Model comparison
GPT-6 Astra vs Llama 3.2 3B
GPT-6 Astra is the stronger model overall, scoring 70.8 to 28.9 on the Noometry Index. Llama 3.2 3B costs 167× less per token, which makes it the better buy when GPT-6 Astra's lead doesn't matter for your workload.
Last verified . 15 shared benchmarks.
MetaSummary
- They share 15 benchmarks with published results for both. GPT-6 Astra 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 reasoning, where GPT-6 Astra leads 85.1 to 21.0.
- The biggest single-benchmark swing is BALROG: 68.3% for GPT-6 Astra and 10.1% for Llama 3.2 3B.
- Llama 3.2 3B is cheaper at $0.05 / $0.33 per million input/output tokens, against $10 / $50 for GPT-6 Astra.
- GPT-6 Astra 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 Astra | Llama 3.2 3B | |
|---|---|---|
| Provider | OpenAI | Meta |
| Noometry Index | 70.8 | 28.9 |
| Released | 2026-09-03 | 2024-09-24 |
| Weights | Proprietary | Open |
| Context window | 1.05M | 131K |
| Max output | 128K | 118K |
| Input $ / M tokens | $10 | $0.05 |
| Output $ / M tokens | $50 | $0.33 |
| Results tracked | 56 | 18 |
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Category by category
Coding GPT-6 Astra leads
GPT-6 Astra: 73.7 (#2), Llama 3.2 3B: 27.6 (#319)
| Benchmark | GPT-6 Astra | Llama 3.2 3B |
|---|---|---|
| LMArena Coding | 1487 | 1098 |
| DeepSWE | 74.1% | — |
| FrontierCode | 53.3% | — |
| LMArena WebDev | 1786 | — |
| FrontierSWE | 65.5% | — |
| SciCode | 56.5% | — |
| GSO | 79.4% | — |
| WeirdML | 93.6% | — |
| BigCodeBench Instruct | — | 23.4% |
| MirrorCode | 46.7% | — |
| BigCodeBench Complete | — | 28.3% |
| ALE-Bench | 2,951 | — |
Agentic & Tool Use GPT-6 Astra leads
GPT-6 Astra: 52.9 (#3), Llama 3.2 3B: 20.1 (#143)
| Benchmark | GPT-6 Astra | Llama 3.2 3B |
|---|---|---|
| BALROG | 68.3% | 10.1% |
| APEX-Agents | 64.7% | — |
| Berkeley Function Calling Leaderboard | — | 21.9% |
| Remote Labor Index | 20.8% | — |
| GDP.pdf | 34.2% | — |
| Vending-Bench 2 | 15,515 | — |
Reasoning GPT-6 Astra leads
GPT-6 Astra: 85.1 (#1), Llama 3.2 3B: 21.0 (#228)
| Benchmark | GPT-6 Astra | Llama 3.2 3B |
|---|---|---|
| LMArena Hard Prompts | 1462 | 1095 |
| ARC-AGI-2 | 95% | — |
| NYT Connections (extended) | 98.1% | — |
| ARC-AGI-1 | 98.5% | — |
| CritPt | 31.7% | — |
| Chess Puzzles | 72% | — |
| EBR-Bench | 76.2% | — |
| Mystery Game Puzzles | 84% | — |
| DTBench | 97.3% | — |
| LMCA | 64.4% | — |
| Bench to the Future 3 | 0.14 | — |
| Epoch Capabilities Index | 166.45 | — |
Math GPT-6 Astra leads
GPT-6 Astra: 93.5 (#2), Llama 3.2 3B: 32.4 (#214)
| Benchmark | GPT-6 Astra | Llama 3.2 3B |
|---|---|---|
| LMArena Math | 1465 | 1126 |
| FrontierMath (Tiers 1-3) | 93.7% | — |
| FrontierMath Tier 4 | 97.6% | — |
| OTIS Mock AIME 2024-2025 | 100% | — |
| ProofBench | 99% | — |
| FrontierMath Erdős | 2.9% | — |
Knowledge GPT-6 Astra leads
GPT-6 Astra: 75.3 (#1), Llama 3.2 3B: 29.7 (#235)
| Benchmark | GPT-6 Astra | Llama 3.2 3B |
|---|---|---|
| LMArena Expert | 1483 | 1090 |
| GPQA Diamond | 95.8% | — |
| Humanity's Last Exam | 54.8% | — |
| SimpleQA Verified | 75.6% | — |
| Vectara Hallucination Rate | 8.7% | — |
Multimodal Not comparable
GPT-6 Astra: 55.0 (#3), Llama 3.2 3B: —
| Benchmark | GPT-6 Astra | Llama 3.2 3B |
|---|---|---|
| LMArena Vision | 1281 | — |
| Blueprint-Bench 2 | 49.7% | — |
| Furniture Assembly | 80% | — |
| LMArena Document | 1468 | — |
Multilingual GPT-6 Astra leads
GPT-6 Astra: 53.7 (#61), Llama 3.2 3B: 26.2 (#281)
| Benchmark | GPT-6 Astra | Llama 3.2 3B |
|---|---|---|
| LMArena Non-English | 1430 | 1019 |
| LMArena Chinese | 1484 | 1017 |
| LMArena German | 1440 | 1056 |
| LMArena Russian | 1436 | 949 |
| LMArena French | 1456 | — |
| LMArena Japanese | 1379 | — |
| LMArena Korean | 1426 | — |
| LMArena Spanish | 1407 | — |
Instruction Following GPT-6 Astra leads
GPT-6 Astra: 76.3 (#44), Llama 3.2 3B: 56.0 (#275)
| Benchmark | GPT-6 Astra | Llama 3.2 3B |
|---|---|---|
| LMArena Instruction Following | 1450 | 1089 |
Long Context GPT-6 Astra leads
GPT-6 Astra: 44.5 (#62), Llama 3.2 3B: 33.4 (#261)
| Benchmark | GPT-6 Astra | Llama 3.2 3B |
|---|---|---|
| LMArena Longer Query | 1456 | 1100 |
Writing & Preference GPT-6 Astra leads
GPT-6 Astra: 75.3 (#7), Llama 3.2 3B: 24.7 (#307)
| Benchmark | GPT-6 Astra | Llama 3.2 3B |
|---|---|---|
| LMArena Text | 1441 | 1110 |
| LMArena Creative Writing | 1418 | 1094 |
| EQ-Bench Creative Writing | 2173 | 595 |
| LMArena Multi-Turn | 1448 | 1105 |
Frequently asked questions
Is GPT-6 Astra better than Llama 3.2 3B?
GPT-6 Astra is the stronger model overall, scoring 70.8 to 28.9 on the Noometry Index. Llama 3.2 3B costs 167× less per token, which makes it the better buy when GPT-6 Astra's lead doesn't matter for your workload.
Which is cheaper, GPT-6 Astra 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 Astra lists at $10 and $50.
Is GPT-6 Astra or Llama 3.2 3B better for coding?
GPT-6 Astra scores higher on coding benchmarks: 73.7 versus 27.6 in the Noometry coding category.
Which has the bigger context window?
GPT-6 Astra does, with 1.05M tokens against 131K.
How many benchmarks do GPT-6 Astra and Llama 3.2 3B share?
15 benchmarks have published results for both models. GPT-6 Astra has 56 scored results on Noometry and Llama 3.2 3B has 18.