Model comparison
GPT-5.4 nano vs Llama 3.1-8B
GPT-5.4 nano is the stronger model overall, scoring 41.9 to 23.0 on the Noometry Index. Llama 3.1-8B costs 8.0× less per token, which makes it the better buy when GPT-5.4 nano's lead doesn't matter for your workload.
Last verified . 26 shared benchmarks.
MetaSummary
- They share 26 benchmarks with published results for both. GPT-5.4 nano scores higher in 8 categories and Llama 3.1-8B in 0 categories; 8 gaps are clear of the uncertainty.
- The widest gap is in knowledge, where GPT-5.4 nano leads 41.9 to 8.0.
- The biggest single-benchmark swing is OTIS Mock AIME 2024-2025: 87.8% for GPT-5.4 nano 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 $0.20 / $1.25 for GPT-5.4 nano.
- GPT-5.4 nano accepts more context: 400K tokens versus 128K.
- Llama 3.1-8B has downloadable open weights; the other is API-only.
Side by side
| GPT-5.4 nano | Llama 3.1-8B | |
|---|---|---|
| Provider | OpenAI | Meta |
| Noometry Index | 41.9 | 23.0 |
| Released | 2026-03-17 | 2024-07-23 |
| Weights | Proprietary | Open |
| Context window | 400K | 128K |
| Max output | 128K | 4K |
| Input $ / M tokens | $0.20 | $0.05 |
| Output $ / M tokens | $1.25 | $0.08 |
| Results tracked | 40 | 43 |
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Category by category
Coding GPT-5.4 nano leads
GPT-5.4 nano: 43.6 (#84), Llama 3.1-8B: 20.2 (#340)
| Benchmark | GPT-5.4 nano | Llama 3.1-8B |
|---|---|---|
| SciCode | 46.9% | 13.2% |
| WeirdML | 49.2% | 1.7% |
| LMArena Coding | 1405 | 1195 |
| BigCodeBench Instruct | — | 32.8% |
| BigCodeBench Complete | — | 40.5% |
| ALE-Bench | 1,005 | — |
| HumanEval+ | — | 62.8% |
| MBPP+ | — | 55.6% |
Agentic & Tool Use Not comparable
GPT-5.4 nano: —, Llama 3.1-8B: 22.5 (#131)
| Benchmark | GPT-5.4 nano | Llama 3.1-8B |
|---|---|---|
| Berkeley Function Calling Leaderboard | — | 25.8% |
| BALROG | — | 15.1% |
Reasoning GPT-5.4 nano leads
GPT-5.4 nano: 23.7 (#173), Llama 3.1-8B: 14.9 (#321)
| Benchmark | GPT-5.4 nano | Llama 3.1-8B |
|---|---|---|
| CritPt | 9.3% | 0% |
| Chess Puzzles | 30% | 0% |
| LMArena Hard Prompts | 1381 | 1175 |
| DTBench | 80.3% | 50.9% |
| LMCA | 36.9% | 5.4% |
| Epoch Capabilities Index | 145.81 | 116.57 |
| ARC-AGI-2 | 5.7% | — |
| Kagi LLM Benchmark | 39.7% | — |
| ARC-AGI-1 | 51.5% | — |
| Mystery Game Puzzles | 9% | — |
| ForecastBench | 57.3 | — |
| PIQA | — | 81.2% |
Math GPT-5.4 nano leads
GPT-5.4 nano: 40.9 (#88), Llama 3.1-8B: 10.2 (#317)
| Benchmark | GPT-5.4 nano | Llama 3.1-8B |
|---|---|---|
| OTIS Mock AIME 2024-2025 | 87.8% | 1.7% |
| LMArena Math | 1406 | 1179 |
| FrontierMath (Tiers 1-3) | 44.9% | — |
| FrontierMath Tier 4 | 12.2% | — |
| ProofBench | 5% | — |
| Omni-MATH | — | 13.7% |
| MATH Level 5 | — | 22.9% |
| FrontierMath (Feb 2025 set) | 25.9% | — |
| FrontierMath Tier 4 (v1) | 6.3% | — |
| GSM8K | — | 82.4% |
Knowledge GPT-5.4 nano leads
GPT-5.4 nano: 41.9 (#103), Llama 3.1-8B: 8.0 (#307)
| Benchmark | GPT-5.4 nano | Llama 3.1-8B |
|---|---|---|
| GPQA Diamond | 78.5% | 27% |
| LMArena Expert | 1396 | 1144 |
| SimpleQA Verified | 11.7% | — |
| MMLU-Pro | — | 40.6% |
| Vectara Hallucination Rate | 3.1% | — |
| GPQA (HELM) | — | 24.7% |
| BoolQ | — | 82.8% |
| MMLU | — | 56.1% |
Multimodal Not comparable
GPT-5.4 nano: 36.7 (#78), Llama 3.1-8B: —
| Benchmark | GPT-5.4 nano | Llama 3.1-8B |
|---|---|---|
| LMArena Vision | 1196 | — |
Multilingual GPT-5.4 nano leads
GPT-5.4 nano: 48.6 (#140), Llama 3.1-8B: 34.0 (#249)
| Benchmark | GPT-5.4 nano | Llama 3.1-8B |
|---|---|---|
| LMArena Non-English | 1359 | 1148 |
| LMArena Chinese | 1392 | 1151 |
| LMArena French | 1396 | 1177 |
| LMArena German | 1367 | 1144 |
| LMArena Japanese | 1343 | 1061 |
| LMArena Korean | 1320 | 1053 |
| LMArena Russian | 1363 | 1158 |
| LMArena Spanish | 1371 | 1169 |
Instruction Following GPT-5.4 nano leads
GPT-5.4 nano: 71.9 (#144), Llama 3.1-8B: 58.9 (#258)
| Benchmark | GPT-5.4 nano | Llama 3.1-8B |
|---|---|---|
| LMArena Instruction Following | 1362 | 1159 |
| IFEval | — | 74.3% |
Long Context GPT-5.4 nano leads
GPT-5.4 nano: 41.6 (#137), Llama 3.1-8B: 35.8 (#238)
| Benchmark | GPT-5.4 nano | Llama 3.1-8B |
|---|---|---|
| LMArena Longer Query | 1366 | 1182 |
Writing & Preference GPT-5.4 nano leads
GPT-5.4 nano: 55.7 (#142), Llama 3.1-8B: 29.7 (#290)
| Benchmark | GPT-5.4 nano | Llama 3.1-8B |
|---|---|---|
| LMArena Text | 1372 | 1187 |
| LMArena Creative Writing | 1314 | 1154 |
| LMArena Multi-Turn | 1382 | 1172 |
| EQ-Bench Creative Writing | — | 713 |
| WildBench | — | 68.7% |
Frequently asked questions
Is GPT-5.4 nano better than Llama 3.1-8B?
GPT-5.4 nano is the stronger model overall, scoring 41.9 to 23.0 on the Noometry Index. Llama 3.1-8B costs 8.0× less per token, which makes it the better buy when GPT-5.4 nano's lead doesn't matter for your workload.
Which is cheaper, GPT-5.4 nano 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-5.4 nano lists at $0.20 and $1.25.
Is GPT-5.4 nano or Llama 3.1-8B better for coding?
GPT-5.4 nano scores higher on coding benchmarks: 43.6 versus 20.2 in the Noometry coding category.
Which has the bigger context window?
GPT-5.4 nano does, with 400K tokens against 128K.
How many benchmarks do GPT-5.4 nano and Llama 3.1-8B share?
26 benchmarks have published results for both models. GPT-5.4 nano has 40 scored results on Noometry and Llama 3.1-8B has 43.