Model comparison
GPT-5.1 vs Llama 3.1-70B
GPT-5.1 is the stronger model overall, scoring 49.0 to 29.6 on the Noometry Index. Llama 3.1-70B costs 8.6× less per token, which makes it the better buy when GPT-5.1's lead doesn't matter for your workload.
Last verified . 28 shared benchmarks.
MetaSummary
- They share 28 benchmarks with published results for both. GPT-5.1 scores higher in 9 categories and Llama 3.1-70B in 0 categories; 9 gaps are clear of the uncertainty.
- The widest gap is in math, where GPT-5.1 leads 52.2 to 13.5.
- The biggest single-benchmark swing is OTIS Mock AIME 2024-2025: 88.6% for GPT-5.1 and 3.6% for Llama 3.1-70B.
- Llama 3.1-70B is cheaper at $0.40 / $0.40 per million input/output tokens, against $1.25 / $10 for GPT-5.1.
- GPT-5.1 accepts more context: 400K tokens versus 128K.
- Llama 3.1-70B has downloadable open weights; the other is API-only.
Side by side
| GPT-5.1 | Llama 3.1-70B | |
|---|---|---|
| Provider | OpenAI | Meta |
| Noometry Index | 49.0 | 29.6 |
| Released | 2025-11-13 | 2024-07-23 |
| Weights | Proprietary | Open |
| Context window | 400K | 128K |
| Max output | 128K | 4K |
| Input $ / M tokens | $1.25 | $0.40 |
| Output $ / M tokens | $10 | $0.40 |
| Results tracked | 63 | 35 |
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Category by category
Coding GPT-5.1 leads
GPT-5.1: 46.4 (#66), Llama 3.1-70B: 30.3 (#296)
| Benchmark | GPT-5.1 | Llama 3.1-70B |
|---|---|---|
| WeirdML | 60.8% | 9% |
| LMArena Coding | 1454 | 1260 |
| SWE-bench Verified | 68% | — |
| SWE-bench Verified (bash only) | 66% | — |
| LMArena WebDev | 1395 | — |
| SciCode | 43.3% | — |
| GSO | 13.7% | — |
| BigCodeBench Instruct | — | 46.1% |
| LiveBench Coding | 72.5% | — |
| BigCodeBench Complete | — | 54.8% |
| ALE-Bench | 1,192 | — |
Agentic & Tool Use GPT-5.1 leads
GPT-5.1: 32.7 (#60), Llama 3.1-70B: 25.1 (#112)
| Benchmark | GPT-5.1 | Llama 3.1-70B |
|---|---|---|
| Terminal-Bench | 47.6% | — |
| TheAgentCompany | — | 6.9% |
| DeepResearch Bench | 42.8% | — |
| BALROG | — | 27.9% |
| LMArena Search | 1199 | — |
| Vending-Bench 2 | 1,473 | — |
Reasoning GPT-5.1 leads
GPT-5.1: 39.8 (#58), Llama 3.1-70B: 21.6 (#220)
| Benchmark | GPT-5.1 | Llama 3.1-70B |
|---|---|---|
| LMArena Hard Prompts | 1457 | 1241 |
| DTBench | 90.1% | 60% |
| LMCA | 43.9% | 14.8% |
| Epoch Capabilities Index | 149.64 | 125.92 |
| ARC-AGI-2 | 17.6% | — |
| SimpleBench | 53.2% | — |
| ARC-AGI-1 | 72.8% | — |
| CritPt | 4.9% | — |
| Chess Puzzles | 32% | — |
| EnigmaEval | 11.2% | — |
| LiveBench Reasoning | 95.8% | — |
| Mystery Game Puzzles | 19% | — |
| LiveBench Data Analysis | 72.1% | — |
| ForecastBench | 58.1 | — |
| LiveBench | 78.8% | — |
Math GPT-5.1 leads
GPT-5.1: 52.2 (#51), Llama 3.1-70B: 13.5 (#304)
| Benchmark | GPT-5.1 | Llama 3.1-70B |
|---|---|---|
| OTIS Mock AIME 2024-2025 | 88.6% | 3.6% |
| Omni-MATH | 46.4% | 21% |
