# GPT-5.4 vs Llama-3.3-70B-Instruct

> GPT-5.4 is the stronger model overall, scoring 59.5 to 30.3 on the Noometry Index. Llama-3.3-70B-Instruct costs 36× less per token, which makes it the better buy when GPT-5.4's lead doesn't matter for your workload.

- Canonical page: https://noometry.com/compare/gpt-5-4-vs-llama-3-3-70b-instruct
- Last updated: 2026-10-10
- Shared benchmarks: 27

## Summary

- They share 27 benchmarks with published results for both. GPT-5.4 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-5.4 leads 73.3 to 14.3.
- The biggest single-benchmark swing is OTIS Mock AIME 2024-2025: 97.8% for GPT-5.4 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 $2.50 / $15 for GPT-5.4.
- GPT-5.4 accepts more context: 1.05M tokens versus 128K.
- Llama-3.3-70B-Instruct has downloadable open weights; the other is API-only.

## Snapshot

| | GPT-5.4 | Llama-3.3-70B-Instruct |
|---|---|---|
| Provider | OpenAI | Meta |
| Noometry Index | 59.5 | 30.3 |
| Rank | 14 | 283 |
| Context | 1.05M | 128K |
| Input $/M | $2.50 | $0.10 |
| Output $/M | $15 | $0.32 |
| Weights | Proprietary | Open |

## Coding

- GPT-5.4: 52.8 (#28)
- Llama-3.3-70B-Instruct: 30.6 (#284)

| Benchmark | GPT-5.4 | Llama-3.3-70B-Instruct |
|---|---|---|
| SciCode | 56.6% | 26% |
| WeirdML | 77.7% | 14.4% |
| LMArena Coding | 1497 | 1268 |
| SWE-bench Verified | 76.9% | — |
| DeepSWE | 51.8% | — |
| LMArena WebDev | 1465 | — |
| GSO | 31.4% | — |
| BigCodeBench Instruct | — | 46.9% |
| LiveBench Coding | — | 36.6% |
| MirrorCode | 15.6% | — |
| BigCodeBench Complete | — | 57.5% |
| ALE-Bench | 1,607 | — |
| AlgoTune | 1.85 | — |

## Agentic & Tool Use

- GPT-5.4: 46.5 (#12)
- Llama-3.3-70B-Instruct: 25.6 (#100)

| Benchmark | GPT-5.4 | Llama-3.3-70B-Instruct |
|---|---|---|
| Terminal-Bench | 81.8% | — |
| APEX-Agents | 52.4% | — |
| Berkeley Function Calling Leaderboard | — | 31.9% |
| τ²-bench Banking | 39.4% | — |
| DeepResearch Bench | 35.1% | — |
| PostTrainBench | 19% | — |
| BALROG | — | 23% |
| GBAEval | 45.1% | — |
| LMArena Search | 1197 | — |
| METR Time Horizons | 74.3% | — |
| Vending-Bench 2 | 6,144 | — |

## Reasoning

- GPT-5.4: 61.7 (#17)
- Llama-3.3-70B-Instruct: 13.7 (#325)

| Benchmark | GPT-5.4 | Llama-3.3-70B-Instruct |
|---|---|---|
| CritPt | 23.4% | 0% |
| LMArena Hard Prompts | 1485 | 1257 |
| DTBench | 94.4% | 59.5% |
| LMCA | 52% | 17.5% |
| Epoch Capabilities Index | 156.92 | 127.32 |
| ForecastBench | 59.5 | 58.6 |
| ARC-AGI-2 | 74% | — |
| SimpleBench | — | 19.9% |
| Kagi LLM Benchmark | 63.8% | — |
| NYT Connections (extended) | 91.3% | — |
| ARC-AGI-1 | 93.7% | — |
| Chess Puzzles | 44% | — |
| EnigmaEval | 16% | — |
| Thematic Generalization | 80% | — |
| EBR-Bench | 25.4% | — |
| LiveBench Reasoning | — | 50.8% |
| Mystery Game Puzzles | 37% | — |
| LiveBench Data Analysis | — | 49.5% |
| LiveBench | — | 50.2% |

