Model comparison
GPT-5.5 Pro vs Llama 3.1-8B
GPT-5.5 Pro is the stronger model overall, scoring 64.3 to 22.8 on the Noometry Index. Llama 3.1-8B costs 1174× less per token, which makes it the better buy when GPT-5.5 Pro's lead doesn't matter for your workload.
Last verified . 7 shared benchmarks.
MetaSummary
- They share 7 benchmarks with published results for both. GPT-5.5 Pro scores higher in 3 categories and Llama 3.1-8B in 0 categories; 3 gaps are clear of the uncertainty.
- The widest gap is in math, where GPT-5.5 Pro leads 83.8 to 9.5.
- The biggest single-benchmark swing is OTIS Mock AIME 2024-2025: 100% for GPT-5.5 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 $30 / $180 for GPT-5.5 Pro.
- GPT-5.5 Pro accepts more context: 1.05M tokens versus 128K.
- Llama 3.1-8B has downloadable open weights; the other is API-only.
Side by side
| GPT-5.5 Pro | Llama 3.1-8B | |
|---|---|---|
| Provider | OpenAI | Meta |
| Noometry Index | 64.3 | 22.8 |
| Released | 2026-04-23 | 2024-07-23 |
| Weights | Proprietary | Open |
| Context window | 1.05M | 128K |
| Max output | 128K | 4K |
| Input $ / M tokens | $30 | $0.05 |
| Output $ / M tokens | $180 | $0.08 |
| Results tracked | 14 | 43 |
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Category by category
Coding Not comparable
GPT-5.5 Pro: —, Llama 3.1-8B: 19.9 (#335)
| Benchmark | GPT-5.5 Pro | Llama 3.1-8B |
|---|---|---|
| SciCode | — | 13.2% |
| WeirdML | — | 1.7% |
| BigCodeBench Instruct | — | 32.8% |
| LMArena Coding | — | 1195 |
| BigCodeBench Complete | — | 40.5% |
| HumanEval+ | — | 62.8% |
| MBPP+ | — | 55.6% |
Agentic & Tool Use Not comparable
GPT-5.5 Pro: —, Llama 3.1-8B: 22.3 (#123)
| Benchmark | GPT-5.5 Pro | Llama 3.1-8B |
|---|---|---|
| Berkeley Function Calling Leaderboard | — | 25.8% |
| BALROG | — | 15.1% |
Reasoning GPT-5.5 Pro leads
GPT-5.5 Pro: 73.6 (#7), Llama 3.1-8B: 14.5 (#316)
| Benchmark | GPT-5.5 Pro | Llama 3.1-8B |
|---|---|---|
| CritPt | 30.6% | 0% |
| Chess Puzzles | 64% | 0% |
| DTBench | 96% | 50.9% |
| LMCA | 53.9% | 5.4% |
| Epoch Capabilities Index | 162.45 | 116.48 |
| ARC-AGI-2 | 84.6% | — |
| SimpleBench | 76.9% | — |
| ARC-AGI-1 | 96.5% | — |
| LMArena Hard Prompts | — | 1175 |
| PIQA | — | 81.2% |
Math GPT-5.5 Pro leads
GPT-5.5 Pro: 83.8 (#8), Llama 3.1-8B: 9.5 (#311)
| Benchmark | GPT-5.5 Pro | Llama 3.1-8B |
|---|---|---|
| OTIS Mock AIME 2024-2025 | 100% | 1.7% |
| FrontierMath (Tiers 1-3) | 87.7% | — |
| FrontierMath Tier 4 | 78% | — |
| Omni-MATH | — | 13.7% |
| LMArena Math | — | 1179 |
| MATH Level 5 | — | 22.9% |
| FrontierMath (Feb 2025 set) | 52.4% | — |
| FrontierMath Tier 4 (v1) | 39.6% | — |
| GSM8K | — | 82.4% |
Knowledge GPT-5.5 Pro leads
GPT-5.5 Pro: 64.2 (#17), Llama 3.1-8B: 8.0 (#302)
| Benchmark | GPT-5.5 Pro | Llama 3.1-8B |
|---|---|---|
| GPQA Diamond | 93.9% | 27% |
| MMLU-Pro | — | 40.6% |
| GPQA (HELM) | — | 24.7% |
| LMArena Expert | — | 1144 |
| BoolQ | — | 82.8% |
| MMLU | — | 56.1% |
Multilingual Not comparable
GPT-5.5 Pro: —, Llama 3.1-8B: 34.2 (#244)
| Benchmark | GPT-5.5 Pro | Llama 3.1-8B |
|---|---|---|
| LMArena Non-English | — | 1148 |
| LMArena Chinese | — | 1151 |
| LMArena French | — | 1176 |
| LMArena German | — | 1144 |
| LMArena Japanese | — | 1061 |
| LMArena Korean | — | 1053 |
| LMArena Russian | — | 1158 |
| LMArena Spanish | — | 1169 |
Instruction Following Not comparable
GPT-5.5 Pro: —, Llama 3.1-8B: 59.1 (#253)
| Benchmark | GPT-5.5 Pro | Llama 3.1-8B |
|---|---|---|
| IFEval | — | 74.3% |
| LMArena Instruction Following | — | 1159 |
Long Context Not comparable
GPT-5.5 Pro: —, Llama 3.1-8B: 35.8 (#233)
| Benchmark | GPT-5.5 Pro | Llama 3.1-8B |
|---|---|---|
| LMArena Longer Query | — | 1182 |
Writing & Preference Not comparable
GPT-5.5 Pro: —, Llama 3.1-8B: 29.9 (#285)
| Benchmark | GPT-5.5 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-5.5 Pro better than Llama 3.1-8B?
GPT-5.5 Pro is the stronger model overall, scoring 64.3 to 22.8 on the Noometry Index. Llama 3.1-8B costs 1174× less per token, which makes it the better buy when GPT-5.5 Pro's lead doesn't matter for your workload.
Which is cheaper, GPT-5.5 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-5.5 Pro lists at $30 and $180.
Which has the bigger context window?
GPT-5.5 Pro does, with 1.05M tokens against 128K.
How many benchmarks do GPT-5.5 Pro and Llama 3.1-8B share?
7 benchmarks have published results for both models. GPT-5.5 Pro has 14 scored results on Noometry and Llama 3.1-8B has 43.