Model comparison
GPT-5.6 Sol vs Llama 3.1-70B
GPT-5.6 Sol is the stronger model overall, scoring 65.0 to 29.6 on the Noometry Index. Llama 3.1-70B costs 20× less per token, which makes it the better buy when GPT-5.6 Sol's lead doesn't matter for your workload.
Last verified . 25 shared benchmarks.
MetaSummary
- They share 25 benchmarks with published results for both. GPT-5.6 Sol scores higher in 9 categories and Llama 3.1-70B in 0 categories; 9 gaps are clear of the uncertainty.
- The widest gap is in math, where GPT-5.6 Sol leads 85.6 to 13.5.
- The biggest single-benchmark swing is OTIS Mock AIME 2024-2025: 100% for GPT-5.6 Sol and 3.6% for Llama 3.1-70B.
- Llama 3.1-70B is cheaper at $0.40 / $0.40 per million input/output tokens, against $4 / $20 for GPT-5.6 Sol.
- GPT-5.6 Sol accepts more context: 1.05M tokens versus 128K.
- Llama 3.1-70B has downloadable open weights; the other is API-only.
Side by side
| GPT-5.6 Sol | Llama 3.1-70B | |
|---|---|---|
| Provider | OpenAI | Meta |
| Noometry Index | 65.0 | 29.6 |
| Released | 2026-07-09 | 2024-07-23 |
| Weights | Proprietary | Open |
| Context window | 1.05M | 128K |
| Max output | 128K | 4K |
| Input $ / M tokens | $4 | $0.40 |
| Output $ / M tokens | $20 | $0.40 |
| Results tracked | 65 | 35 |
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Category by category
Coding GPT-5.6 Sol leads
GPT-5.6 Sol: 65.1 (#7), Llama 3.1-70B: 30.3 (#296)
| Benchmark | GPT-5.6 Sol | Llama 3.1-70B |
|---|---|---|
| WeirdML | 89.4% | 9% |
| LMArena Coding | 1498 | 1260 |
| DeepSWE | 72.7% | — |
| FrontierCode | 47.5% | — |
| CursorBench | 41.7% | — |
| LMArena WebDev | 1618 | — |
| FrontierSWE | 32.2% | — |
| SciCode | 57.1% | — |
| GSO | 76.5% | — |
| BigCodeBench Instruct | — | 46.1% |
| MirrorCode | 20% | — |
| BigCodeBench Complete | — | 54.8% |
| ALE-Bench | 2,177 | — |
Agentic & Tool Use GPT-5.6 Sol leads
GPT-5.6 Sol: 50.3 (#7), Llama 3.1-70B: 25.1 (#112)
| Benchmark | GPT-5.6 Sol | Llama 3.1-70B |
|---|---|---|
| BALROG | 60% | 27.9% |
| APEX-Agents | 51.4% | — |
| OSWorld 2.0 | 27.3% | — |
| TheAgentCompany | — | 6.9% |
| τ²-bench Banking | 46.9% | — |
| PostTrainBench | 36.2% | — |
| GBAEval | 52.6% | — |
| GDP.pdf | 30.7% | — |
| LMArena Search | 1257 | — |
| Vending-Bench 2 | 9,619 | — |
Reasoning GPT-5.6 Sol leads
GPT-5.6 Sol: 74.8 (#8), Llama 3.1-70B: 21.6 (#220)
| Benchmark | GPT-5.6 Sol | Llama 3.1-70B |
|---|---|---|
| LMArena Hard Prompts | 1484 | 1241 |
| DTBench | 96% | 60% |
| LMCA | 59.2% | 14.8% |
| Epoch Capabilities Index | 161.66 | 125.92 |
| ARC-AGI-2 | 92.5% | — |
| SimpleBench | 71.7% | — |
| Kagi LLM Benchmark | 67% | — |
| NYT Connections (extended) | 93.8% | — |
| ARC-AGI-1 | 97.5% | — |
| CritPt | 32.3% | — |
| Chess Puzzles | 64% | — |
| EnigmaEval | 37.1% | — |
| EBR-Bench | 44.8% | — |
| Mystery Game Puzzles | 58% | — |
| Surface Evolver Bench | 93.1% | — |
| Bench to the Future 3 | 0.14 | — |
Math GPT-5.6 Sol leads
GPT-5.6 Sol: 85.6 (#9), Llama 3.1-70B: 13.5 (#304)
