Model comparison
GPT-5.3 Chat vs Llama 4 Scout
GPT-5.3 Chat is the stronger model overall, scoring 42.8 to 27.7 on the Noometry Index. Llama 4 Scout costs 32× less per token, which makes it the better buy when GPT-5.3 Chat's lead doesn't matter for your workload.
Last verified . 18 shared benchmarks.
MetaSummary
- They share 18 benchmarks with published results for both. GPT-5.3 Chat scores higher in 8 categories and Llama 4 Scout in 0 categories; 8 gaps are clear of the uncertainty.
- The widest gap is in writing & preference, where GPT-5.3 Chat leads 63.1 to 37.0.
- Llama 4 Scout is cheaper at $0.10 / $0.30 per million input/output tokens, against $1.75 / $14 for GPT-5.3 Chat.
- Llama 4 Scout has downloadable open weights; the other is API-only.
Side by side
| GPT-5.3 Chat | Llama 4 Scout | |
|---|---|---|
| Provider | OpenAI | Meta |
| Noometry Index | 42.8 | 27.7 |
| Released | 2026-03-03 | 2025-04-05 |
| Weights | Proprietary | Open |
| Context window | 128K | 128K |
| Max output | 16K | 4K |
| Input $ / M tokens | $1.75 | $0.10 |
| Output $ / M tokens | $14 | $0.30 |
| Results tracked | 18 | 43 |
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Category by category
Coding GPT-5.3 Chat leads
GPT-5.3 Chat: 41.4 (#124), Llama 4 Scout: 20.2 (#339)
| Benchmark | GPT-5.3 Chat | Llama 4 Scout |
|---|---|---|
| LMArena Coding | 1408 | 1286 |
| SWE-bench Verified (bash only) | — | 9.1% |
| SciCode | — | 17% |
| BigCodeBench Complete | — | 43.1% |
Agentic & Tool Use Not comparable
GPT-5.3 Chat: —, Llama 4 Scout: 24.6 (#119)
| Benchmark | GPT-5.3 Chat | Llama 4 Scout |
|---|---|---|
| Berkeley Function Calling Leaderboard | — | 28.1% |
Reasoning GPT-5.3 Chat leads
GPT-5.3 Chat: 28.5 (#102), Llama 4 Scout: 9.1 (#345)
| Benchmark | GPT-5.3 Chat | Llama 4 Scout |
|---|---|---|
| LMArena Hard Prompts | 1399 | 1266 |
| ARC-AGI-2 | — | 0% |
| Kagi LLM Benchmark | — | 36.9% |
| ARC-AGI-1 | — | 0.5% |
| CritPt | — | 0% |
| DTBench | — | 57.9% |
| LMCA | — | 12% |
| Epoch Capabilities Index | — | 129.64 |
| ForecastBench | — | 57.5 |
Math GPT-5.3 Chat leads
GPT-5.3 Chat: 38.2 (#142), Llama 4 Scout: 19.6 (#286)
| Benchmark | GPT-5.3 Chat | Llama 4 Scout |
|---|---|---|
| LMArena Math | 1389 | 1287 |
| OTIS Mock AIME 2024-2025 | — | 7.8% |
| Omni-MATH | — | 37.3% |
| MATH Level 5 | — | 62.3% |
| FrontierMath (Feb 2025 set) | — | 0% |
Knowledge GPT-5.3 Chat leads
GPT-5.3 Chat: 38.8 (#140), Llama 4 Scout: 31.9 (#217)
| Benchmark | GPT-5.3 Chat | Llama 4 Scout |
|---|---|---|
| LMArena Expert | 1397 | 1235 |
| GPQA Diamond | — | 51.8% |
| MMLU-Pro | — | 74.2% |
| Vectara Hallucination Rate | — | 7.7% |
| GPQA (HELM) | — | 50.7% |
Multimodal Not comparable
GPT-5.3 Chat: —, Llama 4 Scout: 32.2 (#102)
| Benchmark | GPT-5.3 Chat | Llama 4 Scout |
|---|---|---|
| LMArena Vision | — | 1118 |
| SpatialViz-Bench | — | 34.2% |
Multilingual GPT-5.3 Chat leads
GPT-5.3 Chat: 50.3 (#124), Llama 4 Scout: 41.0 (#212)
| Benchmark | GPT-5.3 Chat | Llama 4 Scout |
|---|---|---|
| LMArena Non-English | 1382 | 1252 |
| LMArena Chinese | 1432 | 1255 |
| LMArena French | 1397 | 1282 |
| LMArena German | 1384 | 1272 |
| LMArena Japanese | 1352 | 1206 |
| LMArena Korean | 1346 | 1207 |
| LMArena Russian | 1400 | 1263 |
| LMArena Spanish | 1371 | 1278 |
Instruction Following GPT-5.3 Chat leads
GPT-5.3 Chat: 72.8 (#129), Llama 4 Scout: 65.8 (#217)
| Benchmark | GPT-5.3 Chat | Llama 4 Scout |
|---|---|---|
| LMArena Instruction Following | 1378 | 1248 |
| IFEval | — | 81.8% |
Long Context GPT-5.3 Chat leads
GPT-5.3 Chat: 42.6 (#120), Llama 4 Scout: 27.5 (#294)
| Benchmark | GPT-5.3 Chat | Llama 4 Scout |
|---|---|---|
| LMArena Longer Query | 1396 | 1265 |
| Fiction.LiveBench | — | 36% |
Writing & Preference GPT-5.3 Chat leads
GPT-5.3 Chat: 63.1 (#68), Llama 4 Scout: 37.0 (#261)
| Benchmark | GPT-5.3 Chat | Llama 4 Scout |
|---|---|---|
| LMArena Text | 1389 | 1279 |
| LMArena Creative Writing | 1355 | 1249 |
| EQ-Bench Creative Writing | 1690 | 783 |
| LMArena Multi-Turn | 1412 | 1280 |
| WildBench | — | 78% |
Frequently asked questions
Is GPT-5.3 Chat better than Llama 4 Scout?
GPT-5.3 Chat is the stronger model overall, scoring 42.8 to 27.7 on the Noometry Index. Llama 4 Scout costs 32× 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 4 Scout?
Llama 4 Scout is cheaper. It lists at $0.10 per million input tokens and $0.30 per million output tokens; GPT-5.3 Chat lists at $1.75 and $14.
Is GPT-5.3 Chat or Llama 4 Scout better for coding?
GPT-5.3 Chat scores higher on coding benchmarks: 41.4 versus 20.2 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 4 Scout share?
18 benchmarks have published results for both models. GPT-5.3 Chat has 18 scored results on Noometry and Llama 4 Scout has 43.