Command A vs Llama 3.3 70B Instruct
Command A leads Llama 3.3 70B Instruct on the Artificial Analysis Intelligence Index, 13.1 to 7.7, and Llama 3.3 70B Instruct costs 13.9x less per output token ($0.72 vs $10.00 per million). Updated October 21, 2026; 43 benchmarks have a result for both.
Command A or Llama 3.3 70B Instruct: which to pick
Neither model has a coding score yet, so the two cannot be ranked on coding.
Pick Command A for a larger context window (256K against 131K tokens) and work measured by LiveCodeBench (Artificial Analysis run) or MMLU Pro (Artificial Analysis run), where it leads.
- Shared results
- 43
- Command A only
- 19
- Llama 3.3 70B Instruct only
- 9
- Like-for-like categories
- 7 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
Neither has a coding score yet.
One side not measured
Intelligence
AA Intelligence Index
Command A
Command A leads, 13.1 against 7.7.
Same board, both measured
Agentic work
AA Agentic Index
No call
Only Command A has a AA Agentic Index result, 1.0.
Directional only
Reasoning
GPQA Diamond
No call
Only Command A has a GPQA Diamond result, 48.5%.
Directional only
Math
AIME
No call
Only Command A has a AIME result, 13.3%.
Directional only
Knowledge
MMLU Pro
No call
Only Command A has a MMLU Pro result, 69.2%.
Directional only
Long documents
Listed context window
Command A
Command A takes 256K tokens against 131K.
Listed by OpenRouter
Everyday cost
1K input + 500 output tokens
Llama 3.3 70B Instruct
Command A $0.0075, Llama 3.3 70B Instruct $0.0008 per turn.
List prices
Cost per solved task
One SWE-bench Verified task, solved
No call
Only Command A has the number, $2.69.
Directional only
At a glance
- Coding index
- Command A not benchmarked, Llama 3.3 70B Instruct not benchmarked.
- Widest category
- Agentic work, 34.3 points apart on average over 2 shared benchmarks.
- Widest single gap
- τ²-Bench Telecom: Command A 80.7% against Llama 3.3 70B Instruct 26.6%, 54.1 points apart on a 0 to 100 scale.
- Output price
- Command A $10.00 against Llama 3.3 70B Instruct $0.72 per million output tokens, 13.9x apart.
- Context window
- Command A takes 256K 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 | Command A | Bars | Llama 3.3 70B Instruct | Ties |
|---|---|---|---|---|
| Coding | 4 | 1 | ||
| Intelligence | 1 | 0 | ||
| Agentic work | 2 | 0 | ||
| Reasoning | 5 | 0 | ||
| Math | 4 | 0 | ||
| Knowledge | 4 | 1 | ||
| General | 20 | 1 | ||
| All shared boards | 40 | 3 |
CodingLike-for-likeSix of these boards feed the Vibe Coding Index41
| Benchmark | Command A | Llama 3.3 70B Instruct | Leader |
|---|---|---|---|
| AA Coding Index | 27.8 | 11.9 | Command A |
| LiveCodeBench (Vals AI) | 35.1% | Not measured | Only Command A measured |
| SWE-bench Verified (Vals AI) | 7.8% | Not measured | Only Command A measured |
| Terminal-Bench 2.1 (Vals AI) | 17.6% | Not measured | Only Command A measured |
| LiveCodeBench (Artificial Analysis run) | 28.7% | 28.8% | Llama 3.3 70B Instruct |
| SciCode | 38.5% | Not measured | Only Command A measured |
| Terminal-Bench Hard (Artificial Analysis run) | 25.0% | 3.0% | Command A |
| WeirdML | Not measured | 14.4% | Only Llama 3.3 70B Instruct measured |
| LMArena Text: coding | 1330 | 1268 | Command A |
| LMArena Text: software and IT | 1336 | 1273 | Command A |
| Neither model has a result on the other 29 boards here | |||
IntelligenceLike-for-likePublic boards, shown as published, kept out of the index10
| Benchmark | Command A | Llama 3.3 70B Instruct | Leader |
|---|---|---|---|
| AA Intelligence Index | 13.1 | 7.7 | Command A |
| Epoch Capabilities Index | Not measured | 127.3 | Only Llama 3.3 70B Instruct measured |
Agentic workLike-for-likePublic boards, shown as published, kept out of the index20
| Benchmark | Command A | Llama 3.3 70B Instruct | Leader |
|---|---|---|---|
| AA Agentic Index | 1.0 | Not measured | Only Command A measured |
| Finance Agent (Vals AI) | 9.0% | Not measured | Only Command A measured |
| τ²-Bench Telecom | 80.7% | 26.6% | Command A |
| BALROG | Not measured | 23.0% | Only Llama 3.3 70B Instruct measured |
| Berkeley Function Calling Leaderboard (BFCL) | 46.5% | 31.9% | Command A |
| Neither model has a result on the other 36 boards here | |||
ReasoningLike-for-likePublic boards, shown as published, kept out of the index50
| Benchmark | Command A | Llama 3.3 70B Instruct | Leader |
|---|---|---|---|
| GPQA Diamond | 48.5% | Not measured | Only Command A measured |
| GPQA Diamond (Artificial Analysis run) | 76.1% | 49.8% | Command A |
| Humanity's Last Exam (Artificial Analysis run) | 12.0% | 3.6% | Command A |
| GPQA Diamond (Epoch AI run) | Not measured | 47.4% | Only Llama 3.3 70B Instruct measured |
| SimpleBench | Not measured | 19.9% | Only Llama 3.3 70B Instruct measured |
| LMArena Text: hard prompts | 1326 | 1257 | Command A |
| LMArena Text: expert | 1296 | 1225 | Command A |
| LMArena Text: hard prompts in English | 1335 | 1277 | Command A |
| Kagi LLM Benchmark | 28.8% | Not measured | Only Command A measured |
| Neither model has a result on the other 13 boards here | |||
