Model comparison
GLM-5 vs Llama 3.1-70B
GLM-5 is the stronger model overall, scoring 46.1 to 29.6 on the Noometry Index. Llama 3.1-70B costs 3.9× less per token, which makes it the better buy when GLM-5'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 scores higher in 9 categories and Llama 3.1-70B in 0 categories; 9 gaps are clear of the uncertainty.
- The widest gap is in math, where GLM-5 leads 46.4 to 13.5.
- The biggest single-benchmark swing is OTIS Mock AIME 2024-2025: 80% for GLM-5 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 / $3.20 for GLM-5.
- GLM-5 accepts more context: 205K tokens versus 128K.
Side by side
| GLM-5 | Llama 3.1-70B | |
|---|---|---|
| Provider | Z.ai (Zhipu) | Meta |
| Noometry Index | 46.1 | 29.6 |
| Released | 2026-02-11 | 2024-07-23 |
| Weights | Open | Open |
| Context window | 205K | 128K |
| Max output | 131K | 4K |
| Input $ / M tokens | $1 | $0.40 |
| Output $ / M tokens | $3.20 | $0.40 |
| Results tracked | 45 | 35 |
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Category by category
Coding GLM-5 leads
GLM-5: 49.0 (#52), Llama 3.1-70B: 30.3 (#296)
| Benchmark | GLM-5 | Llama 3.1-70B |
|---|---|---|
| WeirdML | 48.2% | 9% |
| LMArena Coding | 1461 | 1260 |
| SWE-bench Verified | 72.1% | — |
| SWE-bench Verified (bash only) | 72.8% | — |
| LMArena WebDev | 1434 | — |
| SWE-bench Multilingual | 69.7% | — |
| BigCodeBench Instruct | — | 46.1% |
| BigCodeBench Complete | — | 54.8% |
| ALE-Bench | 765.62 | — |
Agentic & Tool Use GLM-5 leads
GLM-5: 31.1 (#71), Llama 3.1-70B: 25.1 (#112)
| Benchmark | GLM-5 | Llama 3.1-70B |
|---|---|---|
| Terminal-Bench | 52.4% | — |
| TheAgentCompany | — | 6.9% |
| τ²-bench Airline | 82.5% | — |
| τ²-bench Banking | 9.8% | — |
| τ²-bench Retail | 73.7% | — |
| τ²-bench Telecom | 86.8% | — |
| BALROG | — | 27.9% |
| Vending-Bench 2 | 4,432 | — |
Reasoning GLM-5 leads
GLM-5: 27.6 (#116), Llama 3.1-70B: 21.6 (#220)
| Benchmark | GLM-5 | Llama 3.1-70B |
|---|---|---|
| LMArena Hard Prompts | 1452 | 1241 |
| Epoch Capabilities Index | 145.83 | 125.92 |
| ARC-AGI-2 | 4.9% | — |
| SimpleBench | 53.2% | — |
| Kagi LLM Benchmark | 75% | — |
| NYT Connections (extended) | 74.8% | — |
| ARC-AGI-1 | 44.7% | — |
| Chess Puzzles | 10% | — |
| DTBench | — | 60% |
| LMCA | — | 14.8% |
| ForecastBench | 61 | — |
Math GLM-5 leads
GLM-5: 46.4 (#71), Llama 3.1-70B: 13.5 (#304)
| Benchmark | GLM-5 | Llama 3.1-70B |
|---|---|---|
| OTIS Mock AIME 2024-2025 | 80% | 3.6% |
| LMArena Math | 1440 | 1252 |
| MathArena Final-Answer Competitions | 65.7% | — |
| Omni-MATH | — | 21% |
| MATH Level 5 | — | 36.7% |
| FrontierMath (Feb 2025 set) | 16.4% | — |
| FrontierMath Tier 4 (v1) | 2.1% | — |
Knowledge GLM-5 leads
GLM-5: 52.3 (#64), Llama 3.1-70B: 24.2 (#269)
| Benchmark | GLM-5 | Llama 3.1-70B |
|---|---|---|
| GPQA Diamond | 87.8% | 44.2% |
| LMArena Expert | 1454 | 1209 |
| MMLU-Pro | — | 65.3% |
| Vectara Hallucination Rate | 10.1% | — |
| GPQA (HELM) | — | 42.6% |
| MMLU | — | 80.1% |
Multilingual GLM-5 leads
GLM-5: 53.7 (#58), Llama 3.1-70B: 38.8 (#225)
| Benchmark | GLM-5 | Llama 3.1-70B |
|---|---|---|
| LMArena Non-English | 1430 | 1219 |
| LMArena Chinese | 1511 | 1215 |
| LMArena French | 1455 | 1261 |
| LMArena German | 1445 | 1222 |
| LMArena Japanese | 1416 | 1132 |
| LMArena Korean | 1423 | 1140 |
| LMArena Russian | 1436 | 1234 |
| LMArena Spanish | 1454 | 1253 |
Instruction Following GLM-5 leads
GLM-5: 75.2 (#67), Llama 3.1-70B: 65.3 (#223)
| Benchmark | GLM-5 | Llama 3.1-70B |
|---|---|---|
| LMArena Instruction Following | 1428 | 1231 |
| IFEval | — | 82.1% |
Long Context GLM-5 leads
GLM-5: 44.7 (#60), Llama 3.1-70B: 37.6 (#214)
| Benchmark | GLM-5 | Llama 3.1-70B |
|---|---|---|
| LMArena Longer Query | 1446 | 1241 |
| CL-bench | 18.7% | — |
Writing & Preference GLM-5 leads
GLM-5: 66.0 (#38), Llama 3.1-70B: 35.4 (#267)
| Benchmark | GLM-5 | Llama 3.1-70B |
|---|---|---|
| LMArena Text | 1446 | 1261 |
| LMArena Creative Writing | 1439 | 1232 |
| EQ-Bench Creative Writing | 1601 | 784 |
| LMArena Multi-Turn | 1456 | 1256 |
| WildBench | — | 75.8% |
Frequently asked questions
Is GLM-5 better than Llama 3.1-70B?
GLM-5 is the stronger model overall, scoring 46.1 to 29.6 on the Noometry Index. Llama 3.1-70B costs 3.9× less per token, which makes it the better buy when GLM-5's lead doesn't matter for your workload.
Which is cheaper, GLM-5 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 lists at $1 and $3.20.
Is GLM-5 or Llama 3.1-70B better for coding?
GLM-5 scores higher on coding benchmarks: 49.0 versus 30.3 in the Noometry coding category.
Which has the bigger context window?
GLM-5 does, with 205K tokens against 128K.
How many benchmarks do GLM-5 and Llama 3.1-70B share?
22 benchmarks have published results for both models. GLM-5 has 45 scored results on Noometry and Llama 3.1-70B has 35.