Model comparison
Grok 4.3 vs Llama-3.3-70B-Instruct
Grok 4.3 is the stronger model overall, scoring 43.8 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 4.3's lead doesn't matter for your workload.
Last verified . 26 shared benchmarks.
MetaSummary
- They share 26 benchmarks with published results for both. Grok 4.3 scores higher in 9 categories and Llama-3.3-70B-Instruct in 0 categories; 9 gaps are clear of the uncertainty.
- The widest gap is in math, where Grok 4.3 leads 46.0 to 15.3.
- The biggest single-benchmark swing is OTIS Mock AIME 2024-2025: 93.3% for Grok 4.3 and 5.1% for Llama-3.3-70B-Instruct.
- Llama-3.3-70B-Instruct is cheaper at $0.10 / $0.32 per million input/output tokens, against $1.25 / $2.50 for Grok 4.3.
- Grok 4.3 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 4.3 | Llama-3.3-70B-Instruct | |
|---|---|---|
| Provider | xAI | Meta |
| Noometry Index | 43.8 | 30.6 |
| Released | 2026-04-17 | 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 | 40 | 43 |
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Category by category
Coding Grok 4.3 leads
Grok 4.3: 41.6 (#121), Llama-3.3-70B-Instruct: 31.0 (#290)
| Benchmark | Grok 4.3 | Llama-3.3-70B-Instruct |
|---|---|---|
| SciCode | 47.3% | 26% |
| WeirdML | 49.9% | 14.4% |
| LMArena Coding | 1415 | 1268 |
| LMArena WebDev | 1357 | — |
| BigCodeBench Instruct | — | 46.9% |
| LiveBench Coding | — | 36.6% |
| BigCodeBench Complete | — | 57.5% |
| ALE-Bench | 944.17 | — |
Agentic & Tool Use Grok 4.3 leads
Grok 4.3: 27.7 (#99), Llama-3.3-70B-Instruct: 25.8 (#105)
| Benchmark | Grok 4.3 | Llama-3.3-70B-Instruct |
|---|---|---|
| Berkeley Function Calling Leaderboard | — | 31.9% |
| BALROG | — | 23% |
| GDP.pdf | 8% | — |
| LMArena Search | 1165 | — |
| Vending-Bench 2 | 35.26 | — |
Reasoning Grok 4.3 leads
Grok 4.3: 35.9 (#68), Llama-3.3-70B-Instruct: 14.1 (#327)
| Benchmark | Grok 4.3 | Llama-3.3-70B-Instruct |
|---|---|---|
| CritPt | 8% | 0% |
| LMArena Hard Prompts | 1396 | 1257 |
| DTBench | 90.7% | 59.5% |
| LMCA | 38.3% | 17.5% |
| Epoch Capabilities Index | 149.16 | 127.33 |
| ForecastBench | 60.3 | 58.6 |
| SimpleBench | — | 19.9% |
| NYT Connections (extended) | 55.2% | — |
| Chess Puzzles | 25% | — |
| LiveBench Reasoning | — | 50.8% |
| LiveBench Data Analysis | — | 49.5% |
| LiveBench | — | 50.2% |
Math Grok 4.3 leads
Grok 4.3: 46.0 (#74), Llama-3.3-70B-Instruct: 15.3 (#298)
| Benchmark | Grok 4.3 | Llama-3.3-70B-Instruct |
|---|---|---|
| OTIS Mock AIME 2024-2025 | 93.3% | 5.1% |
| LMArena Math | 1388 | 1267 |
| FrontierMath (Tiers 1-3) | 42.8% | — |
| FrontierMath Tier 4 | 14.6% | — |
| ProofBench | 11% | — |
| LiveBench Math | — | 42.2% |
| MATH Level 5 | — | 41.6% |
Knowledge Grok 4.3 leads
Grok 4.3: 52.5 (#62), Llama-3.3-70B-Instruct: 30.6 (#226)
| Benchmark | Grok 4.3 | Llama-3.3-70B-Instruct |
|---|---|---|
| GPQA Diamond | 88.8% | 47.4% |
| LMArena Expert | 1385 | 1225 |
| SimpleQA Verified | 33.2% | — |
| Confabulations | — | 22.8% |
| Vectara Hallucination Rate | — | 4.1% |
| MMLU | — | 86.3% |
Multimodal Not comparable
Grok 4.3: 31.6 (#104), Llama-3.3-70B-Instruct: —
| Benchmark | Grok 4.3 | Llama-3.3-70B-Instruct |
|---|---|---|
| LMArena Vision | 1229 | — |
| Blueprint-Bench 2 | 0% | — |
Multilingual Grok 4.3 leads
Grok 4.3: 50.5 (#120), Llama-3.3-70B-Instruct: 39.9 (#220)
| Benchmark | Grok 4.3 | Llama-3.3-70B-Instruct |
|---|---|---|
| LMArena Non-English | 1385 | 1236 |
| LMArena Chinese | 1422 | 1217 |
| LMArena French | 1412 | 1281 |
| LMArena German | 1395 | 1251 |
| LMArena Japanese | 1379 | 1150 |
| LMArena Korean | 1356 | 1143 |
| LMArena Russian | 1399 | 1252 |
| LMArena Spanish | 1398 | 1270 |
Instruction Following Grok 4.3 leads
Grok 4.3: 72.1 (#140), Llama-3.3-70B-Instruct: 71.1 (#157)
| Benchmark | Grok 4.3 | Llama-3.3-70B-Instruct |
|---|---|---|
| LMArena Instruction Following | 1366 | 1242 |
| LiveBench Instruction Following | — | 82.7% |
Long Context Grok 4.3 leads
Grok 4.3: 42.5 (#123), Llama-3.3-70B-Instruct: 26.4 (#295)
| Benchmark | Grok 4.3 | Llama-3.3-70B-Instruct |
|---|---|---|
| LMArena Longer Query | 1393 | 1256 |
| Fiction.LiveBench | — | 33.3% |
Writing & Preference Grok 4.3 leads
Grok 4.3: 58.5 (#118), Llama-3.3-70B-Instruct: 47.6 (#207)
| Benchmark | Grok 4.3 | Llama-3.3-70B-Instruct |
|---|---|---|
| LMArena Text | 1397 | 1274 |
| LMArena Creative Writing | 1380 | 1250 |
| LMArena Multi-Turn | 1406 | 1280 |
| EQ-Bench 4 | 1075 | — |
| LiveBench Language | — | 39.2% |
Frequently asked questions
Is Grok 4.3 better than Llama-3.3-70B-Instruct?
Grok 4.3 is the stronger model overall, scoring 43.8 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 4.3's lead doesn't matter for your workload.
Which is cheaper, Grok 4.3 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 4.3 lists at $1.25 and $2.50.
Is Grok 4.3 or Llama-3.3-70B-Instruct better for coding?
Grok 4.3 scores higher on coding benchmarks: 41.6 versus 31.0 in the Noometry coding category.
Which has the bigger context window?
Grok 4.3 does, with 1M tokens against 128K.
How many benchmarks do Grok 4.3 and Llama-3.3-70B-Instruct share?
26 benchmarks have published results for both models. Grok 4.3 has 40 scored results on Noometry and Llama-3.3-70B-Instruct has 43.