Model comparison
GPT-5 Pro vs Llama 3.1-8B
GPT-5 Pro is the stronger model overall, scoring 46.4 to 23.0 on the Noometry Index. Llama 3.1-8B costs 717× less per token, which makes it the better buy when GPT-5 Pro's lead doesn't matter for your workload.
Last verified . 2 shared benchmarks.
MetaSummary
- They share 2 benchmarks with published results for both. GPT-5 Pro scores higher in 4 categories and Llama 3.1-8B in 0 categories; 4 gaps are clear of the uncertainty.
- The widest gap is in knowledge, where GPT-5 Pro leads 56.7 to 8.0.
- The biggest single-benchmark swing is WeirdML: 60.4% for GPT-5 Pro 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 $15 / $120 for GPT-5 Pro.
- GPT-5 Pro 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 Pro | Llama 3.1-8B | |
|---|---|---|
| Provider | OpenAI | Meta |
| Noometry Index | 46.4 | 23.0 |
| Released | 2025-10-06 | 2024-07-23 |
| Weights | Proprietary | Open |
| Context window | 400K | 128K |
| Max output | 272K | 4K |
| Input $ / M tokens | $15 | $0.05 |
| Output $ / M tokens | $120 | $0.08 |
| Results tracked | 12 | 43 |
Sponsored placements are available on pages like this one. Advertise on Noometry
Category by category
Coding GPT-5 Pro leads
GPT-5 Pro: 44.0 (#80), Llama 3.1-8B: 20.2 (#340)
| Benchmark | GPT-5 Pro | Llama 3.1-8B |
|---|---|---|
| WeirdML | 60.4% | 1.7% |
| SciCode | — | 13.2% |
| BigCodeBench Instruct | — | 32.8% |
| LMArena Coding | — | 1195 |
| BigCodeBench Complete | — | 40.5% |
| AlgoTune | 1.31 | — |
| HumanEval+ | — | 62.8% |
| MBPP+ | — | 55.6% |
Agentic & Tool Use Not comparable
GPT-5 Pro: —, Llama 3.1-8B: 22.5 (#131)
| Benchmark | GPT-5 Pro | Llama 3.1-8B |
|---|---|---|
| Berkeley Function Calling Leaderboard | — | 25.8% |
| BALROG | — | 15.1% |
Reasoning GPT-5 Pro leads
GPT-5 Pro: 38.9 (#62), Llama 3.1-8B: 14.9 (#321)
| Benchmark | GPT-5 Pro | Llama 3.1-8B |
|---|---|---|
| Epoch Capabilities Index | 150.28 | 116.57 |
| ARC-AGI-2 | 18.3% | — |
| SimpleBench | 61.6% | — |
| Kagi LLM Benchmark | 76.8% | — |
| ARC-AGI-1 | 70.2% | — |
| CritPt | — | 0% |
| Chess Puzzles | — | 0% |
| EnigmaEval | 18.8% | — |
| LMArena Hard Prompts | — | 1175 |
| DTBench | — | 50.9% |
| LMCA | — | 5.4% |
| PIQA | — | 81.2% |
Math GPT-5 Pro leads
GPT-5 Pro: 48.5 (#63), Llama 3.1-8B: 10.2 (#317)
| Benchmark | GPT-5 Pro | Llama 3.1-8B |
|---|---|---|
| FrontierMath (Tiers 1-3) | 55.8% | — |
| FrontierMath Tier 4 | 19.5% | — |
| OTIS Mock AIME 2024-2025 | — | 1.7% |
| Omni-MATH | — | 13.7% |
| LMArena Math | — | 1179 |
| MATH Level 5 | — | 22.9% |
| FrontierMath Tier 4 (v1) | 14.6% | — |
| GSM8K | — | 82.4% |
Knowledge GPT-5 Pro leads
GPT-5 Pro: 56.7 (#42), Llama 3.1-8B: 8.0 (#307)
| Benchmark | GPT-5 Pro | Llama 3.1-8B |
|---|---|---|
| GPQA Diamond | — | 27% |
| Humanity's Last Exam | 31.6% | — |
| MMLU-Pro | — | 40.6% |
| GPQA (HELM) | — | 24.7% |
| LMArena Expert | — | 1144 |
| BoolQ | — | 82.8% |
| MMLU | — | 56.1% |
Multilingual Not comparable
GPT-5 Pro: —, Llama 3.1-8B: 34.0 (#249)
| Benchmark | GPT-5 Pro | Llama 3.1-8B |
|---|---|---|
| LMArena Non-English | — | 1148 |
| LMArena Chinese | — | 1151 |
| LMArena French | — | 1177 |
| LMArena German | — | 1144 |
| LMArena Japanese | — | 1061 |
| LMArena Korean | — | 1053 |
| LMArena Russian | — | 1158 |
| LMArena Spanish | — | 1169 |
Instruction Following Not comparable
GPT-5 Pro: —, Llama 3.1-8B: 58.9 (#258)
| Benchmark | GPT-5 Pro | Llama 3.1-8B |
|---|---|---|
| IFEval | — | 74.3% |
| LMArena Instruction Following | — | 1159 |
Long Context Not comparable
GPT-5 Pro: —, Llama 3.1-8B: 35.8 (#238)
| Benchmark | GPT-5 Pro | Llama 3.1-8B |
|---|---|---|
| LMArena Longer Query | — | 1182 |
Writing & Preference Not comparable
GPT-5 Pro: —, Llama 3.1-8B: 29.7 (#290)
| Benchmark | GPT-5 Pro | Llama 3.1-8B |
|---|---|---|
| LMArena Text | — | 1187 |
| LMArena Creative Writing | — | 1154 |
| EQ-Bench Creative Writing | — | 713 |
| WildBench | — | 68.7% |
| LMArena Multi-Turn | — | 1172 |
Frequently asked questions
Is GPT-5 Pro better than Llama 3.1-8B?
GPT-5 Pro is the stronger model overall, scoring 46.4 to 23.0 on the Noometry Index. Llama 3.1-8B costs 717× less per token, which makes it the better buy when GPT-5 Pro's lead doesn't matter for your workload.
Which is cheaper, GPT-5 Pro 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 Pro lists at $15 and $120.
Is GPT-5 Pro or Llama 3.1-8B better for coding?
GPT-5 Pro scores higher on coding benchmarks: 44.0 versus 20.2 in the Noometry coding category.
Which has the bigger context window?
GPT-5 Pro does, with 400K tokens against 128K.
How many benchmarks do GPT-5 Pro and Llama 3.1-8B share?
2 benchmarks have published results for both models. GPT-5 Pro has 12 scored results on Noometry and Llama 3.1-8B has 43.