Gemini 2.5 Pro vs Llama 3.2 1B Instruct
Gemini 2.5 Pro leads Llama 3.2 1B Instruct on the Artificial Analysis Intelligence Index, 16.1 to 4.8, and Llama 3.2 1B Instruct costs 49.8x less per output token ($0.201 vs $10.00 per million). Updated October 21, 2026; 36 benchmarks have a result for both.
Gemini 2.5 Pro or Llama 3.2 1B Instruct: which to pick
Only Gemini 2.5 Pro has a coding score, 19.1 on the Vibe Coding Index; Llama 3.2 1B Instruct has none, so the two cannot be ranked on coding.
Pick Llama 3.2 1B Instruct for output tokens 49.8x cheaper ($0.201 vs $10.00 per million).
- Shared results
- 36
- Gemini 2.5 Pro only
- 54
- Llama 3.2 1B Instruct only
- 2
- 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
Only Gemini 2.5 Pro has a coding score, so no call is made.
One side not measured
Intelligence
AA Intelligence Index
Gemini 2.5 Pro
Gemini 2.5 Pro leads, 16.1 against 4.8.
Same board, both measured
Agentic work
AA Agentic Index
No call
Only Gemini 2.5 Pro has a AA Agentic Index result, 1.6.
Directional only
Web design
Design Arena: websites
No call
Only Gemini 2.5 Pro has a Design Arena: websites result, 1167.
Directional only
Long documents
Listed context window
Gemini 2.5 Pro
Gemini 2.5 Pro takes 1.05M tokens against 60K.
Listed by OpenRouter
Everyday cost
1K input + 500 output tokens
Llama 3.2 1B Instruct
Gemini 2.5 Pro $0.0063, Llama 3.2 1B Instruct $0.0001 per turn.
List prices
Cost per solved task
One SWE-bench Verified task, solved
No call
Only Gemini 2.5 Pro has the number, $0.64.
Directional only
At a glance
- Coding index
- Gemini 2.5 Pro 19.1 from 3 benchmarks, Llama 3.2 1B Instruct not benchmarked.
- Widest category
- Math, 75.9 points apart on average over 5 shared benchmarks.
- Widest single gap
- AA Math Index: Gemini 2.5 Pro 87.7 against Llama 3.2 1B Instruct 0.0, 87.7 points apart on a 0 to 100 scale.
- Output price
- Gemini 2.5 Pro $10.00 against Llama 3.2 1B Instruct $0.201 per million output tokens, 49.8x apart.
- Context window
- Gemini 2.5 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 | Gemini 2.5 Pro | Bars | Llama 3.2 1B Instruct | Ties |
|---|---|---|---|---|
| Coding | 4 | 0 | ||
| Intelligence | 1 | 0 | ||
| Agentic work | 1 | 0 | ||
| Reasoning | 6 | 0 | ||
| Math | 5 | 0 | ||
| Knowledge | 5 | 0 | ||
| General | 14 | 0 | ||
| All shared boards | 36 | 0 |
CodingLike-for-likeSix of these boards feed the Vibe Coding Index40
| Benchmark | Gemini 2.5 Pro | Llama 3.2 1B Instruct | Leader |
|---|---|---|---|
| SWE-bench Verified | 53.6% | Not measured | Only Gemini 2.5 Pro measured |
| LMArena WebDev | 1226 | Not measured | Only Gemini 2.5 Pro measured |
| AA Coding Index | 33.3 | Not measured | Only Gemini 2.5 Pro measured |
| SWE-bench Verified (Vals AI) | 54.4% | Not measured | Only Gemini 2.5 Pro measured |
| Vibe Code Bench (Vals AI) | 0.4% | Not measured | Only Gemini 2.5 Pro measured |
| LiveCodeBench (Artificial Analysis run) | 80.1% | 1.9% | Gemini 2.5 Pro |
| SciCode | 46.3% | Not measured | Only Gemini 2.5 Pro measured |
| Terminal-Bench Hard (Artificial Analysis run) | 26.5% | 0.0% | Gemini 2.5 Pro |
