GPT-4o vs Llama 3.1 70B Instruct
Llama 3.1 70B Instruct costs 17.9x less per output token than GPT-4o ($0.56 vs $10.00 per million). Updated October 21, 2026; 12 benchmarks have a result for both.
GPT-4o 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-4o for work measured by Humanity's Last Exam (Artificial Analysis run) or IFBench, where it leads.
- Shared results
- 12
- GPT-4o only
- 15
- Llama 3.1 70B Instruct only
- 35
- 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
No call
Only Llama 3.1 70B Instruct has a AA Intelligence Index result, 6.6.
Directional only
Web design
Design Arena: websites
No call
Only GPT-4o has a Design Arena: websites result, 831.
Directional only
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-4o $0.0075, Llama 3.1 70B Instruct $0.0008 per turn.
List prices
At a glance
- Coding index
- GPT-4o not benchmarked, Llama 3.1 70B Instruct not benchmarked.
- Widest category
- Agentic work, 9.9 points apart on average over 1 shared benchmark.
- Widest single gap
- GPQA Diamond (Artificial Analysis run): GPT-4o 54.3% against Llama 3.1 70B Instruct 40.9%, 13.4 points apart on a 0 to 100 scale.
- Output price
- GPT-4o $10.00 against Llama 3.1 70B Instruct $0.56 per million output tokens, 17.9x apart.
- Context window
- GPT-4o 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-4o | Bars | Llama 3.1 70B Instruct | Ties |
|---|---|---|---|---|
| Coding | 2 | 0 | ||
| Agentic work | 1 | 0 | ||
| Reasoning | 1 | 1 | ||
| Math | 2 | 0 | ||
| Knowledge | 2 | 0 | ||
| General | 2 | 1 | ||
| All shared boards | 10 | 2 |
CodingLike-for-likeSix of these boards feed the Vibe Coding Index20
| Benchmark | GPT-4o | Llama 3.1 70B Instruct | Leader |
|---|---|---|---|
| SWE-bench Verified | 38.8% | Not measured | Only GPT-4o measured |
| SWE-bench Lite | 38.0% | Not measured | Only GPT-4o measured |
| SWE-bench (full test set) | 18.8% | Not measured | Only GPT-4o measured |
| SWE-bench Multimodal | 30.4% | Not measured | Only GPT-4o measured |
| LiveCodeBench (Artificial Analysis run) | 30.9% | 23.2% | GPT-4o |
| Terminal-Bench Hard (Artificial Analysis run) | 8.3% | 3.0% | GPT-4o |
| WeirdML | Not measured | 9.0% | Only Llama 3.1 70B Instruct measured |
| LMArena Text: coding | Not measured | 1260 | Only Llama 3.1 70B Instruct measured |
| LMArena Text: software and IT | Not measured | 1260 | Only Llama 3.1 70B Instruct measured |
| SciCode | 1.5% | Not measured | Only GPT-4o measured |
| Neither model has a result on the other 29 boards here | |||
IntelligenceDirectional onlyPublic boards, shown as published, kept out of the index2 boards, none shared
| Benchmark | GPT-4o | Llama 3.1 70B Instruct | Leader |
|---|---|---|---|
| AA Intelligence Index | Not measured | 6.6 | Only Llama 3.1 70B Instruct measured |
| Epoch Capabilities Index | Not measured | 125.9 | Only Llama 3.1 70B Instruct measured |
Agentic workLike-for-likePublic boards, shown as published, kept out of the index10
| Benchmark | GPT-4o | Llama 3.1 70B Instruct | Leader |
|---|---|---|---|
| τ²-Bench Telecom | 25.1% | 15.2% | GPT-4o |
| GDPval | 9.9% | Not measured | Only GPT-4o 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 37 boards here | |||
ReasoningLike-for-likePublic boards, shown as published, kept out of the index11
| Benchmark | GPT-4o | Llama 3.1 70B Instruct | Leader |
|---|---|---|---|
| GPQA Diamond (Artificial Analysis run) | 54.3% | 40.9% | GPT-4o |
| Humanity's Last Exam (Artificial Analysis run) | 2.4% | 4.5% | Llama 3.1 70B Instruct |
| GPQA Diamond (Epoch AI run) | Not measured | 44.2% | Only Llama 3.1 70B Instruct measured |
| ARC-AGI | 4.5% | Not measured | Only GPT-4o measured |
| ARC-AGI-2 | 0.0% | Not measured | Only GPT-4o measured |
| LMArena Text: hard prompts | Not measured | 1241 | Only Llama 3.1 70B Instruct measured |
| LMArena Text: expert | Not measured | 1209 | Only Llama 3.1 70B Instruct measured |
| LMArena Text: hard prompts in English | Not measured | 1261 | Only Llama 3.1 70B Instruct measured |
| Neither model has a result on the other 14 boards here | |||
MathLike-for-likePublic boards, shown as published, kept out of the index20
| Benchmark | GPT-4o | Llama 3.1 70B Instruct | Leader |
|---|---|---|---|
| AA Math Index | 6.0 | 4.0 | GPT-4o |
| AIME 2025 (Artificial Analysis run) | 6.0% | 4.0% | GPT-4o |
