Model comparison
GPT-5.5 Pro vs Llama-3.3-70B-Instruct
GPT-5.5 Pro is the stronger model overall, scoring 64.3 to 30.6 on the Noometry Index. Llama-3.3-70B-Instruct costs 435× less per token, which makes it the better buy when GPT-5.5 Pro's lead doesn't matter for your workload.
Last verified . 7 shared benchmarks.
MetaSummary
- They share 7 benchmarks with published results for both. GPT-5.5 Pro scores higher in 3 categories and Llama-3.3-70B-Instruct in 0 categories; 3 gaps are clear of the uncertainty.
- The widest gap is in math, where GPT-5.5 Pro leads 84.0 to 15.3.
- The biggest single-benchmark swing is OTIS Mock AIME 2024-2025: 100% for GPT-5.5 Pro 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 $30 / $180 for GPT-5.5 Pro.
- GPT-5.5 Pro 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-5.5 Pro | Llama-3.3-70B-Instruct | |
|---|---|---|
| Provider | OpenAI | Meta |
| Noometry Index | 64.3 | 30.6 |
| Released | 2026-04-23 | 2024-12-06 |
| Weights | Proprietary | Open |
| Context window | 1.05M | 128K |
| Max output | 128K | 4K |
| Input $ / M tokens | $30 | $0.10 |
| Output $ / M tokens | $180 | $0.32 |
| Results tracked | 14 | 43 |
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Category by category
Coding Not comparable
GPT-5.5 Pro: —, Llama-3.3-70B-Instruct: 31.0 (#290)
| Benchmark | GPT-5.5 Pro | Llama-3.3-70B-Instruct |
|---|---|---|
| SciCode | — | 26% |
| WeirdML | — | 14.4% |
| BigCodeBench Instruct | — | 46.9% |
| LiveBench Coding | — | 36.6% |
| LMArena Coding | — | 1268 |
| BigCodeBench Complete | — | 57.5% |
Agentic & Tool Use Not comparable
GPT-5.5 Pro: —, Llama-3.3-70B-Instruct: 25.8 (#105)
| Benchmark | GPT-5.5 Pro | Llama-3.3-70B-Instruct |
|---|---|---|
| Berkeley Function Calling Leaderboard | — | 31.9% |
| BALROG | — | 23% |
Reasoning GPT-5.5 Pro leads
GPT-5.5 Pro: 73.3 (#10), Llama-3.3-70B-Instruct: 14.1 (#327)
| Benchmark | GPT-5.5 Pro | Llama-3.3-70B-Instruct |
|---|---|---|
| SimpleBench | 76.9% | 19.9% |
| CritPt | 30.6% | 0% |
| DTBench | 96% | 59.5% |
| LMCA | 53.9% | 17.5% |
| Epoch Capabilities Index | 162.07 | 127.33 |
| ARC-AGI-2 | 84.6% | — |
| ARC-AGI-1 | 96.5% | — |
| Chess Puzzles | 64% | — |
| LiveBench Reasoning | — | 50.8% |
| LMArena Hard Prompts | — | 1257 |
| LiveBench Data Analysis | — | 49.5% |
| ForecastBench | — | 58.6 |
| LiveBench | — | 50.2% |
Math GPT-5.5 Pro leads
GPT-5.5 Pro: 84.0 (#10), Llama-3.3-70B-Instruct: 15.3 (#298)
| Benchmark | GPT-5.5 Pro | Llama-3.3-70B-Instruct |
|---|---|---|
| OTIS Mock AIME 2024-2025 | 100% | 5.1% |
| FrontierMath (Tiers 1-3) | 87.7% | — |
| FrontierMath Tier 4 | 78% | — |
| LiveBench Math | — | 42.2% |
| LMArena Math | — | 1267 |
| MATH Level 5 | — | 41.6% |
| FrontierMath (Feb 2025 set) | 52.4% | — |
| FrontierMath Tier 4 (v1) | 39.6% | — |
Knowledge GPT-5.5 Pro leads
GPT-5.5 Pro: 64.1 (#19), Llama-3.3-70B-Instruct: 30.6 (#226)
| Benchmark | GPT-5.5 Pro | Llama-3.3-70B-Instruct |
|---|---|---|
| GPQA Diamond | 93.9% | 47.4% |
| Confabulations | — | 22.8% |
| Vectara Hallucination Rate | — | 4.1% |
| LMArena Expert | — | 1225 |
| MMLU | — | 86.3% |
Multilingual Not comparable
GPT-5.5 Pro: —, Llama-3.3-70B-Instruct: 39.9 (#220)
| Benchmark | GPT-5.5 Pro | Llama-3.3-70B-Instruct |
|---|---|---|
| LMArena Non-English | — | 1236 |
| LMArena Chinese | — | 1217 |
| LMArena French | — | 1281 |
| LMArena German | — | 1251 |
| LMArena Japanese | — | 1150 |
| LMArena Korean | — | 1143 |
| LMArena Russian | — | 1252 |
| LMArena Spanish | — | 1270 |
Instruction Following Not comparable
GPT-5.5 Pro: —, Llama-3.3-70B-Instruct: 71.1 (#157)
| Benchmark | GPT-5.5 Pro | Llama-3.3-70B-Instruct |
|---|---|---|
| LiveBench Instruction Following | — | 82.7% |
| LMArena Instruction Following | — | 1242 |
Long Context Not comparable
GPT-5.5 Pro: —, Llama-3.3-70B-Instruct: 26.4 (#295)
| Benchmark | GPT-5.5 Pro | Llama-3.3-70B-Instruct |
|---|---|---|
| Fiction.LiveBench | — | 33.3% |
| LMArena Longer Query | — | 1256 |
Writing & Preference Not comparable
GPT-5.5 Pro: —, Llama-3.3-70B-Instruct: 47.6 (#207)
| Benchmark | GPT-5.5 Pro | Llama-3.3-70B-Instruct |
|---|---|---|
| LMArena Text | — | 1274 |
| LMArena Creative Writing | — | 1250 |
| LMArena Multi-Turn | — | 1280 |
| LiveBench Language | — | 39.2% |
Frequently asked questions
Is GPT-5.5 Pro better than Llama-3.3-70B-Instruct?
GPT-5.5 Pro is the stronger model overall, scoring 64.3 to 30.6 on the Noometry Index. Llama-3.3-70B-Instruct costs 435× less per token, which makes it the better buy when GPT-5.5 Pro's lead doesn't matter for your workload.
Which is cheaper, GPT-5.5 Pro 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-5.5 Pro lists at $30 and $180.
Which has the bigger context window?
GPT-5.5 Pro does, with 1.05M tokens against 128K.
How many benchmarks do GPT-5.5 Pro and Llama-3.3-70B-Instruct share?
7 benchmarks have published results for both models. GPT-5.5 Pro has 14 scored results on Noometry and Llama-3.3-70B-Instruct has 43.