Model comparison
GPT-5.3 Codex vs Llama 3.1-70B
GPT-5.3 Codex is the stronger model overall, scoring 45.8 to 29.6 on the Noometry Index. Llama 3.1-70B costs 12× less per token, which makes it the better buy when GPT-5.3 Codex's lead doesn't matter for your workload.
Last verified . 2 shared benchmarks.
MetaSummary
- They share 2 benchmarks with published results for both. GPT-5.3 Codex scores higher in 2 categories and Llama 3.1-70B in 0 categories; 2 gaps are clear of the uncertainty.
- The widest gap is in agentic & tool use, where GPT-5.3 Codex leads 48.0 to 25.1.
- The biggest single-benchmark swing is WeirdML: 79.3% for GPT-5.3 Codex and 9% for Llama 3.1-70B.
- Llama 3.1-70B is cheaper at $0.40 / $0.40 per million input/output tokens, against $1.75 / $14 for GPT-5.3 Codex.
- GPT-5.3 Codex accepts more context: 400K tokens versus 128K.
- Llama 3.1-70B has downloadable open weights; the other is API-only.
Side by side
| GPT-5.3 Codex | Llama 3.1-70B | |
|---|---|---|
| Provider | OpenAI | Meta |
| Noometry Index | 45.8 | 29.6 |
| Released | 2026-02-05 | 2024-07-23 |
| Weights | Proprietary | Open |
| Context window | 400K | 128K |
| Max output | 128K | 4K |
| Input $ / M tokens | $1.75 | $0.40 |
| Output $ / M tokens | $14 | $0.40 |
| Results tracked | 8 | 35 |
Sponsored placements are available on pages like this one. Advertise on Noometry
Category by category
Coding GPT-5.3 Codex leads
GPT-5.3 Codex: 48.6 (#56), Llama 3.1-70B: 30.3 (#296)
| Benchmark | GPT-5.3 Codex | Llama 3.1-70B |
|---|---|---|
| WeirdML | 79.3% | 9% |
| SWE-bench Verified | 74.8% | — |
| LMArena WebDev | 1409 | — |
| BigCodeBench Instruct | — | 46.1% |
| LMArena Coding | — | 1260 |
| BigCodeBench Complete | — | 54.8% |
| ALE-Bench | 1,655 | — |
Agentic & Tool Use GPT-5.3 Codex leads
GPT-5.3 Codex: 48.0 (#9), Llama 3.1-70B: 25.1 (#112)
| Benchmark | GPT-5.3 Codex | Llama 3.1-70B |
|---|---|---|
| Terminal-Bench | 78.4% | — |
| TheAgentCompany | — | 6.9% |
| BALROG | — | 27.9% |
| METR Time Horizons | 74.5% | — |
| Vending-Bench 2 | 5,940 | — |
Reasoning Not comparable
GPT-5.3 Codex: —, Llama 3.1-70B: 21.6 (#220)
| Benchmark | GPT-5.3 Codex | Llama 3.1-70B |
|---|---|---|
| Epoch Capabilities Index | 156.77 | 125.92 |
| LMArena Hard Prompts | — | 1241 |
| DTBench | — | 60% |
| LMCA | — | 14.8% |
Math Not comparable
GPT-5.3 Codex: —, Llama 3.1-70B: 13.5 (#304)
| Benchmark | GPT-5.3 Codex | Llama 3.1-70B |
|---|---|---|
| OTIS Mock AIME 2024-2025 | — | 3.6% |
| Omni-MATH | — | 21% |
| LMArena Math | — | 1252 |
| MATH Level 5 | — | 36.7% |
Knowledge Not comparable
GPT-5.3 Codex: —, Llama 3.1-70B: 24.2 (#269)
| Benchmark | GPT-5.3 Codex | Llama 3.1-70B |
|---|---|---|
| GPQA Diamond | — | 44.2% |
| MMLU-Pro | — | 65.3% |
| GPQA (HELM) | — | 42.6% |
| LMArena Expert | — | 1209 |
| MMLU | — | 80.1% |
Multilingual Not comparable
GPT-5.3 Codex: —, Llama 3.1-70B: 38.8 (#225)
| Benchmark | GPT-5.3 Codex | Llama 3.1-70B |
|---|---|---|
| LMArena Non-English | — | 1219 |
| LMArena Chinese | — | 1215 |
| LMArena French | — | 1261 |
| LMArena German | — | 1222 |
| LMArena Japanese | — | 1132 |
| LMArena Korean | — | 1140 |
| LMArena Russian | — | 1234 |
| LMArena Spanish | — | 1253 |
Instruction Following Not comparable
GPT-5.3 Codex: —, Llama 3.1-70B: 65.3 (#223)
| Benchmark | GPT-5.3 Codex | Llama 3.1-70B |
|---|---|---|
| IFEval | — | 82.1% |
| LMArena Instruction Following | — | 1231 |
Long Context Not comparable
GPT-5.3 Codex: —, Llama 3.1-70B: 37.6 (#214)
| Benchmark | GPT-5.3 Codex | Llama 3.1-70B |
|---|---|---|
| LMArena Longer Query | — | 1241 |
Writing & Preference Not comparable
GPT-5.3 Codex: —, Llama 3.1-70B: 35.4 (#267)
| Benchmark | GPT-5.3 Codex | Llama 3.1-70B |
|---|---|---|
| LMArena Text | — | 1261 |
| LMArena Creative Writing | — | 1232 |
| EQ-Bench Creative Writing | — | 784 |
| WildBench | — | 75.8% |
| LMArena Multi-Turn | — | 1256 |
Frequently asked questions
Is GPT-5.3 Codex better than Llama 3.1-70B?
GPT-5.3 Codex is the stronger model overall, scoring 45.8 to 29.6 on the Noometry Index. Llama 3.1-70B costs 12× less per token, which makes it the better buy when GPT-5.3 Codex's lead doesn't matter for your workload.
Which is cheaper, GPT-5.3 Codex 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-5.3 Codex lists at $1.75 and $14.
Is GPT-5.3 Codex or Llama 3.1-70B better for coding?
GPT-5.3 Codex scores higher on coding benchmarks: 48.6 versus 30.3 in the Noometry coding category.
Which has the bigger context window?
GPT-5.3 Codex does, with 400K tokens against 128K.
How many benchmarks do GPT-5.3 Codex and Llama 3.1-70B share?
2 benchmarks have published results for both models. GPT-5.3 Codex has 8 scored results on Noometry and Llama 3.1-70B has 35.