Llama 3.3 70B Instruct vs o3 Pro
Updated October 21, 2026. 7 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 output tokens cost 111.1x less, and where what matters is WeirdML or AA Intelligence Index, where it leads.
- Shared results
- 7
- Llama 3.3 70B Instruct only
- 44
- o3 Pro only
- 4
- Like-for-like categories
- 4 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
o3 Pro
o3 Pro leads, 21.9 against 7.7.
Same board, both measured
Long documents
Listed context window
o3 Pro
o3 Pro takes 200K tokens against 131K.
Listed by OpenRouter
Everyday cost
1K input + 500 output tokens
Llama 3.3 70B Instruct
Llama 3.3 70B Instruct $0.0008, o3 Pro $0.060 per turn.
List prices
At a glance
- Coding index
- Llama 3.3 70B Instruct not benchmarked, o3 Pro not benchmarked.
- Widest category
- Coding, 43.8 points apart on average over 1 shared benchmark.
- Widest single gap
- Fiction.LiveBench: Llama 3.3 70B Instruct 33.3% against o3 Pro 97.2%, 63.9 points apart on a 0 to 100 scale.
- Output price
- Llama 3.3 70B Instruct $0.72 against o3 Pro $80.00 per million output tokens, 111.1x apart.
- Context window
- Llama 3.3 70B Instruct takes 131K tokens, o3 Pro 200K.
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 | o3 Pro | Ties |
|---|---|---|---|---|
| Coding | 0 | 1 | ||
| Intelligence | 0 | 2 | ||
| Reasoning | 0 | 1 | ||
| General | 0 | 3 | ||
| All shared boards | 0 | 7 |
CodingLike-for-likeSix of these boards feed the Vibe Coding Index01
| Benchmark | Llama 3.3 70B Instruct | o3 Pro | Leader |
|---|---|---|---|
| Aider polyglot | Not measured | 84.9% | Only o3 Pro measured |
| AA Coding Index | 11.9 | Not measured | Only Llama 3.3 70B Instruct measured |
| LiveCodeBench (Artificial Analysis run) | 28.8% | Not measured | Only Llama 3.3 70B Instruct measured |
| Terminal-Bench Hard (Artificial Analysis run) | 3.0% | Not measured | Only Llama 3.3 70B Instruct measured |
| WeirdML | 14.4% | 58.2% | o3 Pro |
| LMArena Text: coding | 1268 | Not measured | Only Llama 3.3 70B Instruct measured |
| LMArena Text: software and IT | 1273 | Not measured | Only Llama 3.3 70B Instruct measured |
| Neither model has a result on the other 31 boards here | |||
IntelligenceLike-for-likePublic boards, shown as published, kept out of the index02
| Benchmark | Llama 3.3 70B Instruct | o3 Pro | Leader |
|---|---|---|---|
| AA Intelligence Index | 7.7 | 21.9 | o3 Pro |
| Epoch Capabilities Index | 127.3 | 147.3 | o3 Pro |
Agentic workDirectional onlyPublic boards, shown as published, kept out of the index2 boards, none shared
| Benchmark | Llama 3.3 70B Instruct | o3 Pro | Leader |
|---|---|---|---|
| τ²-Bench Telecom | 26.6% | Not measured | Only Llama 3.3 70B Instruct measured |
| BALROG | 23.0% | Not measured | Only Llama 3.3 70B Instruct measured |
| Neither model has a result on the other 37 boards here | |||
ReasoningLike-for-likePublic boards, shown as published, kept out of the index01
| Benchmark | Llama 3.3 70B Instruct | o3 Pro | Leader |
|---|---|---|---|
| GPQA Diamond (Artificial Analysis run) | 49.8% | 84.5% | o3 Pro |
| Humanity's Last Exam (Artificial Analysis run) | 3.6% | Not measured | Only Llama 3.3 70B Instruct measured |
| GPQA Diamond (Epoch AI run) | 47.4% | Not measured | Only Llama 3.3 70B Instruct measured |
| ARC-AGI | Not measured | 59.3% | Only o3 Pro measured |
| ARC-AGI-2 | Not measured | 4.9% | Only o3 Pro measured |
| SimpleBench | 19.9% | Not measured | Only Llama 3.3 70B Instruct measured |
| LMArena Text: hard prompts | 1257 | Not measured | Only Llama 3.3 70B Instruct measured |
| LMArena Text: expert | 1225 | Not measured | Only Llama 3.3 70B Instruct measured |
| LMArena Text: hard prompts in English | 1277 | Not measured | Only Llama 3.3 70B Instruct measured |
| Neither model has a result on the other 12 boards here | |||
MathDirectional onlyPublic boards, shown as published, kept out of the index6 boards, none shared
| Benchmark | Llama 3.3 70B Instruct | o3 Pro | Leader |
|---|---|---|---|
| AA Math Index | 7.7 | Not measured | Only Llama 3.3 70B Instruct measured |
| AIME 2025 (Artificial Analysis run) | 7.7% | Not measured | Only Llama 3.3 70B Instruct measured |
| OTIS Mock AIME | 5.1% | Not measured | Only Llama 3.3 70B Instruct measured |
| MATH level 5 | 41.6% | Not measured | Only Llama 3.3 70B Instruct measured |
