Llama 3.3 70B Instruct vs o1
o1 leads Llama 3.3 70B Instruct on the Artificial Analysis Intelligence Index, 15.2 to 7.7, and Llama 3.3 70B Instruct costs 83.3x less per output token ($0.72 vs $60.00 per million). Updated October 21, 2026; 46 benchmarks have a result for both.
Llama 3.3 70B Instruct or o1: which to pick
Neither model has a coding score yet, so the two cannot be ranked on coding.
Pick Llama 3.3 70B Instruct for output tokens 83.3x cheaper ($0.72 vs $60.00 per million) and work measured by AA Coding Index or LiveCodeBench (Artificial Analysis run), where it leads.
- Shared results
- 46
- Llama 3.3 70B Instruct only
- 5
- o1 only
- 22
- 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
o1
o1 leads, 15.2 against 7.7.
Same board, both measured
Reasoning
GPQA Diamond
No call
Only o1 has a GPQA Diamond result, 73.2%.
Directional only
Math
AIME
No call
Only o1 has a AIME result, 71.5%.
Directional only
Knowledge
MMLU Pro
No call
Only o1 has a MMLU Pro result, 83.5%.
Directional only
Long documents
Listed context window
o1
o1 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, o1 $0.045 per turn.
List prices
At a glance
- Coding index
- Llama 3.3 70B Instruct not benchmarked, o1 not benchmarked.
- Widest category
- Math, 40.5 points apart on average over 4 shared benchmarks.
- Widest single gap
- OTIS Mock AIME: Llama 3.3 70B Instruct 5.1% against o1 73.3%, 68.2 points apart on a 0 to 100 scale.
- Output price
- Llama 3.3 70B Instruct $0.72 against o1 $60.00 per million output tokens, 83.3x apart.
- Context window
- Llama 3.3 70B Instruct takes 131K tokens, o1 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 | o1 | Ties |
|---|---|---|---|---|
| Coding | 0 | 6 | ||
| Intelligence | 0 | 2 | ||
| Agentic work | 0 | 1 | ||
| Reasoning | 0 | 7 | ||
| Math | 0 | 4 | ||
| Knowledge | 0 | 5 | ||
| General | 0 | 21 | ||
| All shared boards | 0 | 46 |
CodingLike-for-likeSix of these boards feed the Vibe Coding Index06
| Benchmark | Llama 3.3 70B Instruct | o1 | Leader |
|---|---|---|---|
| Aider polyglot | Not measured | 61.7% | Only o1 measured |
| AA Coding Index | 11.9 | 39.7 | o1 |
| LiveCodeBench (Vals AI) | Not measured | 50.3% | Only o1 measured |
| LiveCodeBench (Artificial Analysis run) | 28.8% | 67.9% | o1 |
| Terminal-Bench Hard (Artificial Analysis run) | 3.0% | 12.9% | o1 |
| WeirdML | 14.4% | 46.1% | o1 |
| LMArena Text: coding | 1268 | 1367 | o1 |
| LMArena Text: software and IT | 1273 | 1366 | o1 |
| Neither model has a result on the other 30 boards here | |||
IntelligenceLike-for-likePublic boards, shown as published, kept out of the index02
| Benchmark | Llama 3.3 70B Instruct | o1 | Leader |
|---|---|---|---|
| AA Intelligence Index | 7.7 | 15.2 | o1 |
| Epoch Capabilities Index | 127.3 | 141.7 | o1 |
Agentic workLike-for-likePublic boards, shown as published, kept out of the index01
| Benchmark | Llama 3.3 70B Instruct | o1 | Leader |
|---|---|---|---|
| τ²-Bench Telecom | 26.6% | 62.6% | o1 |
| METR time horizons | Not measured | 51.1% | Only o1 measured |
| BALROG | 23.0% | Not measured | Only Llama 3.3 70B Instruct measured |
| Neither model has a result on the other 36 boards here | |||
ReasoningLike-for-likePublic boards, shown as published, kept out of the index07
| Benchmark | Llama 3.3 70B Instruct | o1 | Leader |
|---|---|---|---|
| GPQA Diamond | Not measured | 73.2% | Only o1 measured |
| MMMU Pro (Vals AI) | Not measured | 77.4% | Only o1 measured |
| GPQA Diamond (Artificial Analysis run) | 49.8% | 74.7% | o1 |
| Humanity's Last Exam (Artificial Analysis run) | 3.6% | 7.0% | o1 |
