Grok 4.20 vs Llama 3.3 70B Instruct
Grok 4.20 leads Llama 3.3 70B Instruct on the Artificial Analysis Intelligence Index, 25.7 to 7.7, and Llama 3.3 70B Instruct costs 3.5x less per output token ($0.72 vs $2.50 per million). Updated October 21, 2026; 11 benchmarks have a result for both.
Grok 4.20 or Llama 3.3 70B Instruct: which to pick
Only Grok 4.20 has a coding score, 33.3 on the Vibe Coding Index; Llama 3.3 70B Instruct has none, so the two cannot be ranked on coding.
Pick Llama 3.3 70B Instruct for output tokens 3.5x cheaper ($0.72 vs $2.50 per million).
- Shared results
- 11
- Grok 4.20 only
- 44
- Llama 3.3 70B Instruct only
- 41
- Like-for-like categories
- 5 of 9
Only benchmarks that tested both models count toward a pick; a result only one model has is shown but not compared.
Which one for your work
Each card is decided by one number both models carry. Where only one has it, the card says so and makes no call.
Coding
Vibe Coding Index, six public coding benchmarks
No call
Only Grok 4.20 has a coding score, so no call is made.
One side not measured
Intelligence
AA Intelligence Index
Grok 4.20
Grok 4.20 leads, 25.7 against 7.7.
Same board, both measured
Reasoning
GPQA Diamond
No call
Only Grok 4.20 has a GPQA Diamond result, 88.6%.
Directional only
Math
AIME
No call
Only Grok 4.20 has a AIME result, 96.5%.
Directional only
Knowledge
MMLU Pro
No call
Only Grok 4.20 has a MMLU Pro result, 86.3%.
Directional only
Web design
Design Arena: websites
No call
Only Grok 4.20 has a Design Arena: websites result, 1229.
Directional only
Long documents
Listed context window
Grok 4.20
Grok 4.20 takes 2M tokens against 131K.
Listed by OpenRouter
Everyday cost
1K input + 500 output tokens
Llama 3.3 70B Instruct
Grok 4.20 $0.0025, Llama 3.3 70B Instruct $0.0008 per turn.
List prices
Cost per solved task
One SWE-bench Verified task, solved
No call
Only Grok 4.20 has the number, $0.72.
Directional only
At a glance
- Coding index
- Grok 4.20 33.3 from 2 benchmarks, Llama 3.3 70B Instruct not benchmarked.
- Widest category
- Agentic work, 66.4 points apart on average over 1 shared benchmark.
- Widest single gap
- τ²-Bench Telecom: Grok 4.20 93.0% against Llama 3.3 70B Instruct 26.6%, 66.4 points apart on a 0 to 100 scale.
- Output price
- Grok 4.20 $2.50 against Llama 3.3 70B Instruct $0.72 per million output tokens, 3.5x apart.
- Context window
- Grok 4.20 takes 2M tokens, Llama 3.3 70B Instruct 131K.
Benchmark by benchmark
First the score: boards each model leads, per category, where both have a result. Then every board, a category to a fold; bars are each result on a 0 to 100 scale, so compare the two in a row, not one row with another.
