GPT-4.1 Nano vs Llama 3.1 70B Instruct
GPT-4.1 Nano leads Llama 3.1 70B Instruct on the Artificial Analysis Intelligence Index, 7.8 to 6.6, and GPT-4.1 Nano costs 1.4x less per output token ($0.40 vs $0.56 per million). Updated October 22, 2026; 41 benchmarks have a result for both.
GPT-4.1 Nano or Llama 3.1 70B Instruct: which to pick
Neither model has a coding score yet, so the two cannot be ranked on coding.
Pick GPT-4.1 Nano for output tokens 1.4x cheaper ($0.40 vs $0.56 per million) and a larger context window (1.05M against 131K tokens).
- Shared results
- 41
- GPT-4.1 Nano only
- 30
- Llama 3.1 70B Instruct only
- 6
- Like-for-like categories
- 7 of 9
Only benchmarks that tested both models count toward a pick; a result only one model has is shown but not compared.
Which one for your work
Each card is decided by one number both models carry. Where only one has it, the card says so and makes no call.
Coding
Vibe Coding Index, six public coding benchmarks
No call
Neither has a coding score yet.
One side not measured
Intelligence
AA Intelligence Index
GPT-4.1 Nano
GPT-4.1 Nano leads, 7.8 against 6.6.
Same board, both measured
Reasoning
GPQA Diamond
No call
Only GPT-4.1 Nano has a GPQA Diamond result, 50.8%.
Directional only
Math
AIME
No call
Only GPT-4.1 Nano has a AIME result, 26.5%.
Directional only
Knowledge
MMLU Pro
No call
Only GPT-4.1 Nano has a MMLU Pro result, 63.5%.
Directional only
Web design
Design Arena: websites
No call
Only GPT-4.1 Nano has a Design Arena: websites result, 972.
Directional only
Long documents
Listed context window
GPT-4.1 Nano
GPT-4.1 Nano takes 1.05M tokens against 131K.
Listed by OpenRouter
Everyday cost
1K input + 500 output tokens
GPT-4.1 Nano
GPT-4.1 Nano $0.0003, Llama 3.1 70B Instruct $0.0008 per turn.
List prices
At a glance
- Coding index
- GPT-4.1 Nano not benchmarked, Llama 3.1 70B Instruct not benchmarked.
- Widest category
- Math, 17.6 points apart on average over 6 shared benchmarks.
- Widest single gap
- MATH level 5: GPT-4.1 Nano 70.0% against Llama 3.1 70B Instruct 36.7%, 33.3 points apart on a 0 to 100 scale.
- Output price
- GPT-4.1 Nano $0.40 against Llama 3.1 70B Instruct $0.56 per million output tokens, 1.4x apart.
- Context window
- GPT-4.1 Nano takes 1.05M tokens, Llama 3.1 70B Instruct 131K.
Benchmark by benchmark
First the score: boards each model leads, per category, where both have a result. Then every board, a category to a fold; bars are each result on a 0 to 100 scale, so compare the two in a row, not one row with another.
| Category | GPT-4.1 Nano | Bars | Llama 3.1 70B Instruct | Ties |
|---|---|---|---|---|
| Coding | 5 | 0 | ||
| Intelligence | 2 | 0 | ||
| Agentic work | 1 | 0 | ||
| Reasoning | 5 | 1 | ||
| Math | 6 | 0 | ||
| Knowledge | 4 | 1 | ||
| General | 13 | 3 | ||
| All shared boards | 36 | 5 |
CodingLike-for-likeSix of these boards feed the Vibe Coding Index50
| Benchmark | GPT-4.1 Nano | Llama 3.1 70B Instruct | Leader |
|---|---|---|---|
| Aider polyglot | 8.9% | Not measured | Only GPT-4.1 Nano measured |
| AA Coding Index | 11.1 | Not measured | Only GPT-4.1 Nano measured |
| LiveCodeBench (Vals AI) | 42.7% | Not measured | Only GPT-4.1 Nano measured |
| LiveCodeBench (Artificial Analysis run) | 32.6% | 23.2% | GPT-4.1 Nano |
| Terminal-Bench Hard (Artificial Analysis run) | 3.8% | 3.0% | GPT-4.1 Nano |
| WeirdML | 19.0% | 9.0% | GPT-4.1 Nano |
| LMArena Text: coding | 1306 | 1260 | GPT-4.1 Nano |
| LMArena Text: software and IT | 1305 | 1260 | GPT-4.1 Nano |
| Neither model has a result on the other 31 boards here | |||
IntelligenceLike-for-likePublic boards, shown as published, kept out of the index20
| Benchmark | GPT-4.1 Nano | Llama 3.1 70B Instruct | Leader |
|---|---|---|---|
| AA Intelligence Index | 7.8 | 6.6 | GPT-4.1 Nano |
| Epoch Capabilities Index | 129.5 | 125.9 | GPT-4.1 Nano |
Agentic workLike-for-likePublic boards, shown as published, kept out of the index10
| Benchmark | GPT-4.1 Nano | Llama 3.1 70B Instruct | Leader |
|---|---|---|---|
