Model comparison
GPT-4o mini vs Llama 3.1-70B
Llama 3.1-70B is the stronger model overall, scoring 29.6 to 25.5 on the Noometry Index. GPT-4o mini costs 1.5× less per token, which makes it the better buy when Llama 3.1-70B's lead doesn't matter for your workload.
Last verified . 34 shared benchmarks.
MetaSummary
- They share 34 benchmarks with published results for both. GPT-4o mini scores higher in 4 categories and Llama 3.1-70B in 5 categories; 9 gaps are clear of the uncertainty.
- The widest gap is in reasoning, where Llama 3.1-70B leads 21.6 to 8.7.
- The biggest single-benchmark swing is MATH Level 5: 52.6% for GPT-4o mini and 36.7% for Llama 3.1-70B.
- GPT-4o mini is cheaper at $0.15 / $0.60 per million input/output tokens, against $0.40 / $0.40 for Llama 3.1-70B.
- Llama 3.1-70B has downloadable open weights; the other is API-only.
Side by side
| GPT-4o mini | Llama 3.1-70B | |
|---|---|---|
| Provider | OpenAI | Meta |
| Noometry Index | 25.5 | 29.6 |
| Released | 2024-07-18 | 2024-07-23 |
| Weights | Proprietary | Open |
| Context window | 128K | 128K |
| Max output | 16K | 4K |
| Input $ / M tokens | $0.15 | $0.40 |
| Output $ / M tokens | $0.60 | $0.40 |
| Results tracked | 60 | 35 |
Sponsored placements are available on pages like this one. Advertise on Noometry
Category by category
Coding Llama 3.1-70B leads
GPT-4o mini: 22.0 (#335), Llama 3.1-70B: 30.3 (#296)
| Benchmark | GPT-4o mini | Llama 3.1-70B |
|---|---|---|
| WeirdML | 11.8% | 9% |
| BigCodeBench Instruct | 46.1% | 46.1% |
| LMArena Coding | 1290 | 1260 |
| BigCodeBench Complete | 57.4% | 54.8% |
| Aider Polyglot | 3.6% | — |
| LiveBench Coding | 43.1% | — |
| HumanEval+ | 83.5% | — |
| MBPP+ | 72.2% | — |
Agentic & Tool Use GPT-4o mini leads
GPT-4o mini: 27.5 (#101), Llama 3.1-70B: 25.1 (#112)
| Benchmark | GPT-4o mini | Llama 3.1-70B |
|---|---|---|
| BALROG | 17.4% | 27.9% |
| TheAgentCompany | — | 6.9% |
Reasoning Llama 3.1-70B leads
GPT-4o mini: 8.7 (#347), Llama 3.1-70B: 21.6 (#220)
| Benchmark | GPT-4o mini | Llama 3.1-70B |
|---|---|---|
| LMArena Hard Prompts | 1267 | 1241 |
| DTBench | 54.4% | 60% |
| LMCA | 10.4% | 14.8% |
| Epoch Capabilities Index | 126.56 | 125.92 |
| ARC-AGI-2 | 0% | — |
| SimpleBench | 10.7% | — |
| Kagi LLM Benchmark | 28.8% | — |
| Chess Puzzles | 0% | — |
| LiveBench Reasoning | 32.8% | — |
| Mystery Game Puzzles | 12% | — |
| LiveBench Data Analysis | 50% | — |
| LiveBench | 41.3% | — |
| PIQA | 88.7% | — |
Math Llama 3.1-70B leads
GPT-4o mini: 10.4 (#314), Llama 3.1-70B: 13.5 (#304)
| Benchmark | GPT-4o mini | Llama 3.1-70B |
|---|---|---|
| OTIS Mock AIME 2024-2025 | 6.9% | 3.6% |
| Omni-MATH | 28% | 21% |
| LMArena Math | 1267 | 1252 |
| MATH Level 5 | 52.6% | 36.7% |
| FrontierMath (Tiers 1-3) | 0.7% | — |
| LiveBench Math | 36.3% | — |
