Model comparison
GPT-5 Nano vs Llama 3.1-70B
GPT-5 Nano is the stronger model overall, scoring 33.5 to 29.6 on the Noometry Index.
Last verified . 29 shared benchmarks.
MetaSummary
- They share 29 benchmarks with published results for both. GPT-5 Nano scores higher in 7 categories and Llama 3.1-70B in 2 categories; 8 gaps are clear of the uncertainty.
- The widest gap is in math, where GPT-5 Nano leads 29.4 to 13.5.
- The biggest single-benchmark swing is OTIS Mock AIME 2024-2025: 81.1% for GPT-5 Nano and 3.6% for Llama 3.1-70B.
- GPT-5 Nano is cheaper at $0.05 / $0.40 per million input/output tokens, against $0.40 / $0.40 for Llama 3.1-70B.
- GPT-5 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 Nano | Llama 3.1-70B | |
|---|---|---|
| Provider | OpenAI | Meta |
| Noometry Index | 33.5 | 29.6 |
| Released | 2025-08-07 | 2024-07-23 |
| Weights | Proprietary | Open |
| Context window | 400K | 128K |
| Max output | 128K | 4K |
| Input $ / M tokens | $0.05 | $0.40 |
| Output $ / M tokens | $0.40 | $0.40 |
| Results tracked | 49 | 35 |
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Category by category
Coding GPT-5 Nano leads
GPT-5 Nano: 33.6 (#254), Llama 3.1-70B: 30.3 (#296)
| Benchmark | GPT-5 Nano | Llama 3.1-70B |
|---|---|---|
| WeirdML | 38.1% | 9% |
| LMArena Coding | 1351 | 1260 |
| SWE-bench Verified (bash only) | 34.8% | — |
| BigCodeBench Instruct | — | 46.1% |
| BigCodeBench Complete | — | 54.8% |
| ALE-Bench | 718.67 | — |
Agentic & Tool Use Too close to call
GPT-5 Nano: 25.8 (#106), Llama 3.1-70B: 25.1 (#112)
| Benchmark | GPT-5 Nano | Llama 3.1-70B |
|---|---|---|
| Terminal-Bench | 21.8% | — |
| Berkeley Function Calling Leaderboard | 51.5% | — |
| TheAgentCompany | — | 6.9% |
| BALROG | — | 27.9% |
Reasoning Llama 3.1-70B leads
GPT-5 Nano: 16.3 (#306), Llama 3.1-70B: 21.6 (#220)
| Benchmark | GPT-5 Nano | Llama 3.1-70B |
|---|---|---|
| LMArena Hard Prompts | 1328 | 1241 |
| DTBench | 62.7% | 60% |
| LMCA | 7.9% | 14.8% |
| Epoch Capabilities Index | 139.38 | 125.92 |
| ARC-AGI-2 | 2.6% | — |
| Kagi LLM Benchmark | 62.2% | — |
| ARC-AGI-1 | 20.7% | — |
| Chess Puzzles | 27% | — |
| Mystery Game Puzzles | 9% | — |
| ForecastBench | 59.1 | — |
Math GPT-5 Nano leads
GPT-5 Nano: 29.4 (#241), Llama 3.1-70B: 13.5 (#304)
| Benchmark | GPT-5 Nano | Llama 3.1-70B |
|---|---|---|
| OTIS Mock AIME 2024-2025 | 81.1% | 3.6% |
| Omni-MATH | 54.6% | 21% |
| LMArena Math | 1317 | 1252 |
| MATH Level 5 | 95.2% | 36.7% |
| FrontierMath (Tiers 1-3) | 20% | — |
| FrontierMath Tier 4 | 2.4% | — |
| ProofBench | 12% | — |
| FrontierMath (Feb 2025 set) | 8.3% | — |
| FrontierMath Tier 4 (v1) | 2.1% | — |
Knowledge GPT-5 Nano leads
GPT-5 Nano: 35.9 (#178), Llama 3.1-70B: 24.2 (#269)
| Benchmark | GPT-5 Nano | Llama 3.1-70B |
|---|---|---|
| GPQA Diamond | 69.4% | 44.2% |
| MMLU-Pro | 77.8% | 65.3% |
| GPQA (HELM) | 67.9% | 42.6% |
| LMArena Expert | 1321 | 1209 |
| SimpleQA Verified | 11.7% | — |
| Vectara Hallucination Rate | 10.5% | — |
| MMLU | — | 80.1% |
Multimodal Not comparable
GPT-5 Nano: 31.3 (#108), Llama 3.1-70B: —
| Benchmark | GPT-5 Nano | Llama 3.1-70B |
|---|---|---|
| LMArena Vision | 1159 | — |
| VPCT | 37.2% | — |
Multilingual GPT-5 Nano leads
GPT-5 Nano: 45.3 (#172), Llama 3.1-70B: 38.8 (#225)
| Benchmark | GPT-5 Nano | Llama 3.1-70B |
|---|---|---|
| LMArena Non-English | 1313 | 1219 |
| LMArena Chinese | 1356 | 1215 |
| LMArena German | 1327 | 1222 |
| LMArena Japanese | 1226 | 1132 |
| LMArena Korean | 1269 | 1140 |
| LMArena Russian | 1296 | 1234 |
| LMArena Spanish | 1360 | 1253 |
| LMArena French | — | 1261 |
Instruction Following GPT-5 Nano leads
GPT-5 Nano: 75.0 (#79), Llama 3.1-70B: 65.3 (#223)
| Benchmark | GPT-5 Nano | Llama 3.1-70B |
|---|---|---|
| IFEval | 93.2% | 82.1% |
| LMArena Instruction Following | 1306 | 1231 |
Long Context Llama 3.1-70B leads
GPT-5 Nano: 31.3 (#281), Llama 3.1-70B: 37.6 (#214)
| Benchmark | GPT-5 Nano | Llama 3.1-70B |
|---|---|---|
| LMArena Longer Query | 1312 | 1241 |
| Fiction.LiveBench | 44.4% | — |
Writing & Preference GPT-5 Nano leads
GPT-5 Nano: 39.1 (#249), Llama 3.1-70B: 35.4 (#267)
| Benchmark | GPT-5 Nano | Llama 3.1-70B |
|---|---|---|
| LMArena Text | 1320 | 1261 |
| LMArena Creative Writing | 1249 | 1232 |
| EQ-Bench Creative Writing | 705 | 784 |
| WildBench | 80.6% | 75.8% |
| LMArena Multi-Turn | 1311 | 1256 |
Frequently asked questions
Is GPT-5 Nano better than Llama 3.1-70B?
GPT-5 Nano is the stronger model overall, scoring 33.5 to 29.6 on the Noometry Index.
Which is cheaper, GPT-5 Nano or Llama 3.1-70B?
GPT-5 Nano is cheaper. It lists at $0.05 per million input tokens and $0.40 per million output tokens; Llama 3.1-70B lists at $0.40 and $0.40.
Is GPT-5 Nano or Llama 3.1-70B better for coding?
GPT-5 Nano scores higher on coding benchmarks: 33.6 versus 30.3 in the Noometry coding category.
Which has the bigger context window?
GPT-5 Nano does, with 400K tokens against 128K.
How many benchmarks do GPT-5 Nano and Llama 3.1-70B share?
29 benchmarks have published results for both models. GPT-5 Nano has 49 scored results on Noometry and Llama 3.1-70B has 35.