| 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 | Not measured | 1252 | Only Llama 3.1 70B Instruct measured |
| LMArena Text: mathematical work | Not measured | 1253 | Only Llama 3.1 70B Instruct measured |
| Neither model has a result on the other 7 boards here | |||
KnowledgeLike-for-likePublic boards, shown as published, kept out of the index20
| Benchmark | GPT-4o | Llama 3.1 70B Instruct | Leader |
|---|---|---|---|
| MMLU Pro (Artificial Analysis run) | 74.8% | 67.6% | GPT-4o |
| MMLU | 88.1% | 80.1% | GPT-4o |
| LMArena Text: science | Not measured | 1276 | Only Llama 3.1 70B Instruct measured |
| LMArena Text: medicine and healthcare | Not measured | 1257 | Only Llama 3.1 70B Instruct measured |
| LMArena Text: legal and government | Not measured | 1283 | Only Llama 3.1 70B Instruct measured |
| LMArena Text: business and finance | Not measured | 1235 | Only Llama 3.1 70B Instruct measured |
| Neither model has a result on the other 12 boards here | |||
Web designDirectional onlyPublic boards, shown as published, kept out of the index6 boards, none shared
| Benchmark | GPT-4o | Llama 3.1 70B Instruct | Leader |
|---|---|---|---|
| Design Arena: websites | 831 | Not measured | Only GPT-4o measured |
| Design Arena: UI components | 885 | Not measured | Only GPT-4o measured |
| Design Arena: game dev | 906 | Not measured | Only GPT-4o measured |
| Design Arena: data viz | 858 | Not measured | Only GPT-4o measured |
| Design Arena: 3D | 869 | Not measured | Only GPT-4o measured |
| Design Arena: all code categories | 861 | Not measured | Only GPT-4o measured |
| Neither model has a result on the other 3 boards here | |||
GeneralLike-for-likePublic boards, shown as published, kept out of the index21
| Benchmark | GPT-4o | Llama 3.1 70B Instruct | Leader |
|---|---|---|---|
| IFBench | 34.3% | 34.4% | Llama 3.1 70B Instruct |
| Long context reasoning (AA-LCR) | 49.3% | Not measured | Only GPT-4o measured |
| DTBench | 64.5% | 60.0% | GPT-4o |
| LMCA | 16.6% | 14.8% | GPT-4o |
| LMArena Text | Not measured | 1261 | Only Llama 3.1 70B Instruct measured |
| LMArena Text: instruction following | Not measured | 1231 | Only Llama 3.1 70B Instruct measured |
| LMArena Text: creative writing | Not measured | 1232 | Only Llama 3.1 70B Instruct measured |
| LMArena Text: English | Not measured | 1294 | Only Llama 3.1 70B Instruct measured |
| LMArena Text: non-English | Not measured | 1219 | Only Llama 3.1 70B Instruct measured |
| LMArena Text: Chinese | Not measured | 1215 | Only Llama 3.1 70B Instruct measured |
| LMArena Text: German | Not measured | 1222 | Only Llama 3.1 70B Instruct measured |
| LMArena Text: Japanese | Not measured | 1132 | Only Llama 3.1 70B Instruct measured |
| 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 | Not measured | 1234 | Only Llama 3.1 70B Instruct measured |
| LMArena Text: multi-turn | Not measured | 1256 | Only Llama 3.1 70B Instruct measured |
| LMArena Text: longer queries | Not measured | 1241 | Only Llama 3.1 70B Instruct measured |
| LMArena Text: writing and language | Not measured | 1236 | Only Llama 3.1 70B Instruct measured |
| LMArena Text: entertainment and media | Not measured | 1230 | Only Llama 3.1 70B Instruct measured |
| Neither model has a result on the other 26 boards here | |||
Image generationNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
| Benchmark | GPT-4o | 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-4o | Meta Llama 3.1 70B Instruct |
|---|---|---|
| Maker | OpenAI | Meta Llama |
| Weights | No public weights listed | Open weights |
| Released | May 13, 2024 | July 23, 2024 |
| Context window | 128K | 131K |
| Input $/Mtok | $2.50 | $0.56 |
| Output $/Mtok | $10.00 | $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-4o 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-4o for work measured by Humanity's Last Exam (Artificial Analysis run) or IFBench, where it leads.
Which is cheaper, GPT-4o or Llama 3.1 70B Instruct?
GPT-4o costs $2.50 per million input tokens and $10.00 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-4o or Llama 3.1 70B Instruct?
Llama 3.1 70B Instruct, with 131K tokens against 128K.
How many benchmarks is the GPT-4o vs Llama 3.1 70B Instruct comparison based on?
12 benchmarks have a result for both models as of October 21, 2026. GPT-4o'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.