Noometry

Model comparison

GPT-5.5 vs Llama 3.1-8B

GPT-5.5 is the stronger model overall, scoring 63.4 to 22.8 on the Noometry Index. Llama 3.1-8B costs 196× less per token, which makes it the better buy when GPT-5.5's lead doesn't matter for your workload.

Last verified . 27 shared benchmarks.

GPT-5.5 OpenAI

63.4

Rank #8 Confirmed

Llama 3.1-8B Meta

22.8

Rank #347 Confirmed

Summary

  • They share 27 benchmarks with published results for both. GPT-5.5 scores higher in 9 categories and Llama 3.1-8B in 0 categories; 9 gaps are clear of the uncertainty.
  • The widest gap is in math, where GPT-5.5 leads 81.5 to 9.5.
  • The biggest single-benchmark swing is OTIS Mock AIME 2024-2025: 100% for GPT-5.5 and 1.7% for Llama 3.1-8B.
  • Llama 3.1-8B is cheaper at $0.05 / $0.08 per million input/output tokens, against $5 / $30 for GPT-5.5.
  • GPT-5.5 accepts more context: 1.05M tokens versus 128K.
  • Llama 3.1-8B has downloadable open weights; the other is API-only.

Side by side

GPT-5.5Llama 3.1-8B
ProviderOpenAIMeta
Noometry Index63.422.8
Released2026-04-232024-07-23
WeightsProprietaryOpen
Context window1.05M128K
Max output128K4K
Input $ / M tokens$5$0.05
Output $ / M tokens$30$0.08
Results tracked7143

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Category by category

Coding GPT-5.5 leads

GPT-5.5: 58.3 (#13), Llama 3.1-8B: 19.9 (#335)

BenchmarkGPT-5.5Llama 3.1-8B
SciCode56.1%13.2%
WeirdML84.9%1.7%
LMArena Coding14941195
SWE-bench Verified80.6%—
DeepSWE67%—
FrontierCode43%—
LMArena WebDev1512—
GSO40.2%—
BigCodeBench Instruct—32.8%
MirrorCode10%—
BigCodeBench Complete—40.5%
ALE-Bench1,943—
HumanEval+—62.8%
MBPP+—55.6%

Agentic & Tool Use GPT-5.5 leads

GPT-5.5: 50.7 (#6), Llama 3.1-8B: 22.3 (#123)

BenchmarkGPT-5.5Llama 3.1-8B
Terminal-Bench84.7%—
APEX-Agents55.1%—
Berkeley Function Calling Leaderboard—25.8%
OSWorld 2.013%—
Remote Labor Index6.3%—
τ²-bench Banking44.6%—
DeepResearch Bench54%—
PostTrainBench27.2%—
BALROG—15.1%
ExploitBench47.4%—
GBAEval53.2%—
GDP.pdf26%—
LMArena Search1242—
Vending-Bench 27,524—

Reasoning GPT-5.5 leads

GPT-5.5: 72.7 (#8), Llama 3.1-8B: 14.5 (#316)

BenchmarkGPT-5.5Llama 3.1-8B
CritPt27.1%0%
Chess Puzzles54%0%
LMArena Hard Prompts14891175
DTBench96%50.9%
LMCA54.3%5.4%
Epoch Capabilities Index159.25116.48
ARC-AGI-285%—
SimpleBench69%—
Kagi LLM Benchmark88.8%—
NYT Connections (extended)96.2%—
ARC-AGI-195%—
EBR-Bench34.3%—
Mystery Game Puzzles56%—
Surface Evolver Bench88.1%—
Bench to the Future 30.14—
ForecastBench60.6—
PIQA—81.2%

Math GPT-5.5 leads

GPT-5.5: 81.5 (#9), Llama 3.1-8B: 9.5 (#311)

BenchmarkGPT-5.5Llama 3.1-8B
OTIS Mock AIME 2024-2025100%1.7%
LMArena Math14861179
FrontierMath (Tiers 1-3)85.3%—
FrontierMath Tier 472.5%—
MathArena Final-Answer Competitions94.3%—
ProofBench50%—
Omni-MATH—13.7%
MATH Level 5—22.9%
FrontierMath (Feb 2025 set)51.7%—
FrontierMath Erdős0%—
FrontierMath Tier 4 (v1)35.4%—
GSM8K—82.4%

Knowledge GPT-5.5 leads

GPT-5.5: 64.4 (#14), Llama 3.1-8B: 8.0 (#302)

BenchmarkGPT-5.5Llama 3.1-8B
GPQA Diamond94%27%
LMArena Expert15081144
SimpleQA Verified63%—
MMLU-Pro—40.6%
Vectara Hallucination Rate9.3%—
GPQA (HELM)—24.7%
BoolQ—82.8%
MMLU—56.1%

Multimodal Not comparable

GPT-5.5: 47.1 (#11), Llama 3.1-8B: —

BenchmarkGPT-5.5Llama 3.1-8B
LMArena Vision1297—
Blueprint-Bench 236.2%—
Furniture Assembly44.2%—
LMArena Document1486—

Multilingual GPT-5.5 leads

GPT-5.5: 56.5 (#19), Llama 3.1-8B: 34.2 (#244)

BenchmarkGPT-5.5Llama 3.1-8B
LMArena Non-English14671148
LMArena Chinese15351151
LMArena French14861176
LMArena German14781144
LMArena Japanese14981061
LMArena Korean14611053
LMArena Russian14731158
LMArena Spanish14691169

Instruction Following GPT-5.5 leads

GPT-5.5: 77.7 (#18), Llama 3.1-8B: 59.1 (#253)

BenchmarkGPT-5.5Llama 3.1-8B
LMArena Instruction Following14791159
IFEval—74.3%

Long Context GPT-5.5 leads

GPT-5.5: 48.3 (#12), Llama 3.1-8B: 35.8 (#233)

BenchmarkGPT-5.5Llama 3.1-8B
LMArena Longer Query14841182
CL-bench Life22.2%—

Writing & Preference GPT-5.5 leads

GPT-5.5: 72.8 (#13), Llama 3.1-8B: 29.9 (#285)

BenchmarkGPT-5.5Llama 3.1-8B
LMArena Text14711187
LMArena Creative Writing14551154
EQ-Bench Creative Writing1844713
LMArena Multi-Turn14771172
WildBench—68.7%
EQ-Bench 41315—

Frequently asked questions

Is GPT-5.5 better than Llama 3.1-8B?

GPT-5.5 is the stronger model overall, scoring 63.4 to 22.8 on the Noometry Index. Llama 3.1-8B costs 196× less per token, which makes it the better buy when GPT-5.5's lead doesn't matter for your workload.

Which is cheaper, GPT-5.5 or Llama 3.1-8B?

Llama 3.1-8B is cheaper. It lists at $0.05 per million input tokens and $0.08 per million output tokens; GPT-5.5 lists at $5 and $30.

Is GPT-5.5 or Llama 3.1-8B better for coding?

GPT-5.5 scores higher on coding benchmarks: 58.3 versus 19.9 in the Noometry coding category.

Which has the bigger context window?

GPT-5.5 does, with 1.05M tokens against 128K.

How many benchmarks do GPT-5.5 and Llama 3.1-8B share?

27 benchmarks have published results for both models. GPT-5.5 has 71 scored results on Noometry and Llama 3.1-8B has 43.

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