Model comparison
GPT-6 Astra vs Llama 3-8B
GPT-6 Astra is the stronger model overall, scoring 70.3 to 25.3 on the Noometry Index. Llama 3-8B costs 143× less per token, which makes it the better buy when GPT-6 Astra's lead doesn't matter for your workload.
Last verified . 19 shared benchmarks.
MetaSummary
- They share 19 benchmarks with published results for both. GPT-6 Astra scores higher in 8 categories and Llama 3-8B in 0 categories; 8 gaps are clear of the uncertainty.
- The widest gap is in math, where GPT-6 Astra leads 93.4 to 8.3.
- The biggest single-benchmark swing is OTIS Mock AIME 2024-2025: 100% for GPT-6 Astra and 1.9% for Llama 3-8B.
- Llama 3-8B is cheaper at $0.14 / $0.14 per million input/output tokens, against $10 / $50 for GPT-6 Astra.
- GPT-6 Astra accepts more context: 1.05M tokens versus 8K.
- Llama 3-8B has downloadable open weights; the other is API-only.
Side by side
| GPT-6 Astra | Llama 3-8B | |
|---|---|---|
| Provider | OpenAI | Meta |
| Noometry Index | 70.3 | 25.3 |
| Released | 2026-09-03 | 2024-04-18 |
| Weights | Proprietary | Open |
| Context window | 1.05M | 8K |
| Max output | 128K | 8K |
| Input $ / M tokens | $10 | $0.14 |
| Output $ / M tokens | $50 | $0.14 |
| Results tracked | 51 | 34 |
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Category by category
Coding GPT-6 Astra leads
GPT-6 Astra: 70.5 (#3), Llama 3-8B: 30.5 (#285)
| Benchmark | GPT-6 Astra | Llama 3-8B |
|---|---|---|
| LMArena Coding | 1488 | 1152 |
| DeepSWE | 74.1% | — |
| FrontierCode | 53.3% | — |
| LMArena WebDev | 1792 | — |
| FrontierSWE | 65.5% | — |
| SciCode | 56.5% | — |
| WeirdML | 93.6% | — |
| BigCodeBench Instruct | — | 31.9% |
| MirrorCode | 46.7% | — |
| BigCodeBench Complete | — | 36.9% |
| ALE-Bench | 2,951 | — |
| HumanEval+ | — | 56.7% |
| MBPP+ | — | 54.8% |
Agentic & Tool Use Not comparable
GPT-6 Astra: 52.3 (#3), Llama 3-8B: —
| Benchmark | GPT-6 Astra | Llama 3-8B |
|---|---|---|
| APEX-Agents | 64.7% | — |
| Remote Labor Index | 20.8% | — |
| BALROG | 68.3% | — |
| Vending-Bench 2 | 15,515 | — |
Reasoning GPT-6 Astra leads
GPT-6 Astra: 85.1 (#1), Llama 3-8B: 13.8 (#321)
| Benchmark | GPT-6 Astra | Llama 3-8B |
|---|---|---|
| Chess Puzzles | 72% | 0% |
| LMArena Hard Prompts | 1464 | 1133 |
| DTBench | 97.3% | 43.9% |
| Epoch Capabilities Index | 166.6 | 116.33 |
| ARC-AGI-2 | 95% | — |
| NYT Connections (extended) | 98.1% | — |
| ARC-AGI-1 | 98.5% | — |
| CritPt | 31.7% | — |
| EBR-Bench | 76.2% | — |
| Mystery Game Puzzles | 84% | — |
| LMCA | 64.4% | — |
| Adversarial NLI | — | 57.3% |
| Bench to the Future 3 | 0.14 | — |
| ForecastBench | — | 58.6 |
| WinoGrande | — | 75.7% |
Math GPT-6 Astra leads
GPT-6 Astra: 93.4 (#1), Llama 3-8B: 8.3 (#317)
| Benchmark | GPT-6 Astra | Llama 3-8B |
