Pairwise benchmark snapshot · Aug 16, 2026noindex (thin pair)

Llama 3.1 70B vs OpenAI: o3 (batch) benchmark

Name: Llama 3.1 70B vs OpenAI: o3 (batch) AI benchmark comparison
Creator: CompareLLM
License: https://creativecommons.org/licenses/by/4.0/

This page compares Llama 3.1 70B (Meta) and OpenAI: o3 (batch) (OpenAI) using the latest snapshots we have as of Aug 16, 2026. Llama 3.1 70B wins on output price at $0.4/1M. OpenAI: o3 (batch) has the larger context window (200k 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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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.

Llama 3.1 70B (8)vsOpenAI: o3 (batch) (1)

Llama 3.1 70B: 8W (89%)Overall: Llama 3.1 70BOpenAI: o3 (batch): 1W (11%)

← Llama 3.1 70BOpenAI: o3 (batch) →

Llama 3.1 70B (4/5)

Intelligence & Reasoning

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

Preference Elo

1,240vs—

Coding Elo

—vs—

SWE-bench

40.2%vs—

LiveBench

46.8%vs—

GPQA Diamond

48%vs—

Llama 3.1 70B: 4WOpenAI: o3 (batch): 0W

Llama 3.1 70B (2/2)

Speed & Latency

Generation throughput and time to first token responsiveness

Output speed

125 tok/svs—

Time to first token

170 msvs—

Llama 3.1 70B: 2WOpenAI: o3 (batch): 0W

Llama 3.1 70B (2/3)

Pricing & Capacity

Cost per million tokens and max context window length

Output price

$0.4/1Mvs$4/1M

Input price

$0.4/1Mvs$1/1M

Context window

131kvs200k

Llama 3.1 70B: 2WOpenAI: o3 (batch): 1W

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 5/6

Preference Elo

No shared data

Throughput Speed

No shared data

Price Efficiency

Llama 3.1 70B

$3.60/1M cheaper

Full Benchmark Score Matrix

—

Elo

High is better

1,240

EloHigh is better

—

LiveBench

High is better

46.8%

LiveBenchHigh is better

—

SWE-bench

High is better

40.2%

SWE-benchHigh is better

—

GPQA

High is better

48%

GPQAHigh is better

—

TTFT

Low is better

170 ms

TTFTLow is better

—

Speed

High is better

125 tok/s

SpeedHigh is better

—

In $

Low is better

+150%$0.4/1M

In $Low is better

$1/1M

Out $

Low is better

+900%$0.4/1M

Out $Low is better

$4/1M

Context

High is better

131k

ContextHigh is better

200k+53%

Interactive Simulator

Workload Cost & Savings Calculator

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

Save up to 79% 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)

Llama 3.1 70B$20.00 / mo

In: $14.00Out: $6.00

OpenAI: o3 (batch)$95.00 / mo

In: $35.00Out: $60.00

Estimated Cost Delta

Llama 3.1 70B is estimated to save $75.00/month ($900/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 RAGOpenAI: o3 (batch)

Larger window (200k).

Screenshots / visionOpenAI: o3 (batch)

OpenAI: o3 (batch) 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	OpenAI: o3 (batch)	Larger window (200k).
Screenshots / vision	OpenAI: o3 (batch)	OpenAI: o3 (batch) is the side marked multimodal in the catalog.

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LIVE

Llama 3.1 70B

Elo 1,240

LiveBench46.8%

SWE-bench40.2%

Speed125 tok/s

Output cost$0.4/1M

OpenAI

OpenAI: o3 (batch)

Elo n/a

LiveBench—

SWE-bench—

Speed—

Output cost$4/1M

Frequently Asked Questions

Which is better overall, Llama 3.1 70B or OpenAI: o3 (batch)?: 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, Llama 3.1 70B or OpenAI: o3 (batch)?: 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 $4/1M. Input prices and retry rates still move the real bill.
Which is faster, Llama 3.1 70B or OpenAI: o3 (batch)?: We do not have TTFT for both models.
Which has the larger context window?: OpenAI: o3 (batch) accepts 200k tokens versus 131k.
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. Llama 3.1 70B and OpenAI: o3 (batch) Elo cells are dated snapshots from the named source — we do not compute Arena ourselves.

Similar Head-to-Head Comparisons

All Llama 3.1 70B matchups →|All OpenAI: o3 (batch) 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

Llama 3.1 70B vs OpenAI: o3 (batch) benchmark

Interactive Comparison Diagram

Matchup Breakdown

Tug-of-War Matchup Matrix

Category wins across intelligence, speed, and pricing efficiency.

Llama 3.1 70B (8)vsOpenAI: o3 (batch) (1)

Llama 3.1 70B: 8W (89%)Overall: Llama 3.1 70BOpenAI: o3 (batch): 1W (11%)

← Llama 3.1 70BOpenAI: o3 (batch) →

Llama 3.1 70B (4/5)

Intelligence & Reasoning

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

Preference Elo

1,240vs—

Coding Elo

—vs—

SWE-bench

40.2%vs—

LiveBench

46.8%vs—

GPQA Diamond

48%vs—

Llama 3.1 70B: 4WOpenAI: o3 (batch): 0W

Llama 3.1 70B (2/2)

Speed & Latency

Generation throughput and time to first token responsiveness

Output speed

125 tok/svs—

Time to first token

170 msvs—

Llama 3.1 70B: 2WOpenAI: o3 (batch): 0W

Llama 3.1 70B (2/3)

Pricing & Capacity

Cost per million tokens and max context window length

Output price

$0.4/1Mvs$4/1M

Input price

$0.4/1Mvs$1/1M

Context window

131kvs200k

Llama 3.1 70B: 2WOpenAI: o3 (batch): 1W

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 RAGOpenAI: o3 (batch)

Larger window (200k).

Screenshots / visionOpenAI: o3 (batch)

OpenAI: o3 (batch) 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	OpenAI: o3 (batch)	Larger window (200k).
Screenshots / vision	OpenAI: o3 (batch)	OpenAI: o3 (batch) is the side marked multimodal in the catalog.

Frequently Asked Questions

Which is better overall, Llama 3.1 70B or OpenAI: o3 (batch)?

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, Llama 3.1 70B or OpenAI: o3 (batch)?

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 $4/1M. Input prices and retry rates still move the real bill.

Which is faster, Llama 3.1 70B or OpenAI: o3 (batch)?

We do not have TTFT for both models.

Which has the larger context window?

OpenAI: o3 (batch) accepts 200k tokens versus 131k.

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. Llama 3.1 70B and OpenAI: o3 (batch) Elo cells are dated snapshots from the named source — we do not compute Arena ourselves.