Model comparison
GPT-5.1 vs Llama 3.1-8B
GPT-5.1 is the stronger model overall, scoring 48.8 to 22.8 on the Noometry Index. Llama 3.1-8B costs 60× less per token, which makes it the better buy when GPT-5.1's lead doesn't matter for your workload.
Last verified . 31 shared benchmarks.
MetaSummary
- They share 31 benchmarks with published results for both. GPT-5.1 scores higher in 9 categories and Llama 3.1-8B in 0 categories; 9 gaps are clear of the uncertainty.
- The widest gap is in knowledge, where GPT-5.1 leads 50.5 to 8.0.
- The biggest single-benchmark swing is OTIS Mock AIME 2024-2025: 88.6% for GPT-5.1 and 1.7% for Llama 3.1-8B.
- Llama 3.1-8B is cheaper at $0.05 / $0.08 per million input/output tokens, against $1.25 / $10 for GPT-5.1.
- GPT-5.1 accepts more context: 400K tokens versus 128K.
- Llama 3.1-8B has downloadable open weights; the other is API-only.
Side by side
| GPT-5.1 | Llama 3.1-8B | |
|---|---|---|
| Provider | OpenAI | Meta |
| Noometry Index | 48.8 | 22.8 |
| Released | 2025-11-13 | 2024-07-23 |
| Weights | Proprietary | Open |
| Context window | 400K | 128K |
| Max output | 128K | 4K |
| Input $ / M tokens | $1.25 | $0.05 |
| Output $ / M tokens | $10 | $0.08 |
| Results tracked | 63 | 43 |
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Category by category
Coding GPT-5.1 leads
GPT-5.1: 46.9 (#59), Llama 3.1-8B: 19.9 (#335)
| Benchmark | GPT-5.1 | Llama 3.1-8B |
|---|---|---|
| SciCode | 43.3% | 13.2% |
| WeirdML | 60.8% | 1.7% |
| LMArena Coding | 1454 | 1195 |
| SWE-bench Verified | 68% | — |
| SWE-bench Verified (bash only) | 66% | — |
| LMArena WebDev | 1395 | — |
| GSO | 13.7% | — |
| BigCodeBench Instruct | — | 32.8% |
| LiveBench Coding | 72.5% | — |
| BigCodeBench Complete | — | 40.5% |
| ALE-Bench | 1,192 | — |
| HumanEval+ | — | 62.8% |
| MBPP+ | — | 55.6% |
Agentic & Tool Use GPT-5.1 leads
GPT-5.1: 32.5 (#55), Llama 3.1-8B: 22.3 (#123)
| Benchmark | GPT-5.1 | Llama 3.1-8B |
|---|---|---|
| Terminal-Bench | 47.6% | — |
| Berkeley Function Calling Leaderboard | — | 25.8% |
| DeepResearch Bench | 42.8% | — |
| BALROG | — | 15.1% |
| LMArena Search | 1199 | — |
| Vending-Bench 2 | 1,473 | — |
Reasoning GPT-5.1 leads
GPT-5.1: 39.9 (#50), Llama 3.1-8B: 14.5 (#316)
| Benchmark | GPT-5.1 | Llama 3.1-8B |
|---|---|---|
| CritPt | 4.9% | 0% |
| Chess Puzzles | 32% | 0% |
| LMArena Hard Prompts | 1457 | 1175 |
| DTBench | 90.1% | 50.9% |
| LMCA | 43.9% | 5.4% |
| Epoch Capabilities Index | 149.66 | 116.48 |
| ARC-AGI-2 | 17.6% | — |
| SimpleBench | 53.2% | — |
| ARC-AGI-1 | 72.8% | — |
| EnigmaEval | 11.2% | — |
| LiveBench Reasoning | 95.8% | — |
| Mystery Game Puzzles | 19% | — |
| LiveBench Data Analysis | 72.1% | — |
| ForecastBench | 58.1 | — |
| LiveBench | 78.8% | — |
| PIQA | — | 81.2% |
Math GPT-5.1 leads
GPT-5.1: 50.3 (#45), Llama 3.1-8B: 9.5 (#311)