| GSM8K | 91.3% | — |
Knowledge Llama 3.1-70B leads
GPT-4o mini: 17.7 (#284), Llama 3.1-70B: 24.2 (#269)
| Benchmark | GPT-4o mini | Llama 3.1-70B |
|---|---|---|
| GPQA Diamond | 37.7% | 44.2% |
| MMLU-Pro | 60.3% | 65.3% |
| GPQA (HELM) | 36.8% | 42.6% |
| LMArena Expert | 1235 | 1209 |
| MMLU | 81.8% | 80.1% |
| SimpleQA Verified | 8.3% | — |
| Confabulations | 37.2% | — |
| BoolQ | 88.7% | — |
Multimodal Not comparable
GPT-4o mini: 25.9 (#122), Llama 3.1-70B: —
| Benchmark | GPT-4o mini | Llama 3.1-70B |
|---|---|---|
| LMArena Vision | 1066 | — |
| Video-MME | 64.8% | — |
| GeoBench | 64% | — |
| VPCT | 34% | — |
Multilingual GPT-4o mini leads
GPT-4o mini: 42.0 (#199), Llama 3.1-70B: 38.8 (#225)
| Benchmark | GPT-4o mini | Llama 3.1-70B |
|---|---|---|
| LMArena Non-English | 1266 | 1219 |
| LMArena Chinese | 1265 | 1215 |
| LMArena French | 1297 | 1261 |
| LMArena German | 1272 | 1222 |
| LMArena Japanese | 1216 | 1132 |
| LMArena Korean | 1195 | 1140 |
| LMArena Russian | 1275 | 1234 |
| LMArena Spanish | 1276 | 1253 |
Instruction Following Llama 3.1-70B leads
GPT-4o mini: 61.9 (#239), Llama 3.1-70B: 65.3 (#223)
| Benchmark | GPT-4o mini | Llama 3.1-70B |
|---|---|---|
| IFEval | 78.2% | 82.1% |
| LMArena Instruction Following | 1258 | 1231 |
| LiveBench Instruction Following | 56.8% | — |
Long Context GPT-4o mini leads
GPT-4o mini: 39.1 (#186), Llama 3.1-70B: 37.6 (#214)
| Benchmark | GPT-4o mini | Llama 3.1-70B |
|---|---|---|
| LMArena Longer Query | 1289 | 1241 |
Writing & Preference GPT-4o mini leads
GPT-4o mini: 39.5 (#248), Llama 3.1-70B: 35.4 (#267)
| Benchmark | GPT-4o mini | Llama 3.1-70B |
|---|---|---|
| LMArena Text | 1286 | 1261 |
| LMArena Creative Writing | 1268 | 1232 |
| EQ-Bench Creative Writing | 873 | 784 |
| WildBench | 79.1% | 75.8% |
| LMArena Multi-Turn | 1285 | 1256 |
| Short-Story Creative Writing | 67.2% | — |
| LiveBench Language | 28.6% | — |
Frequently asked questions
Is GPT-4o mini better than Llama 3.1-70B?
Llama 3.1-70B is the stronger model overall, scoring 29.6 to 25.5 on the Noometry Index. GPT-4o mini costs 1.5× less per token, which makes it the better buy when Llama 3.1-70B's lead doesn't matter for your workload.
Which is cheaper, GPT-4o mini or Llama 3.1-70B?
GPT-4o mini is cheaper. It lists at $0.15 per million input tokens and $0.60 per million output tokens; Llama 3.1-70B lists at $0.40 and $0.40.
Is GPT-4o mini or Llama 3.1-70B better for coding?
Llama 3.1-70B scores higher on coding benchmarks: 30.3 versus 22.0 in the Noometry coding category.
Which has the bigger context window?
Both accept 128K tokens.
How many benchmarks do GPT-4o mini and Llama 3.1-70B share?
34 benchmarks have published results for both models. GPT-4o mini has 60 scored results on Noometry and Llama 3.1-70B has 35.