| LMArena Text: math | 1267 | Not measured | Only Llama 3.3 70B Instruct measured |
| LMArena Text: mathematical work | 1267 | Not measured | Only Llama 3.3 70B Instruct measured |
| Neither model has a result on the other 7 boards here | |||
KnowledgeDirectional onlyPublic boards, shown as published, kept out of the index6 boards, none shared
| Benchmark | Llama 3.3 70B Instruct | o3 Pro | Leader |
|---|---|---|---|
| MMLU Pro (Artificial Analysis run) | 71.3% | Not measured | Only Llama 3.3 70B Instruct measured |
| MMLU | 86.3% | Not measured | Only Llama 3.3 70B Instruct measured |
| LMArena Text: science | 1284 | Not measured | Only Llama 3.3 70B Instruct measured |
| LMArena Text: medicine and healthcare | 1275 | Not measured | Only Llama 3.3 70B Instruct measured |
| LMArena Text: legal and government | 1284 | Not measured | Only Llama 3.3 70B Instruct measured |
| LMArena Text: business and finance | 1243 | Not measured | Only Llama 3.3 70B Instruct measured |
| 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 | o3 Pro | Leader |
|---|---|---|---|
| Neither model has a result on 9 boards here | |||
GeneralLike-for-likePublic boards, shown as published, kept out of the index03
| Benchmark | Llama 3.3 70B Instruct | o3 Pro | Leader |
|---|---|---|---|
| IFBench | 47.1% | Not measured | Only Llama 3.3 70B Instruct measured |
| Long context reasoning (AA-LCR) | 15.7% | Not measured | Only Llama 3.3 70B Instruct measured |
| DTBench | 59.5% | 86.9% | o3 Pro |
| LMCA | 17.5% | 38.5% | o3 Pro |
| Fiction.LiveBench | 33.3% | 97.2% | o3 Pro |
| Creative writing (Lech Mazur) | Not measured | 84.4% | Only o3 Pro measured |
| LMArena Text | 1274 | Not measured | Only Llama 3.3 70B Instruct measured |
| LMArena Text: instruction following | 1242 | Not measured | Only Llama 3.3 70B Instruct measured |
| LMArena Text: creative writing | 1250 | Not measured | Only Llama 3.3 70B Instruct measured |
| LMArena Text: English | 1303 | Not measured | Only Llama 3.3 70B Instruct measured |
| LMArena Text: non-English | 1236 | Not measured | Only Llama 3.3 70B Instruct measured |
| LMArena Text: Chinese | 1217 | Not measured | Only Llama 3.3 70B Instruct measured |
| LMArena Text: German | 1251 | Not measured | Only Llama 3.3 70B Instruct measured |
| LMArena Text: Japanese | 1150 | Not measured | Only Llama 3.3 70B Instruct measured |
| LMArena Text: Korean | 1142 | Not measured | Only Llama 3.3 70B Instruct measured |
| LMArena Text: French | 1282 | Not measured | Only Llama 3.3 70B Instruct measured |
| LMArena Text: Spanish | 1269 | Not measured | Only Llama 3.3 70B Instruct measured |
| LMArena Text: Russian | 1252 | Not measured | Only Llama 3.3 70B Instruct measured |
| LMArena Text: Polish | 1259 | Not measured | Only Llama 3.3 70B Instruct measured |
| LMArena Text: multi-turn | 1280 | Not measured | Only Llama 3.3 70B Instruct measured |
| LMArena Text: longer queries | 1255 | Not measured | Only Llama 3.3 70B Instruct measured |
| LMArena Text: writing and language | 1255 | Not measured | Only Llama 3.3 70B Instruct measured |
| LMArena Text: entertainment and media | 1241 | Not measured | Only Llama 3.3 70B Instruct measured |
| 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 | o3 Pro | 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 | Meta Llama 3.3 70B Instruct | OpenAI o3 Pro |
|---|---|---|
| Maker | Meta Llama | OpenAI |
| Weights | Open weights | No public weights listed |
| Released | December 6, 2024 | June 10, 2025 |
| Context window | 131K | 200K |
| Input $/Mtok | $0.45 | $20.00 |
| Output $/Mtok | $0.72 | $80.00 |
| 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 o3 Pro 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 output tokens cost 111.1x less, and where what matters is WeirdML or AA Intelligence Index, where it leads.
Which is cheaper, Llama 3.3 70B Instruct or o3 Pro?
Llama 3.3 70B Instruct costs $0.45 per million input tokens and $0.72 per million output tokens; o3 Pro costs $20.00 and $80.00. Cost per solved task is known for neither of them.
Which has the bigger context window, Llama 3.3 70B Instruct or o3 Pro?
o3 Pro, with 200K tokens against 131K.
How many benchmarks is the Llama 3.3 70B Instruct vs o3 Pro comparison based on?
7 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 o3 Pro's on 0. Where only one model has a result, the page says so instead of naming a winner.