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

OpenAI: GPT-5.2 vs Llama 3.1 405B benchmark

Name: OpenAI: GPT-5.2 vs Llama 3.1 405B AI benchmark comparison
Creator: CompareLLM
License: https://creativecommons.org/licenses/by/4.0/

This page compares OpenAI: GPT-5.2 (OpenAI) and Llama 3.1 405B (Meta) using the latest snapshots we have as of Aug 16, 2026. Llama 3.1 405B wins on output price at $3.5/1M. OpenAI: GPT-5.2 has the larger context window (400k 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-5.2

Auto-discovered from OpenRouter (openai/gpt-5.2). Preview until a second source matches.

$14/1M out

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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-5.2 (1)vsLlama 3.1 405B (9)

OpenAI: GPT-5.2: 1W (10%)Overall: Llama 3.1 405BLlama 3.1 405B: 9W (90%)

← OpenAI: GPT-5.2Llama 3.1 405B →

Llama 3.1 405B (5/5)

Intelligence & Reasoning

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

Preference Elo

—vs1,370

Coding Elo

—vs1,385

SWE-bench

—vs58.4%

LiveBench

—vs61.2%

GPQA Diamond

—vs75.2%

OpenAI: GPT-5.2: 0WLlama 3.1 405B: 5W

Llama 3.1 405B (2/2)

Speed & Latency

Generation throughput and time to first token responsiveness

Output speed

—vs42 tok/s

Time to first token

—vs360 ms

OpenAI: GPT-5.2: 0WLlama 3.1 405B: 2W

Llama 3.1 405B (2/3)

Pricing & Capacity

Cost per million tokens and max context window length

Output price

$14/1Mvs$3.5/1M

Input price

$1.75/1Mvs$1.25/1M

Context window

400kvs128k

OpenAI: GPT-5.2: 1WLlama 3.1 405B: 2W

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

Price Efficiency

Llama 3.1 405B

$10.50/1M cheaper

Full Benchmark Score Matrix

—

Elo

High is better

—

EloHigh is better

1,370

Code Elo

High is better

—

Code EloHigh is better

1,385

LiveBench

High is better

—

LiveBenchHigh is better

61.2%

SWE-bench

High is better

—

SWE-benchHigh is better

58.4%

GPQA

High is better

—

GPQAHigh is better

75.2%

TTFT

Low is better

—

TTFTLow is better

360 ms

Speed

High is better

—

SpeedHigh is better

42 tok/s

In $

Low is better

$1.75/1M

In $Low is better

$1.25/1M+40%

Out $

Low is better

$14/1M

Out $Low is better

$3.5/1M+300%

Context

High is better

+213%400k

ContextHigh is better

128k

Interactive Simulator

Workload Cost & Savings Calculator

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

Save up to 65% 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)

OpenAI: GPT-5.2$271.25 / mo

In: $61.25Out: $210.00

Llama 3.1 405B$96.25 / mo

In: $43.75Out: $52.50

Estimated Cost Delta

Llama 3.1 405B is estimated to save $175.00/month ($2,100/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: GPT-5.2

Larger window (400k).

Screenshots / visionLlama 3.1 405B

Llama 3.1 405B 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: GPT-5.2	Larger window (400k).
Screenshots / vision	Llama 3.1 405B	Llama 3.1 405B is the side marked multimodal in the catalog.

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LIVE

OpenAI

OpenAI: GPT-5.2

Elo n/a

LiveBench—

SWE-bench—

Speed—

Output cost$14/1M

Llama 3.1 405B

Elo 1,370

LiveBench61.2%

SWE-bench58.4%

Speed42 tok/s

Output cost$3.5/1M

Frequently Asked Questions

Which is better overall, OpenAI: GPT-5.2 or Llama 3.1 405B?: 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-5.2 or Llama 3.1 405B?: 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 $14/1M. Input prices and retry rates still move the real bill.
Which is faster, OpenAI: GPT-5.2 or Llama 3.1 405B?: We do not have TTFT for both models.
Which has the larger context window?: OpenAI: GPT-5.2 accepts 400k 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. OpenAI: GPT-5.2 and Llama 3.1 405B Elo cells are dated snapshots from the named source — we do not compute Arena ourselves.

Similar Head-to-Head Comparisons

All OpenAI: GPT-5.2 matchups →|All Llama 3.1 405B matchups →

Llama 3.1 405B vs Llama 3.1 70B (next llama)GPT-5 vs Llama 3.1 405B Llama 4 Maverick 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

OpenAI: GPT-5.2 vs Llama 3.1 405B benchmark

Interactive Comparison Diagram

Matchup Breakdown

Tug-of-War Matchup Matrix

Category wins across intelligence, speed, and pricing efficiency.

OpenAI: GPT-5.2 (1)vsLlama 3.1 405B (9)

OpenAI: GPT-5.2: 1W (10%)Overall: Llama 3.1 405BLlama 3.1 405B: 9W (90%)

← OpenAI: GPT-5.2Llama 3.1 405B →

Llama 3.1 405B (5/5)

Intelligence & Reasoning

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

Preference Elo

—vs1,370

Coding Elo

—vs1,385

SWE-bench

—vs58.4%

LiveBench

—vs61.2%

GPQA Diamond

—vs75.2%

OpenAI: GPT-5.2: 0WLlama 3.1 405B: 5W

Llama 3.1 405B (2/2)

Speed & Latency

Generation throughput and time to first token responsiveness

Output speed

—vs42 tok/s

Time to first token

—vs360 ms

OpenAI: GPT-5.2: 0WLlama 3.1 405B: 2W

Llama 3.1 405B (2/3)

Pricing & Capacity

Cost per million tokens and max context window length

Output price

$14/1Mvs$3.5/1M

Input price

$1.75/1Mvs$1.25/1M

Context window

400kvs128k

OpenAI: GPT-5.2: 1WLlama 3.1 405B: 2W

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: GPT-5.2

Larger window (400k).

Screenshots / visionLlama 3.1 405B

Llama 3.1 405B 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: GPT-5.2	Larger window (400k).
Screenshots / vision	Llama 3.1 405B	Llama 3.1 405B is the side marked multimodal in the catalog.

Frequently Asked Questions

Which is better overall, OpenAI: GPT-5.2 or Llama 3.1 405B?

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-5.2 or Llama 3.1 405B?

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 $14/1M. Input prices and retry rates still move the real bill.

Which is faster, OpenAI: GPT-5.2 or Llama 3.1 405B?

We do not have TTFT for both models.

Which has the larger context window?

OpenAI: GPT-5.2 accepts 400k 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. OpenAI: GPT-5.2 and Llama 3.1 405B Elo cells are dated snapshots from the named source — we do not compute Arena ourselves.