# GPT-5.5 vs Llama 3.1-8B

> GPT-5.5 is the stronger model overall, scoring 63.4 to 23.0 on the Noometry Index. Llama 3.1-8B costs 196× less per token, which makes it the better buy when GPT-5.5's lead doesn't matter for your workload.

- Canonical page: https://noometry.com/compare/gpt-5-5-vs-llama-3-1-8b
- Last updated: 2026-10-12
- Shared benchmarks: 27

## Summary

- They share 27 benchmarks with published results for both. GPT-5.5 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 math, where GPT-5.5 leads 81.7 to 10.2.
- The biggest single-benchmark swing is OTIS Mock AIME 2024-2025: 100% for GPT-5.5 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 $5 / $30 for GPT-5.5.
- GPT-5.5 accepts more context: 1.05M tokens versus 128K.
- Llama 3.1-8B has downloadable open weights; the other is API-only.

## Snapshot

| | GPT-5.5 | Llama 3.1-8B |
|---|---|---|
| Provider | OpenAI | Meta |
| Noometry Index | 63.4 | 23.0 |
| Rank | 9 | 352 |
| Context | 1.05M | 128K |
| Input $/M | $5 | $0.05 |
| Output $/M | $30 | $0.08 |
| Weights | Proprietary | Open |

## Coding

- GPT-5.5: 58.2 (#17)
- Llama 3.1-8B: 20.2 (#340)

| Benchmark | GPT-5.5 | Llama 3.1-8B |
|---|---|---|
| SciCode | 56.1% | 13.2% |
| WeirdML | 84.9% | 1.7% |
| LMArena Coding | 1494 | 1195 |
| SWE-bench Verified | 80.6% | — |
| DeepSWE | 67% | — |
| FrontierCode | 43% | — |
| LMArena WebDev | 1513 | — |
| GSO | 40.2% | — |
| BigCodeBench Instruct | — | 32.8% |
| MirrorCode | 10% | — |
| BigCodeBench Complete | — | 40.5% |
| ALE-Bench | 1,943 | — |
| HumanEval+ | — | 62.8% |
| MBPP+ | — | 55.6% |

## Agentic & Tool Use

- GPT-5.5: 50.7 (#6)
- Llama 3.1-8B: 22.5 (#131)

| Benchmark | GPT-5.5 | Llama 3.1-8B |
|---|---|---|
| Terminal-Bench | 84.7% | — |
| APEX-Agents | 55.1% | — |
| Berkeley Function Calling Leaderboard | — | 25.8% |
| OSWorld 2.0 | 13% | — |
| Remote Labor Index | 6.3% | — |
| τ²-bench Banking | 44.6% | — |
| DeepResearch Bench | 54% | — |
| PostTrainBench | 27.2% | — |
| BALROG | — | 15.1% |
| ExploitBench | 47.4% | — |
| GBAEval | 53.2% | — |
| GDP.pdf | 26% | — |
| LMArena Search | 1242 | — |
| Vending-Bench 2 | 7,524 | — |

## Reasoning

- GPT-5.5: 72.8 (#11)
- Llama 3.1-8B: 14.9 (#321)

| Benchmark | GPT-5.5 | Llama 3.1-8B |
|---|---|---|
| CritPt | 27.1% | 0% |
| Chess Puzzles | 54% | 0% |
| LMArena Hard Prompts | 1489 | 1175 |
| DTBench | 96% | 50.9% |
| LMCA | 54.3% | 5.4% |
| Epoch Capabilities Index | 159.1 | 116.57 |
| ARC-AGI-2 | 85% | — |
| SimpleBench | 69% | — |
| Kagi LLM Benchmark | 88.8% | — |
| NYT Connections (extended) | 96.2% | — |
| ARC-AGI-1 | 95% | — |
| EBR-Bench | 34.3% | — |
| Mystery Game Puzzles | 56% | — |
| Surface Evolver Bench | 88.1% | — |
| Bench to the Future 3 | 0.14 | — |
| ForecastBench | 60.6 | — |
| PIQA | — | 81.2% |