MathLike-for-likePublic boards, shown as published, kept out of the index40
| Benchmark | Command A | Llama 3.3 70B Instruct | Leader |
|---|---|---|---|
| AIME | 13.3% | Not measured | Only Command A measured |
| MATH 500 (Vals AI) | 76.2% | Not measured | Only Command A measured |
| MGSM (Vals AI) | 85.7% | Not measured | Only Command A measured |
| AA Math Index | 13.0 | 7.7 | Command A |
| AIME 2025 (Artificial Analysis run) | 13.0% | 7.7% | Command A |
| 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 | 1300 | 1267 | Command A |
| LMArena Text: mathematical work | 1312 | 1267 | Command A |
| Neither model has a result on the other 4 boards here | |||
KnowledgeLike-for-likePublic boards, shown as published, kept out of the index41
| Benchmark | Command A | Llama 3.3 70B Instruct | Leader |
|---|---|---|---|
| MMLU Pro | 69.2% | Not measured | Only Command A measured |
| LegalBench (Vals AI) | 79.7% | Not measured | Only Command A measured |
| CaseLaw (Vals AI) | 64.5% | Not measured | Only Command A measured |
| CorpFin (Vals AI) | 46.0% | Not measured | Only Command A measured |
| TaxEval (Vals AI) | 61.4% | Not measured | Only Command A measured |
| MedQA (Vals AI) | 80.5% | Not measured | Only Command A measured |
| MedCode (Vals AI) | 19.7% | Not measured | Only Command A measured |
| MMLU Pro (Artificial Analysis run) | 71.2% | 71.3% | Llama 3.3 70B Instruct |
| MMLU | Not measured | 86.3% | Only Llama 3.3 70B Instruct measured |
| LMArena Text: science | 1339 | 1284 | Command A |
| LMArena Text: medicine and healthcare | 1334 | 1275 | Command A |
| LMArena Text: legal and government | 1348 | 1284 | Command A |
| LMArena Text: business and finance | 1315 | 1243 | Command A |
| Neither model has a result on the other 5 boards here | |||
Web designNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
| Benchmark | Command A | Llama 3.3 70B Instruct | Leader |
|---|---|---|---|
| Neither model has a result on 9 boards here | |||
GeneralLike-for-likePublic boards, shown as published, kept out of the index201
| Benchmark | Command A | Llama 3.3 70B Instruct | Leader |
|---|---|---|---|
| MedScribe (Vals AI) | 55.7% | Not measured | Only Command A measured |
| IFBench | 73.9% | 47.1% | Command A |
| Long context reasoning (AA-LCR) | 52.7% | 15.7% | Command A |
| DTBench | 61.3% | 59.5% | Command A |
| LMCA | 10.3% | 17.5% | Llama 3.3 70B Instruct |
| Fiction.LiveBench | Not measured | 33.3% | Only Llama 3.3 70B Instruct measured |
| LMArena Text | 1331 | 1274 | Command A |
| LMArena Text: instruction following | 1309 | 1242 | Command A |
| LMArena Text: creative writing | 1319 | 1250 | Command A |
| LMArena Text: English | 1344 | 1303 | Command A |
| LMArena Text: non-English | 1313 | 1236 | Command A |
| LMArena Text: Chinese | 1327 | 1217 | Command A |
| LMArena Text: German | 1342 | 1251 | Command A |
| LMArena Text: Japanese | 1285 | 1150 | Command A |
| LMArena Text: Korean | 1285 | 1142 | Command A |
| LMArena Text: French | 1351 | 1282 | Command A |
| LMArena Text: Spanish | 1347 | 1269 | Command A |
| LMArena Text: Russian | 1314 | 1252 | Command A |
| LMArena Text: Polish | 1324 | 1259 | Command A |
| LMArena Text: multi-turn | 1338 | 1280 | Command A |
| LMArena Text: longer queries | 1334 | 1255 | Command A |
| LMArena Text: writing and language | 1328 | 1255 | Command A |
| LMArena Text: entertainment and media | 1306 | 1241 | Command A |
| Neither model has a result on the other 23 boards here | |||
Image generationNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
| Benchmark | Command A | 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 | Cohere Command A | Meta Llama 3.3 70B Instruct |
|---|---|---|
| Maker | Cohere | Meta Llama |
| Weights | Open weights | Open weights |
| Released | March 13, 2025 | December 6, 2024 |
| Context window | 256K | 131K |
| Input $/Mtok | $2.50 | $0.45 |
| Output $/Mtok | $10.00 | $0.72 |
| Coding index | Not benchmarked | Not benchmarked |
| Independent measurements behind it | 1 | 0 |
| Cost per solved task | $2.69 | Not computable |
| Source | OpenRouter listing | OpenRouter listing |
Questions
Is Command A better than Llama 3.3 70B Instruct for coding?
Neither model has a coding score yet, so the two cannot be ranked on coding. Pick Command A for a larger context window (256K against 131K tokens) and work measured by LiveCodeBench (Artificial Analysis run) or MMLU Pro (Artificial Analysis run), where it leads.
Which is cheaper, Command A or Llama 3.3 70B Instruct?
Command A costs $2.50 per million input tokens and $10.00 per million output tokens; Llama 3.3 70B Instruct costs $0.45 and $0.72. Cost per solved task is known for Command A only.
Which has the bigger context window, Command A or Llama 3.3 70B Instruct?
Command A, with 256K tokens against 131K.
How many benchmarks is the Command A vs Llama 3.3 70B Instruct comparison based on?
43 benchmarks have a result for both models as of October 21, 2026. Command A's coding index rests on 1 independent benchmark 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.