| τ²-Bench Telecom | 17.3% | 15.2% | GPT-4.1 Nano |
| BALROG | Not measured | 27.9% | Only Llama 3.1 70B Instruct measured |
| TheAgentCompany | Not measured | 6.9% | Only Llama 3.1 70B Instruct measured |
| Berkeley Function Calling Leaderboard (BFCL) | 33.1% | Not measured | Only GPT-4.1 Nano measured |
| Neither model has a result on the other 37 boards here | |||
ReasoningLike-for-likePublic boards, shown as published, kept out of the index51
| Benchmark | GPT-4.1 Nano | Llama 3.1 70B Instruct | Leader |
|---|---|---|---|
| GPQA Diamond | 50.8% | Not measured | Only GPT-4.1 Nano measured |
| MMMU Pro (Vals AI) | 55.0% | Not measured | Only GPT-4.1 Nano measured |
| GPQA Diamond (Artificial Analysis run) | 51.2% | 40.9% | GPT-4.1 Nano |
| Humanity's Last Exam (Artificial Analysis run) | 3.8% | 4.5% | Llama 3.1 70B Instruct |
| GPQA Diamond (Epoch AI run) | 48.9% | 44.2% | GPT-4.1 Nano |
| ARC-AGI | 0.0% | Not measured | Only GPT-4.1 Nano measured |
| ARC-AGI-2 | 0.0% | Not measured | Only GPT-4.1 Nano measured |
| Chess puzzles | 0.0% | Not measured | Only GPT-4.1 Nano measured |
| LMArena Text: hard prompts | 1286 | 1241 | GPT-4.1 Nano |
| LMArena Text: expert | 1271 | 1209 | GPT-4.1 Nano |
| LMArena Text: hard prompts in English | 1293 | 1261 | GPT-4.1 Nano |
| Kagi LLM Benchmark | 33.3% | Not measured | Only GPT-4.1 Nano measured |
| Neither model has a result on the other 10 boards here | |||
MathLike-for-likePublic boards, shown as published, kept out of the index60
| Benchmark | GPT-4.1 Nano | Llama 3.1 70B Instruct | Leader |
|---|---|---|---|
| AIME | 26.5% | Not measured | Only GPT-4.1 Nano measured |
| MATH 500 (Vals AI) | 80.2% | Not measured | Only GPT-4.1 Nano measured |
| MGSM (Vals AI) | 69.3% | Not measured | Only GPT-4.1 Nano measured |
| AA Math Index | 24.0 | 4.0 | GPT-4.1 Nano |
| AIME 2025 (Artificial Analysis run) | 24.0% | 4.0% | GPT-4.1 Nano |
| OTIS Mock AIME | 28.9% | 3.6% | GPT-4.1 Nano |
| MATH level 5 | 70.0% | 36.7% | GPT-4.1 Nano |
| LMArena Text: math | 1274 | 1252 | GPT-4.1 Nano |
| LMArena Text: mathematical work | 1274 | 1253 | GPT-4.1 Nano |
| Neither model has a result on the other 4 boards here | |||
KnowledgeLike-for-likePublic boards, shown as published, kept out of the index41
| Benchmark | GPT-4.1 Nano | Llama 3.1 70B Instruct | Leader |
|---|---|---|---|
| MMLU Pro | 63.5% | Not measured | Only GPT-4.1 Nano measured |
| LegalBench (Vals AI) | 61.1% | Not measured | Only GPT-4.1 Nano measured |
| CorpFin (Vals AI) | 42.1% | Not measured | Only GPT-4.1 Nano measured |
| TaxEval (Vals AI) | 60.8% | Not measured | Only GPT-4.1 Nano measured |
| MortgageTax (Vals AI) | 52.8% | Not measured | Only GPT-4.1 Nano measured |
| MedQA (Vals AI) | 68.2% | Not measured | Only GPT-4.1 Nano measured |
| MMLU Pro (Artificial Analysis run) | 65.7% | 67.6% | Llama 3.1 70B Instruct |
| SimpleQA Verified | 6.0% | Not measured | Only GPT-4.1 Nano measured |
| MMLU | Not measured | 80.1% | Only Llama 3.1 70B Instruct measured |
| LMArena Text: science | 1297 | 1276 | GPT-4.1 Nano |
| LMArena Text: medicine and healthcare | 1276 | 1257 | GPT-4.1 Nano |
| LMArena Text: legal and government | 1300 | 1283 | GPT-4.1 Nano |
| LMArena Text: business and finance | 1267 | 1235 | GPT-4.1 Nano |
| Neither model has a result on the other 5 boards here | |||
Web designDirectional onlyPublic boards, shown as published, kept out of the index6 boards, none shared
| Benchmark | GPT-4.1 Nano | Llama 3.1 70B Instruct | Leader |
|---|---|---|---|
| Design Arena: websites | 972 | Not measured | Only GPT-4.1 Nano measured |
| Design Arena: UI components | 913 | Not measured | Only GPT-4.1 Nano measured |
| Design Arena: game dev | 970 | Not measured | Only GPT-4.1 Nano measured |
| Design Arena: data viz | 891 | Not measured | Only GPT-4.1 Nano measured |
| Design Arena: 3D | 924 | Not measured | Only GPT-4.1 Nano measured |
| Design Arena: all code categories | 962 | Not measured | Only GPT-4.1 Nano measured |
| Neither model has a result on the other 3 boards here | |||
