Model comparison
Llama-3.3-70B-Instruct vs o4-mini
o4-mini is the stronger model overall, scoring 41.6 to 30.6 on the Noometry Index. Llama-3.3-70B-Instruct costs 12× less per token, which makes it the better buy when o4-mini's lead doesn't matter for your workload.
Last verified . 31 shared benchmarks.
MetaSummary
- They share 31 benchmarks with published results for both. Llama-3.3-70B-Instruct scores higher in 0 categories and o4-mini in 9 categories; 9 gaps are clear of the uncertainty.
- The widest gap is in math, where o4-mini leads 40.8 to 15.3.
- The biggest single-benchmark swing is OTIS Mock AIME 2024-2025: 5.1% for Llama-3.3-70B-Instruct and 81.7% for o4-mini.
- Llama-3.3-70B-Instruct is cheaper at $0.10 / $0.32 per million input/output tokens, against $1.10 / $4.40 for o4-mini.
- o4-mini accepts more context: 200K tokens versus 128K.
- Llama-3.3-70B-Instruct has downloadable open weights; the other is API-only.
Side by side
| Llama-3.3-70B-Instruct | o4-mini | |
|---|---|---|
| Provider | Meta | OpenAI |
| Noometry Index | 30.6 | 41.6 |
| Released | 2024-12-06 | 2025-04-16 |
| Weights | Open | Proprietary |
| Context window | 128K | 200K |
| Max output | 4K | 100K |
| Input $ / M tokens | $0.10 | $1.10 |
| Output $ / M tokens | $0.32 | $4.40 |
| Results tracked | 43 | 60 |
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Category by category
Coding o4-mini leads
Llama-3.3-70B-Instruct: 31.0 (#290), o4-mini: 40.9 (#127)
| Benchmark | Llama-3.3-70B-Instruct | o4-mini |
|---|---|---|
| WeirdML | 14.4% | 52.6% |
| LMArena Coding | 1268 | 1368 |
| SWE-bench Verified (bash only) | — | 45% |
| Aider Polyglot | — | 72% |
| SciCode | 26% | — |
| GSO | — | 3.6% |
| BigCodeBench Instruct | 46.9% | — |
| LiveBench Coding | 36.6% | — |
| BigCodeBench Complete | 57.5% | — |
| CadEval | — | 62% |
| ALE-Bench | — | 826.17 |
| AlgoTune | — | 1.72 |
Agentic & Tool Use o4-mini leads
Llama-3.3-70B-Instruct: 25.8 (#105), o4-mini: 32.6 (#61)
| Benchmark | Llama-3.3-70B-Instruct | o4-mini |
|---|---|---|
| Berkeley Function Calling Leaderboard | 31.9% | 53.2% |
| GDPval | — | 25.3% |
| BALROG | 23% | — |
| METR Time Horizons | — | 63.9% |
Reasoning o4-mini leads
Llama-3.3-70B-Instruct: 14.1 (#327), o4-mini: 24.6 (#162)
| Benchmark | Llama-3.3-70B-Instruct | o4-mini |
|---|---|---|
| SimpleBench | 19.9% | 38.7% |
| CritPt | 0% | 0.6% |
| LMArena Hard Prompts | 1257 | 1351 |
| DTBench | 59.5% | 77.6% |
| LMCA | 17.5% | 26.5% |
| Epoch Capabilities Index | 127.33 | 145.64 |
| ForecastBench | 58.6 | 61.8 |
| ARC-AGI-2 | — | 6.1% |
| Kagi LLM Benchmark | — | 67.6% |
| ARC-AGI-1 | — | 58.7% |
| Chess Puzzles | — | 26% |
| EnigmaEval | — | 9.2% |
| LiveBench Reasoning | 50.8% | — |
| Mystery Game Puzzles | — | 5% |
| LiveBench Data Analysis | 49.5% | — |
| LiveBench | 50.2% | — |
Math o4-mini leads
Llama-3.3-70B-Instruct: 15.3 (#298), o4-mini: 40.8 (#89)
| Benchmark | Llama-3.3-70B-Instruct | o4-mini |
|---|---|---|
| OTIS Mock AIME 2024-2025 | 5.1% | 81.7% |
| LMArena Math | 1267 | 1389 |
| MATH Level 5 | 41.6% | 97.8% |
