Model comparison
GPT-5.6 Sol vs Llama-3.3-70B-Instruct
GPT-5.6 Sol is the stronger model overall, scoring 65.0 to 30.6 on the Noometry Index. Llama-3.3-70B-Instruct costs 52× less per token, which makes it the better buy when GPT-5.6 Sol's lead doesn't matter for your workload.
Last verified . 28 shared benchmarks.
MetaSummary
- They share 28 benchmarks with published results for both. GPT-5.6 Sol scores higher in 9 categories and Llama-3.3-70B-Instruct 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 15.3.
- The biggest single-benchmark swing is OTIS Mock AIME 2024-2025: 100% for GPT-5.6 Sol and 5.1% for Llama-3.3-70B-Instruct.
- Llama-3.3-70B-Instruct is cheaper at $0.10 / $0.32 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-3.3-70B-Instruct has downloadable open weights; the other is API-only.
Side by side
| GPT-5.6 Sol | Llama-3.3-70B-Instruct | |
|---|---|---|
| Provider | OpenAI | Meta |
| Noometry Index | 65.0 | 30.6 |
| Released | 2026-07-09 | 2024-12-06 |
| Weights | Proprietary | Open |
| Context window | 1.05M | 128K |
| Max output | 128K | 4K |
| Input $ / M tokens | $4 | $0.10 |
| Output $ / M tokens | $20 | $0.32 |
| Results tracked | 65 | 43 |
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Category by category
Coding GPT-5.6 Sol leads
GPT-5.6 Sol: 65.1 (#7), Llama-3.3-70B-Instruct: 31.0 (#290)
| Benchmark | GPT-5.6 Sol | Llama-3.3-70B-Instruct |
|---|---|---|
| SciCode | 57.1% | 26% |
| WeirdML | 89.4% | 14.4% |
| LMArena Coding | 1498 | 1268 |
| DeepSWE | 72.7% | — |
| FrontierCode | 47.5% | — |
| CursorBench | 41.7% | — |
| LMArena WebDev | 1618 | — |
| FrontierSWE | 32.2% | — |
| GSO | 76.5% | — |
| BigCodeBench Instruct | — | 46.9% |
| LiveBench Coding | — | 36.6% |
| MirrorCode | 20% | — |
| BigCodeBench Complete | — | 57.5% |
| ALE-Bench | 2,177 | — |
Agentic & Tool Use GPT-5.6 Sol leads
GPT-5.6 Sol: 50.3 (#7), Llama-3.3-70B-Instruct: 25.8 (#105)
| Benchmark | GPT-5.6 Sol | Llama-3.3-70B-Instruct |
|---|---|---|
| BALROG | 60% | 23% |
| APEX-Agents | 51.4% | — |
| Berkeley Function Calling Leaderboard | — | 31.9% |
| OSWorld 2.0 | 27.3% | — |
| 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-3.3-70B-Instruct: 14.1 (#327)
| Benchmark | GPT-5.6 Sol | Llama-3.3-70B-Instruct |
|---|---|---|
| SimpleBench | 71.7% | 19.9% |
| CritPt | 32.3% | 0% |
| LMArena Hard Prompts | 1484 | 1257 |
| DTBench | 96% | 59.5% |
| LMCA | 59.2% | 17.5% |
| Epoch Capabilities Index | 161.66 | 127.33 |
| ARC-AGI-2 | 92.5% | — |
| Kagi LLM Benchmark | 67% | — |
| NYT Connections (extended) | 93.8% | — |
| ARC-AGI-1 | 97.5% | — |
| Chess Puzzles | 64% | — |
| EnigmaEval | 37.1% | — |
| EBR-Bench | 44.8% | — |
| LiveBench Reasoning | — | 50.8% |
| Mystery Game Puzzles | 58% | — |
| LiveBench Data Analysis | — | 49.5% |
| Surface Evolver Bench | 93.1% | — |
| Bench to the Future 3 | 0.14 | — |
| ForecastBench | — | 58.6 |
| LiveBench | — | 50.2% |
Math GPT-5.6 Sol leads