| Category | Grok 4.20 | Bars | Llama 3.3 70B Instruct | Ties |
|---|---|---|---|---|
| Coding | 2 | 0 | ||
| Intelligence | 2 | 0 | ||
| Agentic work | 1 | 0 | ||
| Reasoning | 2 | 0 | ||
| General | 4 | 0 | ||
| All shared boards | 11 | 0 |
CodingLike-for-likeSix of these boards feed the Vibe Coding Index20
| Benchmark | Grok 4.20 | Llama 3.3 70B Instruct | Leader |
|---|---|---|---|
| AA Coding Index | Not measured | 11.9 | Only Llama 3.3 70B Instruct measured |
| LiveCodeBench (Vals AI) | 84.3% | Not measured | Only Grok 4.20 measured |
| SWE-bench Verified (Vals AI) | 72.2% | Not measured | Only Grok 4.20 measured |
| Vibe Code Bench (Vals AI) | 4.1% | Not measured | Only Grok 4.20 measured |
| Code migration (Vals AI) | 0.3% | Not measured | Only Grok 4.20 measured |
| Terminal-Bench 2.1 (Vals AI) | 44.2% | Not measured | Only Grok 4.20 measured |
| LiveCodeBench (Artificial Analysis run) | Not measured | 28.8% | Only Llama 3.3 70B Instruct measured |
| Terminal-Bench Hard (Artificial Analysis run) | 37.9% | 3.0% | Grok 4.20 |
| Terminal-Bench | 57.3% | Not measured | Only Grok 4.20 measured |
| WeirdML | 52.3% | 14.4% | Grok 4.20 |
| LMArena Text: coding | Not measured | 1268 | Only Llama 3.3 70B Instruct measured |
| LMArena Text: software and IT | Not measured | 1273 | Only Llama 3.3 70B Instruct measured |
| Neither model has a result on the other 27 boards here | |||
IntelligenceLike-for-likePublic boards, shown as published, kept out of the index20
| Benchmark | Grok 4.20 | Llama 3.3 70B Instruct | Leader |
|---|---|---|---|
| AA Intelligence Index | 25.7 | 7.7 | Grok 4.20 |
| Epoch Capabilities Index | 152.1 | 127.3 | Grok 4.20 |
Agentic workLike-for-likePublic boards, shown as published, kept out of the index10
| Benchmark | Grok 4.20 | Llama 3.3 70B Instruct | Leader |
|---|---|---|---|
| Design Arena: agents building web apps | 1144 | Not measured | Only Grok 4.20 measured |
| Design Arena: agents building full-stack apps | 1054 | Not measured | Only Grok 4.20 measured |
| Design Arena: agents building mobile apps | 1090 | Not measured | Only Grok 4.20 measured |
| Design Arena: agents building Android apps | 1052 | Not measured | Only Grok 4.20 measured |
| Design Arena: agents building Godot games | 1089 | Not measured | Only Grok 4.20 measured |
| Design Arena: HTML slides | 1180 | Not measured | Only Grok 4.20 measured |
| Finance Agent (Vals AI) | 28.5% | Not measured | Only Grok 4.20 measured |
| Tax Agent Bench (Vals AI) | 7.0% | Not measured | Only Grok 4.20 measured |
| Legal Research Bench (Vals AI) | 13.9% | Not measured | Only Grok 4.20 measured |
| Excel Modeling Benchmark (Vals AI) | 11.7% | Not measured | Only Grok 4.20 measured |
| τ²-Bench Telecom | 93.0% | 26.6% | Grok 4.20 |
| BALROG | Not measured | 23.0% | Only Llama 3.3 70B Instruct measured |
| Vending-Bench 2 | $4,663 | Not measured | Only Grok 4.20 measured |
| Berkeley Function Calling Leaderboard (BFCL) | Not measured | 31.9% | Only Llama 3.3 70B Instruct measured |
| Neither model has a result on the other 27 boards here | |||
ReasoningLike-for-likePublic boards, shown as published, kept out of the index20
| Benchmark | Grok 4.20 | Llama 3.3 70B Instruct | Leader |
|---|---|---|---|
| GPQA Diamond | 88.6% | Not measured | Only Grok 4.20 measured |
| MMMU Pro (Vals AI) | 83.5% | Not measured | Only Grok 4.20 measured |
| GPQA Diamond (Artificial Analysis run) | 91.1% | 49.8% | Grok 4.20 |
| Humanity's Last Exam (Artificial Analysis run) | 34.5% | 3.6% | Grok 4.20 |
| GPQA Diamond (Epoch AI run) | Not measured | 47.4% | Only Llama 3.3 70B Instruct measured |
| ARC-AGI | 89.5% | Not measured | Only Grok 4.20 measured |
| ARC-AGI-2 | 65.1% | Not measured | Only Grok 4.20 measured |
| SimpleBench | Not measured | 19.9% | Only Llama 3.3 70B Instruct measured |
| LMArena Text: hard prompts | Not measured | 1257 | Only Llama 3.3 70B Instruct measured |
| LMArena Text: expert | Not measured | 1225 | Only Llama 3.3 70B Instruct measured |
