Llama 3.3 70B Instruct vs Phi 4
Updated October 21, 2026. 44 benchmarks have a result for both. Coding is ranked on the Vibe Coding Index; every other board is shown as published.
Decision reading
Neither model has a coding score yet, so the two cannot be ranked on coding.
Llama 3.3 70B Instruct has the edge where the context window is larger, 131K against 16K tokens, and where what matters is Terminal-Bench Hard (Artificial Analysis run) or Epoch Capabilities Index, where it leads.
- Shared results
- 44
- Llama 3.3 70B Instruct only
- 7
- Phi 4 only
- 1
- Like-for-like categories
- 7 of 9
Shared results can support a head-to-head reading. Results only one model has describe coverage, not superiority.
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
Llama 3.3 70B Instruct
Llama 3.3 70B Instruct leads, 7.7 against 5.9.
Same board, both measured
Long documents
Listed context window
Llama 3.3 70B Instruct
Llama 3.3 70B Instruct takes 131K tokens against 16K.
Listed by OpenRouter
Everyday cost
1K input + 500 output tokens
Phi 4
Llama 3.3 70B Instruct $0.0008, Phi 4 $0.0001 per turn.
List prices
At a glance
- Coding index
- Llama 3.3 70B Instruct not benchmarked, Phi 4 not benchmarked.
- Widest category
- Agentic work, 19.0 points apart on average over 2 shared benchmarks.
- Widest single gap
- τ²-Bench Telecom: Llama 3.3 70B Instruct 26.6% against Phi 4 0.0%, 26.6 points apart on a 0 to 100 scale.
- Output price
- Llama 3.3 70B Instruct $0.72 against Phi 4 $0.14 per million output tokens, 5.1x apart.
- Context window
- Llama 3.3 70B Instruct takes 131K tokens, Phi 4 16K.
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 | Llama 3.3 70B Instruct | Bars | Phi 4 | Ties |
|---|---|---|---|---|
| Coding | 3 | 1 | ||
| Intelligence | 1 | 1 | ||
| Agentic work | 2 | 0 | ||
| Reasoning | 3 | 3 | ||
| Math | 2 | 4 | ||
| Knowledge | 5 | 1 | ||
| General | 16 | 2 | ||
| All shared boards | 32 | 12 |
CodingLike-for-likeSix of these boards feed the Vibe Coding Index31
| Benchmark | Llama 3.3 70B Instruct | Phi 4 | Leader |
|---|---|---|---|
| AA Coding Index | 11.9 | Not measured | Only Llama 3.3 70B Instruct measured |
| LiveCodeBench (Artificial Analysis run) | 28.8% | 23.1% | Llama 3.3 70B Instruct |
| Terminal-Bench Hard (Artificial Analysis run) | 3.0% | 3.8% | Phi 4 |
| WeirdML | 14.4% | Not measured | Only Llama 3.3 70B Instruct measured |
| LMArena Text: coding | 1268 | 1231 | Llama 3.3 70B Instruct |
| LMArena Text: software and IT | 1273 | 1228 | Llama 3.3 70B Instruct |
| Neither model has a result on the other 32 boards here | |||
IntelligenceLike-for-likePublic boards, shown as published, kept out of the index11
| Benchmark | Llama 3.3 70B Instruct | Phi 4 | Leader |
|---|---|---|---|
| AA Intelligence Index | 7.7 | 5.9 | Llama 3.3 70B Instruct |
| Epoch Capabilities Index | 127.3 | 130.5 | Phi 4 |
Agentic workLike-for-likePublic boards, shown as published, kept out of the index20
| Benchmark | Llama 3.3 70B Instruct | Phi 4 | Leader |
|---|---|---|---|
| τ²-Bench Telecom | 26.6% | 0.0% | Llama 3.3 70B Instruct |
| BALROG | 23.0% | 11.6% | Llama 3.3 70B Instruct |
| Neither model has a result on the other 37 boards here | |||
ReasoningLike-for-likePublic boards, shown as published, kept out of the index33
| Benchmark | Llama 3.3 70B Instruct | Phi 4 | Leader |
|---|---|---|---|
| GPQA Diamond (Artificial Analysis run) | 49.8% | 57.5% | Phi 4 |
| Humanity's Last Exam (Artificial Analysis run) | 3.6% | 3.8% | Phi 4 |
| GPQA Diamond (Epoch AI run) | 47.4% | 56.1% | Phi 4 |
| SimpleBench | 19.9% | Not measured | Only Llama 3.3 70B Instruct measured |
| LMArena Text: hard prompts | 1257 | 1220 | Llama 3.3 70B Instruct |
| LMArena Text: expert | 1225 | 1203 | Llama 3.3 70B Instruct |
| LMArena Text: hard prompts in English | 1277 | 1230 | Llama 3.3 70B Instruct |
| Neither model has a result on the other 14 boards here | |||
MathLike-for-likePublic boards, shown as published, kept out of the index24
| Benchmark | Llama 3.3 70B Instruct | Phi 4 | Leader |
|---|---|---|---|
| AA Math Index | 7.7 | 18.0 | Phi 4 |
| AIME 2025 (Artificial Analysis run) | 7.7% | 18.0% | Phi 4 |
| OTIS Mock AIME | 5.1% | 13.8% | Phi 4 |
| MATH level 5 | 41.6% | 64.9% | Phi 4 |
| LMArena Text: math | 1267 | 1246 | Llama 3.3 70B Instruct |
| LMArena Text: mathematical work | 1267 | 1254 | Llama 3.3 70B Instruct |
| Neither model has a result on the other 7 boards here | |||
