GPT-4o vs Llama 3.1 8B Instruct
Wins 4 of 6 areas
Coding · Facts · Long documents · Following instructions
Wins 0 of 6 areas
—
GPT-4o is the stronger all-rounder.Llama 3.1 8B Instruct is cheaper.
Scores updated · 32 tests both models report · How we compare
Where each one wins
Tests won in each of the six areas where both have results. Each piece is one test, so a longer bar means more evidence; grey means the two scored within a point of each other.
- CodingWriting and fixing software20GPT-4o2 of 2 tests
- FactsGetting facts right instead of making them up20GPT-4o2 of 2 tests
- Long documentsFinding answers in very long texts10GPT-4o1 of 1 test
- Following instructionsDoing exactly what it is asked10GPT-4o1 of 1 test
- AgentsCarrying out multi-step tasks on its own00Even0 each · 1 tie
- ReasoningHard problems that need careful thinking11Even1 each · 1 tie
Agents, long documents and following instructions rest on a single test each.
What it costs
Prices per million tokens, roughly 750,000 words. The bars show the cost of a million tokens read plus a million written.
Llama 3.1 8B Instruct costs 100% less for the same work.
The biggest differences
The tests each model wins by the widest margin, up to three each. Scores are out of 100.
Where GPT-4o pulls ahead
- Reasons across sets of long documentsAA-LCR+38points ahead
- Graduate-level biology, physics and chemistry questionsGPQA Diamond+26.7points ahead
- Code for real scientific research problemsSciCode+20.1points ahead
Where Llama 3.1 8B Instruct pulls ahead
- Very hard expert questions across many subjectsHumanity's Last Exam+3.5points ahead
Every test, side by side
All 32 tests both models report. The winning score is in its model's colour; marks a score checked independently.
CodingGPT-4o
- SciCodeGPT-4o by 20.133.313.2+20.1
- Terminal-Bench HardGPT-4o by 7.58.30.8+7.5
ReasoningEven
- GPQA DiamondGPT-4o by 26.752.625.9+26.7
- Humanity's Last ExamLlama 3.1 8B Instruct by 3.51.85.3+3.5
- CritPttie00tie
FactsGPT-4o
- AA-Omniscience · AccuracyGPT-4o by 15.223.78.5+15.2
- AA-Omniscience · Non-hallucinationGPT-4o by 5.162.157+5.1
Other results22 tests, not counted
Tests outside the eight areas. They are not counted above: several are summary scores built from other tests, or the same test under another name.
- Arena HardGPT-4o by 42.979.336.4+42.9
- MATHGPT-4o by 33.485.351.9+33.4
- naturalquestions_closedbookGPT-4o by 28.749.620.9+28.7
- MMLU-ProGPT-4o by 26.474.748.3+26.4
- DROPGPT-4o by 23.983.459.5+23.9
- OpenBookQAGPT-4o by 22.896.874+22.8
- MGSMGPT-4o by 21.690.568.9+21.6
- HarmBenchGPT-4o by 21.382.961.6+21.3
- AA-OmniscienceGPT-4o by 20.4-10.5-30.9+20.4
- Artificial Analysis Coding IndexGPT-4o by 18.824.25.4+18.8
- HumanEvalGPT-4o by 17.690.272.6+17.6
- bbqGPT-4o by 16.695.178.5+16.6
- τ²-Bench Telecom (AA run)GPT-4o by 12.528.916.4+12.5
- GSM8KGPT-4o by 11.190.979.8+11.1
- NarrativeQAGPT-4o by 3.979.575.6+3.9
- anthropic_red_teamGPT-4o by 2.399.196.8+2.3
- XSTestGPT-4o by 297.395.3+2
- MMLUtie73.873tie
- IFEvaltie8180.4tie
- AA Intelligencetie7.36.9tie
- simple_safety_teststie98.598.8tie
- AIR-Bench 2024tie62.462.3tie
Questions people ask
Which is better, GPT-4o or Llama 3.1 8B Instruct?
GPT-4o wins four of the six areas where both have results: coding, facts, long documents and following instructions. Llama 3.1 8B Instruct wins none, but costs 100% less. They are level on agents and reasoning.
Which is better for coding?
GPT-4o. It wins 2 of the 2 coding tests both models report; Llama 3.1 8B Instruct wins none.
Which is cheaper?
GPT-4o costs $5.00 per million input tokens and $15.00 per million output tokens; Llama 3.1 8B Instruct costs $0.02 and $0.05. That makes Llama 3.1 8B Instruct about 100% cheaper for the same work.
How do you compare the two?
We use the 32 benchmark tests both models have published scores on. The verdict counts the 10 tests in the eight capability areas, and a gap under one point (ten on rating-style scales) counts as a tie. The other 22 are listed but not counted, because several are summary scores or repeat a test. Each score is the one shown on the model's own page.