GPT-5.4 Pro vs Llama 3.2 1B Instruct
Llama 3.2 1B Instruct costs 895.5x less per output token than GPT-5.4 Pro ($0.201 vs $180.00 per million). Updated October 21, 2026; 1 benchmarks have a result for both.
GPT-5.4 Pro 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-5.4 Pro for a larger context window (1.05M against 60K tokens).
- Shared results
- 1
- GPT-5.4 Pro only
- 9
- Llama 3.2 1B Instruct only
- 37
- Like-for-like categories
- 1 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 3.2 1B Instruct has a AA Intelligence Index result, 4.8.
Directional only
Long documents
Listed context window
GPT-5.4 Pro
GPT-5.4 Pro takes 1.05M tokens against 60K.
Listed by OpenRouter
Everyday cost
1K input + 500 output tokens
Llama 3.2 1B Instruct
GPT-5.4 Pro $0.120, Llama 3.2 1B Instruct $0.0001 per turn.
List prices
At a glance
- Coding index
- GPT-5.4 Pro not benchmarked, Llama 3.2 1B Instruct not benchmarked.
- Widest category
- Reasoning, 70.7 points apart on average over 1 shared benchmark.
- Widest single gap
- GPQA Diamond (Epoch AI run): GPT-5.4 Pro 94.6% against Llama 3.2 1B Instruct 23.9%, 70.7 points apart on a 0 to 100 scale.
- Output price
- GPT-5.4 Pro $180.00 against Llama 3.2 1B Instruct $0.201 per million output tokens, 895.5x apart.
- Context window
- GPT-5.4 Pro takes 1.05M 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-5.4 Pro | Bars | Llama 3.2 1B Instruct | Ties |
|---|---|---|---|---|
| Reasoning | 1 | 0 | ||
| All shared boards | 1 | 0 |
CodingDirectional onlySix of these boards feed the Vibe Coding Index5 boards, none shared
| Benchmark | GPT-5.4 Pro | Llama 3.2 1B Instruct | Leader |
|---|---|---|---|
| LiveCodeBench (Artificial Analysis run) | Not measured | 1.9% | Only Llama 3.2 1B Instruct measured |
| Terminal-Bench Hard (Artificial Analysis run) | Not measured | 0.0% | Only Llama 3.2 1B Instruct measured |
| WeirdML | 57.4% | Not measured | Only GPT-5.4 Pro measured |
| LMArena Text: coding | Not measured | 1070 | Only Llama 3.2 1B Instruct measured |
| LMArena Text: software and IT | Not measured | 1056 | Only Llama 3.2 1B Instruct measured |
| Neither model has a result on the other 34 boards here | |||
IntelligenceDirectional onlyPublic boards, shown as published, kept out of the index2 boards, none shared
| Benchmark | GPT-5.4 Pro | Llama 3.2 1B Instruct | Leader |
|---|---|---|---|
| AA Intelligence Index | Not measured | 4.8 | Only Llama 3.2 1B Instruct measured |
| Epoch Capabilities Index | 159.6 | Not measured | Only GPT-5.4 Pro measured |
Agentic workDirectional onlyPublic boards, shown as published, kept out of the index3 boards, none shared
| Benchmark | GPT-5.4 Pro | 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 index10
| Benchmark | GPT-5.4 Pro | 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) | Not measured | 5.5% | Only Llama 3.2 1B Instruct measured |
| GPQA Diamond (Epoch AI run) | 94.6% | 23.9% | GPT-5.4 Pro |
| ARC-AGI | 94.5% | Not measured | Only GPT-5.4 Pro measured |
| ARC-AGI-2 | 83.3% | Not measured | Only GPT-5.4 Pro measured |
| SimpleBench | 74.1% | Not measured | Only GPT-5.4 Pro measured |
| Chess puzzles | 59.0% | Not measured | Only GPT-5.4 Pro measured |
| LMArena Text: hard prompts | Not measured | 1044 | Only Llama 3.2 1B Instruct measured |
| LMArena Text: expert | Not measured | 1007 | Only Llama 3.2 1B Instruct measured |
| LMArena Text: hard prompts in English | Not measured | 1087 | Only Llama 3.2 1B Instruct measured |
| Neither model has a result on the other 12 boards here | |||
MathDirectional onlyPublic boards, shown as published, kept out of the index7 boards, none shared
| Benchmark | GPT-5.4 Pro | 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 |
| FrontierMath, tiers 1 to 3 | 82.5% | Not measured | Only GPT-5.4 Pro measured |
