Model comparison
GPT-5.4 mini vs Llama-3.3-70B-Instruct
GPT-5.4 mini is the stronger model overall, scoring 44.8 to 30.3 on the Noometry Index. Llama-3.3-70B-Instruct costs 11× less per token, which makes it the better buy when GPT-5.4 mini's lead doesn't matter for your workload.
Last verified . 27 shared benchmarks.
MetaSummary
- They share 27 benchmarks with published results for both. GPT-5.4 mini 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 mini leads 45.4 to 14.3.
- The biggest single-benchmark swing is OTIS Mock AIME 2024-2025: 88.9% for GPT-5.4 mini 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 $0.75 / $4.50 for GPT-5.4 mini.
- GPT-5.4 mini accepts more context: 400K tokens versus 128K.
- Llama-3.3-70B-Instruct has downloadable open weights; the other is API-only.
Side by side
| GPT-5.4 mini | Llama-3.3-70B-Instruct | |
|---|---|---|
| Provider | OpenAI | Meta |
| Noometry Index | 44.8 | 30.3 |
| Released | 2026-03-17 | 2024-12-06 |
| Weights | Proprietary | Open |
| Context window | 400K | 128K |
| Max output | 128K | 4K |
| Input $ / M tokens | $0.75 | $0.10 |
| Output $ / M tokens | $4.50 | $0.32 |
| Results tracked | 46 | 43 |
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Category by category
Coding GPT-5.4 mini leads
GPT-5.4 mini: 45.0 (#67), Llama-3.3-70B-Instruct: 30.6 (#284)
| Benchmark | GPT-5.4 mini | Llama-3.3-70B-Instruct |
|---|---|---|
| SciCode | 49.9% | 26% |
| WeirdML | 60.3% | 14.4% |
| LMArena Coding | 1438 | 1268 |
| FrontierCode | 27% | — |
| LMArena WebDev | 1397 | — |
| BigCodeBench Instruct | — | 46.9% |
| LiveBench Coding | — | 36.6% |
| BigCodeBench Complete | — | 57.5% |
| ALE-Bench | 1,189 | — |
Agentic & Tool Use GPT-5.4 mini leads
GPT-5.4 mini: 29.6 (#74), Llama-3.3-70B-Instruct: 25.6 (#100)
| Benchmark | GPT-5.4 mini | Llama-3.3-70B-Instruct |
|---|---|---|
| Berkeley Function Calling Leaderboard | — | 31.9% |
| DeepResearch Bench | 36.3% | — |
| BALROG | — | 23% |
Reasoning GPT-5.4 mini leads
GPT-5.4 mini: 30.3 (#77), Llama-3.3-70B-Instruct: 13.7 (#325)
| Benchmark | GPT-5.4 mini | Llama-3.3-70B-Instruct |
|---|---|---|
| CritPt | 10% | 0% |
| LMArena Hard Prompts | 1424 | 1257 |
| DTBench | 80% | 59.5% |
| LMCA | 40.8% | 17.5% |
| Epoch Capabilities Index | 148.83 | 127.32 |
| ForecastBench | 57 | 58.6 |
| ARC-AGI-2 | 18.9% | — |
| SimpleBench | — | 19.9% |
| Kagi LLM Benchmark | 37.9% | — |
| NYT Connections (extended) | 61.8% | — |
| ARC-AGI-1 | 63.7% | — |
| Chess Puzzles | 24% | — |
| Thematic Generalization | 61.7% | — |
| LiveBench Reasoning | — | 50.8% |
| Mystery Game Puzzles | 11% | — |
| LiveBench Data Analysis | — | 49.5% |
| LiveBench | — | 50.2% |
Math GPT-5.4 mini leads
GPT-5.4 mini: 45.4 (#63), Llama-3.3-70B-Instruct: 14.3 (#292)
| Benchmark | GPT-5.4 mini | Llama-3.3-70B-Instruct |
|---|---|---|
| OTIS Mock AIME 2024-2025 | 88.9% | 5.1% |
| LMArena Math | 1418 | 1268 |
| FrontierMath (Tiers 1-3) | 51.2% | — |