## Math

- GPT-5.4: 73.3 (#14)
- Llama-3.3-70B-Instruct: 14.3 (#292)

| Benchmark | GPT-5.4 | Llama-3.3-70B-Instruct |
|---|---|---|
| OTIS Mock AIME 2024-2025 | 97.8% | 5.1% |
| LMArena Math | 1488 | 1268 |
| FrontierMath (Tiers 1-3) | 78.6% | — |
| FrontierMath Tier 4 | 49% | — |
| MathArena Final-Answer Competitions | 83.1% | — |
| ProofBench | 56% | — |
| LiveBench Math | — | 42.2% |
| MATH Level 5 | — | 41.6% |
| FrontierMath (Feb 2025 set) | 47.6% | — |
| FrontierMath Tier 4 (v1) | 27.1% | — |

## Knowledge

- GPT-5.4: 65.3 (#13)
- Llama-3.3-70B-Instruct: 30.5 (#220)

| Benchmark | GPT-5.4 | Llama-3.3-70B-Instruct |
|---|---|---|
| GPQA Diamond | 93.3% | 47.4% |
| Vectara Hallucination Rate | 7% | 4.1% |
| LMArena Expert | 1507 | 1225 |
| Humanity's Last Exam | 36.2% | — |
| SimpleQA Verified | 45.1% | — |
| Confabulations | — | 22.8% |
| MMLU | — | 86.3% |

## Multimodal

- GPT-5.4: 43.8 (#19)
- Llama-3.3-70B-Instruct: —

| Benchmark | GPT-5.4 | Llama-3.3-70B-Instruct |
|---|---|---|
| LMArena Vision | 1302 | — |
| Blueprint-Bench 2 | 27.1% | — |
| Furniture Assembly | 37.5% | — |
| LMArena Document | 1471 | — |

## Multilingual

- GPT-5.4: 56.4 (#22)
- Llama-3.3-70B-Instruct: 40.1 (#215)

| Benchmark | GPT-5.4 | Llama-3.3-70B-Instruct |
|---|---|---|
| LMArena Non-English | 1465 | 1236 |
| LMArena Chinese | 1520 | 1217 |
| LMArena French | 1493 | 1280 |
| LMArena German | 1472 | 1251 |
| LMArena Japanese | 1484 | 1149 |
| LMArena Korean | 1449 | 1142 |
| LMArena Russian | 1480 | 1252 |
| LMArena Spanish | 1453 | 1269 |

## Instruction Following

- GPT-5.4: 77.3 (#25)
- Llama-3.3-70B-Instruct: 71.3 (#152)

| Benchmark | GPT-5.4 | Llama-3.3-70B-Instruct |
|---|---|---|
| LMArena Instruction Following | 1469 | 1242 |
| LiveBench Instruction Following | — | 82.7% |

## Long Context

- GPT-5.4: 50.3 (#8)
- Llama-3.3-70B-Instruct: 26.4 (#290)

| Benchmark | GPT-5.4 | Llama-3.3-70B-Instruct |
|---|---|---|
| LMArena Longer Query | 1474 | 1256 |
| Fiction.LiveBench | — | 33.3% |
| CL-bench | 27.9% | — |
| CL-bench Life | 21.7% | — |

## Writing & Preference

- GPT-5.4: 72.1 (#18)
- Llama-3.3-70B-Instruct: 47.8 (#203)

| Benchmark | GPT-5.4 | Llama-3.3-70B-Instruct |
|---|---|---|
| LMArena Text | 1469 | 1274 |
| LMArena Creative Writing | 1439 | 1250 |
| LMArena Multi-Turn | 1482 | 1279 |
| EQ-Bench Creative Writing | 1840 | — |
| EQ-Bench 4 | 1272 | — |
| LiveBench Language | — | 39.2% |

## FAQ

### Is GPT-5.4 better than Llama-3.3-70B-Instruct?

GPT-5.4 is the stronger model overall, scoring 59.5 to 30.3 on the Noometry Index. Llama-3.3-70B-Instruct costs 36× less per token, which makes it the better buy when GPT-5.4's lead doesn't matter for your workload.

### Which is cheaper, GPT-5.4 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-5.4 lists at $2.50 and $15.

### Is GPT-5.4 or Llama-3.3-70B-Instruct better for coding?

GPT-5.4 scores higher on coding benchmarks: 52.8 versus 30.6 in the Noometry coding category.

### Which has the bigger context window?

GPT-5.4 does, with 1.05M tokens against 128K.

### How many benchmarks do GPT-5.4 and Llama-3.3-70B-Instruct share?

27 benchmarks have published results for both models. GPT-5.4 has 68 scored results on Noometry and Llama-3.3-70B-Instruct has 43.