| Terminal-Bench | 32.6% | Not measured | Only Gemini 2.5 Pro measured |
| GSO-Bench | 3.9% | Not measured | Only Gemini 2.5 Pro measured |
| WeirdML | 54.0% | Not measured | Only Gemini 2.5 Pro measured |
| LMArena Text: coding | 1452 | 1070 | Gemini 2.5 Pro |
| LMArena Text: software and IT | 1459 | 1056 | Gemini 2.5 Pro |
| LMArena WebDev: HTML | 1235 | Not measured | Only Gemini 2.5 Pro measured |
| LMArena WebDev: from an image | 1285 | Not measured | Only Gemini 2.5 Pro measured |
| Neither model has a result on the other 24 boards here | |||
IntelligenceLike-for-likePublic boards, shown as published, kept out of the index10
| Benchmark | Gemini 2.5 Pro | Llama 3.2 1B Instruct | Leader |
|---|---|---|---|
| AA Intelligence Index | 16.1 | 4.8 | Gemini 2.5 Pro |
| Epoch Capabilities Index | 145.1 | Not measured | Only Gemini 2.5 Pro measured |
Agentic workLike-for-likePublic boards, shown as published, kept out of the index10
| Benchmark | Gemini 2.5 Pro | Llama 3.2 1B Instruct | Leader |
|---|---|---|---|
| AA Agentic Index | 1.6 | Not measured | Only Gemini 2.5 Pro measured |
| τ²-Bench Telecom | 54.1% | 0.0% | Gemini 2.5 Pro |
| GDPval | 23.3% | Not measured | Only Gemini 2.5 Pro measured |
| BALROG | Not measured | 6.6% | Only Llama 3.2 1B Instruct measured |
| DeepResearch Bench | 41.5% | Not measured | Only Gemini 2.5 Pro measured |
| Vending-Bench 2 | $574 | Not measured | Only Gemini 2.5 Pro measured |
| Berkeley Function Calling Leaderboard (BFCL) | Not measured | 10.8% | Only Llama 3.2 1B Instruct measured |
| BrowseComp-Plus | 29.5% | Not measured | Only Gemini 2.5 Pro measured |
| Neither model has a result on the other 33 boards here | |||
ReasoningLike-for-likePublic boards, shown as published, kept out of the index60
| Benchmark | Gemini 2.5 Pro | Llama 3.2 1B Instruct | Leader |
|---|---|---|---|
| GPQA Diamond (Artificial Analysis run) | 84.4% | 19.6% | Gemini 2.5 Pro |
| Humanity's Last Exam (Artificial Analysis run) | 22.5% | 5.5% | Gemini 2.5 Pro |
| GPQA Diamond (Epoch AI run) | 85.3% | 23.9% | Gemini 2.5 Pro |
| ARC-AGI | 41.0% | Not measured | Only Gemini 2.5 Pro measured |
| ARC-AGI-2 | 4.9% | Not measured | Only Gemini 2.5 Pro measured |
| LMArena Text: hard prompts | 1455 | 1044 | Gemini 2.5 Pro |
| LMArena Text: expert | 1452 | 1007 | Gemini 2.5 Pro |
| LMArena Text: hard prompts in English | 1453 | 1087 | Gemini 2.5 Pro |
| LMArena Vision: diagrams | 1274 | Not measured | Only Gemini 2.5 Pro measured |
| LMArena Vision: homework | 1281 | Not measured | Only Gemini 2.5 Pro measured |
| Kagi LLM Benchmark | 70.3% | Not measured | Only Gemini 2.5 Pro measured |
| Neither model has a result on the other 11 boards here | |||
MathLike-for-likePublic boards, shown as published, kept out of the index50
| Benchmark | Gemini 2.5 Pro | Llama 3.2 1B Instruct | Leader |
|---|---|---|---|
| AA Math Index | 87.7 | 0.0 | Gemini 2.5 Pro |
| AIME 2025 (Artificial Analysis run) | 87.7% | 0.0% | Gemini 2.5 Pro |
| OTIS Mock AIME | 84.7% | 0.6% | Gemini 2.5 Pro |
