Model comparison
GPT-5.3 Chat vs Llama-3.3-70B-Instruct
GPT-5.3 Chat is the stronger model overall, scoring 42.1 to 30.3 on the Noometry Index. Llama-3.3-70B-Instruct costs 31× less per token, which makes it the better buy when GPT-5.3 Chat's lead doesn't matter for your workload.
Last verified . 17 shared benchmarks.
MetaSummary
- They share 17 benchmarks with published results for both. GPT-5.3 Chat scores higher in 8 categories and Llama-3.3-70B-Instruct in 0 categories; 8 gaps are clear of the uncertainty.
- The widest gap is in math, where GPT-5.3 Chat leads 35.4 to 14.3.
- Llama-3.3-70B-Instruct is cheaper at $0.10 / $0.32 per million input/output tokens, against $1.75 / $14 for GPT-5.3 Chat.
- Llama-3.3-70B-Instruct has downloadable open weights; the other is API-only.
Side by side
| GPT-5.3 Chat | Llama-3.3-70B-Instruct | |
|---|---|---|
| Provider | OpenAI | Meta |
| Noometry Index | 42.1 | 30.3 |
| Released | 2026-03-03 | 2024-12-06 |
| Weights | Proprietary | Open |
| Context window | 128K | 128K |
| Max output | 16K | 4K |
| Input $ / M tokens | $1.75 | $0.10 |
| Output $ / M tokens | $14 | $0.32 |
| Results tracked | 18 | 43 |
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Category by category
Coding GPT-5.3 Chat leads
GPT-5.3 Chat: 40.8 (#124), Llama-3.3-70B-Instruct: 30.6 (#284)
| Benchmark | GPT-5.3 Chat | Llama-3.3-70B-Instruct |
|---|---|---|
| LMArena Coding | 1408 | 1268 |
| SciCode | — | 26% |
| WeirdML | — | 14.4% |
| BigCodeBench Instruct | — | 46.9% |
| LiveBench Coding | — | 36.6% |
| BigCodeBench Complete | — | 57.5% |
Agentic & Tool Use Not comparable
GPT-5.3 Chat: —, Llama-3.3-70B-Instruct: 25.6 (#100)
| Benchmark | GPT-5.3 Chat | Llama-3.3-70B-Instruct |
|---|---|---|
| Berkeley Function Calling Leaderboard | — | 31.9% |
| BALROG | — | 23% |
Reasoning GPT-5.3 Chat leads
GPT-5.3 Chat: 27.3 (#105), Llama-3.3-70B-Instruct: 13.7 (#325)
| Benchmark | GPT-5.3 Chat | Llama-3.3-70B-Instruct |
|---|---|---|
| LMArena Hard Prompts | 1399 | 1257 |
| SimpleBench | — | 19.9% |
| CritPt | — | 0% |
| LiveBench Reasoning | — | 50.8% |
| DTBench | — | 59.5% |
| LiveBench Data Analysis | — | 49.5% |
| LMCA | — | 17.5% |
| Epoch Capabilities Index | — | 127.32 |
| ForecastBench | — | 58.6 |
| LiveBench | — | 50.2% |
Math GPT-5.3 Chat leads
GPT-5.3 Chat: 35.4 (#146), Llama-3.3-70B-Instruct: 14.3 (#292)
| Benchmark | GPT-5.3 Chat | Llama-3.3-70B-Instruct |
|---|---|---|
| LMArena Math | 1389 | 1268 |
| OTIS Mock AIME 2024-2025 | — | 5.1% |
| LiveBench Math | — | 42.2% |
| MATH Level 5 | — | 41.6% |
Knowledge GPT-5.3 Chat leads
GPT-5.3 Chat: 38.2 (#137), Llama-3.3-70B-Instruct: 30.5 (#220)
| Benchmark | GPT-5.3 Chat | Llama-3.3-70B-Instruct |
|---|---|---|
| LMArena Expert | 1397 | 1225 |
| GPQA Diamond | — | 47.4% |
| Confabulations | — | 22.8% |
| Vectara Hallucination Rate | — | 4.1% |
| MMLU | — | 86.3% |
Multilingual GPT-5.3 Chat leads
GPT-5.3 Chat: 50.5 (#119), Llama-3.3-70B-Instruct: 40.1 (#215)
| Benchmark | GPT-5.3 Chat | Llama-3.3-70B-Instruct |
|---|---|---|
| LMArena Non-English | 1383 | 1236 |
| LMArena Chinese | 1433 | 1217 |
| LMArena French | 1398 | 1280 |
| LMArena German | 1384 | 1251 |
| LMArena Japanese | 1351 | 1149 |
| LMArena Korean | 1347 | 1142 |
| LMArena Russian | 1400 | 1252 |
| LMArena Spanish | 1371 | 1269 |
Instruction Following GPT-5.3 Chat leads
GPT-5.3 Chat: 73.0 (#124), Llama-3.3-70B-Instruct: 71.3 (#152)
| Benchmark | GPT-5.3 Chat | Llama-3.3-70B-Instruct |
|---|---|---|
| LMArena Instruction Following | 1378 | 1242 |
| LiveBench Instruction Following | — | 82.7% |
Long Context GPT-5.3 Chat leads
GPT-5.3 Chat: 42.6 (#115), Llama-3.3-70B-Instruct: 26.4 (#290)
| Benchmark | GPT-5.3 Chat | Llama-3.3-70B-Instruct |
|---|---|---|
| LMArena Longer Query | 1396 | 1256 |
| Fiction.LiveBench | — | 33.3% |
Writing & Preference GPT-5.3 Chat leads
GPT-5.3 Chat: 63.3 (#65), Llama-3.3-70B-Instruct: 47.8 (#203)
| Benchmark | GPT-5.3 Chat | Llama-3.3-70B-Instruct |
|---|---|---|
| LMArena Text | 1389 | 1274 |
| LMArena Creative Writing | 1356 | 1250 |
| LMArena Multi-Turn | 1413 | 1279 |
| EQ-Bench Creative Writing | 1690 | — |
| LiveBench Language | — | 39.2% |
Frequently asked questions
Is GPT-5.3 Chat better than Llama-3.3-70B-Instruct?
GPT-5.3 Chat is the stronger model overall, scoring 42.1 to 30.3 on the Noometry Index. Llama-3.3-70B-Instruct costs 31× less per token, which makes it the better buy when GPT-5.3 Chat's lead doesn't matter for your workload.
Which is cheaper, GPT-5.3 Chat 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-5.3 Chat lists at $1.75 and $14.
Is GPT-5.3 Chat or Llama-3.3-70B-Instruct better for coding?
GPT-5.3 Chat scores higher on coding benchmarks: 40.8 versus 30.6 in the Noometry coding category.
Which has the bigger context window?
Both accept 128K tokens.
How many benchmarks do GPT-5.3 Chat and Llama-3.3-70B-Instruct share?
17 benchmarks have published results for both models. GPT-5.3 Chat has 18 scored results on Noometry and Llama-3.3-70B-Instruct has 43.