| GPQA Diamond (Epoch AI run) | 47.4% | 76.8% | o1 |
| ARC-AGI | Not measured | 30.7% | Only o1 measured |
| SimpleBench | 19.9% | 40.1% | o1 |
| Chess puzzles | Not measured | 10.0% | Only o1 measured |
| VPCT | Not measured | 37.0% | Only o1 measured |
| LMArena Text: hard prompts | 1257 | 1371 | o1 |
| LMArena Text: expert | 1225 | 1361 | o1 |
| LMArena Text: hard prompts in English | 1277 | 1372 | o1 |
| Neither model has a result on the other 9 boards here | |||
MathLike-for-likePublic boards, shown as published, kept out of the index04
| Benchmark | Llama 3.3 70B Instruct | o1 | Leader |
|---|---|---|---|
| AIME | Not measured | 71.5% | Only o1 measured |
| MATH 500 (Vals AI) | Not measured | 90.4% | Only o1 measured |
| MGSM (Vals AI) | Not measured | 89.3% | Only o1 measured |
| 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% | 73.3% | o1 |
| FrontierMath, tiers 1 to 3 | Not measured | 14.7% | Only o1 measured |
| MATH level 5 | 41.6% | 94.7% | o1 |
| LMArena Text: math | 1267 | 1388 | o1 |
| LMArena Text: mathematical work | 1267 | 1390 | o1 |
| Neither model has a result on the other 3 boards here | |||
KnowledgeLike-for-likePublic boards, shown as published, kept out of the index05
| Benchmark | Llama 3.3 70B Instruct | o1 | Leader |
|---|---|---|---|
| MMLU Pro | Not measured | 83.5% | Only o1 measured |
| LegalBench (Vals AI) | Not measured | 80.4% | Only o1 measured |
| TaxEval (Vals AI) | Not measured | 74.3% | Only o1 measured |
| MedQA (Vals AI) | Not measured | 96.5% | Only o1 measured |
| MMLU Pro (Artificial Analysis run) | 71.3% | 84.1% | o1 |
| SimpleQA Verified | Not measured | 41.1% | Only o1 measured |
| MMLU | 86.3% | Not measured | Only Llama 3.3 70B Instruct measured |
| GeoBench | Not measured | 80.0% | Only o1 measured |
| LMArena Text: science | 1284 | 1369 | o1 |
| LMArena Text: medicine and healthcare | 1275 | 1355 | o1 |
| LMArena Text: legal and government | 1284 | 1368 | o1 |
| LMArena Text: business and finance | 1243 | 1328 | o1 |
| 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 | Llama 3.3 70B Instruct | o1 | Leader |
|---|---|---|---|
| Neither model has a result on 9 boards here | |||
GeneralLike-for-likePublic boards, shown as published, kept out of the index021
Image generationNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
| Benchmark | Llama 3.3 70B Instruct | o1 | 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 o1 |
|---|---|---|
| Maker | Meta Llama | OpenAI |
| Weights | Open weights | No public weights listed |
| Released | December 6, 2024 | December 17, 2024 |
| Context window | 131K | 200K |
| Input $/Mtok | $0.45 | $15.00 |
| Output $/Mtok | $0.72 | $60.00 |
| 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 o1 for coding?
Neither model has a coding score yet, so the two cannot be ranked on coding. Pick Llama 3.3 70B Instruct for output tokens 83.3x cheaper ($0.72 vs $60.00 per million) and work measured by AA Coding Index or LiveCodeBench (Artificial Analysis run), where it leads.
Which is cheaper, Llama 3.3 70B Instruct or o1?
Llama 3.3 70B Instruct costs $0.45 per million input tokens and $0.72 per million output tokens; o1 costs $15.00 and $60.00. Cost per solved task is known for neither of them.
Which has the bigger context window, Llama 3.3 70B Instruct or o1?
o1, with 200K tokens against 131K.
How many benchmarks is the Llama 3.3 70B Instruct vs o1 comparison based on?
46 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 o1's on 0. Where only one model has a result, the page says so instead of naming a winner.