GPT-4 Turbo vs Llama 3.1 70B Instruct
GPT-4 Turbo leads Llama 3.1 70B Instruct on the Artificial Analysis Intelligence Index, 7.0 to 6.6, and Llama 3.1 70B Instruct costs 53.6x less per output token ($0.56 vs $30.00 per million). Updated October 22, 2026; 35 benchmarks have a result for both.
GPT-4 Turbo 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 Turbo for work measured by Humanity's Last Exam (Artificial Analysis run) or LMArena Text: science, where it leads.
- Shared results
- 35
- GPT-4 Turbo only
- 7
- Llama 3.1 70B Instruct only
- 12
- Like-for-like categories
- 6 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 Turbo
GPT-4 Turbo leads, 7.0 against 6.6.
Same board, both measured
Long documents
Listed context window
Llama 3.1 70B Instruct
Llama 3.1 70B Instruct takes 131K tokens against 128K.
Listed by OpenRouter
Everyday cost
1K input + 500 output tokens
Llama 3.1 70B Instruct
GPT-4 Turbo $0.025, Llama 3.1 70B Instruct $0.0008 per turn.
List prices
At a glance
- Coding index
- GPT-4 Turbo not benchmarked, Llama 3.1 70B Instruct not benchmarked.
- Widest category
- General, 4.2 points apart on average over 18 shared benchmarks.
- Widest single gap
- LMArena Text: Japanese: GPT-4 Turbo 1194 against Llama 3.1 70B Instruct 1132, 10.2 points apart on a 0 to 100 scale.
- Output price
- GPT-4 Turbo $30.00 against Llama 3.1 70B Instruct $0.56 per million output tokens, 53.6x apart.
- Context window
- GPT-4 Turbo takes 128K 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 Turbo | Bars | Llama 3.1 70B Instruct | Ties |
|---|---|---|---|---|
| Coding | 3 | 0 | ||
| Intelligence | 2 | 0 | ||
| Reasoning | 3 | 1 | ||
| Math | 2 | 0 | ||
| Knowledge | 2 | 4 | ||
| General | 16 | 2 | ||
| All shared boards | 28 | 7 |
CodingLike-for-likeSix of these boards feed the Vibe Coding Index30
| Benchmark | GPT-4 Turbo | Llama 3.1 70B Instruct | Leader |
|---|---|---|---|
| AA Coding Index | 21.5 | Not measured | Only GPT-4 Turbo measured |
| LiveCodeBench (Artificial Analysis run) | 29.1% | 23.2% | GPT-4 Turbo |
| Terminal-Bench Hard (Artificial Analysis run) | Not measured | 3.0% | Only Llama 3.1 70B Instruct measured |
| WeirdML | Not measured | 9.0% | Only Llama 3.1 70B Instruct measured |
| LMArena Text: coding | 1268 | 1260 | GPT-4 Turbo |
| LMArena Text: software and IT | 1267 | 1260 | GPT-4 Turbo |
| Neither model has a result on the other 33 boards here | |||
IntelligenceLike-for-likePublic boards, shown as published, kept out of the index20
| Benchmark | GPT-4 Turbo | Llama 3.1 70B Instruct | Leader |
|---|---|---|---|
| AA Intelligence Index | 7.0 | 6.6 | GPT-4 Turbo |
| Epoch Capabilities Index | 127.3 | 125.9 | GPT-4 Turbo |
Agentic workDirectional onlyPublic boards, shown as published, kept out of the index3 boards, none shared
| Benchmark | GPT-4 Turbo | Llama 3.1 70B Instruct | Leader |
|---|---|---|---|
| τ²-Bench Telecom | Not measured | 15.2% | Only Llama 3.1 70B Instruct measured |
| BALROG | Not measured | 27.9% | Only Llama 3.1 70B Instruct measured |
| TheAgentCompany | Not measured | 6.9% | Only Llama 3.1 70B Instruct measured |
| Neither model has a result on the other 38 boards here | |||
ReasoningLike-for-likePublic boards, shown as published, kept out of the index31
| Benchmark | GPT-4 Turbo | Llama 3.1 70B Instruct | Leader |
|---|---|---|---|
| GPQA Diamond (Artificial Analysis run) | Not measured | 40.9% | Only Llama 3.1 70B Instruct measured |
| Humanity's Last Exam (Artificial Analysis run) | 3.1% | 4.5% | Llama 3.1 70B Instruct |
| GPQA Diamond (Epoch AI run) | Not measured | 44.2% | Only Llama 3.1 70B Instruct measured |
| LMArena Text: hard prompts | 1251 | 1241 | GPT-4 Turbo |
| LMArena Text: expert | 1223 | 1209 | GPT-4 Turbo |
| LMArena Text: hard prompts in English | 1266 | 1261 | GPT-4 Turbo |
| Neither model has a result on the other 16 boards here | |||
MathLike-for-likePublic boards, shown as published, kept out of the index20
| Benchmark | GPT-4 Turbo | Llama 3.1 70B Instruct | Leader |
|---|---|---|---|
| AA Math Index | Not measured | 4.0 | Only Llama 3.1 70B Instruct measured |
| AIME 2025 (Artificial Analysis run) | Not measured | 4.0% | Only Llama 3.1 70B Instruct measured |