| LMArena Math | 1447 | 1252 |
| LiveBench Math | 94.5% | — |
| MATH Level 5 | — | 36.7% |
| FrontierMath (Feb 2025 set) | 31% | — |
| FrontierMath Tier 4 (v1) | 12.5% | — |
Knowledge GPT-5.1 leads
GPT-5.1: 50.6 (#71), Llama 3.1-70B: 24.2 (#269)
| Benchmark | GPT-5.1 | Llama 3.1-70B |
|---|---|---|
| GPQA Diamond | 87.6% | 44.2% |
| MMLU-Pro | 57.9% | 65.3% |
| GPQA (HELM) | 44.2% | 42.6% |
| LMArena Expert | 1470 | 1209 |
| Humanity's Last Exam | 23.7% | — |
| SimpleQA Verified | 48% | — |
| Vectara Hallucination Rate | 10.9% | — |
| MMLU | — | 80.1% |
Multimodal Not comparable
GPT-5.1: 44.8 (#19), Llama 3.1-70B: —
| Benchmark | GPT-5.1 | Llama 3.1-70B |
|---|---|---|
| LMArena Vision | 1250 | — |
| VPCT | 58.7% | — |
| LMArena Document | 1403 | — |
Multilingual GPT-5.1 leads
GPT-5.1: 53.8 (#56), Llama 3.1-70B: 38.8 (#225)
| Benchmark | GPT-5.1 | Llama 3.1-70B |
|---|---|---|
| LMArena Non-English | 1431 | 1219 |
| LMArena Chinese | 1495 | 1215 |
| LMArena French | 1450 | 1261 |
| LMArena German | 1438 | 1222 |
| LMArena Japanese | 1453 | 1132 |
| LMArena Korean | 1401 | 1140 |
| LMArena Russian | 1435 | 1234 |
| LMArena Spanish | 1433 | 1253 |
Instruction Following GPT-5.1 leads
GPT-5.1: 83.9 (#1), Llama 3.1-70B: 65.3 (#223)
| Benchmark | GPT-5.1 | Llama 3.1-70B |
|---|---|---|
| IFEval | 93.5% | 82.1% |
| LMArena Instruction Following | 1443 | 1231 |
| LiveBench Instruction Following | 93.3% | — |
Long Context GPT-5.1 leads
GPT-5.1: 47.6 (#14), Llama 3.1-70B: 37.6 (#214)
| Benchmark | GPT-5.1 | Llama 3.1-70B |
|---|---|---|
| LMArena Longer Query | 1447 | 1241 |
| CL-bench | 23.7% | — |
| CL-bench Life | 17.3% | — |
Writing & Preference GPT-5.1 leads
GPT-5.1: 64.5 (#55), Llama 3.1-70B: 35.4 (#267)
| Benchmark | GPT-5.1 | Llama 3.1-70B |
|---|---|---|
| LMArena Text | 1443 | 1261 |
| LMArena Creative Writing | 1427 | 1232 |
| WildBench | 86.3% | 75.8% |
| LMArena Multi-Turn | 1450 | 1256 |
| EQ-Bench Creative Writing | — | 784 |
| LiveBench Language | 80.2% | — |
Frequently asked questions
Is GPT-5.1 better than Llama 3.1-70B?
GPT-5.1 is the stronger model overall, scoring 49.0 to 29.6 on the Noometry Index. Llama 3.1-70B costs 8.6× less per token, which makes it the better buy when GPT-5.1's lead doesn't matter for your workload.
Which is cheaper, GPT-5.1 or Llama 3.1-70B?
Llama 3.1-70B is cheaper. It lists at $0.40 per million input tokens and $0.40 per million output tokens; GPT-5.1 lists at $1.25 and $10.
Is GPT-5.1 or Llama 3.1-70B better for coding?
GPT-5.1 scores higher on coding benchmarks: 46.4 versus 30.3 in the Noometry coding category.
Which has the bigger context window?
GPT-5.1 does, with 400K tokens against 128K.
How many benchmarks do GPT-5.1 and Llama 3.1-70B share?
28 benchmarks have published results for both models. GPT-5.1 has 63 scored results on Noometry and Llama 3.1-70B has 35.