GPT-5.6 Sol: 85.6 (#9), Llama-3.3-70B-Instruct: 15.3 (#298)
| Benchmark | GPT-5.6 Sol | Llama-3.3-70B-Instruct |
|---|---|---|
| OTIS Mock AIME 2024-2025 | 100% | 5.1% |
| LMArena Math | 1474 | 1267 |
| FrontierMath (Tiers 1-3) | 89.1% | — |
| FrontierMath Tier 4 | 82.9% | — |
| ProofBench | 83% | — |
| LiveBench Math | — | 42.2% |
| MATH Level 5 | — | 41.6% |
| FrontierMath Erdős | 0% | — |
Knowledge GPT-5.6 Sol leads
GPT-5.6 Sol: 64.3 (#18), Llama-3.3-70B-Instruct: 30.6 (#226)
| Benchmark | GPT-5.6 Sol | Llama-3.3-70B-Instruct |
|---|---|---|
| GPQA Diamond | 93.5% | 47.4% |
| Vectara Hallucination Rate | 12.4% | 4.1% |
| LMArena Expert | 1516 | 1225 |
| SimpleQA Verified | 69.7% | — |
| Confabulations | — | 22.8% |
| MMLU | — | 86.3% |
Multimodal Not comparable
GPT-5.6 Sol: 48.6 (#9), Llama-3.3-70B-Instruct: —
| Benchmark | GPT-5.6 Sol | Llama-3.3-70B-Instruct |
|---|---|---|
| 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-3.3-70B-Instruct: 39.9 (#220)
| Benchmark | GPT-5.6 Sol | Llama-3.3-70B-Instruct |
|---|---|---|
| LMArena Non-English | 1452 | 1236 |
| LMArena Chinese | 1527 | 1217 |
| LMArena French | 1477 | 1281 |
| LMArena German | 1476 | 1251 |
| LMArena Japanese | 1471 | 1150 |
| LMArena Korean | 1442 | 1143 |
| LMArena Russian | 1468 | 1252 |
| LMArena Spanish | 1441 | 1270 |
Instruction Following GPT-5.6 Sol leads
GPT-5.6 Sol: 77.7 (#16), Llama-3.3-70B-Instruct: 71.1 (#157)
| Benchmark | GPT-5.6 Sol | Llama-3.3-70B-Instruct |
|---|---|---|
| LMArena Instruction Following | 1482 | 1242 |
| LiveBench Instruction Following | — | 82.7% |
Long Context GPT-5.6 Sol leads
GPT-5.6 Sol: 45.4 (#42), Llama-3.3-70B-Instruct: 26.4 (#295)
| Benchmark | GPT-5.6 Sol | Llama-3.3-70B-Instruct |
|---|---|---|
| LMArena Longer Query | 1480 | 1256 |
| Fiction.LiveBench | — | 33.3% |
Writing & Preference GPT-5.6 Sol leads
GPT-5.6 Sol: 73.3 (#12), Llama-3.3-70B-Instruct: 47.6 (#207)
| Benchmark | GPT-5.6 Sol | Llama-3.3-70B-Instruct |
|---|---|---|
| LMArena Text | 1457 | 1274 |
| LMArena Creative Writing | 1448 | 1250 |
| LMArena Multi-Turn | 1460 | 1280 |
| EQ-Bench Creative Writing | 1972 | — |
| EQ-Bench 4 | 1250 | — |
| LiveBench Language | — | 39.2% |
Frequently asked questions
Is GPT-5.6 Sol better than Llama-3.3-70B-Instruct?
GPT-5.6 Sol is the stronger model overall, scoring 65.0 to 30.6 on the Noometry Index. Llama-3.3-70B-Instruct costs 52× 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-3.3-70B-Instruct?
Llama-3.3-70B-Instruct is cheaper. It lists at $0.10 per million input tokens and $0.32 per million output tokens; GPT-5.6 Sol lists at $4 and $20.
Is GPT-5.6 Sol or Llama-3.3-70B-Instruct better for coding?
GPT-5.6 Sol scores higher on coding benchmarks: 65.1 versus 31.0 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-3.3-70B-Instruct share?
28 benchmarks have published results for both models. GPT-5.6 Sol has 65 scored results on Noometry and Llama-3.3-70B-Instruct has 43.