Noometry

Model comparison

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

GPT-5.4 is the stronger model overall, scoring 59.4 to 30.6 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.

Last verified . 27 shared benchmarks.

GPT-5.4 OpenAI

59.4

Rank #16 Confirmed

Llama-3.3-70B-Instruct Meta

30.6

Rank #291 Confirmed

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 73.5 to 15.3.
  • 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.

Side by side

GPT-5.4Llama-3.3-70B-Instruct
ProviderOpenAIMeta
Noometry Index59.430.6
Released2026-03-052024-12-06
WeightsProprietaryOpen
Context window1.05M128K
Max output128K4K
Input $ / M tokens$2.50$0.10
Output $ / M tokens$15$0.32
Results tracked6843

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Category by category

Coding GPT-5.4 leads

GPT-5.4: 52.6 (#33), Llama-3.3-70B-Instruct: 31.0 (#290)

BenchmarkGPT-5.4Llama-3.3-70B-Instruct
SciCode56.6%26%
WeirdML77.7%14.4%
LMArena Coding14971268
SWE-bench Verified76.9%—
DeepSWE51.8%—
LMArena WebDev1465—
GSO31.4%—
BigCodeBench Instruct—46.9%
LiveBench Coding—36.6%
MirrorCode15.6%—
BigCodeBench Complete—57.5%
ALE-Bench1,607—
AlgoTune1.85—

Agentic & Tool Use GPT-5.4 leads

GPT-5.4: 46.5 (#13), Llama-3.3-70B-Instruct: 25.8 (#105)

BenchmarkGPT-5.4Llama-3.3-70B-Instruct
Terminal-Bench81.8%—
APEX-Agents52.4%—
Berkeley Function Calling Leaderboard—31.9%
τ²-bench Banking39.4%—
DeepResearch Bench35.1%—
PostTrainBench19%—
BALROG—23%
GBAEval45.1%—
LMArena Search1197—
METR Time Horizons74.3%—
Vending-Bench 26,144—

Reasoning GPT-5.4 leads

GPT-5.4: 61.8 (#19), Llama-3.3-70B-Instruct: 14.1 (#327)

BenchmarkGPT-5.4Llama-3.3-70B-Instruct
CritPt23.4%0%
LMArena Hard Prompts14851257
DTBench94.4%59.5%
LMCA52%17.5%
Epoch Capabilities Index156.81127.33
ForecastBench59.558.6
ARC-AGI-274%—
SimpleBench—19.9%
Kagi LLM Benchmark63.8%—
NYT Connections (extended)91.3%—
ARC-AGI-193.7%—
Chess Puzzles44%—
EnigmaEval16%—
Thematic Generalization80%—
EBR-Bench25.4%—
LiveBench Reasoning—50.8%
Mystery Game Puzzles37%—
LiveBench Data Analysis—49.5%
LiveBench—50.2%

Math GPT-5.4 leads

GPT-5.4: 73.5 (#19), Llama-3.3-70B-Instruct: 15.3 (#298)

BenchmarkGPT-5.4Llama-3.3-70B-Instruct
OTIS Mock AIME 2024-202597.8%5.1%
LMArena Math14881267
FrontierMath (Tiers 1-3)78.6%—
FrontierMath Tier 449%—
MathArena Final-Answer Competitions83.1%—
ProofBench56%—
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 leads

GPT-5.4: 65.3 (#14), Llama-3.3-70B-Instruct: 30.6 (#226)

BenchmarkGPT-5.4Llama-3.3-70B-Instruct
GPQA Diamond93.3%47.4%
Vectara Hallucination Rate7%4.1%
LMArena Expert15071225
Humanity's Last Exam36.2%—
SimpleQA Verified45.1%—
Confabulations—22.8%
MMLU—86.3%

Multimodal Not comparable

GPT-5.4: 43.7 (#20), Llama-3.3-70B-Instruct: —

BenchmarkGPT-5.4Llama-3.3-70B-Instruct
LMArena Vision1303—
Blueprint-Bench 227.1%—
Furniture Assembly37.5%—
LMArena Document1471—

Multilingual GPT-5.4 leads

GPT-5.4: 56.2 (#23), Llama-3.3-70B-Instruct: 39.9 (#220)

BenchmarkGPT-5.4Llama-3.3-70B-Instruct
LMArena Non-English14651236
LMArena Chinese15191217
LMArena French14931281
LMArena German14721251
LMArena Japanese14851150
LMArena Korean14481143
LMArena Russian14801252
LMArena Spanish14541270

Instruction Following GPT-5.4 leads

GPT-5.4: 77.1 (#27), Llama-3.3-70B-Instruct: 71.1 (#157)

BenchmarkGPT-5.4Llama-3.3-70B-Instruct
LMArena Instruction Following14691242
LiveBench Instruction Following—82.7%

Long Context GPT-5.4 leads

GPT-5.4: 50.3 (#8), Llama-3.3-70B-Instruct: 26.4 (#295)

BenchmarkGPT-5.4Llama-3.3-70B-Instruct
LMArena Longer Query14731256
Fiction.LiveBench—33.3%
CL-bench27.9%—
CL-bench Life21.7%—

Writing & Preference GPT-5.4 leads

GPT-5.4: 71.9 (#17), Llama-3.3-70B-Instruct: 47.6 (#207)

BenchmarkGPT-5.4Llama-3.3-70B-Instruct
LMArena Text14691274
LMArena Creative Writing14391250
LMArena Multi-Turn14821280
EQ-Bench Creative Writing1840—
EQ-Bench 41272—
LiveBench Language—39.2%

Frequently asked questions

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

GPT-5.4 is the stronger model overall, scoring 59.4 to 30.6 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: 52.6 versus 31.0 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.

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