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

Llama 3.1 405B vs OpenAI: o3 Pro (batch) benchmark

Name: Llama 3.1 405B vs OpenAI: o3 Pro (batch) AI benchmark comparison
Creator: CompareLLM
License: https://creativecommons.org/licenses/by/4.0/

This page compares Llama 3.1 405B (Meta) and OpenAI: o3 Pro (batch) (OpenAI) using the latest snapshots we have as of Aug 17, 2026. Llama 3.1 405B wins on output price at $3.5/1M. OpenAI: o3 Pro (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 B

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Interactive Comparison Diagram

Matchup Breakdown

Tug-of-War Matchup Matrix

Category wins across intelligence, speed, and pricing efficiency.

Llama 3.1 405B (9)vsOpenAI: o3 Pro (batch) (1)

Llama 3.1 405B: 9W (90%)Overall: Llama 3.1 405BOpenAI: o3 Pro (batch): 1W (10%)

← Llama 3.1 405BOpenAI: o3 Pro (batch) →

Llama 3.1 405B (5/5)

Intelligence & Reasoning

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

Preference Elo

1,370vs—

Coding Elo

1,385vs—

SWE-bench

58.4%vs—

LiveBench

61.2%vs—

GPQA Diamond

75.2%vs—

Llama 3.1 405B: 5WOpenAI: o3 Pro (batch): 0W

Llama 3.1 405B (2/2)

Speed & Latency

Generation throughput and time to first token responsiveness

Output speed

42 tok/svs—

Time to first token

360 msvs—

Llama 3.1 405B: 2WOpenAI: o3 Pro (batch): 0W

Llama 3.1 405B (2/3)

Pricing & Capacity

Cost per million tokens and max context window length

Output price

$3.5/1Mvs$40/1M

Input price

$1.25/1Mvs$10/1M

Context window

128kvs200k

Llama 3.1 405B: 2WOpenAI: o3 Pro (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 405B 5/6

Preference Elo

No shared data

Throughput Speed

No shared data

Token Price Efficiency

Llama 3.1 405B

$36.50/1M cheaper

Full Benchmark Score Matrix

—

Elo

High is better

1,370

EloHigh is better

—

Code Elo

High is better

1,385

Code EloHigh is better

—

LiveBench

High is better

61.2%

LiveBenchHigh is better

—

SWE-bench

High is better

58.4%

SWE-benchHigh is better

—

GPQA

High is better

75.2%

GPQAHigh is better

—

TTFT

Low is better

360 ms

TTFTLow is better

—

Speed

High is better

42 tok/s

SpeedHigh is better

—

In $

Low is better

+700%$1.25 / 1M

In $Low is better

$10.00 / 1M

Out $

Low is better

+1043%$3.50 / 1M

Out $Low is better

$40.00 / 1M

Context

High is better

128k

ContextHigh is better

200k+56%

Interactive Simulator (USD)

Workload Cost & Savings Calculator

Simulate monthly production API costs in USD (US Dollar).

Save up to 90% with Llama 3.1 405B

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 405B$96.25 / mo

In: $43.75Out: $52.5

OpenAI: o3 Pro (batch)$950.00 / mo

In: $350Out: $600

Estimated Cost Delta

Llama 3.1 405B is estimated to save $853.75/month ($10,245/year).

Based on 50M tokens (30% gen)

Key Selection Recommendations

1
Llama 3.1 405B:Pick Llama 3.1 405B when you are optimizing output cost.

Recommended Workload Routing

Repo / coding agentsinsufficient data

Need SWE-bench on both sides.

High-volume chatLlama 3.1 405B

Lower output list price ($3.5/1M).

Voice / low-latency UIinsufficient data

Need TTFT on both sides.

Long-document RAGOpenAI: o3 Pro (batch)

Larger window (200k).

Screenshots / visionOpenAI: o3 Pro (batch)

OpenAI: o3 Pro (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 405B	Lower output list price ($3.5/1M).
Voice / low-latency UI	insufficient data	Need TTFT on both sides.
Long-document RAG	OpenAI: o3 Pro (batch)	Larger window (200k).
Screenshots / vision	OpenAI: o3 Pro (batch)	OpenAI: o3 Pro (batch) is the side marked multimodal in the catalog.

