B has 35.