| LMArena Text: hard prompts in English | Not measured | 1277 | Only Llama 3.3 70B Instruct measured |
| Kagi LLM Benchmark | 75.0% | Not measured | Only Grok 4.20 measured |
| Neither model has a result on the other 10 boards here | |||
MathDirectional onlyPublic boards, shown as published, kept out of the index7 boards, none shared
| Benchmark | Grok 4.20 | Llama 3.3 70B Instruct | Leader |
|---|---|---|---|
| AIME | 96.5% | Not measured | Only Grok 4.20 measured |
| AA Math Index | Not measured | 7.7 | Only Llama 3.3 70B Instruct measured |
| AIME 2025 (Artificial Analysis run) | Not measured | 7.7% | Only Llama 3.3 70B Instruct measured |
| OTIS Mock AIME | Not measured | 5.1% | Only Llama 3.3 70B Instruct measured |
| MATH level 5 | Not measured | 41.6% | Only Llama 3.3 70B Instruct measured |
| LMArena Text: math | Not measured | 1267 | Only Llama 3.3 70B Instruct measured |
| LMArena Text: mathematical work | Not measured | 1267 | Only Llama 3.3 70B Instruct measured |
| Neither model has a result on the other 6 boards here | |||
KnowledgeDirectional onlyPublic boards, shown as published, kept out of the index14 boards, none shared
| Benchmark | Grok 4.20 | Llama 3.3 70B Instruct | Leader |
|---|---|---|---|
| MMLU Pro | 86.3% | Not measured | Only Grok 4.20 measured |
| LegalBench (Vals AI) | 77.7% | Not measured | Only Grok 4.20 measured |
| CaseLaw (Vals AI) | 54.4% | Not measured | Only Grok 4.20 measured |
| CorpFin (Vals AI) | 63.7% | Not measured | Only Grok 4.20 measured |
| TaxEval (Vals AI) | 74.1% | Not measured | Only Grok 4.20 measured |
| MortgageTax (Vals AI) | 45.4% | Not measured | Only Grok 4.20 measured |
| MedQA (Vals AI) | 94.5% | Not measured | Only Grok 4.20 measured |
| MedCode (Vals AI) | 32.2% | Not measured | Only Grok 4.20 measured |
| MMLU Pro (Artificial Analysis run) | Not measured | 71.3% | Only Llama 3.3 70B Instruct measured |
| MMLU | Not measured | 86.3% | Only Llama 3.3 70B Instruct measured |
| LMArena Text: science | Not measured | 1284 | Only Llama 3.3 70B Instruct measured |
| LMArena Text: medicine and healthcare | Not measured | 1275 | Only Llama 3.3 70B Instruct measured |
| LMArena Text: legal and government | Not measured | 1284 | Only Llama 3.3 70B Instruct measured |
| LMArena Text: business and finance | Not measured | 1243 | Only Llama 3.3 70B Instruct measured |
| Neither model has a result on the other 4 boards here | |||
Web designDirectional onlyPublic boards, shown as published, kept out of the index8 boards, none shared
| Benchmark | Grok 4.20 | Llama 3.3 70B Instruct | Leader |
|---|---|---|---|
| Design Arena: websites | 1229 | Not measured | Only Grok 4.20 measured |
| Design Arena: UI components | 1199 | Not measured | Only Grok 4.20 measured |
| Design Arena: game dev | 1192 | Not measured | Only Grok 4.20 measured |
| Design Arena: data viz | 1217 | Not measured | Only Grok 4.20 measured |
| Design Arena: 3D | 1193 | Not measured | Only Grok 4.20 measured |
| Design Arena: all code categories | 1221 | Not measured | Only Grok 4.20 measured |
| Design Arena: SVG | 1164 | Not measured | Only Grok 4.20 measured |
| Design Arena: ASCII art | 1174 | Not measured | Only Grok 4.20 measured |
| Neither model has a result on the other 1 board here | |||
GeneralLike-for-likePublic boards, shown as published, kept out of the index40
| Benchmark | Grok 4.20 | Llama 3.3 70B Instruct | Leader |
|---|---|---|---|
| SAGE (Vals AI) | 38.2% | Not measured | Only Grok 4.20 measured |
| MedScribe (Vals AI) | 63.4% | Not measured | Only Grok 4.20 measured |
| Vals Multimodal Index | 39.1% | Not measured | Only Grok 4.20 measured |
| IFBench | 81.2% | 47.1% | Grok 4.20 |
| Long context reasoning (AA-LCR) | 69.0% | 15.7% | Grok 4.20 |
| DTBench | 90.1% | 59.5% | Grok 4.20 |
| LMCA | 38.7% | 17.5% | Grok 4.20 |
| Fiction.LiveBench | Not measured | 33.3% | Only Llama 3.3 70B Instruct measured |
| CL-bench | 22.2% | Not measured | Only Grok 4.20 measured |