| FrontierMath (Tiers 1-3) | — | 36.1% |
| FrontierMath Tier 4 | — | 4.9% |
| Omni-MATH | — | 72% |
| LiveBench Math | 42.2% | — |
| FrontierMath (Feb 2025 set) | — | 24.8% |
| FrontierMath Tier 4 (v1) | — | 6.3% |
Knowledge o4-mini leads
Llama-3.3-70B-Instruct: 30.6 (#226), o4-mini: 43.6 (#91)
| Benchmark | Llama-3.3-70B-Instruct | o4-mini |
|---|---|---|
| GPQA Diamond | 47.4% | 79.6% |
| Confabulations | 22.8% | 15.8% |
| Vectara Hallucination Rate | 4.1% | 18.6% |
| LMArena Expert | 1225 | 1343 |
| Humanity's Last Exam | — | 18.1% |
| SimpleQA Verified | — | 19.6% |
| MMLU-Pro | — | 82% |
| GPQA (HELM) | — | 73.5% |
| MMLU | 86.3% | — |
Multimodal Not comparable
Llama-3.3-70B-Instruct: —, o4-mini: 40.2 (#49)
| Benchmark | Llama-3.3-70B-Instruct | o4-mini |
|---|---|---|
| LMArena Vision | — | 1194 |
| GeoBench | — | 64% |
| VPCT | — | 57.5% |
Multilingual o4-mini leads
Llama-3.3-70B-Instruct: 39.9 (#220), o4-mini: 47.0 (#154)
| Benchmark | Llama-3.3-70B-Instruct | o4-mini |
|---|---|---|
| LMArena Non-English | 1236 | 1337 |
| LMArena Chinese | 1217 | 1354 |
| LMArena French | 1281 | 1364 |
| LMArena German | 1251 | 1336 |
| LMArena Japanese | 1150 | 1308 |
| LMArena Korean | 1143 | 1312 |
| LMArena Russian | 1252 | 1334 |
| LMArena Spanish | 1270 | 1347 |
Instruction Following o4-mini leads
Llama-3.3-70B-Instruct: 71.1 (#157), o4-mini: 75.2 (#68)
| Benchmark | Llama-3.3-70B-Instruct | o4-mini |
|---|---|---|
| LMArena Instruction Following | 1242 | 1321 |
| LiveBench Instruction Following | 82.7% | — |
| IFEval | — | 92.8% |
Long Context o4-mini leads
Llama-3.3-70B-Instruct: 26.4 (#295), o4-mini: 45.5 (#33)
| Benchmark | Llama-3.3-70B-Instruct | o4-mini |
|---|---|---|
| Fiction.LiveBench | 33.3% | 77.8% |
| LMArena Longer Query | 1256 | 1315 |
Writing & Preference o4-mini leads
Llama-3.3-70B-Instruct: 47.6 (#207), o4-mini: 54.0 (#152)
| Benchmark | Llama-3.3-70B-Instruct | o4-mini |
|---|---|---|
| LMArena Text | 1274 | 1353 |
| LMArena Creative Writing | 1250 | 1294 |
| LMArena Multi-Turn | 1280 | 1350 |
| Short-Story Creative Writing | — | 75% |
| WildBench | — | 85.4% |
| LiveBench Language | 39.2% | — |
Frequently asked questions
Is Llama-3.3-70B-Instruct better than o4-mini?
o4-mini is the stronger model overall, scoring 41.6 to 30.6 on the Noometry Index. Llama-3.3-70B-Instruct costs 12× less per token, which makes it the better buy when o4-mini's lead doesn't matter for your workload.
Which is cheaper, Llama-3.3-70B-Instruct or o4-mini?
Llama-3.3-70B-Instruct is cheaper. It lists at $0.10 per million input tokens and $0.32 per million output tokens; o4-mini lists at $1.10 and $4.40.
Is Llama-3.3-70B-Instruct or o4-mini better for coding?
o4-mini scores higher on coding benchmarks: 40.9 versus 31.0 in the Noometry coding category.
Which has the bigger context window?
o4-mini does, with 200K tokens against 128K.
How many benchmarks do Llama-3.3-70B-Instruct and o4-mini share?
31 benchmarks have published results for both models. Llama-3.3-70B-Instruct has 43 scored results on Noometry and o4-mini has 60.