KnowledgeLike-for-likePublic boards, shown as published, kept out of the index51
| Benchmark | Llama 3.3 70B Instruct | Phi 4 | Leader |
|---|---|---|---|
| MMLU Pro (Artificial Analysis run) | 71.3% | 71.4% | Phi 4 |
| MMLU | 86.3% | 84.8% | Llama 3.3 70B Instruct |
| LMArena Text: science | 1284 | 1206 | Llama 3.3 70B Instruct |
| LMArena Text: medicine and healthcare | 1275 | 1202 | Llama 3.3 70B Instruct |
| LMArena Text: legal and government | 1284 | 1241 | Llama 3.3 70B Instruct |
| LMArena Text: business and finance | 1243 | 1199 | Llama 3.3 70B Instruct |
| Neither model has a result on the other 11 boards here | |||
Web designNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
| Benchmark | Llama 3.3 70B Instruct | Phi 4 | Leader |
|---|---|---|---|
| Neither model has a result on 9 boards here | |||
GeneralLike-for-likePublic boards, shown as published, kept out of the index162
| Benchmark | Llama 3.3 70B Instruct | Phi 4 | Leader |
|---|---|---|---|
| IFBench | 47.1% | 23.5% | Llama 3.3 70B Instruct |
| Long context reasoning (AA-LCR) | 15.7% | 0.0% | Llama 3.3 70B Instruct |
| DTBench | 59.5% | Not measured | Only Llama 3.3 70B Instruct measured |
| LMCA | 17.5% | Not measured | Only Llama 3.3 70B Instruct measured |
| Fiction.LiveBench | 33.3% | Not measured | Only Llama 3.3 70B Instruct measured |
| Creative writing (Lech Mazur) | Not measured | 62.6% | Only Phi 4 measured |
| LMArena Text | 1274 | 1217 | Llama 3.3 70B Instruct |
| LMArena Text: instruction following | 1242 | 1201 | Llama 3.3 70B Instruct |
| LMArena Text: creative writing | 1250 | 1181 | Llama 3.3 70B Instruct |
| LMArena Text: English | 1303 | 1231 | Llama 3.3 70B Instruct |
| LMArena Text: non-English | 1236 | 1197 | Llama 3.3 70B Instruct |
| LMArena Text: Chinese | 1217 | 1212 | Llama 3.3 70B Instruct |
| LMArena Text: German | 1251 | 1222 | Llama 3.3 70B Instruct |
| LMArena Text: Japanese | 1150 | 1158 | Phi 4 |
| LMArena Text: Korean | 1142 | 1151 | Phi 4 |
| LMArena Text: French | 1282 | 1224 | Llama 3.3 70B Instruct |
| LMArena Text: Spanish | 1269 | 1234 | Llama 3.3 70B Instruct |
| LMArena Text: Russian | 1252 | 1209 | Llama 3.3 70B Instruct |
| LMArena Text: Polish | 1259 | Not measured | Only Llama 3.3 70B Instruct measured |
| LMArena Text: multi-turn | 1280 | 1206 | Llama 3.3 70B Instruct |
| LMArena Text: longer queries | 1255 | 1217 | Llama 3.3 70B Instruct |
| LMArena Text: writing and language | 1255 | 1198 | Llama 3.3 70B Instruct |
| LMArena Text: entertainment and media | 1241 | 1162 | Llama 3.3 70B Instruct |
| 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 | Llama 3.3 70B Instruct | Phi 4 | 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
Phi 4 costs less
Repository review
50K input + 3K output tokens
Phi 4 costs less
Agent loop
200K input + 10K output tokens
Phi 4 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 | Meta Llama 3.3 70B Instruct | Microsoft Phi 4 |
|---|---|---|
| Maker | Meta Llama | Microsoft |
| Weights | Open weights | Open weights |
| Released | December 6, 2024 | January 10, 2025 |
| Context window | 131K | 16K |
| Input $/Mtok | $0.45 | $0.07 |
| Output $/Mtok | $0.72 | $0.14 |
| Coding index | Not benchmarked | Not benchmarked |
| Independent measurements behind it | 0 | 0 |
| Cost per solved task | Not computable | Not computable |
| Source | OpenRouter listing | OpenRouter listing |
Questions
Is Llama 3.3 70B Instruct better than Phi 4 for coding?
Neither model has a coding score yet, so the two cannot be ranked on coding. Llama 3.3 70B Instruct has the edge where the context window is larger, 131K against 16K tokens, and where what matters is Terminal-Bench Hard (Artificial Analysis run) or Epoch Capabilities Index, where it leads.
Which is cheaper, Llama 3.3 70B Instruct or Phi 4?
Llama 3.3 70B Instruct costs $0.45 per million input tokens and $0.72 per million output tokens; Phi 4 costs $0.07 and $0.14. Cost per solved task is known for neither of them.
Which has the bigger context window, Llama 3.3 70B Instruct or Phi 4?
Llama 3.3 70B Instruct, with 131K tokens against 16K.
How many benchmarks is the Llama 3.3 70B Instruct vs Phi 4 comparison based on?
44 benchmarks have a result for both models as of October 21, 2026. Llama 3.3 70B Instruct's coding index rests on 0 independent benchmarks and Phi 4's on 0. Where only one model has a result, the page says so instead of naming a winner.