Pairwise benchmark snapshot · Aug 17, 2026noindex (thin pair)

OpenAI: GPT-4o (2024-11-20) vs Llama 3.1 70B benchmark

Name: OpenAI: GPT-4o (2024-11-20) vs Llama 3.1 70B AI benchmark comparison
Creator: CompareLLM
License: https://creativecommons.org/licenses/by/4.0/

This page compares OpenAI: GPT-4o (2024-11-20) (OpenAI) and Llama 3.1 70B (Meta) using the latest snapshots we have as of Aug 17, 2026. Llama 3.1 70B wins on output price at $0.4/1M. Llama 3.1 70B has the larger context window (131k tokens). Every cell below is a dated snapshot from a named public source — we do not invent missing scores, and we do not run SWE-bench ourselves.

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OpenAI

OpenAI: GPT-4o (2024-11-20)

Auto-discovered from OpenRouter (openai/gpt-4o-2024-11-20). Preview until a second source matches.

$10/1M out

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Model A

Model B

Quick vs:vs Claude Opus 4.5 vs Claude Opus 4.6 vs Claude Sonnet 4.5 vs Claude 3.7 Sonnet

Interactive Comparison Diagram

Matchup Breakdown

Tug-of-War Matchup Matrix

Category wins across intelligence, speed, and pricing efficiency.

OpenAI: GPT-4o (2024-11-20) (0)vsLlama 3.1 70B (9)

OpenAI: GPT-4o (2024-11-20): 0W (0%)Overall: Llama 3.1 70BLlama 3.1 70B: 9W (100%)

← OpenAI: GPT-4o (2024-11-20)Llama 3.1 70B →

Llama 3.1 70B (4/5)

Intelligence & Reasoning

Preference Elo, Coding proficiency, SWE-bench & LiveBench accuracy

Preference Elo

—vs1,240

Coding Elo

—vs—

SWE-bench

—vs40.2%

LiveBench

—vs46.8%

GPQA Diamond

—vs48%

OpenAI: GPT-4o (2024-11-20): 0WLlama 3.1 70B: 4W

Llama 3.1 70B (2/2)

Speed & Latency

Generation throughput and time to first token responsiveness

Output speed

—vs125 tok/s

Time to first token

—vs170 ms

OpenAI: GPT-4o (2024-11-20): 0WLlama 3.1 70B: 2W

Llama 3.1 70B (3/3)

Pricing & Capacity

Cost per million tokens and max context window length

Output price

$10/1Mvs$0.4/1M

Input price

$2.5/1Mvs$0.4/1M

Context window

128kvs131k

OpenAI: GPT-4o (2024-11-20): 0WLlama 3.1 70B: 3W

Capability Matchup Radar

Multi-Dimensional Capability Radar

Each spoke is a skill. Farther from the center is better (0–100th percentile). Tap any dot to inspect details.

Tap any node to inspect

Percentile 0–100

Dimensional AdvantageLlama 3.1 70B 6/6

Preference Elo

No shared data

Throughput Speed

No shared data

Price Efficiency

Llama 3.1 70B

$9.60/1M cheaper

Full Benchmark Score Matrix

—

Elo

High is better

—

EloHigh is better

1,240

LiveBench

High is better

—

LiveBenchHigh is better

46.8%

SWE-bench

High is better

—

SWE-benchHigh is better

40.2%

GPQA

High is better

—

GPQAHigh is better

48%

TTFT

Low is better

—

TTFTLow is better

170 ms

Speed

High is better

—

SpeedHigh is better

125 tok/s

In $

Low is better

$2.5/1M

In $Low is better

$0.4/1M+525%

Out $

Low is better

$10/1M

Out $Low is better

$0.4/1M+2400%

Context

High is better

128k

ContextHigh is better

131k+2%

Interactive Simulator

Workload Cost & Savings Calculator

Simulate monthly production API costs based on published $/1M tokens.