| OTIS Mock AIME | Not measured | 3.6% | Only Llama 3.1 70B Instruct measured |
| MATH level 5 | Not measured | 36.7% | Only Llama 3.1 70B Instruct measured |
| LMArena Text: math | 1272 | 1252 | GPT-4 Turbo |
| LMArena Text: mathematical work | 1268 | 1253 | GPT-4 Turbo |
| Neither model has a result on the other 7 boards here | |||
KnowledgeLike-for-likePublic boards, shown as published, kept out of the index24
| Benchmark | GPT-4 Turbo | Llama 3.1 70B Instruct | Leader |
|---|---|---|---|
| LegalBench (Vals AI) | 80.5% | Not measured | Only GPT-4 Turbo measured |
| MedQA (Vals AI) | 82.0% | Not measured | Only GPT-4 Turbo measured |
| MMLU Pro (Artificial Analysis run) | 69.4% | 67.6% | GPT-4 Turbo |
| MMLU | 81.3% | 80.1% | GPT-4 Turbo |
| LMArena Text: science | 1256 | 1276 | Llama 3.1 70B Instruct |
| LMArena Text: medicine and healthcare | 1239 | 1257 | Llama 3.1 70B Instruct |
| LMArena Text: legal and government | 1265 | 1283 | Llama 3.1 70B Instruct |
| LMArena Text: business and finance | 1222 | 1235 | Llama 3.1 70B Instruct |
| Neither model has a result on the other 10 boards here | |||
Web designNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
| Benchmark | GPT-4 Turbo | Llama 3.1 70B Instruct | Leader |
|---|---|---|---|
| Neither model has a result on 9 boards here | |||
GeneralLike-for-likePublic boards, shown as published, kept out of the index162
| Benchmark | GPT-4 Turbo | Llama 3.1 70B Instruct | Leader |
|---|---|---|---|
| IFBench | Not measured | 34.4% | Only Llama 3.1 70B Instruct measured |
| DTBench | 61.6% | 60.0% | GPT-4 Turbo |
| LMCA | 9.8% | 14.8% | Llama 3.1 70B Instruct |
| LMArena Text | 1272 | 1261 | GPT-4 Turbo |
| LMArena Text: instruction following | 1249 | 1231 | GPT-4 Turbo |
| LMArena Text: creative writing | 1268 | 1232 | GPT-4 Turbo |
| LMArena Vision | 1090 | Not measured | Only GPT-4 Turbo measured |
| LMArena Text: English | 1291 | 1294 | Llama 3.1 70B Instruct |
| LMArena Text: non-English | 1245 | 1219 | GPT-4 Turbo |
| LMArena Text: Chinese | 1242 | 1215 | GPT-4 Turbo |
| LMArena Text: German | 1259 | 1222 | GPT-4 Turbo |
| LMArena Text: Japanese | 1194 | 1132 | GPT-4 Turbo |
| LMArena Text: Korean | 1187 | 1140 | GPT-4 Turbo |
| LMArena Text: French | 1276 | 1262 | GPT-4 Turbo |
| LMArena Text: Spanish | 1260 | 1253 | GPT-4 Turbo |
| LMArena Text: Russian | 1259 | 1234 | GPT-4 Turbo |
| LMArena Text: Polish | 1302 | Not measured | Only GPT-4 Turbo measured |
| LMArena Text: multi-turn | 1267 | 1256 | GPT-4 Turbo |
| LMArena Text: longer queries | 1254 | 1241 | GPT-4 Turbo |
| LMArena Text: writing and language | 1279 | 1236 | GPT-4 Turbo |
| LMArena Text: entertainment and media | 1258 | 1230 | GPT-4 Turbo |
| LMArena Vision: English | 1097 | Not measured | Only GPT-4 Turbo measured |
| LMArena Vision: Chinese | 1045 | Not measured | Only GPT-4 Turbo 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 Turbo | 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
Llama 3.1 70B Instruct costs less
Repository review
50K input + 3K output tokens
Llama 3.1 70B Instruct costs less
Agent loop
200K input + 10K output tokens
Llama 3.1 70B Instruct 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 Turbo | Meta Llama 3.1 70B Instruct |
|---|---|---|
| Maker | OpenAI | Meta Llama |
| Weights | No public weights listed | Open weights |
| Released | April 9, 2024 | July 23, 2024 |
| Context window | 128K | 131K |
| Input $/Mtok | $10.00 | $0.56 |
| Output $/Mtok | $30.00 | $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 Turbo 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 Turbo for work measured by Humanity's Last Exam (Artificial Analysis run) or LMArena Text: science, where it leads.
Which is cheaper, GPT-4 Turbo or Llama 3.1 70B Instruct?
GPT-4 Turbo costs $10.00 per million input tokens and $30.00 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 Turbo or Llama 3.1 70B Instruct?
Llama 3.1 70B Instruct, with 131K tokens against 128K.
How many benchmarks is the GPT-4 Turbo vs Llama 3.1 70B Instruct comparison based on?
35 benchmarks have a result for both models as of October 22, 2026. GPT-4 Turbo'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.