| FrontierMath, tiers 1 to 3 | 24.6% | Not measured | Only Gemini 2.5 Pro measured |
| FrontierMath, tier 4 | 0.0% | Not measured | Only Gemini 2.5 Pro measured |
| LMArena Text: math | 1450 | 1086 | Gemini 2.5 Pro |
| LMArena Text: mathematical work | 1454 | 1098 | Gemini 2.5 Pro |
| Neither model has a result on the other 6 boards here | |||
KnowledgeLike-for-likePublic boards, shown as published, kept out of the index50
| Benchmark | Gemini 2.5 Pro | Llama 3.2 1B Instruct | Leader |
|---|---|---|---|
| CaseLaw (Vals AI) | 63.9% | Not measured | Only Gemini 2.5 Pro measured |
| CorpFin (Vals AI) | 60.8% | Not measured | Only Gemini 2.5 Pro measured |
| MortgageTax (Vals AI) | 68.9% | Not measured | Only Gemini 2.5 Pro measured |
| MedCode (Vals AI) | 50.6% | Not measured | Only Gemini 2.5 Pro measured |
| MMLU Pro (Artificial Analysis run) | 86.2% | 20.0% | Gemini 2.5 Pro |
| LMArena Text: science | 1480 | 1055 | Gemini 2.5 Pro |
| LMArena Text: medicine and healthcare | 1476 | 1009 | Gemini 2.5 Pro |
| LMArena Text: legal and government | 1480 | 1071 | Gemini 2.5 Pro |
| LMArena Text: business and finance | 1439 | 1000 | Gemini 2.5 Pro |
| FACTS Benchmark Suite | 61.5% | Not measured | Only Gemini 2.5 Pro measured |
| Neither model has a result on the other 8 boards here | |||
Web designDirectional onlyPublic boards, shown as published, kept out of the index6 boards, none shared
| Benchmark | Gemini 2.5 Pro | Llama 3.2 1B Instruct | Leader |
|---|---|---|---|
| Design Arena: websites | 1167 | Not measured | Only Gemini 2.5 Pro measured |
| Design Arena: UI components | 1140 | Not measured | Only Gemini 2.5 Pro measured |
| Design Arena: game dev | 1111 | Not measured | Only Gemini 2.5 Pro measured |
| Design Arena: data viz | 1221 | Not measured | Only Gemini 2.5 Pro measured |
| Design Arena: 3D | 1081 | Not measured | Only Gemini 2.5 Pro measured |
| Design Arena: all code categories | 1152 | Not measured | Only Gemini 2.5 Pro measured |
| Neither model has a result on the other 3 boards here | |||
GeneralLike-for-likePublic boards, shown as published, kept out of the index140
| Benchmark | Gemini 2.5 Pro | Llama 3.2 1B Instruct | Leader |
|---|---|---|---|
| SAGE (Vals AI) | 41.9% | Not measured | Only Gemini 2.5 Pro measured |
| MedScribe (Vals AI) | 73.5% | Not measured | Only Gemini 2.5 Pro measured |
| IFBench | 48.7% | 22.8% | Gemini 2.5 Pro |
| Long context reasoning (AA-LCR) | 69.0% | 6.7% | Gemini 2.5 Pro |
| DTBench | 82.4% | Not measured | Only Gemini 2.5 Pro measured |
| LMCA | 34.8% | Not measured | Only Gemini 2.5 Pro measured |
| Creative writing (Lech Mazur) | 83.8% | Not measured | Only Gemini 2.5 Pro measured |
| LMArena Text | 1458 | 1055 | Gemini 2.5 Pro |
| LMArena Text: instruction following | 1437 | 1031 | Gemini 2.5 Pro |
| LMArena Text: creative writing | 1454 | 1033 | Gemini 2.5 Pro |
| LMArena Vision | 1263 | Not measured | Only Gemini 2.5 Pro measured |
| LMArena Text: English | 1458 | 1117 | Gemini 2.5 Pro |
| LMArena Text: non-English | 1452 | 973 | Gemini 2.5 Pro |
| LMArena Text: Chinese | 1507 | 959 | Gemini 2.5 Pro |