Save up to 92% with Llama 3.1 70B

Monthly Volume50M tokens/mo

Quick Presets:

Token Ratio (In vs Out)70% In / 30% Out

RAG / Search (10% out)Coding / Chat (50% out)

OpenAI: GPT-4o (2024-11-20)$237.50 / mo

In: $87.50Out: $150.00

Llama 3.1 70B$20.00 / mo

In: $14.00Out: $6.00

Estimated Cost Delta

Llama 3.1 70B is estimated to save $217.50/month ($2,610/year).

Based on 50M tokens (30% gen)

Key Selection Recommendations

1
Llama 3.1 70B:Pick Llama 3.1 70B when you are optimizing output cost.

Recommended Workload Routing

Repo / coding agentsinsufficient data

Need SWE-bench on both sides.

High-volume chatLlama 3.1 70B

Lower output list price ($0.4/1M).

Voice / low-latency UIinsufficient data

Need TTFT on both sides.

Long-document RAGLlama 3.1 70B

Larger window (131k).

Screenshots / visionLlama 3.1 70B

Llama 3.1 70B is the side marked multimodal in the catalog.

Target Workload	Recommended Pick	Evaluation Rationale
Repo / coding agents	insufficient data	Need SWE-bench on both sides.
High-volume chat	Llama 3.1 70B	Lower output list price ($0.4/1M).
Voice / low-latency UI	insufficient data	Need TTFT on both sides.
Long-document RAG	Llama 3.1 70B	Larger window (131k).
Screenshots / vision	Llama 3.1 70B	Llama 3.1 70B is the side marked multimodal in the catalog.

Community Sentiment

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LIVE

OpenAI

OpenAI: GPT-4o (2024-11-20)

Elo n/a

LiveBench—

SWE-bench—

Speed—

Output cost$10/1M

Llama 3.1 70B

Elo 1,240

LiveBench46.8%

SWE-bench40.2%

Speed125 tok/s

Output cost$0.4/1M

Frequently Asked Questions

Which is better overall, OpenAI: GPT-4o (2024-11-20) or Llama 3.1 70B?: We do not have preference Elo for both models, so we do not declare an overall winner. Compare the metrics that exist.
Which is better at coding, OpenAI: GPT-4o (2024-11-20) or Llama 3.1 70B?: SWE-bench is missing for at least one model, so we do not rank coding from a single number.
Which is cheaper to run in production?: Llama 3.1 70B output tokens are $0.4/1M versus $10/1M. Input prices and retry rates still move the real bill.
Which is faster, OpenAI: GPT-4o (2024-11-20) or Llama 3.1 70B?: We do not have TTFT for both models.
Which has the larger context window?: Llama 3.1 70B accepts 131k tokens versus 128k.
Can I self-host either model?: Llama 3.1 70B is marked open-weights in our catalog. The other side is a closed API. Check the provider license before you ship weights.
Are these scores from CompareLLM’s own evals?: No. V1 aggregates public leaderboards (and optional first-party latency pings). Each cell names its source and date. Read /methodology.
What does preference Elo mean on this page?: Preference Elo is a crowd vote from LMArena / Arena. People see two hidden answers and pick the one they like more. The model that wins more often gets a higher Elo. That means people preferred it — not that it passed a school test. It is not SWE-bench, not accuracy, and not a number we invent. OpenAI: GPT-4o (2024-11-20) and Llama 3.1 70B Elo cells are dated snapshots from the named source — we do not compute Arena ourselves.

Similar Head-to-Head Comparisons

All OpenAI: GPT-4o (2024-11-20) matchups →|All Llama 3.1 70B matchups →

Llama 3.1 70B vs Llama 3.1 405B (previous llama)Llama 3.1 70B vs Llama 3.3 70B (next llama)GPT-5 vs Llama 3.1 70B Llama 4 Maverick vs Llama 3.1 70B GPT-4.5 Orion vs Llama 3.1 70B GPT-5.6 Sol vs Llama 3.1 70B

OpenAI: GPT-4o (2024-11-20) vs Llama 3.1 70B benchmark

Interactive Comparison Diagram

Matchup Breakdown

Tug-of-War Matchup Matrix

Category wins across intelligence, speed, and pricing efficiency.

