Model comparison
GPT-5.4 nano vs Llama 3.1-70B
GPT-5.4 nano is the stronger model overall, scoring 41.9 to 29.6 on the Noometry Index.
Last verified . 23 shared benchmarks.
MetaSummary
- They share 23 benchmarks with published results for both. GPT-5.4 nano scores higher in 8 categories and Llama 3.1-70B in 0 categories; 8 gaps are clear of the uncertainty.
- The widest gap is in math, where GPT-5.4 nano leads 40.9 to 13.5.
- The biggest single-benchmark swing is OTIS Mock AIME 2024-2025: 87.8% for GPT-5.4 nano and 3.6% for Llama 3.1-70B.
- Llama 3.1-70B is cheaper at $0.40 / $0.40 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 3.1-70B has downloadable open weights; the other is API-only.
Side by side
| GPT-5.4 nano | Llama 3.1-70B | |
|---|---|---|
| Provider | OpenAI | Meta |
| Noometry Index | 41.9 | 29.6 |
| Released | 2026-03-17 | 2024-07-23 |
| Weights | Proprietary | Open |
| Context window | 400K | 128K |
| Max output | 128K | 4K |
| Input $ / M tokens | $0.20 | $0.40 |
| Output $ / M tokens | $1.25 | $0.40 |
| Results tracked | 40 | 35 |
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Category by category
Coding GPT-5.4 nano leads
GPT-5.4 nano: 43.6 (#84), Llama 3.1-70B: 30.3 (#296)
| Benchmark | GPT-5.4 nano | Llama 3.1-70B |
|---|---|---|
| WeirdML | 49.2% | 9% |
| LMArena Coding | 1405 | 1260 |
| SciCode | 46.9% | — |
| BigCodeBench Instruct | — | 46.1% |
| BigCodeBench Complete | — | 54.8% |
| ALE-Bench | 1,005 | — |
Agentic & Tool Use Not comparable
GPT-5.4 nano: —, Llama 3.1-70B: 25.1 (#112)
| Benchmark | GPT-5.4 nano | Llama 3.1-70B |
|---|---|---|
| TheAgentCompany | — | 6.9% |
| BALROG | — | 27.9% |
Reasoning GPT-5.4 nano leads
GPT-5.4 nano: 23.7 (#173), Llama 3.1-70B: 21.6 (#220)
| Benchmark | GPT-5.4 nano | Llama 3.1-70B |
|---|---|---|
| LMArena Hard Prompts | 1381 | 1241 |
| DTBench | 80.3% | 60% |
| LMCA | 36.9% | 14.8% |
| Epoch Capabilities Index | 145.81 | 125.92 |
| ARC-AGI-2 | 5.7% | — |
| Kagi LLM Benchmark | 39.7% | — |
| ARC-AGI-1 | 51.5% | — |
| CritPt | 9.3% | — |
| Chess Puzzles | 30% | — |
| Mystery Game Puzzles | 9% | — |
| ForecastBench | 57.3 | — |
Math GPT-5.4 nano leads
GPT-5.4 nano: 40.9 (#88), Llama 3.1-70B: 13.5 (#304)
| Benchmark | GPT-5.4 nano | Llama 3.1-70B |
|---|---|---|
| OTIS Mock AIME 2024-2025 | 87.8% | 3.6% |
| LMArena Math | 1406 | 1252 |
| FrontierMath (Tiers 1-3) | 44.9% | — |
| FrontierMath Tier 4 | 12.2% | — |
| ProofBench | 5% | — |
| Omni-MATH | — | 21% |
| MATH Level 5 | — | 36.7% |
| FrontierMath (Feb 2025 set) | 25.9% | — |
| FrontierMath Tier 4 (v1) | 6.3% | — |
Knowledge GPT-5.4 nano leads
GPT-5.4 nano: 41.9 (#103), Llama 3.1-70B: 24.2 (#269)
| Benchmark | GPT-5.4 nano | Llama 3.1-70B |
|---|---|---|
| GPQA Diamond | 78.5% | 44.2% |
| LMArena Expert | 1396 | 1209 |
| SimpleQA Verified | 11.7% | — |
| MMLU-Pro | — | 65.3% |
| Vectara Hallucination Rate | 3.1% | — |
| GPQA (HELM) | — | 42.6% |
| MMLU | — | 80.1% |
Multimodal Not comparable
GPT-5.4 nano: 36.7 (#78), Llama 3.1-70B: —
| Benchmark | GPT-5.4 nano | Llama 3.1-70B |
|---|---|---|
| LMArena Vision | 1196 | — |
Multilingual GPT-5.4 nano leads
GPT-5.4 nano: 48.6 (#140), Llama 3.1-70B: 38.8 (#225)
| Benchmark | GPT-5.4 nano | Llama 3.1-70B |
|---|---|---|
| LMArena Non-English | 1359 | 1219 |
| LMArena Chinese | 1392 | 1215 |
| LMArena French | 1396 | 1261 |
| LMArena German | 1367 | 1222 |
| LMArena Japanese | 1343 | 1132 |
| LMArena Korean | 1320 | 1140 |
| LMArena Russian | 1363 | 1234 |
| LMArena Spanish | 1371 | 1253 |
Instruction Following GPT-5.4 nano leads
GPT-5.4 nano: 71.9 (#144), Llama 3.1-70B: 65.3 (#223)
| Benchmark | GPT-5.4 nano | Llama 3.1-70B |
|---|---|---|
| LMArena Instruction Following | 1362 | 1231 |
| IFEval | — | 82.1% |
Long Context GPT-5.4 nano leads
GPT-5.4 nano: 41.6 (#137), Llama 3.1-70B: 37.6 (#214)
| Benchmark | GPT-5.4 nano | Llama 3.1-70B |
|---|---|---|
| LMArena Longer Query | 1366 | 1241 |
Writing & Preference GPT-5.4 nano leads
GPT-5.4 nano: 55.7 (#142), Llama 3.1-70B: 35.4 (#267)
| Benchmark | GPT-5.4 nano | Llama 3.1-70B |
|---|---|---|
| LMArena Text | 1372 | 1261 |
| LMArena Creative Writing | 1314 | 1232 |
| LMArena Multi-Turn | 1382 | 1256 |
| EQ-Bench Creative Writing | — | 784 |
| WildBench | — | 75.8% |
Frequently asked questions
Is GPT-5.4 nano better than Llama 3.1-70B?
GPT-5.4 nano is the stronger model overall, scoring 41.9 to 29.6 on the Noometry Index.
Which is cheaper, GPT-5.4 nano or Llama 3.1-70B?
Llama 3.1-70B is cheaper. It lists at $0.40 per million input tokens and $0.40 per million output tokens; GPT-5.4 nano lists at $0.20 and $1.25.
Is GPT-5.4 nano or Llama 3.1-70B better for coding?
GPT-5.4 nano scores higher on coding benchmarks: 43.6 versus 30.3 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 3.1-70B share?
23 benchmarks have published results for both models. GPT-5.4 nano has 40 scored results on Noometry and Llama 3.1-70B has 35.