| LMArena Text: German | 1487 | 1014 | Gemini 2.5 Pro |
| LMArena Text: Japanese | 1461 | Not measured | Only Gemini 2.5 Pro measured |
| LMArena Text: Korean | 1434 | Not measured | Only Gemini 2.5 Pro measured |
| LMArena Text: French | 1473 | Not measured | Only Gemini 2.5 Pro measured |
| LMArena Text: Spanish | 1474 | Not measured | Only Gemini 2.5 Pro measured |
| LMArena Text: Russian | 1461 | 941 | Gemini 2.5 Pro |
| LMArena Text: Polish | 1479 | Not measured | Only Gemini 2.5 Pro measured |
| LMArena Text: multi-turn | 1453 | 1030 | Gemini 2.5 Pro |
| LMArena Text: longer queries | 1449 | 1050 | Gemini 2.5 Pro |
| LMArena Text: writing and language | 1454 | 1030 | Gemini 2.5 Pro |
| LMArena Text: entertainment and media | 1441 | 1025 | Gemini 2.5 Pro |
| LMArena Vision: English | 1261 | Not measured | Only Gemini 2.5 Pro measured |
| LMArena Vision: Chinese | 1300 | Not measured | Only Gemini 2.5 Pro measured |
| LMArena Vision: reading text | 1270 | Not measured | Only Gemini 2.5 Pro measured |
| LMArena Vision: humor | 1262 | Not measured | Only Gemini 2.5 Pro measured |
| LMArena Vision: captioning | 1304 | Not measured | Only Gemini 2.5 Pro measured |
| LMArena Vision: recognizing things | 1283 | Not measured | Only Gemini 2.5 Pro measured |
| LMArena Vision: creative writing | 1265 | Not measured | Only Gemini 2.5 Pro measured |
| LMArena Document | 1421 | Not measured | Only Gemini 2.5 Pro measured |
| Neither model has a result on the other 13 boards here | |||
Image generationNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
| Benchmark | Gemini 2.5 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 | Google Gemini 2.5 Pro | Meta Llama 3.2 1B Instruct |
|---|---|---|
| Maker | Meta Llama | |
| Weights | No public weights listed | Open weights |
| Released | June 17, 2025 | September 25, 2024 |
| Context window | 1.05M | 60K |
| Input $/Mtok | $1.25 | $0.027 |
| Output $/Mtok | $10.00 | $0.201 |
| Coding index | 19.1 | Not benchmarked |
| Independent measurements behind it | 3 | 0 |
| Cost per solved task | $0.64 | Not computable |
| Source | OpenRouter listing | OpenRouter listing |
Questions
Is Gemini 2.5 Pro better than Llama 3.2 1B Instruct for coding?
Only Gemini 2.5 Pro has a coding score, 19.1 on the Vibe Coding Index; Llama 3.2 1B Instruct has none, so the two cannot be ranked on coding. Pick Llama 3.2 1B Instruct for output tokens 49.8x cheaper ($0.201 vs $10.00 per million).
Which is cheaper, Gemini 2.5 Pro or Llama 3.2 1B Instruct?
Gemini 2.5 Pro costs $1.25 per million input tokens and $10.00 per million output tokens; Llama 3.2 1B Instruct costs $0.027 and $0.201. Cost per solved task is known for Gemini 2.5 Pro only.
Which has the bigger context window, Gemini 2.5 Pro or Llama 3.2 1B Instruct?
Gemini 2.5 Pro, with 1.05M tokens against 60K.
How many benchmarks is the Gemini 2.5 Pro vs Llama 3.2 1B Instruct comparison based on?
36 benchmarks have a result for both models as of October 21, 2026. Gemini 2.5 Pro's coding index rests on 3 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.