| FrontierMath, tier 4 | 58.5% | Not measured | Only GPT-5.4 Pro measured |
| LMArena Text: math | Not measured | 1086 | Only Llama 3.2 1B Instruct measured |
| LMArena Text: mathematical work | Not measured | 1098 | Only Llama 3.2 1B Instruct measured |
| Neither model has a result on the other 6 boards here | |||
KnowledgeDirectional onlyPublic boards, shown as published, kept out of the index6 boards, none shared
| Benchmark | GPT-5.4 Pro | Llama 3.2 1B Instruct | Leader |
|---|---|---|---|
| MMLU Pro (Artificial Analysis run) | Not measured | 20.0% | Only Llama 3.2 1B Instruct measured |
| SimpleQA Verified | 46.3% | Not measured | Only GPT-5.4 Pro measured |
| LMArena Text: science | Not measured | 1055 | Only Llama 3.2 1B Instruct measured |
| LMArena Text: medicine and healthcare | Not measured | 1009 | Only Llama 3.2 1B Instruct measured |
| LMArena Text: legal and government | Not measured | 1071 | Only Llama 3.2 1B Instruct measured |
| LMArena Text: business and finance | Not measured | 1000 | Only Llama 3.2 1B Instruct measured |
| Neither model has a result on the other 12 boards here | |||
Web designNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
| Benchmark | GPT-5.4 Pro | Llama 3.2 1B Instruct | Leader |
|---|---|---|---|
| Neither model has a result on 9 boards here | |||
GeneralDirectional onlyPublic boards, shown as published, kept out of the index14 boards, none shared
| Benchmark | GPT-5.4 Pro | 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 |
| LMArena Text | Not measured | 1055 | Only Llama 3.2 1B Instruct measured |
| LMArena Text: instruction following | Not measured | 1031 | Only Llama 3.2 1B Instruct measured |
| LMArena Text: creative writing | Not measured | 1033 | Only Llama 3.2 1B Instruct measured |
| LMArena Text: English | Not measured | 1117 | Only Llama 3.2 1B Instruct measured |
| LMArena Text: non-English | Not measured | 973 | Only Llama 3.2 1B Instruct measured |
| LMArena Text: Chinese | Not measured | 959 | Only Llama 3.2 1B Instruct measured |
| LMArena Text: German | Not measured | 1014 | Only Llama 3.2 1B Instruct measured |
| LMArena Text: Russian | Not measured | 941 | Only Llama 3.2 1B Instruct measured |
| LMArena Text: multi-turn | Not measured | 1030 | Only Llama 3.2 1B Instruct measured |
| LMArena Text: longer queries | Not measured | 1050 | Only Llama 3.2 1B Instruct measured |
| LMArena Text: writing and language | Not measured | 1030 | Only Llama 3.2 1B Instruct measured |
| LMArena Text: entertainment and media | Not measured | 1025 | Only Llama 3.2 1B Instruct measured |
| Neither model has a result on the other 32 boards here | |||
Image generationNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
| Benchmark | GPT-5.4 Pro | 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-5.4 Pro | Meta Llama 3.2 1B Instruct |
|---|---|---|
| Maker | OpenAI | Meta Llama |
| Weights | No public weights listed | Open weights |
| Released | March 5, 2026 | September 25, 2024 |
| Context window | 1.05M | 60K |
| Input $/Mtok | $30.00 | $0.027 |
| Output $/Mtok | $180.00 | $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-5.4 Pro 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-5.4 Pro for a larger context window (1.05M against 60K tokens).
Which is cheaper, GPT-5.4 Pro or Llama 3.2 1B Instruct?
GPT-5.4 Pro costs $30.00 per million input tokens and $180.00 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-5.4 Pro or Llama 3.2 1B Instruct?
GPT-5.4 Pro, with 1.05M tokens against 60K.
How many benchmarks is the GPT-5.4 Pro vs Llama 3.2 1B Instruct comparison based on?
1 benchmarks have a result for both models as of October 21, 2026. GPT-5.4 Pro'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.