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LIVE

Llama 3.1 405B

Elo 1,370

LiveBench61.2%

SWE-bench58.4%

Speed42 tok/s

Output cost$3.5/1M

OpenAI

OpenAI: o3 Pro (batch)

Elo n/a

LiveBench—

SWE-bench—

Speed—

Output cost$40/1M

Frequently Asked Questions

Which is better overall, Llama 3.1 405B or OpenAI: o3 Pro (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 405B or OpenAI: o3 Pro (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 405B output tokens are $3.5/1M versus $40/1M. Input prices and retry rates still move the real bill.
Which is faster, Llama 3.1 405B or OpenAI: o3 Pro (batch)?: We do not have TTFT for both models.
Which has the larger context window?: OpenAI: o3 Pro (batch) accepts 200k tokens versus 128k.
Can I self-host either model?: Llama 3.1 405B 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 405B and OpenAI: o3 Pro (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 405B matchups →|All OpenAI: o3 Pro (batch) matchups →

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

Llama 3.1 405B vs OpenAI: o3 Pro (batch) benchmark

Interactive Comparison Diagram

Matchup Breakdown

Tug-of-War Matchup Matrix

Category wins across intelligence, speed, and pricing efficiency.

Llama 3.1 405B (9)vsOpenAI: o3 Pro (batch) (1)

Llama 3.1 405B: 9W (90%)Overall: Llama 3.1 405BOpenAI: o3 Pro (batch): 1W (10%)

← Llama 3.1 405BOpenAI: o3 Pro (batch) →

Llama 3.1 405B (5/5)

Intelligence & Reasoning

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

Preference Elo

1,370vs—

Coding Elo

1,385vs—

SWE-bench

58.4%vs—

LiveBench

61.2%vs—

GPQA Diamond

75.2%vs—

Llama 3.1 405B: 5WOpenAI: o3 Pro (batch): 0W

Llama 3.1 405B (2/2)

Speed & Latency

Generation throughput and time to first token responsiveness

Output speed

42 tok/svs—

Time to first token

360 msvs—

Llama 3.1 405B: 2WOpenAI: o3 Pro (batch): 0W

Llama 3.1 405B (2/3)

Pricing & Capacity

Cost per million tokens and max context window length

Output price

$3.5/1Mvs$40/1M

Input price

$1.25/1Mvs$10/1M

Context window

128kvs200k

Llama 3.1 405B: 2WOpenAI: o3 Pro (batch): 1W

Recommended Workload Routing

Repo / coding agentsinsufficient data

Need SWE-bench on both sides.

High-volume chatLlama 3.1 405B

Lower output list price ($3.5/1M).

Voice / low-latency UIinsufficient data

Need TTFT on both sides.

Long-document RAGOpenAI: o3 Pro (batch)

Larger window (200k).

Screenshots / visionOpenAI: o3 Pro (batch)

OpenAI: o3 Pro (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 405B	Lower output list price ($3.5/1M).
Voice / low-latency UI	insufficient data	Need TTFT on both sides.
Long-document RAG	OpenAI: o3 Pro (batch)	Larger window (200k).
Screenshots / vision	OpenAI: o3 Pro (batch)	OpenAI: o3 Pro (batch) is the side marked multimodal in the catalog.

Frequently Asked Questions

Which is better overall, Llama 3.1 405B or OpenAI: o3 Pro (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 405B or OpenAI: o3 Pro (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 405B output tokens are $3.5/1M versus $40/1M. Input prices and retry rates still move the real bill.

Which is faster, Llama 3.1 405B or OpenAI: o3 Pro (batch)?

We do not have TTFT for both models.

Which has the larger context window?

OpenAI: o3 Pro (batch) accepts 200k tokens versus 128k.

Can I self-host either model?

Llama 3.1 405B 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 405B and OpenAI: o3 Pro (batch) Elo cells are dated snapshots from the named source — we do not compute Arena ourselves.