Model comparison
GPT-6.1 Sol vs Llama 3.2 3B
GPT-6.1 Sol is the stronger model overall, scoring 65.6 to 28.9 on the Noometry Index. Llama 3.2 3B costs 33× less per token, which makes it the better buy when GPT-6.1 Sol's lead doesn't matter for your workload.
Last verified . 12 shared benchmarks.
MetaSummary
- They share 12 benchmarks with published results for both. GPT-6.1 Sol scores higher in 9 categories and Llama 3.2 3B in 0 categories; 9 gaps are clear of the uncertainty.
- The widest gap is in math, where GPT-6.1 Sol leads 93.7 to 32.4.
- Llama 3.2 3B is cheaper at $0.05 / $0.33 per million input/output tokens, against $2 / $10 for GPT-6.1 Sol.
- GPT-6.1 Sol accepts more context: 1.05M tokens versus 131K.
- Llama 3.2 3B has downloadable open weights; the other is API-only.
Side by side
| GPT-6.1 Sol | Llama 3.2 3B | |
|---|---|---|
| Provider | OpenAI | Meta |
| Noometry Index | 65.6 | 28.9 |
| Released | 2026-09-29 | 2024-09-24 |
| Weights | Proprietary | Open |
| Context window | 1.05M | 131K |
| Max output | 128K | 118K |
| Input $ / M tokens | $2 | $0.05 |
| Output $ / M tokens | $10 | $0.33 |
| Results tracked | 34 | 18 |
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Category by category
Coding GPT-6.1 Sol leads
GPT-6.1 Sol: 63.2 (#8), Llama 3.2 3B: 27.6 (#319)
| Benchmark | GPT-6.1 Sol | Llama 3.2 3B |
|---|---|---|
| LMArena Coding | 1487 | 1098 |
| DeepSWE | 75.2% | — |
| FrontierCode | 50.2% | — |
| LMArena WebDev | 1755 | — |
| SciCode | 55.8% | — |
| BigCodeBench Instruct | — | 23.4% |
| BigCodeBench Complete | — | 28.3% |
Agentic & Tool Use GPT-6.1 Sol leads
GPT-6.1 Sol: 39.6 (#26), Llama 3.2 3B: 20.1 (#143)
| Benchmark | GPT-6.1 Sol | Llama 3.2 3B |
|---|---|---|
| APEX-Agents | 60% | — |
| Berkeley Function Calling Leaderboard | — | 21.9% |
| BALROG | — | 10.1% |
| GDP.pdf | 32% | — |
Reasoning GPT-6.1 Sol leads
GPT-6.1 Sol: 81.9 (#2), Llama 3.2 3B: 21.0 (#228)
| Benchmark | GPT-6.1 Sol | Llama 3.2 3B |
|---|---|---|
| LMArena Hard Prompts | 1466 | 1095 |
| ARC-AGI-2 | 94.2% | — |
| NYT Connections (extended) | 95.5% | — |
| ARC-AGI-1 | 98.5% | — |
| CritPt | 31.7% | — |
| Chess Puzzles | 61% | — |
| EBR-Bench | 54.3% | — |
| Mystery Game Puzzles | 80% | — |
| Epoch Capabilities Index | 166.09 | — |
Math GPT-6.1 Sol leads
GPT-6.1 Sol: 93.7 (#1), Llama 3.2 3B: 32.4 (#214)
| Benchmark | GPT-6.1 Sol | Llama 3.2 3B |
|---|---|---|
| LMArena Math | 1464 | 1126 |
| FrontierMath (Tiers 1-3) | 93.7% | — |
| FrontierMath Tier 4 | 100% | — |
| OTIS Mock AIME 2024-2025 | 100% | — |
| ProofBench | 99% | — |
Knowledge GPT-6.1 Sol leads
GPT-6.1 Sol: 71.8 (#4), Llama 3.2 3B: 29.7 (#235)
| Benchmark | GPT-6.1 Sol | Llama 3.2 3B |
|---|---|---|
| LMArena Expert | 1502 | 1090 |
| GPQA Diamond | 95.4% | — |
| SimpleQA Verified | 73.9% | — |
Multimodal Not comparable
GPT-6.1 Sol: 52.7 (#5), Llama 3.2 3B: —
| Benchmark | GPT-6.1 Sol | Llama 3.2 3B |
|---|---|---|
| LMArena Vision | 1288 | — |
| Furniture Assembly | 80% | — |
Multilingual GPT-6.1 Sol leads
GPT-6.1 Sol: 54.3 (#46), Llama 3.2 3B: 26.2 (#281)
| Benchmark | GPT-6.1 Sol | Llama 3.2 3B |
|---|---|---|
| LMArena Non-English | 1438 | 1019 |
| LMArena Chinese | 1477 | 1017 |
| LMArena Russian | 1455 | 949 |
| LMArena German | — | 1056 |
Instruction Following GPT-6.1 Sol leads
GPT-6.1 Sol: 77.0 (#29), Llama 3.2 3B: 56.0 (#275)
| Benchmark | GPT-6.1 Sol | Llama 3.2 3B |
|---|---|---|
| LMArena Instruction Following | 1468 | 1089 |
Long Context GPT-6.1 Sol leads
GPT-6.1 Sol: 44.9 (#54), Llama 3.2 3B: 33.4 (#261)
| Benchmark | GPT-6.1 Sol | Llama 3.2 3B |
|---|---|---|
| LMArena Longer Query | 1465 | 1100 |
Writing & Preference GPT-6.1 Sol leads
GPT-6.1 Sol: 63.6 (#63), Llama 3.2 3B: 24.7 (#307)
| Benchmark | GPT-6.1 Sol | Llama 3.2 3B |
|---|---|---|
| LMArena Text | 1447 | 1110 |
| LMArena Creative Writing | 1432 | 1094 |
| LMArena Multi-Turn | 1449 | 1105 |
| EQ-Bench Creative Writing | — | 595 |
Frequently asked questions
Is GPT-6.1 Sol better than Llama 3.2 3B?
GPT-6.1 Sol is the stronger model overall, scoring 65.6 to 28.9 on the Noometry Index. Llama 3.2 3B costs 33× less per token, which makes it the better buy when GPT-6.1 Sol's lead doesn't matter for your workload.
Which is cheaper, GPT-6.1 Sol or Llama 3.2 3B?
Llama 3.2 3B is cheaper. It lists at $0.05 per million input tokens and $0.33 per million output tokens; GPT-6.1 Sol lists at $2 and $10.
Is GPT-6.1 Sol or Llama 3.2 3B better for coding?
GPT-6.1 Sol scores higher on coding benchmarks: 63.2 versus 27.6 in the Noometry coding category.
Which has the bigger context window?
GPT-6.1 Sol does, with 1.05M tokens against 131K.
How many benchmarks do GPT-6.1 Sol and Llama 3.2 3B share?
12 benchmarks have published results for both models. GPT-6.1 Sol has 34 scored results on Noometry and Llama 3.2 3B has 18.