| CL-bench Life | 11.9% | Not measured | Only Grok 4.20 measured |
| LMArena Text | Not measured | 1274 | Only Llama 3.3 70B Instruct measured |
| LMArena Text: instruction following | Not measured | 1242 | Only Llama 3.3 70B Instruct measured |
| LMArena Text: creative writing | Not measured | 1250 | Only Llama 3.3 70B Instruct measured |
| LMArena Text: English | Not measured | 1303 | Only Llama 3.3 70B Instruct measured |
| LMArena Text: non-English | Not measured | 1236 | Only Llama 3.3 70B Instruct measured |
| LMArena Text: Chinese | Not measured | 1217 | Only Llama 3.3 70B Instruct measured |
| LMArena Text: German | Not measured | 1251 | Only Llama 3.3 70B Instruct measured |
| LMArena Text: Japanese | Not measured | 1150 | Only Llama 3.3 70B Instruct measured |
| LMArena Text: Korean | Not measured | 1142 | Only Llama 3.3 70B Instruct measured |
| LMArena Text: French | Not measured | 1282 | Only Llama 3.3 70B Instruct measured |
| LMArena Text: Spanish | Not measured | 1269 | Only Llama 3.3 70B Instruct measured |
| LMArena Text: Russian | Not measured | 1252 | Only Llama 3.3 70B Instruct measured |
| LMArena Text: Polish | Not measured | 1259 | Only Llama 3.3 70B Instruct measured |
| LMArena Text: multi-turn | Not measured | 1280 | Only Llama 3.3 70B Instruct measured |
| LMArena Text: longer queries | Not measured | 1255 | Only Llama 3.3 70B Instruct measured |
| LMArena Text: writing and language | Not measured | 1255 | Only Llama 3.3 70B Instruct measured |
| LMArena Text: entertainment and media | Not measured | 1241 | Only Llama 3.3 70B Instruct measured |
| Neither model has a result on the other 19 boards here | |||
Image generationNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
| Benchmark | Grok 4.20 | Llama 3.3 70B Instruct | Leader |
|---|---|---|---|
| Neither model has a result on 5 boards here | |||
What each workload costs
Three fixed token mixes at each model's listed price, so per-token rates become a bill.
Chat turn
1K input + 500 output tokens
Llama 3.3 70B Instruct costs less
Repository review
50K input + 3K output tokens
Llama 3.3 70B Instruct costs less
Agent loop
200K input + 10K output tokens
Llama 3.3 70B Instruct costs less
Listed OpenRouter rates, without cache or batch discounts. A workload that does not fit a model's context window would need splitting.
Specification differences
The leading value in a row is marked; a row with a missing value names no leader. Our two rows are marked in red.
| Specification | SpaceXAI Grok 4.20 | Meta Llama 3.3 70B Instruct |
|---|---|---|
| Maker | xAI | Meta Llama |
| Weights | No public weights listed | Open weights |
| Released | March 31, 2026 | December 6, 2024 |
| Context window | 2M | 131K |
| Input $/Mtok | $1.25 | $0.45 |
| Output $/Mtok | $2.50 | $0.72 |
| Coding index | 33.3 | Not benchmarked |
| Independent measurements behind it | 2 | 0 |
| Cost per solved task | $0.72 | Not computable |
| Source | OpenRouter listing | OpenRouter listing |
Questions
Is Grok 4.20 better than Llama 3.3 70B Instruct for coding?
Only Grok 4.20 has a coding score, 33.3 on the Vibe Coding Index; Llama 3.3 70B Instruct has none, so the two cannot be ranked on coding. Pick Llama 3.3 70B Instruct for output tokens 3.5x cheaper ($0.72 vs $2.50 per million).
Which is cheaper, Grok 4.20 or Llama 3.3 70B Instruct?
Grok 4.20 costs $1.25 per million input tokens and $2.50 per million output tokens; Llama 3.3 70B Instruct costs $0.45 and $0.72. Cost per solved task is known for Grok 4.20 only.
Which has the bigger context window, Grok 4.20 or Llama 3.3 70B Instruct?
Grok 4.20, with 2M tokens against 131K.
How many benchmarks is the Grok 4.20 vs Llama 3.3 70B Instruct comparison based on?
11 benchmarks have a result for both models as of October 21, 2026. Grok 4.20's coding index rests on 2 independent benchmarks and Llama 3.3 70B Instruct's on 0. Where only one model has a result, the page says so instead of naming a winner.