## Math

- GPT-5.5: 81.7 (#11)
- Llama 3.1-8B: 10.2 (#317)

| Benchmark | GPT-5.5 | Llama 3.1-8B |
|---|---|---|
| OTIS Mock AIME 2024-2025 | 100% | 1.7% |
| LMArena Math | 1486 | 1179 |
| FrontierMath (Tiers 1-3) | 85.3% | — |
| FrontierMath Tier 4 | 72.5% | — |
| MathArena Final-Answer Competitions | 94.3% | — |
| ProofBench | 50% | — |
| Omni-MATH | — | 13.7% |
| MATH Level 5 | — | 22.9% |
| FrontierMath (Feb 2025 set) | 51.7% | — |
| FrontierMath Erdős | 0% | — |
| FrontierMath Tier 4 (v1) | 35.4% | — |
| GSM8K | — | 82.4% |

## Knowledge

- GPT-5.5: 64.4 (#17)
- Llama 3.1-8B: 8.0 (#307)

| Benchmark | GPT-5.5 | Llama 3.1-8B |
|---|---|---|
| GPQA Diamond | 94% | 27% |
| LMArena Expert | 1508 | 1144 |
| SimpleQA Verified | 63% | — |
| MMLU-Pro | — | 40.6% |
| Vectara Hallucination Rate | 9.3% | — |
| GPQA (HELM) | — | 24.7% |
| BoolQ | — | 82.8% |
| MMLU | — | 56.1% |

## Multimodal

- GPT-5.5: 46.9 (#12)
- Llama 3.1-8B: —

| Benchmark | GPT-5.5 | Llama 3.1-8B |
|---|---|---|
| LMArena Vision | 1297 | — |
| Blueprint-Bench 2 | 36.2% | — |
| Furniture Assembly | 44.2% | — |
| LMArena Document | 1486 | — |

## Multilingual

- GPT-5.5: 56.4 (#20)
- Llama 3.1-8B: 34.0 (#249)

| Benchmark | GPT-5.5 | Llama 3.1-8B |
|---|---|---|
| LMArena Non-English | 1467 | 1148 |
| LMArena Chinese | 1533 | 1151 |
| LMArena French | 1486 | 1177 |
| LMArena German | 1480 | 1144 |
| LMArena Japanese | 1498 | 1061 |
| LMArena Korean | 1460 | 1053 |
| LMArena Russian | 1473 | 1158 |
| LMArena Spanish | 1468 | 1169 |

## Instruction Following

- GPT-5.5: 77.5 (#18)
- Llama 3.1-8B: 58.9 (#258)

| Benchmark | GPT-5.5 | Llama 3.1-8B |
|---|---|---|
| LMArena Instruction Following | 1479 | 1159 |
| IFEval | — | 74.3% |

## Long Context

- GPT-5.5: 48.3 (#12)
- Llama 3.1-8B: 35.8 (#238)

| Benchmark | GPT-5.5 | Llama 3.1-8B |
|---|---|---|
| LMArena Longer Query | 1484 | 1182 |
| CL-bench Life | 22.2% | — |

## Writing & Preference

- GPT-5.5: 72.7 (#13)
- Llama 3.1-8B: 29.7 (#290)

| Benchmark | GPT-5.5 | Llama 3.1-8B |
|---|---|---|
| LMArena Text | 1472 | 1187 |
| LMArena Creative Writing | 1455 | 1154 |
| EQ-Bench Creative Writing | 1844 | 713 |
| LMArena Multi-Turn | 1476 | 1172 |
| WildBench | — | 68.7% |
| EQ-Bench 4 | 1315 | — |

## FAQ

### Is GPT-5.5 better than Llama 3.1-8B?

GPT-5.5 is the stronger model overall, scoring 63.4 to 23.0 on the Noometry Index. Llama 3.1-8B costs 196× less per token, which makes it the better buy when GPT-5.5's lead doesn't matter for your workload.

### Which is cheaper, GPT-5.5 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 lists at $5 and $30.

### Is GPT-5.5 or Llama 3.1-8B better for coding?

GPT-5.5 scores higher on coding benchmarks: 58.2 versus 20.2 in the Noometry coding category.

### Which has the bigger context window?

GPT-5.5 does, with 1.05M tokens against 128K.

### How many benchmarks do GPT-5.5 and Llama 3.1-8B share?

27 benchmarks have published results for both models. GPT-5.5 has 71 scored results on Noometry and Llama 3.1-8B has 43.