GPT-4 Turbo vs Llama 3.2 1B Instruct
GPT-4 Turbo leads Llama 3.2 1B Instruct on the Artificial Analysis Intelligence Index, 7.0 to 4.8, and Llama 3.2 1B Instruct costs 149.3x less per output token ($0.201 vs $30.00 per million). Updated October 22, 2026; 27 benchmarks have a result for both.
GPT-4 Turbo or Llama 3.2 1B Instruct: which to pick
Neither model has a coding score yet, so the two cannot be ranked on coding.
Pick GPT-4 Turbo for a larger context window (128K against 60K tokens) and work measured by Humanity's Last Exam (Artificial Analysis run), where it leads.
- Shared results
- 27
- GPT-4 Turbo only
- 15
- Llama 3.2 1B Instruct only
- 11
- 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 4.8.
Same board, both measured
Long documents
Listed context window
GPT-4 Turbo
GPT-4 Turbo takes 128K tokens against 60K.
Listed by OpenRouter
Everyday cost
1K input + 500 output tokens
Llama 3.2 1B Instruct
GPT-4 Turbo $0.025, Llama 3.2 1B Instruct $0.0001 per turn.
List prices
At a glance
- Coding index
- GPT-4 Turbo not benchmarked, Llama 3.2 1B Instruct not benchmarked.
- Widest category
- General, 38.4 points apart on average over 12 shared benchmarks.
- Widest single gap
- MMLU Pro (Artificial Analysis run): GPT-4 Turbo 69.4% against Llama 3.2 1B Instruct 20.0%, 49.4 points apart on a 0 to 100 scale.
- Output price
- GPT-4 Turbo $30.00 against Llama 3.2 1B Instruct $0.201 per million output tokens, 149.3x apart.
- Context window
- GPT-4 Turbo takes 128K tokens, Llama 3.2 1B Instruct 60K.
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 3.2 1B Instruct | Ties |
|---|---|---|---|---|
| Coding | 3 | 0 | ||
| Intelligence | 1 | 0 | ||
| Reasoning | 3 | 1 | ||
| Math | 2 | 0 | ||
| Knowledge | 5 | 0 | ||
| General | 12 | 0 | ||
| All shared boards | 26 | 1 |
CodingLike-for-likeSix of these boards feed the Vibe Coding Index30
| Benchmark | GPT-4 Turbo | Llama 3.2 1B Instruct | Leader |
|---|---|---|---|
| AA Coding Index | 21.5 | Not measured | Only GPT-4 Turbo measured |
| LiveCodeBench (Artificial Analysis run) | 29.1% | 1.9% | GPT-4 Turbo |
| Terminal-Bench Hard (Artificial Analysis run) | Not measured | 0.0% | Only Llama 3.2 1B Instruct measured |
| LMArena Text: coding | 1268 | 1070 | GPT-4 Turbo |
| LMArena Text: software and IT | 1267 | 1056 | GPT-4 Turbo |
| Neither model has a result on the other 34 boards here | |||
IntelligenceLike-for-likePublic boards, shown as published, kept out of the index10
| Benchmark | GPT-4 Turbo | Llama 3.2 1B Instruct | Leader |
|---|---|---|---|
| AA Intelligence Index | 7.0 | 4.8 | GPT-4 Turbo |
| Epoch Capabilities Index | 127.3 | Not measured | Only GPT-4 Turbo measured |
Agentic workDirectional onlyPublic boards, shown as published, kept out of the index3 boards, none shared
| Benchmark | GPT-4 Turbo | Llama 3.2 1B Instruct | Leader |
|---|---|---|---|
| τ²-Bench Telecom | Not measured | 0.0% | Only Llama 3.2 1B Instruct measured |
| BALROG | Not measured | 6.6% | Only Llama 3.2 1B Instruct measured |
| Berkeley Function Calling Leaderboard (BFCL) | Not measured | 10.8% | Only Llama 3.2 1B 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 3.2 1B Instruct | Leader |
|---|---|---|---|
| GPQA Diamond (Artificial Analysis run) | Not measured | 19.6% | Only Llama 3.2 1B Instruct measured |
| Humanity's Last Exam (Artificial Analysis run) | 3.1% | 5.5% | Llama 3.2 1B Instruct |
| GPQA Diamond (Epoch AI run) | Not measured | 23.9% | Only Llama 3.2 1B Instruct measured |
| LMArena Text: hard prompts | 1251 | 1044 | GPT-4 Turbo |
| LMArena Text: expert | 1223 | 1007 | GPT-4 Turbo |
| LMArena Text: hard prompts in English | 1266 | 1087 | 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 3.2 1B Instruct | Leader |
|---|---|---|---|
| AA Math Index | Not measured | 0.0 | Only Llama 3.2 1B Instruct measured |
| AIME 2025 (Artificial Analysis run) | Not measured | 0.0% | Only Llama 3.2 1B Instruct measured |
| OTIS Mock AIME | Not measured | 0.6% | Only Llama 3.2 1B Instruct measured |
| LMArena Text: math | 1272 | 1086 | GPT-4 Turbo |
