Model comparison
Grok 4.20 Multi-Agent vs Llama-3.3-70B-Instruct
Grok 4.20 Multi-Agent is the stronger model overall, scoring 46.2 to 30.6 on the Noometry Index. Llama-3.3-70B-Instruct costs 10× less per token, which makes it the better buy when Grok 4.20 Multi-Agent's lead doesn't matter for your workload.
Last verified . 17 shared benchmarks.
MetaSummary
- They share 17 benchmarks with published results for both. Grok 4.20 Multi-Agent scores higher in 8 categories and Llama-3.3-70B-Instruct in 0 categories; 8 gaps are clear of the uncertainty.
- The widest gap is in reasoning, where Grok 4.20 Multi-Agent leads 43.9 to 14.1.
- Llama-3.3-70B-Instruct is cheaper at $0.10 / $0.32 per million input/output tokens, against $1.25 / $2.50 for Grok 4.20 Multi-Agent.
- Grok 4.20 Multi-Agent accepts more context: 1M tokens versus 128K.
- Llama-3.3-70B-Instruct has downloadable open weights; the other is API-only.
Side by side
| Grok 4.20 Multi-Agent | Llama-3.3-70B-Instruct | |
|---|---|---|
| Provider | xAI | Meta |
| Noometry Index | 46.2 | 30.6 |
| Released | 2026-03-09 | 2024-12-06 |
| Weights | Proprietary | Open |
| Context window | 1M | 128K |
| Max output | 30K | 4K |
| Input $ / M tokens | $1.25 | $0.10 |
| Output $ / M tokens | $2.50 | $0.32 |
| Results tracked | 20 | 43 |
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Category by category
Coding Grok 4.20 Multi-Agent leads
Grok 4.20 Multi-Agent: 43.0 (#92), Llama-3.3-70B-Instruct: 31.0 (#290)
| Benchmark | Grok 4.20 Multi-Agent | Llama-3.3-70B-Instruct |
|---|---|---|
| LMArena Coding | 1457 | 1268 |
| SciCode | — | 26% |
| WeirdML | — | 14.4% |
| BigCodeBench Instruct | — | 46.9% |
| LiveBench Coding | — | 36.6% |
| BigCodeBench Complete | — | 57.5% |
Agentic & Tool Use Not comparable
Grok 4.20 Multi-Agent: —, Llama-3.3-70B-Instruct: 25.8 (#105)
| Benchmark | Grok 4.20 Multi-Agent | Llama-3.3-70B-Instruct |
|---|---|---|
| Berkeley Function Calling Leaderboard | — | 31.9% |
| BALROG | — | 23% |
| LMArena Search | 1204 | — |
Reasoning Grok 4.20 Multi-Agent leads
Grok 4.20 Multi-Agent: 43.9 (#48), Llama-3.3-70B-Instruct: 14.1 (#327)
| Benchmark | Grok 4.20 Multi-Agent | Llama-3.3-70B-Instruct |
|---|---|---|
| LMArena Hard Prompts | 1448 | 1257 |
| SimpleBench | — | 19.9% |
| NYT Connections (extended) | 89.6% | — |
| CritPt | — | 0% |
| LiveBench Reasoning | — | 50.8% |
| DTBench | — | 59.5% |
| LiveBench Data Analysis | — | 49.5% |
| LMCA | — | 17.5% |
| Epoch Capabilities Index | — | 127.33 |
| ForecastBench | — | 58.6 |
| LiveBench | — | 50.2% |
Math Grok 4.20 Multi-Agent leads
Grok 4.20 Multi-Agent: 39.4 (#104), Llama-3.3-70B-Instruct: 15.3 (#298)
| Benchmark | Grok 4.20 Multi-Agent | Llama-3.3-70B-Instruct |
|---|---|---|
| LMArena Math | 1442 | 1267 |
| OTIS Mock AIME 2024-2025 | — | 5.1% |
| LiveBench Math | — | 42.2% |
| MATH Level 5 | — | 41.6% |
Knowledge Grok 4.20 Multi-Agent leads