| Benchmark | GPT-5.1 | Llama 3.1-8B |
|---|---|---|
| OTIS Mock AIME 2024-2025 | 88.6% | 1.7% |
| Omni-MATH | 46.4% | 13.7% |
| LMArena Math | 1446 | 1179 |
| LiveBench Math | 94.5% | — |
| MATH Level 5 | — | 22.9% |
| FrontierMath (Feb 2025 set) | 31% | — |
| FrontierMath Tier 4 (v1) | 12.5% | — |
| GSM8K | — | 82.4% |
Knowledge GPT-5.1 leads
GPT-5.1: 50.5 (#65), Llama 3.1-8B: 8.0 (#302)
| Benchmark | GPT-5.1 | Llama 3.1-8B |
|---|---|---|
| GPQA Diamond | 87.6% | 27% |
| MMLU-Pro | 57.9% | 40.6% |
| GPQA (HELM) | 44.2% | 24.7% |
| LMArena Expert | 1470 | 1144 |
| Humanity's Last Exam | 23.7% | — |
| SimpleQA Verified | 48% | — |
| Vectara Hallucination Rate | 10.9% | — |
| BoolQ | — | 82.8% |
| MMLU | — | 56.1% |
Multimodal Not comparable
GPT-5.1: 44.0 (#17), Llama 3.1-8B: —
| Benchmark | GPT-5.1 | Llama 3.1-8B |
|---|---|---|
| LMArena Vision | 1250 | — |
| VPCT | 58.7% | — |
| LMArena Document | 1403 | — |
Multilingual GPT-5.1 leads
GPT-5.1: 54.0 (#51), Llama 3.1-8B: 34.2 (#244)
| Benchmark | GPT-5.1 | Llama 3.1-8B |
|---|---|---|
| LMArena Non-English | 1431 | 1148 |
| LMArena Chinese | 1495 | 1151 |
| LMArena French | 1451 | 1176 |
| LMArena German | 1439 | 1144 |
| LMArena Japanese | 1453 | 1061 |
| LMArena Korean | 1401 | 1053 |
| LMArena Russian | 1435 | 1158 |
| LMArena Spanish | 1430 | 1169 |
Instruction Following GPT-5.1 leads
GPT-5.1: 84.0 (#1), Llama 3.1-8B: 59.1 (#253)
| Benchmark | GPT-5.1 | Llama 3.1-8B |
|---|---|---|
| IFEval | 93.5% | 74.3% |
| LMArena Instruction Following | 1442 | 1159 |
| LiveBench Instruction Following | 93.3% | — |
Long Context GPT-5.1 leads
GPT-5.1: 47.6 (#14), Llama 3.1-8B: 35.8 (#233)
| Benchmark | GPT-5.1 | Llama 3.1-8B |
|---|---|---|
| LMArena Longer Query | 1448 | 1182 |
| CL-bench | 23.7% | — |
| CL-bench Life | 17.3% | — |
Writing & Preference GPT-5.1 leads
GPT-5.1: 64.7 (#53), Llama 3.1-8B: 29.9 (#285)
| Benchmark | GPT-5.1 | Llama 3.1-8B |
|---|---|---|
| LMArena Text | 1443 | 1187 |
| LMArena Creative Writing | 1427 | 1154 |
| WildBench | 86.3% | 68.7% |
| LMArena Multi-Turn | 1449 | 1172 |
| EQ-Bench Creative Writing | — | 713 |
| LiveBench Language | 80.2% | — |
Frequently asked questions
Is GPT-5.1 better than Llama 3.1-8B?
GPT-5.1 is the stronger model overall, scoring 48.8 to 22.8 on the Noometry Index. Llama 3.1-8B costs 60× 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 3.1-8B?
Llama 3.1-8B is cheaper. It lists at $0.05 per million input tokens and $0.08 per million output tokens; GPT-5.1 lists at $1.25 and $10.
Is GPT-5.1 or Llama 3.1-8B better for coding?
GPT-5.1 scores higher on coding benchmarks: 46.9 versus 19.9 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 3.1-8B share?
31 benchmarks have published results for both models. GPT-5.1 has 63 scored results on Noometry and Llama 3.1-8B has 43.