| Benchmark | GPT-5.6 Sol | Llama 3.1-70B |
|---|---|---|
| OTIS Mock AIME 2024-2025 | 100% | 3.6% |
| LMArena Math | 1474 | 1252 |
| FrontierMath (Tiers 1-3) | 89.1% | — |
| FrontierMath Tier 4 | 82.9% | — |
| ProofBench | 83% | — |
| Omni-MATH | — | 21% |
| MATH Level 5 | — | 36.7% |
| FrontierMath Erdős | 0% | — |
Knowledge GPT-5.6 Sol leads
GPT-5.6 Sol: 64.3 (#18), Llama 3.1-70B: 24.2 (#269)
| Benchmark | GPT-5.6 Sol | Llama 3.1-70B |
|---|---|---|
| GPQA Diamond | 93.5% | 44.2% |
| LMArena Expert | 1516 | 1209 |
| SimpleQA Verified | 69.7% | — |
| MMLU-Pro | — | 65.3% |
| Vectara Hallucination Rate | 12.4% | — |
| GPQA (HELM) | — | 42.6% |
| MMLU | — | 80.1% |
Multimodal Not comparable
GPT-5.6 Sol: 48.6 (#9), Llama 3.1-70B: —
| Benchmark | GPT-5.6 Sol | Llama 3.1-70B |
|---|---|---|
| LMArena Vision | 1281 | — |
| Blueprint-Bench 2 | 33.6% | — |
| Furniture Assembly | 56.7% | — |
| LMArena Document | 1483 | — |
Multilingual GPT-5.6 Sol leads
GPT-5.6 Sol: 55.3 (#32), Llama 3.1-70B: 38.8 (#225)
| Benchmark | GPT-5.6 Sol | Llama 3.1-70B |
|---|---|---|
| LMArena Non-English | 1452 | 1219 |
| LMArena Chinese | 1527 | 1215 |
| LMArena French | 1477 | 1261 |
| LMArena German | 1476 | 1222 |
| LMArena Japanese | 1471 | 1132 |
| LMArena Korean | 1442 | 1140 |
| LMArena Russian | 1468 | 1234 |
| LMArena Spanish | 1441 | 1253 |
Instruction Following GPT-5.6 Sol leads
GPT-5.6 Sol: 77.7 (#16), Llama 3.1-70B: 65.3 (#223)
| Benchmark | GPT-5.6 Sol | Llama 3.1-70B |
|---|---|---|
| LMArena Instruction Following | 1482 | 1231 |
| IFEval | — | 82.1% |
Long Context GPT-5.6 Sol leads
GPT-5.6 Sol: 45.4 (#42), Llama 3.1-70B: 37.6 (#214)
| Benchmark | GPT-5.6 Sol | Llama 3.1-70B |
|---|---|---|
| LMArena Longer Query | 1480 | 1241 |
Writing & Preference GPT-5.6 Sol leads
GPT-5.6 Sol: 73.3 (#12), Llama 3.1-70B: 35.4 (#267)
| Benchmark | GPT-5.6 Sol | Llama 3.1-70B |
|---|---|---|
| LMArena Text | 1457 | 1261 |
| LMArena Creative Writing | 1448 | 1232 |
| EQ-Bench Creative Writing | 1972 | 784 |
| LMArena Multi-Turn | 1460 | 1256 |
| WildBench | — | 75.8% |
| EQ-Bench 4 | 1250 | — |
Frequently asked questions
Is GPT-5.6 Sol better than Llama 3.1-70B?
GPT-5.6 Sol is the stronger model overall, scoring 65.0 to 29.6 on the Noometry Index. Llama 3.1-70B costs 20× less per token, which makes it the better buy when GPT-5.6 Sol's lead doesn't matter for your workload.
Which is cheaper, GPT-5.6 Sol or Llama 3.1-70B?
Llama 3.1-70B is cheaper. It lists at $0.40 per million input tokens and $0.40 per million output tokens; GPT-5.6 Sol lists at $4 and $20.
Is GPT-5.6 Sol or Llama 3.1-70B better for coding?
GPT-5.6 Sol scores higher on coding benchmarks: 65.1 versus 30.3 in the Noometry coding category.
Which has the bigger context window?
GPT-5.6 Sol does, with 1.05M tokens against 128K.
How many benchmarks do GPT-5.6 Sol and Llama 3.1-70B share?
25 benchmarks have published results for both models. GPT-5.6 Sol has 65 scored results on Noometry and Llama 3.1-70B has 35.