Grok 4.20 Multi-Agent: 40.4 (#119), Llama-3.3-70B-Instruct: 30.6 (#226)
| Benchmark | Grok 4.20 Multi-Agent | Llama-3.3-70B-Instruct |
|---|---|---|
| LMArena Expert | 1445 | 1225 |
| GPQA Diamond | — | 47.4% |
| Confabulations | — | 22.8% |
| Vectara Hallucination Rate | — | 4.1% |
| MMLU | — | 86.3% |
Multimodal Not comparable
Grok 4.20 Multi-Agent: 40.5 (#48), Llama-3.3-70B-Instruct: —
| Benchmark | Grok 4.20 Multi-Agent | Llama-3.3-70B-Instruct |
|---|---|---|
| LMArena Vision | 1259 | — |
Multilingual Grok 4.20 Multi-Agent leads
Grok 4.20 Multi-Agent: 54.4 (#43), Llama-3.3-70B-Instruct: 39.9 (#220)
| Benchmark | Grok 4.20 Multi-Agent | Llama-3.3-70B-Instruct |
|---|---|---|
| LMArena Non-English | 1440 | 1236 |
| LMArena Chinese | 1475 | 1217 |
| LMArena French | 1466 | 1281 |
| LMArena German | 1456 | 1251 |
| LMArena Japanese | 1405 | 1150 |
| LMArena Korean | 1416 | 1143 |
| LMArena Russian | 1457 | 1252 |
| LMArena Spanish | 1447 | 1270 |
Instruction Following Grok 4.20 Multi-Agent leads
Grok 4.20 Multi-Agent: 74.8 (#84), Llama-3.3-70B-Instruct: 71.1 (#157)
| Benchmark | Grok 4.20 Multi-Agent | Llama-3.3-70B-Instruct |
|---|---|---|
| LMArena Instruction Following | 1420 | 1242 |
| LiveBench Instruction Following | — | 82.7% |
Long Context Grok 4.20 Multi-Agent leads
Grok 4.20 Multi-Agent: 43.7 (#88), Llama-3.3-70B-Instruct: 26.4 (#295)
| Benchmark | Grok 4.20 Multi-Agent | Llama-3.3-70B-Instruct |
|---|---|---|
| LMArena Longer Query | 1431 | 1256 |
| Fiction.LiveBench | — | 33.3% |
Writing & Preference Grok 4.20 Multi-Agent leads
Grok 4.20 Multi-Agent: 64.0 (#59), Llama-3.3-70B-Instruct: 47.6 (#207)
| Benchmark | Grok 4.20 Multi-Agent | Llama-3.3-70B-Instruct |
|---|---|---|
| LMArena Text | 1450 | 1274 |
| LMArena Creative Writing | 1436 | 1250 |
| LMArena Multi-Turn | 1452 | 1280 |
| LiveBench Language | — | 39.2% |
Frequently asked questions
Is Grok 4.20 Multi-Agent better than Llama-3.3-70B-Instruct?
Grok 4.20 Multi-Agent is the stronger model overall, scoring 46.2 to 30.6 on the Noometry Index. Llama-3.3-70B-Instruct costs 10× less per token, which makes it the better buy when Grok 4.20 Multi-Agent's lead doesn't matter for your workload.
Which is cheaper, Grok 4.20 Multi-Agent or Llama-3.3-70B-Instruct?
Llama-3.3-70B-Instruct is cheaper. It lists at $0.10 per million input tokens and $0.32 per million output tokens; Grok 4.20 Multi-Agent lists at $1.25 and $2.50.
Is Grok 4.20 Multi-Agent or Llama-3.3-70B-Instruct better for coding?
Grok 4.20 Multi-Agent scores higher on coding benchmarks: 43.0 versus 31.0 in the Noometry coding category.
Which has the bigger context window?
Grok 4.20 Multi-Agent does, with 1M tokens against 128K.
How many benchmarks do Grok 4.20 Multi-Agent and Llama-3.3-70B-Instruct share?
17 benchmarks have published results for both models. Grok 4.20 Multi-Agent has 20 scored results on Noometry and Llama-3.3-70B-Instruct has 43.