| LMArena Text: mathematical work | 1268 | 1098 | GPT-4 Turbo |
| Neither model has a result on the other 8 boards here | |||
KnowledgeLike-for-likePublic boards, shown as published, kept out of the index50
| Benchmark | GPT-4 Turbo | Llama 3.2 1B 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% | 20.0% | GPT-4 Turbo |
| MMLU | 81.3% | Not measured | Only GPT-4 Turbo measured |
| LMArena Text: science | 1256 | 1055 | GPT-4 Turbo |
| LMArena Text: medicine and healthcare | 1239 | 1009 | GPT-4 Turbo |
| LMArena Text: legal and government | 1265 | 1071 | GPT-4 Turbo |
| LMArena Text: business and finance | 1222 | 1000 | GPT-4 Turbo |
| 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 3.2 1B Instruct | Leader |
|---|---|---|---|
| Neither model has a result on 9 boards here | |||
GeneralLike-for-likePublic boards, shown as published, kept out of the index120
| Benchmark | GPT-4 Turbo | Llama 3.2 1B Instruct | Leader |
|---|---|---|---|
| IFBench | Not measured | 22.8% | Only Llama 3.2 1B Instruct measured |
| Long context reasoning (AA-LCR) | Not measured | 6.7% | Only Llama 3.2 1B Instruct measured |
| DTBench | 61.6% | Not measured | Only GPT-4 Turbo measured |
| LMCA | 9.8% | Not measured | Only GPT-4 Turbo measured |
| LMArena Text | 1272 | 1055 | GPT-4 Turbo |
| LMArena Text: instruction following | 1249 | 1031 | GPT-4 Turbo |
| LMArena Text: creative writing | 1268 | 1033 | GPT-4 Turbo |
| LMArena Vision | 1090 | Not measured | Only GPT-4 Turbo measured |
| LMArena Text: English | 1291 | 1117 | GPT-4 Turbo |
| LMArena Text: non-English | 1245 | 973 | GPT-4 Turbo |
| LMArena Text: Chinese | 1242 | 959 | GPT-4 Turbo |
| LMArena Text: German | 1259 | 1014 | GPT-4 Turbo |
| LMArena Text: Japanese | 1194 | Not measured | Only GPT-4 Turbo measured |
| LMArena Text: Korean | 1187 | Not measured | Only GPT-4 Turbo measured |
| LMArena Text: French | 1276 | Not measured | Only GPT-4 Turbo measured |
| LMArena Text: Spanish | 1260 | Not measured | Only GPT-4 Turbo measured |
| LMArena Text: Russian | 1259 | 941 | GPT-4 Turbo |
| LMArena Text: Polish | 1302 | Not measured | Only GPT-4 Turbo measured |
| LMArena Text: multi-turn | 1267 | 1030 | GPT-4 Turbo |
| LMArena Text: longer queries | 1254 | 1050 | GPT-4 Turbo |
| LMArena Text: writing and language | 1279 | 1030 | GPT-4 Turbo |
| LMArena Text: entertainment and media | 1258 | 1025 | 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 22 boards here | |||
Image generationNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
| Benchmark | GPT-4 Turbo | Llama 3.2 1B 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 3.2 1B Instruct costs less
Repository review
50K input + 3K output tokens
Llama 3.2 1B Instruct costs less
Agent loop
200K input + 10K output tokens
Llama 3.2 1B 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 3.2 1B Instruct |
|---|---|---|
| Maker | OpenAI | Meta Llama |
| Weights | No public weights listed | Open weights |
| Released | April 9, 2024 | September 25, 2024 |
| Context window | 128K | 60K |
| Input $/Mtok | $10.00 | $0.027 |
| Output $/Mtok | $30.00 | $0.201 |
| 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 3.2 1B Instruct for coding?
Neither model has a coding score yet, so the two cannot be ranked on coding. Pick GPT-4 Turbo for a larger context window (128K against 60K tokens) and work measured by Humanity's Last Exam (Artificial Analysis run), where it leads.
Which is cheaper, GPT-4 Turbo or Llama 3.2 1B Instruct?
GPT-4 Turbo costs $10.00 per million input tokens and $30.00 per million output tokens; Llama 3.2 1B Instruct costs $0.027 and $0.201. Cost per solved task is known for neither of them.
Which has the bigger context window, GPT-4 Turbo or Llama 3.2 1B Instruct?
GPT-4 Turbo, with 128K tokens against 60K.
How many benchmarks is the GPT-4 Turbo vs Llama 3.2 1B Instruct comparison based on?
27 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 3.2 1B Instruct's on 0. Where only one model has a result, the page says so instead of naming a winner.