GeneralLike-for-likePublic boards, shown as published, kept out of the index133
| Benchmark | GPT-4.1 Nano | Llama 3.1 70B Instruct | Leader |
|---|---|---|---|
| IFBench | 32.0% | 34.4% | Llama 3.1 70B Instruct |
| Long context reasoning (AA-LCR) | 20.3% | Not measured | Only GPT-4.1 Nano measured |
| DTBench | 52.5% | 60.0% | Llama 3.1 70B Instruct |
| LMCA | 5.5% | 14.8% | Llama 3.1 70B Instruct |
| Fiction.LiveBench | 25.0% | Not measured | Only GPT-4.1 Nano measured |
| LMArena Text | 1285 | 1261 | GPT-4.1 Nano |
| LMArena Text: instruction following | 1266 | 1231 | GPT-4.1 Nano |
| LMArena Text: creative writing | 1260 | 1232 | GPT-4.1 Nano |
| LMArena Vision | 1063 | Not measured | Only GPT-4.1 Nano measured |
| LMArena Text: English | 1303 | 1294 | GPT-4.1 Nano |
| LMArena Text: non-English | 1260 | 1219 | GPT-4.1 Nano |
| LMArena Text: Chinese | 1270 | 1215 | GPT-4.1 Nano |
| LMArena Text: German | 1288 | 1222 | GPT-4.1 Nano |
| LMArena Text: Japanese | 1198 | 1132 | GPT-4.1 Nano |
| LMArena Text: Korean | Not measured | 1140 | Only Llama 3.1 70B Instruct measured |
| LMArena Text: French | Not measured | 1262 | Only Llama 3.1 70B Instruct measured |
| LMArena Text: Spanish | Not measured | 1253 | Only Llama 3.1 70B Instruct measured |
| LMArena Text: Russian | 1261 | 1234 | GPT-4.1 Nano |
| LMArena Text: multi-turn | 1277 | 1256 | GPT-4.1 Nano |
| LMArena Text: longer queries | 1283 | 1241 | GPT-4.1 Nano |
| LMArena Text: writing and language | 1261 | 1236 | GPT-4.1 Nano |
| LMArena Text: entertainment and media | 1243 | 1230 | GPT-4.1 Nano |
| LMArena Vision: English | 1101 | Not measured | Only GPT-4.1 Nano measured |
| Neither model has a result on the other 23 boards here | |||
Image generationNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
| Benchmark | GPT-4.1 Nano | Llama 3.1 70B Instruct | Leader |
|---|---|---|---|
| Neither model has a result on 5 boards here | |||
What each workload costs
Three fixed token mixes at each model's listed price, so per-token rates become a bill.
Chat turn
1K input + 500 output tokens
GPT-4.1 Nano costs less
Repository review
50K input + 3K output tokens
GPT-4.1 Nano costs less
Agent loop
200K input + 10K output tokens
GPT-4.1 Nano costs less
Listed OpenRouter rates, without cache or batch discounts. A workload that does not fit a model's context window would need splitting.
Specification differences
The leading value in a row is marked; a row with a missing value names no leader. Our two rows are marked in red.
| Specification | OpenAI GPT-4.1 Nano | Meta Llama 3.1 70B Instruct |
|---|---|---|
| Maker | OpenAI | Meta Llama |
| Weights | No public weights listed | Open weights |
| Released | April 14, 2025 | July 23, 2024 |
| Context window | 1.05M | 131K |
| Input $/Mtok | $0.10 | $0.56 |
| Output $/Mtok | $0.40 | $0.56 |
| Coding index | Not benchmarked | Not benchmarked |
| Independent measurements behind it | 0 | 0 |
| Cost per solved task | Not computable | Not computable |
| Source | OpenRouter listing | OpenRouter listing |
Questions
Is GPT-4.1 Nano better than Llama 3.1 70B Instruct for coding?
Neither model has a coding score yet, so the two cannot be ranked on coding. Pick GPT-4.1 Nano for output tokens 1.4x cheaper ($0.40 vs $0.56 per million) and a larger context window (1.05M against 131K tokens).
Which is cheaper, GPT-4.1 Nano or Llama 3.1 70B Instruct?
GPT-4.1 Nano costs $0.10 per million input tokens and $0.40 per million output tokens; Llama 3.1 70B Instruct costs $0.56 and $0.56. Cost per solved task is known for neither of them.
Which has the bigger context window, GPT-4.1 Nano or Llama 3.1 70B Instruct?
GPT-4.1 Nano, with 1.05M tokens against 131K.
How many benchmarks is the GPT-4.1 Nano vs Llama 3.1 70B Instruct comparison based on?
41 benchmarks have a result for both models as of October 22, 2026. GPT-4.1 Nano's coding index rests on 0 independent benchmarks and Llama 3.1 70B Instruct's on 0. Where only one model has a result, the page says so instead of naming a winner.