OpenAI: GPT-4o (2024-11-20) (0)vsLlama 3.1 70B (9)

OpenAI: GPT-4o (2024-11-20): 0W (0%)Overall: Llama 3.1 70BLlama 3.1 70B: 9W (100%)

← OpenAI: GPT-4o (2024-11-20)Llama 3.1 70B →

Llama 3.1 70B (4/5)

Intelligence & Reasoning

Preference Elo, Coding proficiency, SWE-bench & LiveBench accuracy

Preference Elo

—vs1,240

Coding Elo

—vs—

SWE-bench

—vs40.2%

LiveBench

—vs46.8%

GPQA Diamond

—vs48%

OpenAI: GPT-4o (2024-11-20): 0WLlama 3.1 70B: 4W

Llama 3.1 70B (2/2)

Speed & Latency

Generation throughput and time to first token responsiveness

Output speed

—vs125 tok/s

Time to first token

—vs170 ms

OpenAI: GPT-4o (2024-11-20): 0WLlama 3.1 70B: 2W

Llama 3.1 70B (3/3)

Pricing & Capacity

Cost per million tokens and max context window length

Output price

$10/1Mvs$0.4/1M

Input price

$2.5/1Mvs$0.4/1M

Context window

128kvs131k

OpenAI: GPT-4o (2024-11-20): 0WLlama 3.1 70B: 3W

Recommended Workload Routing

Repo / coding agentsinsufficient data

Need SWE-bench on both sides.

High-volume chatLlama 3.1 70B

Lower output list price ($0.4/1M).

Voice / low-latency UIinsufficient data

Need TTFT on both sides.

Long-document RAGLlama 3.1 70B

Larger window (131k).

Screenshots / visionLlama 3.1 70B

Llama 3.1 70B is the side marked multimodal in the catalog.

Target Workload	Recommended Pick	Evaluation Rationale
Repo / coding agents	insufficient data	Need SWE-bench on both sides.
High-volume chat	Llama 3.1 70B	Lower output list price ($0.4/1M).
Voice / low-latency UI	insufficient data	Need TTFT on both sides.
Long-document RAG	Llama 3.1 70B	Larger window (131k).
Screenshots / vision	Llama 3.1 70B	Llama 3.1 70B is the side marked multimodal in the catalog.

Frequently Asked Questions

Which is better overall, OpenAI: GPT-4o (2024-11-20) or Llama 3.1 70B?

We do not have preference Elo for both models, so we do not declare an overall winner. Compare the metrics that exist.

Which is better at coding, OpenAI: GPT-4o (2024-11-20) or Llama 3.1 70B?

SWE-bench is missing for at least one model, so we do not rank coding from a single number.

Which is cheaper to run in production?

Llama 3.1 70B output tokens are $0.4/1M versus $10/1M. Input prices and retry rates still move the real bill.

Which is faster, OpenAI: GPT-4o (2024-11-20) or Llama 3.1 70B?

We do not have TTFT for both models.

Which has the larger context window?

Llama 3.1 70B accepts 131k tokens versus 128k.

Can I self-host either model?

Llama 3.1 70B is marked open-weights in our catalog. The other side is a closed API. Check the provider license before you ship weights.

Are these scores from CompareLLM’s own evals?

No. V1 aggregates public leaderboards (and optional first-party latency pings). Each cell names its source and date. Read /methodology.

What does preference Elo mean on this page?

Preference Elo is a crowd vote from LMArena / Arena. People see two hidden answers and pick the one they like more. The model that wins more often gets a higher Elo. That means people preferred it — not that it passed a school test. It is not SWE-bench, not accuracy, and not a number we invent. OpenAI: GPT-4o (2024-11-20) and Llama 3.1 70B Elo cells are dated snapshots from the named source — we do not compute Arena ourselves.