|---|---|---|
| OTIS Mock AIME 2024-2025 | 100% | 1.9% |
| LMArena Math | 1463 | 1151 |
| FrontierMath (Tiers 1-3) | 93.7% | — |
| FrontierMath Tier 4 | 97.6% | — |
| ProofBench | 99% | — |
| MATH Level 5 | — | 6.1% |
| FrontierMath Erdős | 2.9% | — |
Knowledge GPT-6 Astra leads
GPT-6 Astra: 75.3 (#1), Llama 3-8B: 7.8 (#303)
| Benchmark | GPT-6 Astra | Llama 3-8B |
|---|---|---|
| GPQA Diamond | 95.8% | 26.1% |
| LMArena Expert | 1486 | 1113 |
| Humanity's Last Exam | 54.8% | — |
| SimpleQA Verified | 75.6% | — |
| Vectara Hallucination Rate | 8.7% | — |
| ARC (AI2) Challenge | — | 82.8% |
| MMLU | — | 68.8% |
| OpenBookQA | — | 82.6% |
| TriviaQA | — | 67.7% |
Multimodal Not comparable
GPT-6 Astra: 55.1 (#3), Llama 3-8B: —
| Benchmark | GPT-6 Astra | Llama 3-8B |
|---|---|---|
| LMArena Vision | 1280 | — |
| Blueprint-Bench 2 | 49.7% | — |
| Furniture Assembly | 80% | — |
| LMArena Document | 1468 | — |
Multilingual GPT-6 Astra leads
GPT-6 Astra: 54.0 (#53), Llama 3-8B: 31.0 (#256)
| Benchmark | GPT-6 Astra | Llama 3-8B |
|---|---|---|
| LMArena Non-English | 1431 | 1098 |
| LMArena Chinese | 1495 | 1076 |
| LMArena French | 1467 | 1158 |
| LMArena Russian | 1434 | 1109 |
| LMArena Spanish | 1412 | 1172 |
| LMArena German | — | 1104 |
| LMArena Japanese | — | 967 |
| LMArena Korean | — | 1003 |
Instruction Following GPT-6 Astra leads
GPT-6 Astra: 76.6 (#37), Llama 3-8B: 58.7 (#255)
| Benchmark | GPT-6 Astra | Llama 3-8B |
|---|---|---|
| LMArena Instruction Following | 1454 | 1127 |
Long Context GPT-6 Astra leads
GPT-6 Astra: 44.7 (#57), Llama 3-8B: 34.2 (#246)
| Benchmark | GPT-6 Astra | Llama 3-8B |
|---|---|---|
| LMArena Longer Query | 1460 | 1128 |
Writing & Preference GPT-6 Astra leads
GPT-6 Astra: 75.7 (#7), Llama 3-8B: 37.7 (#251)
| Benchmark | GPT-6 Astra | Llama 3-8B |
|---|---|---|
| LMArena Text | 1443 | 1166 |
| LMArena Creative Writing | 1423 | 1150 |
| LMArena Multi-Turn | 1457 | 1152 |
| EQ-Bench Creative Writing | 2173 | — |
Frequently asked questions
Is GPT-6 Astra better than Llama 3-8B?
GPT-6 Astra is the stronger model overall, scoring 70.3 to 25.3 on the Noometry Index. Llama 3-8B costs 143× 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 3-8B?
Llama 3-8B is cheaper. It lists at $0.14 per million input tokens and $0.14 per million output tokens; GPT-6 Astra lists at $10 and $50.
Is GPT-6 Astra or Llama 3-8B better for coding?
GPT-6 Astra scores higher on coding benchmarks: 70.5 versus 30.5 in the Noometry coding category.
Which has the bigger context window?
GPT-6 Astra does, with 1.05M tokens against 8K.
How many benchmarks do GPT-6 Astra and Llama 3-8B share?
19 benchmarks have published results for both models. GPT-6 Astra has 51 scored results on Noometry and Llama 3-8B has 34.