| FrontierMath Tier 4 | 9.8% | — |
| ProofBench | 21% | — |
| LiveBench Math | — | 42.2% |
| MATH Level 5 | — | 41.6% |
| FrontierMath (Feb 2025 set) | 28.3% | — |
| FrontierMath Tier 4 (v1) | 2.1% | — |
Knowledge GPT-5.4 mini leads
GPT-5.4 mini: 51.4 (#62), Llama-3.3-70B-Instruct: 30.5 (#220)
| Benchmark | GPT-5.4 mini | Llama-3.3-70B-Instruct |
|---|---|---|
| GPQA Diamond | 86.9% | 47.4% |
| Vectara Hallucination Rate | 5.5% | 4.1% |
| LMArena Expert | 1434 | 1225 |
| SimpleQA Verified | 29.4% | — |
| Confabulations | — | 22.8% |
| MMLU | — | 86.3% |
Multimodal Not comparable
GPT-5.4 mini: 39.0 (#51), Llama-3.3-70B-Instruct: —
| Benchmark | GPT-5.4 mini | Llama-3.3-70B-Instruct |
|---|---|---|
| LMArena Vision | 1245 | — |
Multilingual GPT-5.4 mini leads
GPT-5.4 mini: 52.1 (#94), Llama-3.3-70B-Instruct: 40.1 (#215)
| Benchmark | GPT-5.4 mini | Llama-3.3-70B-Instruct |
|---|---|---|
| LMArena Non-English | 1405 | 1236 |
| LMArena Chinese | 1446 | 1217 |
| LMArena French | 1440 | 1280 |
| LMArena German | 1407 | 1251 |
| LMArena Japanese | 1375 | 1149 |
| LMArena Korean | 1369 | 1142 |
| LMArena Russian | 1417 | 1252 |
| LMArena Spanish | 1403 | 1269 |
Instruction Following GPT-5.4 mini leads
GPT-5.4 mini: 74.3 (#99), Llama-3.3-70B-Instruct: 71.3 (#152)
| Benchmark | GPT-5.4 mini | Llama-3.3-70B-Instruct |
|---|---|---|
| LMArena Instruction Following | 1405 | 1242 |
| LiveBench Instruction Following | — | 82.7% |
Long Context GPT-5.4 mini leads
GPT-5.4 mini: 42.9 (#107), Llama-3.3-70B-Instruct: 26.4 (#290)
| Benchmark | GPT-5.4 mini | Llama-3.3-70B-Instruct |
|---|---|---|
| LMArena Longer Query | 1407 | 1256 |
| Fiction.LiveBench | — | 33.3% |
Writing & Preference GPT-5.4 mini leads
GPT-5.4 mini: 64.2 (#56), Llama-3.3-70B-Instruct: 47.8 (#203)
| Benchmark | GPT-5.4 mini | Llama-3.3-70B-Instruct |
|---|---|---|
| LMArena Text | 1411 | 1274 |
| LMArena Creative Writing | 1370 | 1250 |
| LMArena Multi-Turn | 1429 | 1279 |
| EQ-Bench Creative Writing | 1665 | — |
| LiveBench Language | — | 39.2% |
Frequently asked questions
Is GPT-5.4 mini better than Llama-3.3-70B-Instruct?
GPT-5.4 mini is the stronger model overall, scoring 44.8 to 30.3 on the Noometry Index. Llama-3.3-70B-Instruct costs 11× less per token, which makes it the better buy when GPT-5.4 mini's lead doesn't matter for your workload.
Which is cheaper, GPT-5.4 mini 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 mini lists at $0.75 and $4.50.
Is GPT-5.4 mini or Llama-3.3-70B-Instruct better for coding?
GPT-5.4 mini scores higher on coding benchmarks: 45.0 versus 30.6 in the Noometry coding category.
Which has the bigger context window?
GPT-5.4 mini does, with 400K tokens against 128K.
How many benchmarks do GPT-5.4 mini and Llama-3.3-70B-Instruct share?
27 benchmarks have published results for both models. GPT-5.4 mini has 46 scored results on Noometry and Llama-3.3-70B-Instruct has 43.